esa-sceva/satcom-chunk-collection huggingface dataset:
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 7 Experiments  
### Goal and Strategy  
Overall, the goal of the following two experiments was to verify the effectiveness of the designed feedback mechanism/variables, so the two AA-HMM models being tested adopted the default settings and the initial matrices (transition and emission) were set with balanced parameters (Section 7.2.1) for better demonstrations. Therefore, considerable amounts of work needed to be completed to further improve the performance of the specific data set.  
In the first experiment, we employed NSL-KDD [34] as the benchmark data set. Given the most recent evaluation of the NIDS data sets in [28], NSL-KDD is one of the two most frequently used data sets for evaluating the NIDS and has no distinct disadvantage compared with the newest data sets. Since numerous researchers have trusted the effectiveness and quality of and tested their algorithms on the NSL-KDD, it is the best data set for a comprehensive performance comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Efficiency Matrix** & **Precision** & **Recall** \\ \hline Benign & \(\mathrm{E_{11}}\) & \(\mathrm{E_{12}}\) \\ Malicious & \(\mathrm{E_{21}}\) & \(\mathrm{E_{22}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of Efficiency Matrix (EM).  
In order to conduct a rigorous comparison, we selected the algorithms/papers for comparison based on the following criteria: (1) the cited paper has to clearly state that it used the two-label version of NSL-KDD instead of the five-label version; (2) the cited paper had to clearly state that it used Test+ (includes all the difficult samples that do not contained in the other test set, test-21) as the test set, because the NSL-KDD is valuable at providing researchers a great test set Test+ with distinct patterns from the training sets, which better reveal the performance difference between the different models; (3) the cited paper had to use Test+ as a separate test set, so that we could ensure that the difficult patterns/samples would not leak to the model during training phase through other evaluation approaches such as cross-validation; and (4) the cited paper had to publish enough metrics for a comprehensive comparison. Therefore, we selected three deep-learning algorithms: (1) deep neural network (DNN) [35], (2) soft-max regression (SMR), and (3) self-taught learning (STL) [36] as the comparison algorithms based on the aforementioned four criteria, which were evaluated on NSL-KDD in terms of accuracy, precision, recall, and efficiency.  
In the second experiment, we verified the effectiveness of the designed mechanisms on a new data set (patterns), CTU-13 [37]. Specifically, in order to evaluate the AA-HMM's abilities to adapt and act as an anti-adversary on the current and intensively changed traffic patterns, we employed the no. 10 data set of CTU-13, which is composed of violently fluctuating traffic patterns caused by an intensive distributed denial-of-service (DDoS) attack.
## 2 Gain and Noise in Avalanche Photodiodes
We can see that the performance of an APD is defined by its dark current, the excess noise factors, and an optimum \(M\). The excess noise factors are often simplified by equaling \(F_{A}=F_{dm}=F_{sm}\), and it quantifies the additional noise caused by the fluctuation of the avalanche multiplication process [41]:  
\[F_{A}=\frac{\left\langle M^{2}\right\rangle}{\left\langle M\right\rangle^{2}} \tag{3}\]  
\(F_{A}\) depends on the ratio of electron-hole generation \(k_{A}=\alpha_{h}/\alpha_{e}\) in the specific APD design and material (0 \(<k_{A}<\) 1), by defining \(k_{A}\) as the smaller of the ratios between holes vs. electrons. \(F_{A}\) has been derived as  
\[F_{A}(M)=k_{A}\cdot M+2(1-k_{A})+(k_{A}-1)/M \tag{4}\]  
For current InGaAs-APDs, we find \(F_{A}\) typically ranging from 3 to 5 for \(M=10\), and from 4 to 8 for \(M=20\). Different approximations have been suggested for (4) to allow for the further analytical evaluation of APD performance. The exponential approximation  
\[F_{A}\approx M^{k_{A}} \tag{5}\]models well for a smaller \(M\), and the exponent can be adopted to other ranges of \(M\). Furthermore, a linear approximation is in use [42]:  
\[F_{A}\approx 2-2k_{A}+k_{A}M \tag{6}\]  
From the comparison in Figure 2, we find that both approximations provide a good fit for \(10<M<20\).
# Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
## 2 Simplified Digital Image Correlation Method  
### Digital Image Correlation  
DIC is a non-contact method for measuring the amount of movement (displacement amount) on the specimen surface. A picture of the specimen surface is taken before and after deformation using a digital camera. An identical point on the specimen is determined in the images before and after deformation; the amount of movement of this point is used to obtain the amount of displacement undergone by the specimen surface. To obtain the amount of movement, the correlation of the light intensity value distribution, which defines the deformed position of the calculation region composed of a subset of pixels (see Figure 1), is expressed as Equation (1).  
\[\Delta\mathrm{x}_{1}=\left(\mathrm{x}_{1}^{\prime}-\mathrm{x}_{1}\right)C( \mathrm{x},\;\mathrm{y},\;\mathrm{x}^{*},\;\mathrm{y}^{*})=\frac{\sum F( \mathrm{x},\mathrm{y})G(\mathrm{x}^{*},\;\mathrm{y}^{*})}{\sqrt{\sum F( \mathrm{x},\;\mathrm{y})^{2}G(\mathrm{x}^{*},\;\mathrm{y}^{*})^{2}}} \tag{1}\]  
F: Light intensity value before deformation G: Light intensity value after deformation (x, y): Coordinates before deformation (x*, y*): Coordinates after deformation  
Figure 1: Process for calculation of deformation by DIC. (**a**) Reference image; (**b**) Deformed image.
## 5 Experiments and Results
### Ablation Study
#### 5.1.3 Which Backbone Model Is Better?  
The question was which of the backbone models used in the experiments performs the best among selected SOTA pre-trained CNN and transformer models? We used the CBIS-DDSM dataset to test those two models. As shown in Table 4, ConvNeXt was selected as a backbone model for our proposed model as it obtained the best result. This decision was made because ConvNeXt outperformed the Swin transformer model in terms of F1-score, RL, and Cov.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fusion Method** & **F1\%** & **mAP\%** & **EM\%** \\ \hline Concatenation & 94.29\(\%\) \(\pm\) 0.011 & 90.54\(\%\)\(\pm\) 0.016 & 86.69\(\%\)\(\pm\) 0.016 \\ \hline Multiply & 93.97\(\%\)\(\pm\) 0.01 & 89.8\(\%\)\(\pm\) 0.013 & 85.6 \(\%\)\(\pm\) 0.023 \\ \hline Average & 94.7\(\%\)\(\pm\) 0.01 & 91.3\(\%\)\(\pm\) 0.017 & 86.9\(\%\)\(\pm\) 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance results for CBIS-DDSM with different fusion methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# Layers** &
\begin{tabular}{c} **\# of Neurons in** \\ **Each Layer** \\ \end{tabular} & **F1\%** & **mAP\%** & **EM\%** \\ \hline
1 & 256 & 94.44 & 90.69 & 86.69 \\ \hline
2 & 256 and 128 & 94.72 & 91.33 & 86.86 \\ \hline
3 & 512, 256, and 128 & 94.27 & 90.5 & 86.45 \\ \hline
4 &
\begin{tabular}{c} 1024, 512, 256, \\ and 128 \\ \end{tabular} & 94.24 & 90.4 & 86.53 \\ \hline
5 &
\begin{tabular}{c} 2048, 1024, 512, \\ and 256 \\ \end{tabular} & 94.48 & 90.9 & 86.69 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of a multi-branch block fusion model with different configurations.
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 2 Sensor Fusion Architecture
### Sensor Fusion Architecture
#### 2.1.2 Sensor Fusion  
The reliability of the values of the system signals in \(\Sigma\) is increased if there exists at least one homogenizer \(P_{i}\). In this case, the system outputs \(y\) and the signals in \(\tilde{\Sigma}^{i}\) are smartly merged by using an extended Kalman filter (\(EKF_{i}\)) to obtain a more accurate value of signals in \(\Sigma\)[18]. Note that the EKF algorithm requires a discrete system, so the system in Equation (1) is discretized with the Euler method or any other discretization method. In addition, herein the subscript \(k\) is used to denote the discrete time index.  
For the sensor fusion, the signals in the output vector \(y\) and the virtual sensors \(\tilde{\Sigma}^{i}\) are arranged into an augmented measurement vector as follows [19]:  
\[\tilde{y}^{i}_{k}=[(y_{k})^{T}\ (y^{\Sigma^{i}}_{k})^{T}]^{T} \tag{4}\]  
where \((y^{\Sigma^{i}}_{k})^{T}\) is the vector formed with the virtual sensors \(\tilde{\Sigma}^{i}\). While the augmented covariance matrix of the measurement noise is given by  
\[\tilde{R}^{i}_{k}=\begin{pmatrix}R^{y}_{k}&0\\ 0&R^{y^{\Sigma^{i}}}_{k}\end{pmatrix} \tag{5}\]  
Figure 2: Implementation of the signal homogenizer \(i\) to obtain the virtual sensors \(\tilde{\Sigma}^{i}\), an estimate of the state \(\tilde{x}^{i}\), and an estimate for the input \(\tilde{u}^{i}\).  
