esa-sceva/satcom-chunk-collection huggingface dataset:
# PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach
## 6 Results and Discussion
### Robustness Against Adversarial Attack Scenarios
#### 6.5.1 Parameter Pruning Attack  
Though parameter pruning is often used for genuine optimization purposes [72], in an adversarial setting, it can pose threats to watermarking mechanisms, especially if the watermark is manifested through the model's parameters. We subjected PK-Judge to various levels of pruning, ranging from low (10%) to severe (90+%), covering a comprehensive scope of conditions that a model might encounter in practical applications. Illustrative results across the tested models shown in Figures 9-11 and Table 4. Notably, PK-Judge has been shown to maintain an EBER of _zero_ consistently across various degrees of pruning. Such an outcome suggests that PK-Judge's watermarking mechanism is intricately designed to withstand the loss of parameters, thereby ensuring the authenticity of the watermark remains verifiable.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline
**Metric Model** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **91\%** & **92\%** & **93\%** & **94\%** & **95\%** & **96\%** & **97\%** & **98\%** & **99\%** \\ \hline Acc\% & RN101 & 81.06 & 82.02 & 79.16 & 78.57 & 82.64 & 79.78 & 81.10 & 82.13 & 83.33 & 82.83 & 76.84 & 80.31 & 83.04 & 80.66 & 79.64 & 79.81 & 78.98 & 70.66 \\ BER & 0 & 0 & 0.021 & 0.038 & 0 & 0 & 0 & 0.019 & 0 & 0 & 0.003 & 0.003 & 0 & 0 & 0 & 0.019 & 0.018 & 0.014 \\ EBER & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Acc\% & DN201 & 84.16 & 84.16 & 84.16 & 84.16 & 84.16 & 84.16 & 84.16 & 84.16 & 84.18 & 84.17 & 84.17 & 84.26 & 84.11 & 83.96 & 84.02 & 83.50 & 83.14 & 72.60 \\ BER & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.001 & 0.019 & 0.013 & 0.013 & 0.003 & 0.031 & 0.031 & 0.031 \\ EBER & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics for pruning at different percentages on ResNet101 (RN101) and DenseNet201 (DN201). All percentages represent pruning percentages.  
Figure 9: Robustness against pruning for MNIST on MLP model.
## 2 Experiment  
### Equipment for Calibration  
The primary equipment used for calibration included a dedicated oil-free vacuum system, ensuring the optical components of the instrument remained uncontaminated. The vacuum system maintained a level of \(6\times 10^{-4}\) Pa, with a cleanliness rating of 1000. The UVS comprised a thermal control system to ensure that the temperature was consistent with the in-orbit conditions. A solar simulator was used to provide ultraviolet-visible collimated light. In addition, a five-dimensional mobile station was incorporated to provide various test positions and angles for the instrument, and a standard diffuser calibrated by NIST was adopted as a reference for the BRDF measurements. Additional auxiliary equipment was also used. All tests and calibrations had to be conducted within a clean room environment. Figures 3 and 4 illustrate the schematic diagrams of the vacuum calibration testing system. The inner walls of the vacuum chamber were coated with a black, light-absorbing layer to minimize the impact of stray light. The super-black coating had a thickness of 50 \(\upmu\)m and exhibited a visible and infrared absorption rate of 0.98 \(\pm\) 0.01. During calibration, we used liquid nitrogen to lower the temperature of the environment inside the tank, and the UVS was equipped with a temperature control system to ensure that the working environment during calibration matched that in orbit.  
Figure 3: Schematic diagram of calibration in the sun observation mod.  
1. Solar simulator In laboratory radiometric calibration, we employed a 200 cm long solar simulator with a 20 cm diameter exit port. It featured a reflective off-axis optical design, with interior mirrors coated in UV high-reflection film. The simulator utilized a single 3000 W lamp as a light source. The key specifications are as follows: Wavelength range: 250-1100 nm; Light output caliber: 200 mm; Non-uniformity: 1.5%; Divergence angle: \(\pm\)0.5\({}^{\circ}\); Power adjustment range: 0.4-0.8 solar constants (continuously adjustable); Stability: 1%/h.
