esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Weak Radar Return Signals Based on Pseudo-Time Domain Algorithm
## 4 Conclusions  
This study, based on signal spectra, defines the frequency domain as a \"pseudo-time domain\" and employs wavelet transform to identify specific structures within the pseudo-time domain waveforms. By applying wavelet transform in the \"pseudo-time domain\", thresholds are established according to the characteristics of the transmitted signal and background noise after wavelet decomposition. This process effectively removes noise, significantly enhances the signal-to-noise ratio (SNR), and achieves a high detection success rate. Numerical simulations demonstrate that this method can handle received signals with SNR as low as \(-34\) dB or even lower, substantially increasing the SNR and achieving a notable denoising effect. When the SNR is \(-40\) dB, the maximum detection success rate can exceed \(95\%\). Outdoor experimental results show that the accuracy remains at \(90\%\) even when the SNR is \(-34\) dB.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Paper Number** & **SNR** & **Method** \\ \hline Reference [10] & \(8\) dB, \(1\) dB & Wavelet Decomposition + Max Modulus Detection \\ Reference [11] & \(-20\) dB & Frequency-domain Accumulation + Wavelet Transform \\ Reference [12] & \(-18\) dB, \(-20\) dB & Multi-layer Autocorrelation + Wavelet Transform \\ Reference [13] & \(1.37\) dB & Wavelet Transform + Range Guidance \\ Reference [14] & \(-19\) dB & Two-step De-noising Strategy (TSDS) \\ proposed method & \(-40\) dB to \(-10\) dB & Pseudo-time De-noising Domain + \\  & & Wavelet Decomposition + \\  & & AdaptiveThreshold Detection \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Comparison of Different Methods.  
Figure 11: Detection probability plots for 1000 groups, each with a distinct SNR.  
Therefore, this method can accurately detect weak signals with very low SNRs and is compatible with existing time-domain denoising methods. It effectively improves the weak signal detection accuracy of low-power radars installed on small platforms such as unmanned aerial vehicles and satellites, enabling the detection of signals at longer distances without altering hardware conditions.  
However, it should be noted that the denoising and threshold determination in this method are based on the analysis of reference signals and background noise, which can be computationally intensive. Future work will therefore focus on incorporating deep learning techniques to reduce this computational load, while further optimizing the method to enhance its robustness in more complex environments and exploring its potential applications in other low-SNR scenarios.
## 3 Result and Discussion
### Discussion  
In this paper, the feasibility of using ATR-FTIR spectra combined with multivariate model for predicting the methanol content in methanol gasoline was investigated. According to the assessment criteria [34], a model with an RPD value in the range of 2.5-3, can obtain a good prediction result. If the RPD value of a model is larger than three, it means the excellent prediction performance can be achieved. Based on the above criteria and calculated result in Table 1, the full variables and UVE regression model have the RPD value larger than three. Therefore, an excellent prediction result for methanol content in methanol gasoline can be achieved using the full variables model and the UVE regression model. Furthermore, it can also be concluded that it is feasible to qualitatively and quantitatively analyze the methanol content in methanol gasoline.  
To get the best regression model, two quantitatively multivariate models of PLSR and LS-SVM and two classic variables selection methods were carried out and combined to establish the more accurate prediction model. All results from the six prediction model were presented in Table 1, and two conclusions can be drawn through comprehensive comparisons among these six models. The first one is that the PLSR model has better prediction performance than the LS-SVM model. This conclusion is consistent with the Lambert-Beer law that is the basis of quantitative analysis of an infrared spectra. Therefore, the linear PLSR regression model is more suitable to quantitatively analyze the methanol content in methanol gasoline. Additionally, it can be found that the UVE not only can greatly reduce the variables number but also improve the accuracy and robustness. The other conclusion is that the variable selection method is a preferential choice to simplify the calculation and improve the performance of model. Generally speaking, UVE coupled with PLSR model is a better choice than others to quantitatively analyze the methanol content.  
