esa-sceva/satcom-chunk-collection huggingface dataset:
# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 4 Experiments
### Comparison with State-of-the-Art Methods  
Table 5 compares our AdaFER with 13 state-of-the-art (SOTA) methods in the CD-FER task. The methods from the bottom table use RAF-DB as the source dataset, and those from the upper table use other datasets. As shown in Table 5, our AdaFER achieves competitive results compared to the SOTA methods. [PERSON] et al. [56] achieve SOTA quality on CK+ using six datasets. ECAN [42] obtain SOTA quality on the JAFFE and FER2013 datasets using a model pretrained on VGGFace2 and then fine-tuned on RAF-DB2.0, which is not publicly available. For fair comparison, the methods in the bottom part of Table 5 use the same source dataset and backbone. The mean accuracy over all target datasets is also computed for easy comparison. Our AdaFER obtains accuracy values of **81.40%**, **61.37%**, **57.29%**, and **70.86%** on the CK+, JAFFE, FER2013, and ExpW datasets, respectively, which are the new state-of-the-art CD-FER results for these datasets. Moreover, our AdaFER does not increase any computing cost in the inference phase. AGRN [17] needs to extract holistic and local features to initialize the nodes of the target domain in the inference stage, which is time-cosuming. LPL [11] relies on the assumption of both the source and target domains.  
To evaluate the robustness of our method on different source datasets, we also show the performance of CD-FER using FERPlus as the source dataset. Note that the FER2013 dataset has the same images as FERPlus; therefore, we ignore its performance on FER2013. Our AdaFER also improves the baseline method (#3) by a large margin in terms of mean accuracy.  
**Understanding AU-guided learning**. To better understand the differences between the baseline method (#3) and our AdaFER, we utilize the T-SNE method to illustrate the feature distributions of the test sets of the CK+ and FERPlus datasets. The results are shown in Figure 7. We can find that our AdaFER can learn more compact features than the baseline in unsupervised CD-FER tasks. For CK+, \"Neutral\" samples are the most scattered ones for the baseline method; AdaFER can cluster them dramatically. For FERPlus, the samples of same categories are clustered compactly, while those of different categories are largely separated. These illustrates the reason why AdaFER improves the baseline dramatically.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threshold** & **CK+** & **ExpW** & **FER2013** & **FERPlus** \\ \hline
0 & 68.99 & 65.24 & 52.21 & 66.68 \\
0.25 & 72.03 & 68.23 & 56.23 & 74.23 \\
0.5 & **81.40** & **70.86** & **57.29** & **78.22** \\
0.75 & 80.96 & 69.12 & 55.80 & 77.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of the threshold for hard-negative mining.
# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 3 Technical Construction  
### Data Collection and Processing  
Taking the dress category in the cross-border e-commerce market as an example, data from the product detail pages of 862 dresses on the Amazon platform were collected. The data include variables such as Style, Price, Size, Season, Waistline, Neckline, Sleeve Length, Material, Fabric Type, Decoration, Pattern Type, and Rating. Among these, Style, Price, Size, Season, and Waistline are market positioning feature variables, while Neckline, Sleeve Length, Material, Fabric Type, Decoration, and Pattern Type are product attribute feature variables. Rating is used as the label variable. These 12 variables were further transformed into feature values, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Name** & **Feature Value** \\ \hline Style & 1—Bohemian; 2—brief; 3—casual; 4—cute; 5—fashion; 6—flare; 7—novelty; 8—OL party; 9—sexy; 10—vintage; 11—work \\ \hline Price & 1—low; 2—average; 3—medium; 4—high; 5—very high \\ \hline Size & 1—small; 2—S; 3—M; 4—L; 5—XL; 6—free \\ \hline Season & 1—autumn; 2—winter; 3—spring; 4—summer \\ \hline Waistline & 1—dropped; 2—empire; 3—natural; 4—princess; 5—null \\ \hline Neckline & 1—O-neck; 2—backless; 3—boat-neck; 4—bowneck; 5—halter; 6—mandarin collar; 7—open; 8—Peter Pan collar; 9—ruffled; 10—scoop; 11—slash-neck; 12—square collar; 13—sweetheart; 14—turndown collar; 15—V-neck \\ \hline Sleeve Length & 1—full; 2—half sleeve; 3—butterfly; 4—sleeveless; 5—short; 6—three-quarter; 7—turndown; 8—cap sleeves \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables and feature values.  