2. Five-dimensional mobile station The measured load was positioned on a five-dimensional mobile platform, enabling translation in three directions and rotation along two axes. Therefore, the platform facilitated field scanning, allowing calibration for each pixel on the image plane.
3. Standard diffuser The simulator emitted collimated light, and the standard irradiance of the light source was measured by a radiometer. Calibration of the radiance (sensitivity) of the load to be measured was achieved through the scattering of a standard diffuser plate. Key indicators of the standard diffuser plate are as follows: Spectral range: 200-700 nm; Reflectivity: >98% (250-500 nm); Uniformity: greater than 95%; Diffuser plate diameter: \(\geq\)150 mm \(\times\) 150 mm; Cosine characteristic error: \(\leq\)1% As in Equation (5), the BRDF of UVS diffusers was calculated through transfer computations based on a NIST-calibrated standard diffuser.
## 4 System Measurement Campaigns
### Case Study: Measurement Campaign in the Engine Compartment of a Car  
In order to validate the overall system in a realistic scenario, a measurement campaign has been carried out in a hatchback engine compartment, which can be considered a practical example of a harsh environment from the EM point of view because it is full of metal parts and obstacles causing multiple reflections of the EM waves.  
The setup consists of several sensor nodes placed in different engine locations and at different distances from the illuminator. The pictures of the two measurement setups are shown in Figure 10 (setup 1: the illuminator is up-frontal; setup 2: the illuminator is down, left-sided): with six nodes distributed all over the engine compartment and only one illuminator present at a time.
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
###### Abstract  
In this article, we aim to achieve manual guidance of a robot manipulator to perform tasks that require strict path following and would benefit from collaboration with a human to guide the motion. The robot can be used as a tool to increase the accuracy of a human operator while remaining compliant with the human instructions. We propose a dual-loop control structure where the outer admittance control loop allows the robot to be compliant along a path considering the projection of the external force to the tangential-normal-binormal (TNB) frame associated with the path. The inner motion control loop is designed based on a modified sliding mode control (SMC) law. We evaluate the system behavior to forces applied from different directions to the end-effector of a 6-DOF industrial robot in a linear motion test. Next, a second test using a 3D path as a tracking task is conducted, where we specify three interaction types: free motion (FM), force-applied motion (FAM), and combined motion with virtual forces (CVF). Results show that the difference of root mean square error (RMSE) among the cases is less than 0.1 mm, which proves the feasibility of applying this method for various path-tracking applications in compliant human-robot collaboration.  
Keywords:path tracking; compliance control; human-robot interaction; sliding mode controller; manual guidance +
Footnote †: These authors contributed equally to this work.
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.5. Resisting Replay Attacks (Threat Model 1)  
All communication messages among \(Pat\), \(Doc\) and \(MCS\) contain timestamps \(TS_{i}\) to guarantee message freshness so the proposed scheme is secure against replay attacks.
## 3 Selected Elements of IoT System Architecture
### Real-Time Database Systems  
To ensure the best possible QoS performance in real-time transmission, the solutions described in the previous sections are crucial from the points of view of hardware technologies and transmission and data processing concepts. At the same time, as the data volume generated by IoT systems continues to grow, there is also a steady increase in the need for solutions to store that data while supporting mechanisms that consider real-time requirements [77]. One of the directions is the concept \"fast data\", which involves developing database technology to ensure the fastest possible execution of queries. Examples of such databases are Druid [78] and Volt Active Data [79].  
Some of the most well-known approaches for storing large datasets are _SDDS_ (Scalable Distributed Data Structure) structures, which enable the virtualization of distributed memory by aggregating resources at network nodes [80]. Considering real-time requirements and the need for QoS parameters, an extension of this concept in this direction are SDDS LH/RP (Scalable Distributed Data Structure Linear Hashing/Range Partitioning) approaches dedicated to real-time cloud applications [81]. An SDDS server schedules queries using real-time queuing methods that consider importance attributes, which enables the achievement of a higher level of execution of queries in a certain time in the case of SDDS LH/RP approaches than in the case of FIFO (First In, First Out) queuing approaches [82]. With such an implementation, QoS metrics in real-time IoT systems can be significantly improved [83]. The steady increase in demand for big data processing environments has led to the development of more dedicated data storage models, such as NoSQL [84] and NewSQL [85]. These IoT system solutions are beginning to gain value from the point of view of time requirements due to the need to address the processing of large volumes of data while maintaining QoS parameters.  