Methanol gasoline is a more environmentally friendly and economical energy source than gasoline. Qualitative and quantitative analysis plays an important in ensuring the quality of the methanol gasoline. To this end, ATR-FTIR spectra and chemometrics were used to nondestructively determine the methanol content. Furthermore, the corresponding excellent result shows that it is feasible to quantitative analyze using ATR-FTIR technology. Compared with traditional methods, ATR-FTIR has shown its speed, accuracy and online detectability. Although an excellent prediction result for the detection of methanol content was obtained, there are many works needed to be done. In the future, more samples and more types of gasoline need to be considered in calibration model. More powerful chemometric methods and cheaper IR spectrometer need to be developed. Nevertheless, the result of this study indicates that using IR spectra to qualitative and quantitative methanol gasoline is feasible, which provides an alternative method to analyze and ensure the quality of gasoline for gas industry.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 4 Results Analysis and Discussion
\begin{table}
\begin{tabular}{l l l c c c c} \hline \hline
**Time Lags** & **Models** & **Error** & **T + 1** & **T + 3** & **T + 6** & **T + 12** \\ \hline \multirow{8}{*}{3} & FCN & MAE & 6.53 & 5.03 & 5.61 & 7.60 \\  & GRU & MAE & 1.89 & 2.45 & 3.43 & 3.30 \\  & LSTM & MAE & 1.94 & 2.64 & 2.83 & 2.67 \\  & LSTM-DNN & MAE & 1.86 & 2.32 & 2.37 & 2.75 \\  & LSTM-FCN & MAE & 1.54 & 2.21 & 2.24 & 2.31 \\  & BDLSTM & MAE & 1.40 & 2.18 & 2.22 & 2.26 \\  & PROPOSED-MODEL & MAE & 1.17 & 2.10 & 2.16 & 2.11 \\ \hline \multirow{8}{*}{6} & FCN & MAE & 6.58 & 5.28 & 5.58 & 8.27 \\  & GRU & MAE & 1.80 & 2.66 & 3.80 & 3.53 \\  & LSTM & MAE & 1.74 & 2.87 & 3.14 & 2.69 \\  & LSTM-DNN & MAE & 2.09 & 2.74 & 2.80 & 2.61 \\  & LSTM-FCN & MAE & 1.45 & 2.82 & 2.66 & 2.50 \\  & BDLSTM & MAE & 1.42 & 2.62 & 2.71 & 2.61 \\  & PROPOSED-MODEL & MAE & 1.19 & 2.25 & 2.56 & 2.44 \\ \hline \multirow{8}{*}{12} & FCN & MAE & 8.09 & 5.29 & 7.70 & 12.06 \\  & GRU & MAE & 2.17 & 3.36 & 3.77 & 3.52 \\ \cline{1-1}  & LSTM & MAE & 2.29 & 3.51 & 3.54 & 3.11 \\ \cline{1-1}  & LSTM-DNN & MAE & 2.62 & 3.45 & 3.38 & 3.09 \\ \cline{1-1}  & LSTM-FCN & MAE & 1.71 & 3.43 & 3.33 & 2.71 \\ \cline{1-1}  & BDLSTM & MAE & 1.54 & 3.20 & 3.31 & 2.75 \\ \cline{1-1}  & PROPOSED-MODEL & MAE & 1.30 & 3.06 & 3.21 & 2.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with baseline models(MAE). Figure 10: Performance prediction of the proposed model on different time lags.
## 4 Framework Development and Validation
### Performance Validation and Framework Development  
To evaluate the generalization performance of the lane identification algorithm, accuracy was tested on new images. Six arterial road images were selected from each of the KITTI and TuSimple datasets. As shown in Figure 15, the average intersection over union (AIoU) ranged from 0.64 to 0.92, reflecting the accuracy of the predicted object locations.  
Figure 14: Classification of normal and faded lanes based on white pixel ratio using defined thresholds.  
Figure 15: Average IOU analysis results per image.  
Figure 16 presents the results of lane condition assessment. While most intact and worn lanes were accurately classified, several issues were identified. First, non-lane areas were occasionally misclassified as worn lanes. Second, lanes located farther from the image center were sometimes misclassified due to reduced resolution. Third, in twilight conditions, decreased brightness led to the false identification of intact lanes as worn.  
Figure 17 illustrates the structure of the lane identification and assessment framework developed in this study. The framework consists of two main stages: lane identification and lane assessment. In the identification stage, a YOLO-based object detection model is used to detect road elements, including lanes, from the input image and classify lane objects. In the assessment stage, each detected lane is individually analyzed. Pixel occupancy is calculated, and Canny edge detection along with morphological operations is applied to determine whether the lane is in a normal condition. This approach enables reliable lane detection and evaluation under diverse road conditions and generates an output image containing both the detected lanes and their condition status.  