The feature values in the dataset vary across different ranges, resulting in certain discrepancies. Before training, the feature values were standardized using the formula (X-mean)/std to ensure that all data are distributed with a variance of 1 within the range of \(-1\) to 1. The holdout method was employed for data splitting, dividing the 862 records into two mutually exclusive sets: 80% of the data (689 records) was used for training, and 20% (173 records) was used for testing.
## 4 Experimental Methodology  
The details of the satellite nodes participating in a communication mission and the topology details are described in a JSON file named contactPlan.json. The JSON file contains  
Figure 1: Cognizant controller running on satellites autonomously selects links at each satellite to optimize latency and pack loss in a possible interplanetary network.  
information such as the source, destination, availability of the satellite contacts (line-of-sight) to each other, data rate, start time, and end time of the contacts. This file will be distributed to all nodes from a mission-specific control center through a separate channel and updated at scheduled intervals [48]. The SNN on each node decides on an outbound link from the set of available links to the next hop on the shortest path from the source node to the destination node, computed using the contacts' availability from the contact plan file. The SNN autonomously learns to select outbound links at each node based on their performances in the previous instances.
# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
## 4 Results and Discussion  
### Simulation Settings  
The MATLAB-based Vienna LTE-A system level simulator is used to implement the proposed method [32; 33]. A number of simulations have been performed to evaluate the performance and compare with other well-known algorithms. Urban area with the random user deployment in the cell is considered where users are equally distributed and scattered all over the coverage area. The number of user varies from 10 to 50 in each cell with combination of active and inactive user. In this simulation, active users are transmitting and receiving data whereas the inactive users are connected to eNB but not requesting any data. The average speed of the user is 5 kmph. Four downlink CCs of 10 MHz bandwidth are used. Two CCs each are at 900 MHz and 2100 MHz frequency band. The transmission mode is single antenna and eNodeB power transmission is 46 dBm. The duration of the simulation process is set to 1000 TTI. The performance is assessed by three different types of data, namely Video, HTTP, and VoIP. According to [29], each type of these data has its own delay threshold and priority level. Video has the lowest priority among the above types of data and its delay threshold is also low. Therefore, Video type data packet would be discarded first in a congested queue. HTTP has higher priority and highest delay threshold whereas VoIP has the highest priority but it has lower delay threshold. These characteristics can affect the performance of these data types.  
The performance of the proposed method is compared with largest gain PF (LG-PF) [27], least load PF (LL-PF) [28], and RR scheme [9]. In [27], joint CC assignment and PF packet scheduling are implemented. Largest gain is considered to select the CC without considering the queue length of all CCs. After assigning CC, the scheme in [27] assigns RB to the user. The scheme in [28] selects CC with lowest load and does not consider any delay or packet loss. Whereas in [9], CCs are randomly assigned  
Figure 2: Simplified steps of the proposed algorithm.  
to the user without considering any other parameter. However, it is less complex than the schemes are reported in [27; 28]. Table 5 shows a list of simulation parameters with corresponding values.
