esa-sceva/satcom-chunk-collection huggingface dataset:
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 4 Results
### Multi-Level Classification and Future Directions  
Given the positive results achieved in the preceding sub-sections, expansion to multi-level malware classification is one of the future directions that can be explored in this work. Furthermore, we could also assess the inclusion of other file formats, such as images, audio, and video. In this section, we present the preliminary research work conducted on the multi-level classification of Portable Executable (PE) files, which is achieved using the Malimg dataset [9]. The Malimg dataset consists of PE files organized into 25 different classes, which are named according to the detailed malware family name. However, due to the limited number of files per category, we conducted this experiment by combining the malware types based on their main malware category, as per Table 6. This approach organized the files into seven main categories: Backdoor, Dialer, PWS, Rouge, Trojan, Trojan Downloader, and Worm.  
The training resulted in an accuracy of approximately 98% for both AlexNet and SqueezeNet pre-trained models, as seen in the confusion matrices presented in Figure 8. Specifically, this resulted in an accuracy of 98.55% for AlexNet and 98.18% for SqueezeNet. Hence, although we are currently limited by the availability of pre-categorized data for PDF and MS document files, these preliminary results show that the work can possibly be extended to these file types as well.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Class** & **Main Family** & **Number of Files** \\ \hline
1 & Worm & 5854 \\ \hline
2 & PWS & 679 \\ \hline
3 & Trojan & 760 \\ \hline
4 & Dialer & 733 \\ \hline
5 & Trojan Downloader & 661 \\ \hline
6 & Rogue & 381 \\ \hline
7 & Backdoor & 274 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Malimg dataset: main malware family categories [9].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**File Type** & **Dataset** & **Number of Files** & **Static Analysis Accuracy** & **Image-Based Analysis Accuracy** \\ \hline \multirow{4}{*}{**Benign**} & Contagio (MS) & 2210 & 95.79\% & 62.8\% \\ \cline{2-6}  & Dike (MS) & 100 & 99\% & 84\% \\ \cline{2-6}  & Contagio (PDF) & 87 & 54\% & 35.6\% \\ \hline \multirow{4}{*}{**Malware**} & Contagio (MS) & 26 & 0 & 88.46\% \\ \cline{2-6}  & Dike (MS) & 1871 & 91.2\% & 92.5\% \\ \cline{2-6}  & Contagio (PDF) & 124 & 95.16\% & 59.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of static analysis model and image-based model for classifying MS files correctly.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 5 Analysis
### Path Expected Energy Cost  
Let \(\mathrm{E}_{p_{n_{s}}^{k}}\) be the expected energy cost of the path \(p_{n_{s}}^{k}\) transmitting a packet of size k from the source node \(n_{s}\) to the sink node \(n_{b}\) via a tree structure, such that \(p_{n_{s}}^{k}=\{n_{s},n_{v},n_{v},n_{w},\ldots,n_{l},n_{b}\}\). \(E_{p_{n_{s}}^{k}}\) is given Equation (21) and defined as the sum of the energy cost of all path nodes. From Equations (1) and (2), we get:  
\[\mathrm{E}_{p_{n_{s}}^{k}}=\sum_{\forall(n_{i},n_{j})\in p^{n_{s}}}E_{Tx} \Big{(}\big{(}n_{i},n_{j}\big{)},k\Big{)}+\sum_{\forall(n_{i},n_{j})\in p^{n_{ s}}}\ E_{Rx}\Big{(}n_{j},k\Big{)} \tag{21}\]
## 2 Materials and Methods
### Smart Drifter Cluster: Network Architecture  
Within the cluster, the individual drifter element is identified by an electronic code and also a visual element. The elements of the cluster exchange data related first of all to geolocation and possibly other data obtained from environmental devices and sensors installed on the cluster elements. Each element has the dual role of generating and transmitting data related to the same element and bridging the transmission of data from the elements connected to it. Note that, in this way, for the Smart Drifter Cluster to remain connected, it is enough for each element to be able to communicate with its nearest neighbors. A cluster consisting of hundreds of Smart Drifter Clusters can, therefore, remain connected even when dispersion due to sea currents brings the distance between its ends to hundreds of kilometers. When an element of the cluster is intercepted wirelessly by a device capable of bringing information to the user, it is able to provide information about all the elements of the cluster itself.  