Figure 16: Visualization of lane assessment results generated by the proposed algorithm.
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 2 Preliminaries
### Chosen Plaintext Power Analysis for SM4  
Reference [14] describes the chosen plaintext attack of SM4, and the details are described as below: First, select special plaintext with certain constraints, so that the output _res_ after L transformation is fixed. Then, the round output \(X_{i+4}\) is selected as the attack object (\(X_{i+4}=X_{i}\oplus\textit{res}\), where \(X_{i}\) is the known random value and _res_ is the fixed unknown value), and the fixed value _res_ is obtained through power analysis, and then the round key can be deduced. The key of SM4 can be recovered by executing the chosen plaintext attack on the first four rounds successively. The attack of the first round is taken as an example:  
1. Let \(\textit{res}=T(X_{1}\oplus X_{2}\oplus X_{3}\oplus rk_{0})\), choose some constraint special plaintext, make sure \(X_{1}\oplus X_{2}\oplus X_{3}\) is fixed, so make sure _res_ is fixed;
2. The output \(X_{4}(X_{4}=X_{0}\oplus\textit{res})\) of the first round is chosen as the attack point to perform CPA analysis and recover _res_;
3. Derive the round key \(rk_{0}\).  
The attack on round 2-4 is similar to the first round mentioned above, one round key is recovered each time, and the initial key is finally recovered through key extension.
## 4 Experiments and Analysis
### Experimental Scenario  
To evaluate the effectiveness of the proposed meta-learning-based model selection framework in an energy management context, we designed a series of experiments that reflect practical energy control service conditions--in particular, limited computing resources and the need for fast response times. The experiments aim to assess whether the framework can deliver accurate forecasting results within acceptable service latency, comprising both model selection and inference time.  
The evaluation was conducted using the preprocessed LPG usage dataset, consisting of 566 sensors out of 887 sensors, as described in Section 3.3. The experiments were structured along two key dimensions: computing resource levels and the number of sensors served concurrently.  
First, five levels of CPU availability were considered: 20%, 40%, 60%, 80%, and 100%. These levels simulate a range of computing environments, from highly constrained computing resources to fully available ones. The execution time of each forecasting model was pre-measured from 1% to 100% of the CPU level and used as part of the decision-making input for the meta-learner, enabling resource-aware model selection.  
Second, the scalability of the proposed system was tested by varying the number of sensors requesting model selection and forecasting simultaneously. Two execution types were defined: (1) single-sensor execution, where one sensor can fully use the available computing resources, and (2) multi-sensor execution, where 10, 20, or 30 sensors concurrently trigger model selection and prediction while competing in the resource-limited environment.  
For each combination of CPU level and sensor request size, the proposed framework selected forecasting models based on extracted meta-features and predicted trade-offs between execution cost and forecasting performance. The selected models were then applied to their respective sensor data for evaluation.  
The following performance metrics were measured:  
* Forecasting accuracy (measured using the RMSE);
* Model selection time;
* Inference time of the selected model;
* Total service time (sum of selection and inference times);
* Performance deviation from the globally optimal model selected via exhaustive search.  
The results were compared against an exhaustive search baseline, which evaluated all candidate models to select the best one for each sensor without considering time or resource constraints. This comparison enables a holistic assessment of the proposed system's ability to deliver fast and accurate forecasting services while adapting to real-world computing limitations.
## 2 Materials and Methods
### Population Selection  
The tumor registry database of the Department of Oncology of our hospital was queried for patients with RPS between 2013 and 2021. Patients with sarcomas that originated in the gastrointestinal tract (namely gastrointestinal stroma tumors) or in other abdominal visceral organs were excluded from the study due to the different imaging appearances and tumor shapes. The search retrieved 56 patients with histological diagnoses of RPS. From these patients, we identified 20 who underwent contrast-enhanced CT at our hospital. Thirteen of these patients had only CT examinations performed at the time of the diagnosis, while seven patients had CT examinations at the time of diagnosis and after treatments (one patient had three post-treatment CT examinations, two patients had four CTs, two patients had five, one patient had six, and one patient had eight). All CT examinations' images were reviewed by a radiologist (G.S.) with 20 years of experience inabdominal radiology on a Pictures Archiving and Communication System (PACS--Impax, Agfa-Gevaert, Mortsel, Belgium), confirming the presence of RPS and, eventually, the recurrence in post-treatment CT scan examinations. When recurrence was found, post-treatment CT examinations were also included in the present study. Six post-treatment CT examinations were excluded for the lack of recurrence.