# Deep Learning of GNSS Acquisition
## 4 Model Training
In order to train the NN-based classifier, the generated dataset contained either signal-plus-noise (\(\mathcal{H}_{1}\)) or noise only (\(\mathcal{H}_{0}\)) snapshots, which were then split into subimages, as shown in Figure 4. Since there will be many subimages that contain the CAF peak, in this method, the specific subimage that contains the complete CAF peak exactly in the middle of the subimage is considered as a correctly detected peak, and these types of snapshots are fed to the NN for training. The classifier learned its parameters by observing a set of 3000 input/output pairs in a supervised manner. The output of the NN was a _softmax_ layerwith dropout, such that the resulting outcome of the NN are the binary class probabilities required to compute the test in (19), or its noncoherent version in (23). The particular convolutional neural network's structure was based on the architecture in [52], containing 7 convolution layers and 3 fully connected layers. Each convolutional layer was followed by a batch normalization layer and a ReLu activation function. The batch normalization layers are used to normalize the activation and gradients propagation through the network between the convolution and ReLu layers, which is known to speed up network training tasks [55]. Each fully connected layer follows up with the ReLu activation function and a dropout layer with \(1/2\) probability rate. Since the task is a binary classification, the last fully connected layer contains two neurons, predicting the posterior probabilities of each hypothesis. Other relevant training options were specified, such as the use of a stochastic gradient descent with momentum (SGDM) optimizer with an initial learning rate of 0.001. The maximum number of epochs, which is a full training cycle on the entire training dataset, was set to 30 and, at every epoch, the data were shuffled. After 20 epochs, the learning rate dropped by a factor of 0.1. The loss, which SGDM optimizes, was the cross-entropy loss, and the accuracy was defined as the percentage of inputs that the network classified correctly. Particularly, the validation accuracy after training the model reached 93%, which is deemed a high enough rate to consider the NN ready for deployment. Section 5 provides testing results of the trained and validated model, showing ROC performances and other relevant metrics.
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
## 4 Experiments
### BI-LSTM Module  
With certain efforts of investigation, we arrived at a BI-LSTM architecture with configuration in Figure 7 and parameter settings in Table 3. The kernel, bias, and activation are constrained by L2 regularization. We adopted a 10-fold cross-validation procedure in the training and testing. The dropout mechanism was invoked to avoid the problem of over-fitting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted Actual** & **Attack** & **Normal** \\ \hline Attack & TP (True Positive) & FP (False Positive) \\ Normal & FN (False Negative) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Figure 7: Configuration of the BI-LSTM.  
We first examined the effectiveness of the BI-LSTM in serving as a DDoS detection module. \"CIC-IDS2017/Wednesday\" was used as the training data set to train the BI-LSTM. After training, the same data set, \"CIC-IDS2017/Wednesday\" was used to test the BI-LSTM. As indicated in the first row of Table 4, the BI-LSTM was well-trained, well-behaved, as intended. The BI-LSTM is highly capable of discrimination between malicious traffics and legitimate ones from the training data set.  
We now turn our attention to the impact of the OSR problem. That is, we investigated how a well-trained BI-LSTM reacts to novel instances never learned beforehand. \"CIC-IDS2017/Friday\" was used to test the BI-LSTM trained with \"CIC-IDS2017/Wednesday\". As shown in the second row of Table 4, the performance, recall and accuracy dropped dramatically to approximately half, which is essentially a random guess. The reason behind this phenomenon is that the BI-LSTM has no built-in mechanism for knowing what it does not know. For a novel instance not drawn from the distribution model of the training set, the BI-LSTM compulsorily determines the category based on a mismatched model, and in general, this results in incorrect classification.  
Notably, the precision does not significantly degrade in Table 4. Because of that, the legitimate traffic in \"CIC-IDS2017/Friday\" and \"CIC-IDS2017/Wednesday\" share a similar distribution pattern in the feature space.