However, this ideal organization of the integrated network has to contend with the limited bit rate of LPWAN communication technology. This constraint makes it necessary to minimize the amount of data transmitted. It is easy to see that if each element of the Smart Drifter Cluster has to bridge the data transmission of all the other drifter elements in the cluster, the amount of data that each has to transmit increases exponentially with both the number of cluster elements and time. For this reason, it is necessary for each drifter element to have the capacity to partially process its own data in order to \"clean\" and compact it, thus drastically reducing its size. To do this, it is necessary that the transmission does not take place in real time, with respect to the acquisition of the data, but with a small delay, which is appropriate for transmission in compressed packets.  
Another delicate step in network planning is the integration of all outputs from each subsystem (in this case, the remote primary elements) with known or reference data, e.g., obtained from other information sources (satellites, the Internet, etc.) in order to establish a coherent database. This is not a task that can be performed by the drifter elements in the cluster, but will instead be assigned to a centralized processing system located in a ground facility. This is in order to contribute directly to the Pan-European networks, \"SeaDataNet, EMODnet, and Copernicus CMEMS\", which provide online integrated marine databases of standardized quality.
## 1 Introduction  
Soda-lime glass is a kind of amorphous non-crystalline material that is made of quartz sand (SiO\({}_{2}\)), soda ash (Na\({}_{2}\)CO\({}_{3}\)), and limestone (CaCO\({}_{3}\)). It is the largest producer of industrial glass components [1; 2; 3; 4].  
When using traditional metal-processing methods for processing hard and brittle materials, it is easy to break the workpiece. Moreover, there is low processing efficiency, high energy consumption, poor processing quality, and many design requirements that cannot be achieved; thus, these methods are a long-term plague and impact production via their major problems [5; 6; 7].  
In recent years, ultrashort pulsed lasers have become a research hotspot in the field of processing with their advantages of high precision, non-contact, and high energy density [8; 9; 10; 11; 12; 13; 14; 15; 16]. Pulsed lasers based on Bessel beams are widely used in micro- and nanofabrication of hard and brittle materials, such as surface microstructuring [17; 18], drilling, and microchannel fabrication [19; 20; 21; 22; 23]. Relevant studies have confirmed the positive impact of laser spatiotemporal shaping on the processing of glass materials [24; 25; 26; 27; 28; 29]. The work of several scholars has shown that the processing parameters related to spot spacing, laser incident power, and burst mode have a significant effect on the processing quality of the samples [30; 31; 32; 33; 34; 35]. Multiple groups of scholars agree that the roughness of the sample cross-section is a key indicator of the laser-cut samples [33; 34; 36; 37].  
[PERSON] et al. investigated the nano-limitation of energy deposition in glass using double ultrafast Bessel pulses [20] but did not study the effect of the number of burst modes on glass processing. [PERSON] et al. used an ultrafast laser Bessel beam to cut glass and sapphire, which narrowed the heat-affected zone of the cut samples and improved the sidewall quality by adjusting the energy deposition [35], but there was a lack of exploration of the machining point spacing. [PERSON] et al. used a femtosecond laser Bessel beam to cut thin glass via internal scratching and mechanical crushing and investigated the effect of pulse energy and processing point spacing on the strength of the specimen and edge cross-section morphology [38]. However, they did not investigate the roughness of the cross-section. [PERSON] et al. investigated the effect of laser incident power, processing point spacing, and defocus distance on quartz glass [36], but the optimal roughness of their samples was still higher than 320 nm.  
This paper mainly used roughness as a measurement index. In multiple batches of experiments, processing a total of 100 pieces of cutting samples, we studied the impact of different processing parameters on the processing quality. We came up with better processing parameters and processing indicators. The average roughness of the sample with the best processing effect under the processing parameters of this paper was 158 nm, which was a significant improvement.
## 5 Real-Time Experimental Results
### Set-Point Tracking without Disturbance and the Payload  
Before testing with the unknown payload, all control methods and their control parameters are tested without a connected payload. The reason for this is that the defined controller parameters may need further fine-tuning.  
In Figures 12 and 13, the PID, T1-FPID and IT2-FPID position results can be seen. The reference, PID, T1-FPID, and IT2-FPID are coloured black, green, blue, and red, respectively. The starting point of the UAV is selected as \(x_{0}=0.2\) m, \(y_{0}=0.2\) m, and the reference points are chosen as \(x_{ref}=1.1\) m, \(y_{ref}=0.52\) m.  
In Figure 12, the IT2-FPID results show the best performance compared to other controllers regarding settling time, oscillation, steady-state error, and overshoot. PID gives more overshoot and then arrives at coverage of the reference area after 31 s. T1-FPID has a slower response compared with IT2-FPID, but it also reaches the reference faster than PID.  