## 5 Formal Verification of the 5 GAKA-LCCO Protocol
### Security Simulation Based on the Scyther Tool  
The Scyther tool [32; 33] is a protocol formal analysis tool, which can provide explicit termination for unlimited sessions and infinite state aggregation protocols, and support multiprotocol parallel analysis. The security protocol description language (SPDL) is used in the Scyther tool to describe and analyze security protocols. It provides a set of claims to test various security goals, such as secrecy and authentication. The secret claim is applied to state confidentiality. In order to provide different degrees of authentication strength, several forms of authentication claims including Alive (i.e., aliveness), Weakagree (i.e., weak agreement), Niagree (i.e., non-injection agreement), and Nisynch (i.e., non-injection synchronization) are employed to detect replay, reflection, MitM attacks, and so on. The detailed description of formal definitions for all Scyther claims can be found in [33].  
We model our proposed 5 GAKA-LCCO protocol in SPDL and specify the security properties of the 5 GAKA-LCCO protocol by a series of claims of Scyther, as shown in Figure 6.  
From Figure 6, our proposed 5 GAKA-LCCO protocol successfully makes certain all Scyther claims and there are no attacks found under the test of the Scyther tool.  
According to Figure 6, \(SUPI\) (i.e., UE in this figure) is secret, and \(K_{SEAF}\) (i.e., the session key established between the UE and HN and distributed from the HN to SN) is also secret. Additionally, aliveness, weak agreement, non-injection agreement, and non-injection synchronization among the UE, SN, and HN are established, so replay and MitM attacks among the UE, SN, and HN are overcome [33].  
Similarly, because MitM attacks among the UE, SN, and HN are overcome, \(K_{SEAF}\) can reach an agreement among the UE, SN, and HN. In addition, replay attacks among the UE, SN, and HN are overcome, so all the messages among the UE, SN, and HN cannot be replayed. As a result, \(SUCI\) cannot be replayed, the location privacy of the UE cannot be compromised, and DoS attacks against both the SN and HN cannot be formed.
## 3 Resonators: Materials and Geometries
### Geometric Shapes
The slanted sides and non-uniform cross-section significantly alter the electromagnetic field distribution, making these geometries highly efficient for compact antennas with enhanced gain and BW. The tapering structure improves coupling with patch antennas by reducing reflections and minimizing energy losses, broadening operational BW. Additionally, these geometries support TE and TM modes while inherently suppressing higher-order modes, leading to cleaner and more stable frequency responses. Precision in fabrication is required for trapezoidal prism-based geometries, particularly at higher frequencies, where minor dimensional inaccuracies can degrade performance. Thermal stability is another important consideration, as materials with low thermal expansion coefficients are required to ensure consistent performance under varying environmental conditions. In this work, we focus on designing resonators using the following trapezoidal prism-based geometries: Figure 2m inverted trapezoidal prism, Figure 2n hollow inverted trapezoidal prism, and Figure 2o trapezoidal prism. The inverted trapezoidal prism offers improved coupling and broader BW due to its optimized field distribution, making it suitable for wideband applications with moderate fabrication complexity. The hollow inverted trapezoidal prism is the most appropriate choice when maximum gain, BW, and weight reduction are required. Its hollow structure maintains strong electromagnetic confinement while minimizing material losses, although it poses more significant challenges in fabrication. Lastly, the trapezoidal prism is an effective and cost-efficient option for narrowband applications, offering simplicity in design and fabrication alongside moderate performance improvements [38]. Finally, a comprehensive comparison of these geometries is provided in Table 2, highlighting their distinct characteristics and suitability for various design applications.