where \(R_{k}^{y}\) is the covariance matrix of the noise of the real sensors' measurements and \(R_{k}^{\Sigma}\) is the covariance matrix of the noise of the virtual sensors obtained from the homogenizer \(i\). The entries of the covariance matrix in Equation (5) are selected based on the respective measurement noise variance. Similarly, the entries of the process noise covariance matrix \(Q_{k}^{i}\) are chosen. Once the parameters of the \(EKF_{i}\) are set, the standard EKF algorithm [20] is used to obtain the required sensor fusion.
## 3 Method
### Reconstruction Losses  
The reconstruction loss contains two parts: waveform reconstruction and duration reconstruction. For the waveform reconstruction loss, we use the multi-precision STFT loss [15] instead of calculating the waveform directly. It is equivalent to the sum of multiple spectrogram losses computed using various STFT parameter sets, consisting of a spectral convergence loss and a logarithmic STFT magnitude loss. It is defined as follows:  
\[L_{sc}(s,\hat{s})=\frac{\|s-\hat{s}\|_{F}}{\|s\|_{F}},L_{\text{mag}}\left(s, \hat{s}\right)=\frac{1}{\hat{s}}\|(\log(s)-\log(\hat{s}))\|_{1} \tag{5}\]  
\[L_{stft}=\frac{1}{M}\sum_{m=1}^{M}\mathbb{E}_{x,\hat{x}}\big{[}L_{sc}(s_{m}, \hat{s}_{m})+L_{mag}(s_{m},\hat{s}_{m})\big{]} \tag{6}\]  
where \(\|.\|_{F}\) and \(\|.\|_{1}\) represent the Frobenius and L1 norm, respectively. \(s\) denotes the number of elements in the spectrum, and \(M\) is the number of sets of STFT parameters chosen. Similarly to VITS and FastSpeech2s [16], we employ a window training strategy, only taking fragments of speech generated for calculating the waveform reconstruction loss.  
For the duration reconstruction loss, we first get the phoneme duration as the target with the help of an external alignment tool and then optimize the duration reconstruction using the log-scale L2 loss.  
\[L_{\text{dur}}=\Big{\|}\log\big{(}\mathcal{D}_{\text{target}}\big{)}-\log \big{(}\mathcal{D}_{\text{predict}}\big{)}\Big{\|}_{2} \tag{7}\]  
Figure 1: The architecture of HierTTS.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology
### Phase 1: Baseline Model  
We assume an offline training set, largely comprising normal data (or at least predominantly normal). Our aim is to learn the baseline representation or distribution of normal operating behavior using self-supervised or unsupervised techniques. If multiple machines are connected via adjacency (e.g., shared process lines or spatial proximity), a graph structure can be leveraged.  
1. Self-Supervision (Reconstruction + Contrastive): 1. Masked Reconstruction: A portion of \(\mathbf{z}^{(m)}(t)\) is masked or noised, and the model \(f_{\theta}\) (e.g., a Transformer) attempts to reconstruct the following: \[\mathcal{L}_{recon}(\theta)=\sum_{m=1}^{M}\sum_{t\in T_{train}}\left\|\mathbf{z}^ {(m)}(t)-f_{\theta}\Big{(}\mathbf{z}^{(m)}(t-\Delta,\ldots)\Big{)}\right\|^{2}.\] (4) 2. Contrastive Learning: If some pairs \((i,j)\) are known to be under similar operating conditions and others are not, we include a contrastive loss [20]: \[\mathcal{L}_{contrast}(\theta)=\sum_{(i,j)\in\mathcal{P}}d\big{(}\mathbf{h}_{i},\mathbf{h}_{j}\big{)}-\sum_{(i,j)\in\mathcal{N}}\max\big{\{}0,\alpha-d\big{(} \mathbf{h}_{i},\mathbf{h}_{j}\big{)}\big{\}}.\] (5) Here, \(\mathbf{h}_{i}\) is the latent embedding from \(f_{\theta}\).
2. Graph-Structured GNN: If machines form a graph \(G=(V,E)\), GNN can be integrated to capture cross-machine dependencies [21].