Figure 12: Set-point tracking results on the x-axis.  
In Figure 13, unlike the results on the x-axis, all the controllers do not achieve good results on the y-axis. Significantly, the PID controller has an overshoot problem and does not reach the reference or converge to the circle. Moreover, IT2-FPID looks faster than T1-FPID, and the system response is the best.
## 5 System Modeling and Simulation
### System Modeling of Traction Condition  
It can also be divided into the longitudinal and lateral traction performed by EMPU and ERSD, respectively, when EASIST performs the traction task. Unlike the capture mode,EMPU and ERSD are affected by the motion resistance from the helicopter. Taking EMPU pulling the helicopter along the negative direction of the \(Y\)-axis as an example, the bond graph model is shown in Figure 16. And the bond graph model of ERSD laterally towing the helicopter is shown in Figure 17.  
In the same way, for EMPU, take the state equation as follows:  
\[X_{3}^{\prime}=A_{3}\cdot X_{3}+B_{3}\cdot U_{3}. \tag{49}\]  
The equation of system state variables is shown as follows:  
\[X_{3}=\big{[}\begin{array}{cccc}p_{2}&q_{14}&p_{16}&q_{22}&p_{25}\end{array} \big{]}^{\text{T}}. \tag{50}\]  
The equation of system input is shown as follows:  
\[U_{3}=\big{[}\begin{array}{cc}Se_{1}&Se_{2}\end{array}\big{]}. \tag{51}\]  
The constant-coefficient matrix \(A_{3}\) and control matrix \(B_{3}\) of the system are shown as follows:  
\[A_{3}=\left[\begin{array}{cccc}-\frac{m_{1}^{2}m_{2}m_{3}R_{3}+m_{3}R_{12}+ m_{2}R_{20}}{E}&-\frac{m_{1}m_{2}m_{3}l_{2}}{\text{C}_{14}E}&0&-\frac{m_{1}m_{2}m_{3}l_ {2}}{\text{C}_{22}E}&0\\ \frac{1}{m_{1}m_{2}I_{2}}&0&-\frac{1}{l_{16}}&0&0\\ 0&\frac{1}{\text{C}_{14}}&-\frac{R_{17}}{l_{16}}&0&0\\ \frac{1}{m_{1}m_{3}l_{2}}&0&0&0&-\frac{m_{4}}{l_{25}}\\ 0&0&0&\frac{m_{4}}{C_{22}}&-\frac{R_{26}}{l_{25}}\end{array}\right], \tag{52}\]  
Figure 16: The bond graph of the longitudinal traction system.  
Figure 17: The bond graph of the lateral traction system.  
\[B_{3}=\left[\begin{array}{cc}\frac{m_{1}^{2}m_{2}m_{3}z_{1}}{E}&0\\ 0&0\\ 0&-1\\ 0&0\\ 0&0\end{array}\right]. \tag{53}\]  
where \(E=m_{1}^{2}m_{2}m_{3}I_{2}+m_{2}m_{3}I_{6}+m_{2}m_{3}R_{7}+m_{3}I_{11}+m_{2}I_{ 19}\).  