In terms of real-time database systems, issues remain to be addressed in terms of ensuring that hard real-time policies are met for queries that update data that are already recorded/stored and for bucket splitting operations [86; 87].
# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 3 Experiments and Discussion
### Experimental Setup and Evaluation Index  
The designed method is implemented using the deep learning framework Pytorch1.1.0-cuda10-py36. The hardware configuration used in the experiment is as follows. Inspur artificial intelligence development platform server GPU is 2080 Ti (12 GB video memory), ResNet-50 is used as the backbone network, and the input image size is adjusted to \(384\times 384\). The parsing model was trained on the dataset for 80 epochs. The model optimizer chooses Adam, the data batch is 18, and the initial learning rate is 0.0001. It drops by 10% after the 20 th epoch and then drops by 10% every 15 epochs. Data enhancement methods such as horizontal flipping and random erasure are still used.  
The evaluation metrics use mean precision (mAP) and cumulative matching curves (CMC) to measure ReID performance. For each probe image b, the average precision is calculated as follows.  
\[AP=\frac{\sum_{k=1}^{n}P(k)\times rel(k)}{N_{gt}} \tag{14}\]  
where \(k\) defines the ranking in the list of retrieved vehicles, \(n\) represents the number of retrieved vehicles, and \(N_{gt}\) represents the number of ground truth retrievals by the detector. \(P(k)\) represents the precision at the cutoff point \(k\), and \(rel(k)\) represents whether the \(k\)-th recall image is correctly matched.  
The mean precision is calculated as follows.  
\[mAP=\frac{\sum_{b=1}^{Q}\rho}{Q} \tag{15}\]  
Here, \(Q\) represents the number of probe images.  
Cumulative matching features use cumulative matching curves (CMC.) The CMC curve describes the expected value of positive samples in the first \(k\) levels, and the CMC values for the first \(k\) levels are calculated as follows.  
\[CMC@k=\frac{\sum_{i=1}^{Q}f(b_{i},k)}{Q} \tag{16}\]  
where \(b_{i}\) is the \(i\)-th rover, \(f(b_{i},k)\) is an indicator function, and \(f(b_{i},k)\) is equal to 1 when the positive sample is within the first \(k\) ranks and equal to 0 otherwise.
# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 3 Research Methodology
### Acquisition of Image  
The sample images are collected by capturing them with a low-cost onboard digital camera (Canon Power Shot SX530 HS (Canon Inc., Tokyo, Japan)) which is placed in a moving vehicle. Under the several environmental conditions, sample images were captured from different route locations in Malaysia. As the Malaysian road is left handed, an onboard digital camera is mounted on the left-hand side of the dashboard. This camera position is suitable for capturing the left sided images of traffic signs. The main objective of this segment is to implement a traffic image database. It should be noted that our collected image datasets include standard, non-standard, maintained and the un-maintained set of traffic sign images. The training and testing images were captured in all kinds of weather such as rainy, sunny, and cloudy. The images were captured at various day and evening times ranging from 7:00 AM to 7:00 PM. The frame rate of the captured real-time images was 29 frames per second with an average of 60 km/h vehicle speed.  