3. Baseline Distribution: After training, we derive either a reconstruction error distribution, \[E_{recon}^{(m)}(t)=\left\|\mathbf{z}^{(m)}(t)-\mathbf{z}^{(m)}(t)\right\|^{2},\] (6) or a latent embedding distribution, \(\mathbf{h}^{(m)}(t)=f_{\theta}\Big{(}\mathbf{z}^{(m)}(t)\Big{)}\). These constitute the baseline representations of normal states. This involves a baseline mode \(\theta^{*}\), capturing normal multi-modal data behavior, plus a reference distribution, \(p(\mathbf{h})\) or \(p(E_{recon})\), to which new data can be compared.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature Name** & **Transformed Vector** \\ \hline Time & Time \\  & (1 January 2025 \(\to\) 1234567) \\ \hline Machine ID & One-Hot Encoding (Machine\_1 \(\to\) [1, 0]) \\ \hline Recipe ID & One-Hot Encoding (Recipe A \(\to\) [1, 0]) \\ \hline Wafer ID & One-Hot Encoding (Wafer\_001 \(\to\) [1, 0]) \\ \hline Product type & One-Hot Encoding (Type X \(\to\) [1, 0, 0]) \\ \hline Temperature & 850 \\ \hline Pressure & 1.5 \\ \hline Current & 2.1 \\ \hline \end{tabular}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{Text embedding vector} \\ (BERT/Word2 Vec output) \\ \hline \end{tabular}
\end{table}
Table 2: Example of feature transformation table.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Second-Order Stokes Light Based on Brillouin Scattering  
In 2010, [PERSON] et al. proposed a linewidth measurement method based on SBS [53], as shown in Figure 9. The principle is to use nonlinear SBS in the low-frequency range to lock the optical frequency of a narrow-linewidth tunable laser source and the measured signal, driving heterodyne mixing. High-power pump light generates first-order Stokes waves with Brillouin inversion and second-order Stokes waves in the same direction. The fluctuation of the optical frequency does not affect the measured beat spectrum, with a resolution of up to 300 Hz, and no longer requires a fiber delay.  
Figure 8: The BEFL self\(-\)heterodyne spectrum and its fitting curve were measured using a 100 km delay fiber [49].  
Figure 9: Delayed self-heterodyne measurement system based on SBS [53].  
In 2019, [PERSON] et al. reported and analyzed a linewidth measurement scheme based on stimulated Brillouin-induced self-heterodyne (SISH) technology [103]. The delayed self-heterodyne method achieved a 20 dB linewidth of 15 kHz, which is more effective than the SISH method. However, the SISH method reduced jitter by 10 dB. The SISH method has a lower dependence on the fiber length for linewidth measurement, with a 20 dB linewidth of 23 kHz for a 2 km fiber, which is higher than the measurement value for a 25 km fiber.  
In 2023, [PERSON] et al. investigated the effect of pump power density on the Stokes linewidth of different SBS liquid media and achieved controllable linewidth output in the range of 200-400 MHz using FC-43 with a wide Brillouin gain linewidth [104]. The Brillouin gain linewidth and pump power density are the main factors affecting the Stokes pulse linewidth. As the pump power density increases, the Stokes linewidth tends to narrow and approach the pump linewidth. This is the first report to reveal that the narrowing of the Stokes linewidth is limited by the pump linewidth.  
Brillouin-induced Stokes light does not rely on delayed fibers compared with other measurement techniques, such as self-interferometry, because the two contributions detected come from the same source and do not require high-frequency modulation such as delayed self-homodyne/self-heterodyne techniques.
## 5 The Influence of Surface Residual Stress on Hydrogen Permeation Resistance
### The Analysis of Tensile Fracture Morphology  
Figure 13 shows the tensile fracture morphology of specimens with different surface residual stresses after hydrogen charging for 5 h. From the observation results shown in Figure 13, as the specimen's surface residual compress stress increases, the proportion of plastic fracture features on the specimen fracture surface gradually increases. Among these, many cleavage planes are distributed accompanied by intergranular fracture features on the fracture surface of the specimen with a surface residual compress stress of 120 MPa, which is a typical brittle fracture feature, as shown in Figure 13a. It is evident that the specimen with a surface residual compress stress of 120 MPa is significantly affected by hydrogen charging, resulting in a severe embrittlement of specimen material. It can be seen from Figure 13b that the ratio of cleavage planes and intergranular fracture features decreases on the fracture surface of the specimen with a surface residual compress stress of 215 MPa. The ratio of the dimple feature increases and the plastic properties of the fractured specimen significantly improve compared to specimens with a surface residual compress stress of 120 MPa. The fracture morphologies of the other two groups of specimens are shown in Figure 13c,d. It can be observed from Figure 13c,d that a large number of dimples  
are distributed on the fracture surface of the specimens, the cleavage planes are locally distributed, and the plastic fractures dominate the fracture form.  