For ERSD, take the state equation as follows:  
\[X_{4}^{\prime}=A_{4}\cdot X_{4}+B_{4}\cdot U_{4}. \tag{54}\]  
The equation of system state variables is shown as follows:  
\[X_{4}=\big{[}\begin{array}{ccc}p_{2}&q_{6}&p_{9}&q_{21}&p_{23}\end{array} \big{]}^{\text{T}}. \tag{55}\]  
The equation of system input is shown as follows:  
\[U_{4}=\big{[}\begin{array}{cc}Se_{1}&Se_{2}\end{array}\big{]}. \tag{56}\]  
The constant-coefficient matrix \(A_{2}\) and control matrix \(B_{2}\) of the system are shown as follows:  
\[A_{4}=\left[\begin{array}{cccc}-\frac{R_{3}}{l_{2}}&-\frac{1}{m_{1}C_{6}}&0& 0&0\\ \frac{1}{m_{1}I_{2}}&0&-\frac{m_{2}}{l_{9}}&0&0\\ 0&\frac{m_{2}m_{3}^{2}m_{4}^{2}l_{9}}{C_{6}\big{(}m_{3}^{2}m_{4}^{2}l_{9}+m_{ 4}^{2}l_{13}+l_{17}\big{)}}&-\frac{R_{18}+m_{2}^{2}R_{14}+m_{2}^{2}l_{13}}{m_{ 3}^{2}m_{4}^{2}l_{9}+m_{4}^{2}l_{13}+l_{17}}&-\frac{m_{2}m_{4}l_{9}}{m_{5}C_{2 1}(l_{17}+m_{3}^{2}m_{4}^{2}l_{9}+m_{4}^{2}l_{13})}&0\\ 0&0&\frac{1}{m_{3}m_{4}m_{5}l_{9}}&0&-\frac{1}{l_{23}}\\ 0&0&0&\frac{1}{C_{21}}&-\frac{R_{24}}{l_{23}}\end{array}\right] \tag{57}\]  
\[B_{4}=\left[\begin{array}{ccc}1&0\\ 0&0\\ 0&0\\ 0&1\end{array}\right] \tag{58}\]
## 1 Introduction
Embedded micro-optics in phones constantly improve, and algorithms for pupil distance measurement can be integrated into applications. With advancements in computer vision and machine learning technology, it is now possible to automatically and accurately measure pupillary distance using computers or mobile devices. By utilizing computer vision and machine learning techniques to process and analyze eye images, automatic measurement of pupillary distance can be achieved. This method is fast, highly accurate, and stable, significantly reducing the need for human and material resources and improving the portability of measuring pupillary distance and height during eyeglass fitting. Therefore, we will conduct our research from the following directions:  
1. Capture facial images and process them using image processing techniques;
2. Use integrated regression tree algorithms to locate the pupils;
3. Use the BlendMask algorithm to locate the eyeglass frames;
4. Calculate the pupil distance and height based on defined parameters;  
Figure 1: Definition and manual measurement. (a) Definition of pupillary distance and height. (b) Manual measurement using a ruler. 5. Integrate the above steps into a single model for simultaneous measurement of pupil distance and height;
6. Compare the values of pupil distance calculated by our model with those measured by a ruler and an auto-refractometer, and analyze the consistency among the three measurement methods. The primary focus of our study is to research measurement methods for pupil distance and pupil height, and compared with existing approaches, the main contributions of this research are as follows. We propose a method that combines ensemble regression trees with the BlendMask algorithm, which can be integrated into devices such as smartphones and computers, enabling simultaneous and automated measurements of interpupillary distance and pupil height. Furthermore, the method we propose offers numerous advantages, including high precision, ease of operation, non-contact measurement, low cost, and rapid measurement. Our method empowers users to provide measurement results to online eyewear retailers, facilitating eyeglass fitting services at home without the need to visit physical eyewear stores. Through continued research and improvement, our goal is to provide an accurate, convenient, and practical method for measuring pupil distance and pupil height, addressing the needs of eyewear wearers and related fields. This section provides an explanation of pupil distance and height definitions, summarizes existing methods for measuring pupil distance, and briefly introduces the objectives of this study. The remainder of the article is organized as follows. Section 2 provides a brief summary of related work relevant to this research. Section 3 then provides a detailed description of the work carried out in this study, followed by Section 4, which describes the experiments, results, and discussion. Finally, Section 5 presents the conclusions and limitations of the proposed method, along with suggestions for further research.
## 4 Numerical Results
### Probability of Missed Spoofing Detection  
The second performance indicator is the mis-detection of an unauthorized user access, that is \(P_{msd}\). We want to verify what happens when a spoofing node is present. This node can be in any position; hence, first of all, we want to verify if there is a relation between the spoofing node position and its classification. As an example, Figure 7 shows a scenario with \(N=8\) authorized nodes, whose positions are indicated with the red triangles, and each one is identified by a different color (i.e., each color corresponds to a different class). The sink node is considered in the center of the area, even if not represented. The area \(\mathcal{A}\) is divided in \(10\times 10\) squares, and the malicious node classification is performed placing the malicious user in the center of each square not occupied by an authorized node. The color of the square indicates the output of the classification (i.e., the unauthorized node in each specific position is classified as the authorized node that has the same color). We can see that even if there is a certain spatial correlation, the classification of a malicious user in different positions is quite mixed in the area.  
Since the attacker tries to embody another node by transmitting a packet to the sink with the label of the node whose identity it is trying to spoof, we consider two different cases.  