Our image dataset contains 12 different classes of traffic signs, namely Caution! Hump, Give Way, Towing Zone, Traffic Lights Ahead, No Entry, Stop, Speed Limit, Pedestrian Crossing, Keep Left Curve Chevron, Keep Right Curve Chevron, No Stopping, and No Parking. These 12 classes of traffic signs were captured because these signs have different colors, shapes, and pictograms throughout Malaysia. Moreover, these signs are frequently installed in many places. For each class, 100 samples were used for training purposes. For testing, 1200 real-time test images were used to detect and recognize the class of particular traffic sign. Hence, overall, our dataset comprised of 2400 training and testing images.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 3 Results
### Performance Analysis
According to Figure 5(b), the CNN exhibited the highest MAE values, while the LSTM and GRU models demonstrated lower MAE than the CNN but higher values than ConvLSTM, LSTM-ED, LSTM-GRU, and GCSG. When \(d\leq 11\), BiLSTM's MAE was lower than those of the LSTM and GRU. However, when \(d>11\), its MAE became comparable to that of the LSTM. Additionally, the combined models (ConvLSTM, LSTM-ED, and LSTM-GRU) maintained similar MAE values across different \(d\) values, with LSTM-ED exhibiting marginally lower MAE than LSTM-GRU and ConvLSTM. The GCSG model consistently achieved the lowest MAE values across all prediction steps \(d\). Figure 5(c) reveals that the MSE of the GCSG model follows a nearly linear growth trend with increasing prediction step \(d\), reflecting the expected accumulation of error. In contrast, the LSTM, GRU, LSTM-ED, ConvLSTM, BiLSTM, and LSTM-GRU models exhibited fluctuating MSE growth patterns. This demonstrates the GCSG model's stable linear MSE\(-d\) relationship in load forecasting applications.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 1 Introduction
The main contributions of this paper are summarized as follows:  
* A double-layer compressive sensing framework is proposed to eliminate the adverse effects of block data loss on DOA estimation in WSAN. Specifically, we model the random packet loss during transmission as a passive CS process and introduce an active CS process at each array sensor to address the block data loss problem. * We present the mutual coherence of the equivalent measurement matrix and the absolute off-diagonal entries' distribution of the corresponding Gram matrix under the double-layer CS framework, which account for the satisfactory DOA estimation performance. * A direct DOA estimation technique (DCS-DDOA) is proposed under the double-layer CS framework to avoid the error propagation problem in DCS-DOA. A joint frequency and spatial domain sparse representation of the sensor array data is constructed and exploited to directly perform DOA estimation at the FC. The remainder of this paper is organized as follows. Section 2 describes the preliminaries of compressive sensing, array signal model, lossy wireless links and block data loss briefly. In Section 3, we elaborate the double-layer CS framework and the two DOA estimation techniques. The performance evaluation for DCS-DOA and DCS-DDOA is presented in Section 4, followed by conclusions in Section 5.
## 4 Experimental Results
### Experiment Results
#### 4.3.2 Results of Case 3  
In Case 3, we only used two class labelled samples to build prediction models. For Case 3, we focused on the Excellent and Poor classes. Table 5 lists results of Case 3. From this table, we can find that the mean values (standard deviation) of overall accuracy are 79.82% (0.91%) for CART, 74.52% (0.41%) for C5.0, 79.02% (4.43%) for RF, and 69.02% (7.28%) for MLP.  
In order to validate the difference between CART, RF, C5.0, and MLP, we implemented one way ANOVA. Null hypothesis is \"All means are equal\" and alternative hypothesis is \"At least one mean is different\". The significance level (\(\alpha\)) is set as 0.05. From Table 6, we can reject null hypothesis due to the \(p\)-value (0.000) is less than 0.05.  