As the specimen's surface residual compress stress increases, the area of the cleavage planes distributed on the specimen surface decreases, and the feature of intergranular fractures disappears. Simultaneously, the proportion of plastic fracture features dominated by dimples increases, and the shapes of the dimples gradually become apparent. The specimens with surface residual compress stresses of 215 MPa, 293 MPa, and 336 MPa after processed by shot peening during the pretreatment could not easily be affected by hydrogen charging. The specimen could retain good plastic properties and demonstrates good hydrogen permeation resistance. Among these specimens, the shape of dimples on the fracture surfaces of the specimens with a surface residual compress stress of 336 MPa is the most obvious, the amount of quasi-cleavage plane features is small, and the area of the quasi-cleavage plane is relatively small. The specimen material has good plastic properties and the best hydrogen permeation resistance among the four groups of specimens.
## 2 Materials and Methods
### Ionospheric Data and Methodologies
#### 2.3.1 CSES Electron Density Data Processing  
CSES data were first analysed by constructing a night-time and day-time electron density (Ne) time series and secondly by checking the latitudinal profiles. The time series was built by computing the daily median value of all the Ne samples acquired in a circled area centred on the volcano. Only the tracks acquired during quiet geomagnetic conditions determined with Dst and ap geomagnetic indexes were considered. The ap index is a global 3-h index obtained from several geomagnetic observatories placed at different latitudes and gives an indication of the status of the geomagnetic activity on Earth [58]. Dst index has been selected as it is obtained from 4 geomagnetic observatories close to the geomagnetic equator, and it is representative of the geomagnetic activity in the equatorial region where \"La Palma\" volcano is placed [59]. Dst has recently been proved to be a good estimator of the ionosphere's geomagnetic conditions; recent works reproduced the Dst index from data of Swarm and CSES satellites (the same satellites used in this work) [60; 61].  
In particular, we impose that \(|\)Dst\(|\)\(\leq\) 20 nT and ap \(\leq\) 10 nT for quiet conditions. If no tracks are available in quiet geomagnetic conditions or for other reasons, the day is skipped on the time series. Even if the samples are always acquired at the same local time for the Sun-synchronous orbit of CSES, a seasonal trend is still present in the time series. Consequently, we fitted a polynomial of 5 degrees for night-time and 7 degrees for day-time and removed it to construct a final detrended time series. The polynomial grade was empirically determined in previous studies based on the same CSES Ne time series, for example, by [PERSON] et al. [62]. On this detrended time series, the median and interquartile range were calculated in order to define the anomalies as the values that overpass the median plus 1.5 times the interquartile range. Further details of the CSES Ne analyses are provided in Appendix A. Since LAIC models predict an enhancement of electron density [2; 63; 64], we do not consider eventual negative anomalies, i.e., low electron density values.
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 3 Methodology
### Evaluation and Performance Metrics  
Regarding model evaluation, a 5-fold cross-validation approach was used to assess performance. This method splits the data into five folds while maintaining the class distribution of spam and ham images. Each model was trained and tested across five iterations, with one fold held out for testing and the others used for training in each round. The final performance metrics are the average scores across all folds. This approach reduces the risk of overfitting and provides a more robust estimation of the model's performance compared to a single train/test split.  
Furthermore, the following metrics were used for the evaluation of the models:  
* **Accuracy:** Identifies the percentage of correctly classified instances.
* **AUC (Area Under the Curve):** Measures the model's ability to distinguish between spam and ham images. A larger AUC value indicates better performance.
* **Precision, Recall, and F1-Score:** These metrics are particularly useful when utilizing imbalanced datasets, as they offer insights into how well the model identifies spam (precision), avoids false negatives (recall) and calculates the mean of precision and recall (F1-score)
* **ROC Curve:** Plots true positive rate against false positive rate to evaluate classifier performance. A curve closer to the top left indicates better classification.
* **AUC Variance:** Estimated through bootstrapping, it indicates the consistency of the model's performance, with lower variance suggesting greater reliability.
* **Confusion Matrix:** The confusion matrix highlights the performance of the model. It displays the number of true positives, true negatives, false positives, and false negatives.  
* **Classification Report:** Provides a comprehensive view of the model's performance across various metrics like precision, recall, F1-score, and support.