First, we assume that the malicious node randomly selects one of the available node IDs in the network (with probability \(1/N\)). Hence, \(P_{msd}\) goes as \(\frac{1}{N}\); indeed, given the classification results, the probability of selecting the ID that matches with the resulting class is \(1/N\). This is shown in Figure 8. These results were derived averaging the \(P_{msd}\) over all the possible positions of the malicious user. Obviously if the number of nodes is low, \(1/N\) is high, and hence, the \(P_{msd}\) is high. To overcome this problem in small networks, _sentinel nodes_ can be introduced, each one with its assigned ID. For example, adding \(N_{S}=10\) sentinel nodes, the \(P_{mds}\) is significantly reduced as shown in Figure 8. Sentinel nodes are randomly placed in the area.  
\begin{table}
\begin{tabular}{c c c c} \hline
**CART** & **RF** & **SVM** & **k-NN** \\ \hline
95.43\% & 95.49 & 95.59\% & 94.59\% \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of classifiers accuracy.  
As a second scenario, we considered the worst case in which the malicious node tries to impersonate the nearest authorized node (i.e., it is able to intercept its ID). Results are shown in Figure 9 in the case without sentinel nodes. We can see that performance slightly worsens, due to the spatial correlation of the classification results shown in Figure 7, but it is still close to the \(1/N\) curve because the spatial correlation is not so high.  
Figure 8: \(P_{msd}\) vs. number of authorized nodes in the area, with and without sentinel nodes (\(N_{S}=10\)).  
Figure 7: Example of unauthorized node classification depending on its position in the area.
# Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
## 4 Methodology
### Technologies for Model Compression
#### 4.1.2 Quantization and Sharing  
Network weight quantization compresses the model by reducing the number of bits required to represent each weight. It generally divides continuous variation data into discrete values and assigns each specific datum to a fixed value. For example, if a weight is represented with a 32-bit floating-point number and we want to indicate a weight with 100 quantified values, then 7-bit representation is sufficient.  
\[argmin_{C}\sum_{i=1}^{k}\sum_{w\in{\cal C}_{i}}|w-c_{i}|^{2} \tag{2}\]  
Generally, K-means clustering is a simple and convenient solution to solve the problem of quantization of CNNs weights [35], which is shown in Equation (2). \(C=\{c_{1},c_{2},...,c_{k}\}\) denotes the cluster centers we want to compute, and \(w\) means original weight. The objective function is to minimize the squared error between all weight and center it belongs to. As a result, each \(w\) is quantized to one cluster center. If the number of cluster centers is set with \(k\), then \(log_{2}(k)\) bits are used to represent the weight value. [PERSON] et al. [36; 37] proposed 8-bit quantization and 16-bit fixed-pointrepresentation. They brought significant speedup, reduce memory usage and decrease loss in accuracy. There were also many methods that directly train CNNs with binary weights, e.g., Binary-Connect [38], BinaryNet [39], and XNORNetworks [40]. The main idea was to learn binary weights or activations during the model training directly. The method in Ref. [41] reduced the precision of weights to ternary values. A HashedNets model was proposed, in which the low cost hash function is used to group weights into hash buckets for sharing [42]. In Ref. [43], a simple regularization method based on soft weight-sharing was proposed.
## 3 Results and Discussion  
### Characterization of the MWCNTs  
#### 3.1.1 FT-IR Analysis  
Infrared spectroscopy is a useful qualitative tool for determining the composition of compounds. Figure 1 shows the typical FTIR spectra in the range of 500-4000 cm\({}^{-1}\) for MWCNTs. In the spectra of the functionalized MWCNT material, the broad peaks at 3510-3780 cm\({}^{-1}\) are assigned to N-H and O-H stretching of the carboxylic group, respectively. The increased intensity of the OH stretching broadband at \(\sim\)3510 cm\({}^{-1}\) suggests a greater proportion of defective CNTs in the sample, which can be easily oxidized in the air [43]. However, the band at 2358 cm\({}^{-1}\) is recognized as thiol S-H stretching. The sharp band at 1730 cm\({}^{-1}\) corresponds to C-O stretching vibration of COOH. The FTIR of amine-functionalized CNT shows new bands at 1654 cm\({}^{-1}\) and corresponds to C=C stretching, C=N due to the reaction with hydroxyl amine, and N-H deformation [44]. The difference in the FTIR spectra of the MWCNTs in the 1000-1270 cm\({}^{-1}\) region can be attributed to C-N stretching due to carbon atom and terminal amino group bonding, and confirms the presence of amine groups on the MWCNTs [44].  
Figure 1: FTIR pattern of MWCNTs.