## 2 Materials and Methods
### Cyclic Fatigue Resistance  
The cyclic fatigue resistance of the instruments was evaluated using a customized device with a static model [27]. Twenty instruments of each brand (EDM, CM, OF) were tested within a curved artificial canal made of lithium disilicate CAD/CAM block (E. max HT A2 shade, Dentsply Sirona), which provided a reproducible curved path to the NiTi files with a 50\({}^{\circ}\) angle and 6.4 mm-radius according to [PERSON]'s method (Figure 1A). The diameter of the artificial root canal was 1.5 mm, the total length was 12 mm, and the center of the curvature was 4.5 mm from the tip of the instrument. Instruments were tested at RT (n = 10) and BT (n = 10). The instrument was mounted to the hand-piece of the endodontic motor (X-smart), which was fixed by a wooden block (Figure 1B,C). The instrument was continuously rotated according to the manufacturers' instructions: 400 rpm for EDM, 500 rpm for CM, and 300 rpm for OF. The time to fracture (seconds) was recorded, then it was multiplied by the operating rpm of the instrument to obtain the number of cycles to failure (NCF). The length of the fractured instrument was measured using a digital caliper (ABS Digimatic caliper CD-10 APX; Mitutoyo, Kawasaki, Japan). The NCF data and fracture length of the tested files were analyzed by two-way ANOVA (brand of NiTi file vs. experimental temperature) and [PERSON]'s test. Additionally, fractured files were examined with scanning electron microscopy (SEM, S-4700, Hitachi, Ltd., Tokyo, Japan).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Brand** & **Size and Taper** & **Length** & **Manufacturer** \\ \hline HyFlex CM \#25/08 (CM) & \#25, 8\% taper & 19 mm & Coltene/Whaledent, Altsätten, Switzerland \\ \hline HyFlex EDM orifice opener (EDM) & \#25, 12\% taper & 15 mm & Coltene/Whaledent, Altsätten, Switzerland \\ \hline One Flare (OF) & \#25, 9\% taper & 17 mm & MicroMega, Besancon, France \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of tested files.
## 3 Materials and Methods
### Headspace--SPME Methodology  
The Headspace (HS) SPME extraction of standard aqueous solutions composed of DL-menthol, (IR)-(+)-camphor (\(S\))-(\(-\))-\(\beta\)-citronello1; 3-pentanone, 2-hexanone, cyclopentanone, 2-heptanone; 1-butanol, 2-heptanol, 1-octanol, and benzyl alcohol, was carried out using the homemade coated fibers along with commercial fibers for comparison purpose. All fibers were preconditioned prior their first use and conditioned during 10 min, at recommended specific temperature, before their daily use. For the SPME procedure, 4 mL of each standard solution were transferred into 10 mL glass vials and the HS--SPME parameters such as stirring rate, extraction temperature, salt addition or extraction time were controlled depending on the desired study condition. After extraction, the fibers were immediately inserted in the GC injector and kept there for 3 min. Between each set of experiments, a blank injection was used to confirm the absence of analytes from previous extractions (carryover). All of the experiments were done in triplicate to estimate uncertainties. The analytical performance and efficiency of each SPME fiber coating was carried out in a Trace 1300 gas chromatograph (GC) from Thermo Scientific (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a flame ionization detector (FID). Helium was used as carrier gas at 1.5 mL\(\cdot\)min\({}^{-1}\). The GC injector was maintainedat 200 \({}^{\circ}\)C with a splitless time of 0.2 min and FID was maintained with a temperature of 250 \({}^{\circ}\)C. A TG-17 MS (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) capillary column from Thermo Scientific was used with the following temperature program: initial temperature of 35 \({}^{\circ}\)C during 3 min, followed by a ramp at 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 100 \({}^{\circ}\)C, held during 3 min, and then increased to 280 \({}^{\circ}\)C at a ramp of 25 \({}^{\circ}\)C min\({}^{-1}\).
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 6 Results and Discussion
### Precision-Recall Curve (PRC)  
Precision-recall curves (PRC) are visual indicators of which classes the model can detect with high precision while the recall increases. Perfect performance for a class (precision 100%; recall 100%) is shown as a horizontal line along Y-axis 1.0 connected with a vertical line at X-axis 1.0. This means that an ideal PRC should pass from the top-left corner to the top-right corner and then straight down. If a model has ideal or close to ideal results, this means that the model has good precision and recall scores. As can be seen in Figure 5, the PRC is different for each algorithm. The closest to an ideal PRC is Figure 4(a) for the random forest model. This figure shows an overlap of six classes as an ideal curve, but it has one class that has a lower rate, as seen in green (class 5). This means that the RF model was able to correctly predict all types of traffic (benign or attacks) except for one class, which has an 81 percent recall score, as seen in the classification report. Despite the less impressive performance in class 5, the result for the overall precision-recall relation in the total model is close to a perfect score.  