To find the best prediction models, 6 statistical hypotheses under 95% confidence level have been carried out using two-sample \(t\)-test. Table 7 lists the results of statistical hypotheses tests. From the results of H1 and H2, we can find CART has no significant difference compared to RF. From H3 to H6, the \(p\)-values are all less than 0.05. Consequently, for these four hypotheses, we reject all null hypotheses. It means CART is better than C5.0 and MLP; RF is better than C5.0 and MLP. In sum, it can be concluded that CART is slightly better than RF since the difference is not significant. And both CART and RF are significantly superior to C5.0 and MLP. In this case, CART is superior to MLP, C5.0, and RF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** \\ \hline Factor & 3 & 826.5 & 275.49 & 15.43 & 0.000 \\ Error & 36 & 642.6 & 17.85 & & \\ Total & 39 & 1469.0 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of Variance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Experiment No.**} & \multicolumn{4}{c}{**Case 3: Focus on Minority**} \\ \cline{2-5}  & \multicolumn{4}{c}{**EX-Poor Classification**} \\ \cline{2-5}  & **CART** & **C5.0** & **RF** & **MLP** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 80.04 & 75.20 & 82.92 & 73.17 \\
2 & 80.04 & 74.60 & 73.17 & 60.97 \\
3 & 80.48 & 74.10 & 75.60 & 63.41 \\
4 & 78.04 & 74.60 & 80.48 & 60.97 \\
5 & 80.48 & 74.10 & 82.92 & 63.41 \\
6 & 78.04 & 72.70 & 82.92 & 65.85 \\
7 & 80.48 & 72.70 & 82.92 & 82.92 \\
8 & 81.48 & 75.20 & 82.92 & 73.17 \\
9 & 82.92 & 76.60 & 80.48 & 70.73 \\
10 & 78.04 & 76.10 & 75.60 & 75.60 \\ Mean & 80.00 & 74.59 & 79.99 & 69.02 \\ Standard & 1.60 & 1.28 & 3.78 & 7.28 \\ Deviation & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of Case 3.  
To make a fair comparison, we also provided a confusion matrix of C5.0 in Table 8. From Tables 4 and 8, we can establish the ability of identifying excellent students is increased from 0% to 69.59%. The accuracy of predicting the learning poor has been improved from 0% to 83.33%. These four algorithms can precisely predict Excellent and Poor classes.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 3 Developed Measurement Techniques and Their Imaging Evolution
### Measurement Techniques Using the Doppler Principle
#### 3.2.2 Frequency-Based Signal Evaluation
However, the necessity of acquiring multiple frames for one velocity image is a critical point.
## 3 Proposed FRESH-Filter Receivers  
Figure 3 details the signal processing blocks used in the proposed FRESH-filter receiver, which comprises one finite impulse response (FIR) filter, two FRESH filters, and one block for estimating and compensating the cyclic frequency offset associated the CSIN. We first use the pilot signals \(s_{p}[n]\) to estimate the channel response of the powerline \(h[n]\), which makes it possible to estimate the received signal without AWGN and CSIN interference. Note that the desired signal to train \(h[n]\) is \(r[n]\), whereas the error signal used to adapt \(h[n]\) is \(e[n]=r[n]-y_{p}[n]\), where \(y_{p}[n]=s_{p}[n]\otimes h[n]\) and \(\otimes\) denotes a convolution operation. To improve convergence results when dealing with the noisy received signals, we suggest using the recursive least-squares (RLS) algorithm to adapt \(h[n]\). Note that following convergence of the adaptation process, the output of \(h[n]\) is used to approximate the output of the  
powerline channel without additive noise; i.e., \(y_{p}[n]\approx y[n]\). Note also that \(e[n]\) could be treated as an additive noise term, including AWGN and CSIN.  
Next, our objective in using an adaptive FRESH filter \(\mathbf{h}_{1}[n]\) is to make predictions related to the additive CSIN signals \(\hat{\eta}[n]\). The desired signal to train \(\mathbf{h}_{1}[n]\) is \(e[n]\), which contains only additive noise following the convergence of \(h[n]\). The error signal used to adapt \(\mathbf{h}_{1}[n]\) is \(e_{1}[n]=e[n]-\hat{\eta}[n]\). Note that the cyclic frequency offset estimation exploits the phase rotation of the coefficients of \(\mathbf{h}_{1}[n]\) in estimating the offset of the \(k\)-th branch, i.e., \(\hat{\Delta}_{k}\), of the FRESH filter (see Section 3.2).  
Third, the adaptive FRESH filter \(\mathbf{h}_{2}[n]\) is meant to restore the OFDM signal from the denoised signal \(r_{m}[n]=r[n]-\hat{\eta}[n]\). The desired signal to train \(\mathbf{h}_{2}[n]\) is \(y_{p}[n]\), whereas the error signal used to adapt \(\mathbf{h}_{2}[n]\) is \(e_{2}[n]=y_{p}[n]-\hat{r}[n]\). Note that the output of \(\mathbf{h}_{2}[n]\) is fed into the CP removal block for further demodulation.