## 2 Materials and Methods
### Measurements  
Measurements were conducted using the PRIMUS RS dynamometer from BTE Technologies (Hanover, MD, USA), a validated and verified device [19]. The participants were placed in a seated position with the hip flexed at 110\({}^{\circ}\). No support straps were placed on the trunk, hip, or thigh to avoid additional fixed support points other than the pad itself. The axis of the dynamometer was correctly aligned with the axis of motion of the knee joint. The pad was positioned with a 30 cm lever arm on all subjects. Six measurements were performed on each knee: three with the pad on the anterior aspect (AA) and three on the posterior aspect (PA) of the tibia (Figure 1). Measurements were performed at three different angular velocities: at 30\({}^{\circ}\)/s, three flexion/extension repetitions were performed; at 120\({}^{\circ}\)/s, five repetitions were performed; and at 180\({}^{\circ}\)/s, ten repetitions were performed. Before the measurements, ten submaximal repetitions at 180\({}^{\circ}\)/s were performed as a warm-up to familiarize the athletes with the device. A 2 min rest period was allowed between each measurement. The measurements were first taken with the pad on the AA position, followed by the PA position.  
The H/Q ratio was determined by dividing the hamstrings' maximum peak strength by the quadriceps' maximum peak strength for each measurement. Additionally, the value of the joint work was extracted from the device after finishing the measurements.  
Figure 1: Measurement positions: anterior aspect (**left**) and posterior aspect (**right**).
## 4 Experimental Settings
### Voice Conversion Baselines  
The first baseline considered was the method based on the **McAdams** coefficient [45], which is a purely signal-processing approach. The power spectrum of a short-term segment of speech is fit with an all-pole model using linear predictive coding analysis. The formant frequencies were determined by the angles of the corresponding complex poles. The speaker anonymization process was, therefore, achieved by varying the pole angles to shift the formants using the McAdams coefficient. Another signal-based technique used as a baseline applies **pitch and formant** changes and resembles the procedure based on the popular Praat toolkit [77]. Given the efficacy of the speech modification approaches used in ContentVec [36], we also consider it as a signal-processing baseline. Three modifications were applied to the signals, i.e., a random shifting of the formants (achieved by scaling by a random factor \(\rho_{1}\)), random pitch scaling (by a random factor \(\rho_{2}\)), and random frequency shaping using a parametric equalizer [65].  
The next baseline considered was the Activation Guidance and Adaptive Instance Normalization-VC method **(AGAIN-VC)**[78], which is an auto-encoder-based model that has a single encoder and a decoder. The application of activation as a bottleneck on content embeddings greatly improves the trade-off between the synthesis quality and the speaker similarity in the converted speech. The related code is publicly available ([[https://github.com/KimythAnly/AGAIN-VC](https://github.com/KimythAnly/AGAIN-VC)]([https://github.com/KimythAnly/AGAIN-VC](https://github.com/KimythAnly/AGAIN-VC)) (accessed on 1 March 2024)). Finally, the original Phonetic Posterior-based VC method **(PPG-VC)**[19] was also considered and included as a baseline. In this case, phonetic posteriors were used as content representations, and the speaker information was therefore effectively obfuscated.  
We also considered models based on our approach [17], i.e., ones that encode the speech content through the popular SSL representation, such as **HuBert**[79] or **WavLM**[59]. The last baseline model was based on the aforementioned S3 PRL toolkit and a standard WavLM representation ([[https://huggingface.co/patrickvonplaten/wavlm-libri-clean-10](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-10)]([https://huggingface.co/patrickvonplaten/wavlm-libri-clean-10](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-10)) Oh-base-plus (accessed on 1 March 2024)).
## 2 Overview of Reinforcement Learning
### Reinforcement Learning
#### 2.1.1 Q-Learning  
As RL has various classes, an off-policy RL algorithm that seeks to learn a policy that maximizes the Q-values is called Q-Learning [10]. It is a lookup table based with one entry per (\(s\), \(a\)) pair and initialized with random Q-values. We then use the Bellman update equation to iteratively update \(Q(s,\,a)\) estimates. Q-values converge to optimal \(Q(s,a)\to Q^{*}(s,a)\). This algorithm is only feasible for small state-action spaces; in large state-action spaces, many regions may never be visited, and it does not handle generalization to unvisited state-action pairs. That is why we use deep Q-learning.
## 6 Security Analysis  
To ensure the security of this system, the correctness of introduced operations is proved. And it is necessary to ensure that one user or multiple colluding users are unable to decrypt other encrypted data according to the threat model. Besides, it is necessary to ensure that the Server or an adversary could not obtain the plaintext to all ciphertext in this system. And we would explain the usage of BCP enhance method to deal with probabilistic forgery attacks.