The PRC figures for other models are unsatisfactory, as they are not close to the top in most cases. This indicates the poor performance of these models. For example, the SVM graph in Figure 5 is a representation of a poorly performing model (most of the classes in this graph are below the diagonal line, which represents low recall and precision scores).  
Figure 4: Results as classification reports. The classes are denoted as follows: Benign \(\to\) 1, DoS \(\to\) 2, Port Scan \(\to\) 3, Bot \(\to\) 4, Infiltration \(\to\) 5, Web Attack \(\to\) 6, Brute Force \(\to\) 7.
## 2 System and Channel Models
### Pathloss  
Based on [13], pathloss can be assumed as  
\[I_{l}=\exp[-\gamma(\lambda)\times l], \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fog Type** & **Dense** & **Thick** & **Moderate** & **Light** \\ \hline \(V(m)\) & 0–50 & 50–200 & 200–500 & 500–1000 \\ \(k\) & 36.05 & 6.00 & 5.49 & 2.32 \\ \(\beta\) & 11.91 & 23.00 & 12.06 & 13.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values for different types of fog.  
Figure 1: System and channel models of FSO communication between mobile platforms in maritime environments.  
where \(\gamma(\lambda)\) represents the attenuation coefficient. In order to model the attenuation caused by fog, the Kim model is used to predict the attenuation coefficient [14; 15]:  
\[\gamma(\lambda)\simeq\frac{3.912}{V}\bigg{(}\frac{\lambda}{550}\bigg{)}^{-q}, \tag{4}\]  
\[q=\left\{\begin{array}{ll}1.6&\text{if }\ V\ >\ 50\ \text{km}\\ 1.3&\text{if }\ 6\ \text{km }<V<\ 50\ \text{km}\\ 0.16V+0.34&\text{if }\ 1\ \text{km }<V<\ 6\ \text{km}\\ V-0.5&\text{if }\ 0.5\ \text{km }<V<\ 1\ \text{km}\\ 0&\text{if }\ V<\ 0.5\ \text{km}\end{array}\right.,\]  
where \(\lambda\) is the wavelength, \(V=\frac{3.912}{7550\ \text{nm}}\) is the visibility, and \(q\) represents the size distribution of the scattering particles.
# BoostPolyGlot: A Structured IR Generation-Based Fuzz Testing Framework for GCC Compiler Frontend
###### Abstract  
The compiler serves as a bridge connecting hardware architecture and application software, converting source code into executable files and optimizing code. Fuzz testing is an automated testing technology that evaluates software reliability by providing a large amount of random or mutated input data to the target system to trigger abnormal program behavior. When existing fuzz testing methods are applied to compiler testing, although they can detect common errors like lexical and syntax errors, there are issues such as insufficient pertinence in constructing the input corpus, limited support for structured Intermediate Representation (IR) node manipulation, and limited perfection of the mutation strategy. This study proposes a deep fuzz testing framework named BoostPolyGlot for GCC compiler frontend IR generation, which effectively covers the code-execution paths and improves the code-coverage rate through constructing an input corpus, employing translation by a master-slave IR translator, conducting operations on structured program characteristic IR nodes, and implementing an IR mutation strategy with dynamic weight adjustment. This study evaluates the fuzz testing capabilities of BoostPolyGlot based on dependency relationships, loop structures, and their synergistic effect. The experimental outcomes confirm that, when measured against five crucial performance indicators including total paths, count coverage, favored paths rate, new edges on rate, and level, BoostPolyGlot demonstrated statistically significant improvements compared with American Fuzzy Lop (AFL) and PolyGlot. These findings validate the effectiveness and practicality of the proposed framework.  
fuzz testing; IR generation; dependency relationship; loop structure; IR mutation strategy +
Footnote †: journal: Applied sciences