esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Encrypted Traffic Features
### Analysis of Encrypted Malicious Communication Node Relationships  
To enhance our understanding of behavioral patterns in encrypted malicious communication, this study conducts a comprehensive analysis of communication node relationships within the dataset. We observed that source IP addresses of the same type--either malicious or benign--demonstrate significant correlation and clustering. Specifically, multiple malicious or benign source IPs often connect to the same destination IP, forming tightly knit communication groups.  
For example, one destination IP (e.g., 115.159.156.123) was found to communicate with 548 malicious source IPs, representing 100% of the source IPs linked to that destination. Similar high aggregation patterns were observed for other destination IPs, such as 83.136.254.57 (100%), 60.190.116.31 (100%), and 180.101.49.206 (97.28%). Statistical analysis further reveals that 87.8% of malicious IPs establish connections with the same destination IP.  
To facilitate understanding, we employed visualization techniques, as shown in Figure 2. In this figure, red nodes denote malicious source IPs, green nodes denote benign source IPs, and black nodes represent destination IPs. Communication links are depicted with red lines for connections from malicious source IPs to destination IPs, and green lines for links from benign source IPs. Figure 2 visually confirms the correlation and clustering of malicious IPs, highlighting the importance of destination IP-based communication clustering in distinguishing between malicious and benign source IPs.  
These results not only reveal the complex patterns within encrypted communication networks but also highlight the importance of communication relationships in feature representation learning. To achieve more accurate and comprehensive analysis of encrypted traffic, this study suggests that future feature representation learning methods should fully account for the complexity and diversity of communication relationships. By integrating communication relationships, these methods can construct a feature space that deeply reflects the underlying semantics of encrypted communication.  
In summary, this paper reveals the deeper semantics and behavioral patterns underlying encrypted communication through the analysis of base features and combined features of encrypted traffic, as well as the visualization of relationships between malicious encrypted communication nodes. These findings not only deepen our understanding of the characteristics of encrypted traffic but also provide direction for constructing efficient feature representation learning models for encrypted traffic. Based on these insights, this paper will focus on how to achieve deep automatic cross-fusion of base features and how to leverage node relationship information to optimize feature representation learning algorithms, with the aim of further enhancing the accuracy of encrypted traffic identification.  
Figure 2: Visual analysis of node relationships in encrypted malicious communications.
## 2 Materials and Methods
### Measurement Setup  
The measurement setup used to characterize the RF exposure from NR base stations consisted of a Rohde and Schwarz FSV spectrum and signal analyzer (FSV30) connected to a tri-axial Satimo INSITE Free electric-field probe (with a nominal frequency range of 2 GHz to 6 GHz and a dynamic range of 5 mV/m to 200 V/m; Figure 1) and a laptop with Matlab software to control the measurement equipment. The R&S FSV30 came with an optional R&S FSV-K14 to use in'spectrogram mode' in order to store a high number of measurement traces (up to 20,000 for the R&S FSV-30) and exporting them with a minimal blind time between traces. The expanded measurement uncertainty of this setup is \(\pm\)3 dB [19,20], and the measurement settings for each step can be found in [13].  
In addition, the contributions of other frequency bands used by wireless telecommunications, present at the measurement sites, to the RF exposure were characterized using a Narda SRM-3006 field strength analyzer with a three-axis electric-field antenna of type Narda 3502/01 (frequency range: 420 MHz-6 GHz; dynamic range: 0.14 mV/m-160 V/m). The measurement uncertainty of this setup is also \(\pm\)3 dB. For this type of measurement, a frequency range of 600 MHz to 4 GHz, a resolution bandwidth (RBW) of 300 kHz, and an averaging time of 6 min were selected. The sweep time (SWT; i.e., the duration of a single measurement trace) of the SRM cannot be configured and amounted here to 1104 ms. Finally, it should be noted that the SRM measurements were performed simultaneously with the FSV spectrum analyzer measurements detailed above, but at other positions assumed to lie within the same PDSCH beam. The average distance between the simultaneous measurement positions was 18.5 m (range: 1.6-64.0 m).
## 1 Introduction  
Tidal volume is defined as the amount of air that moves in or out of the lungs with each respiratory cycle [1]. Changes in tidal volume may reveal pathologies such as restrictive or obstructive lung diseases, acute respiratory distress syndrome, and diseases affecting the respiratory muscles [1]. Of note, the information provided by tidal volume may not be replaced by breathing rate [2, 3]. Indeed, tidal volume and breathing rate are differentially regulated, with the regulation of tidal volume being mainly mediated by metabolic inputs [2, 3]. The existence of an unbalanced interdependence between tidal volume and breathing rate has also been suggested, in which tidal volume continuously adjusts on the basis of breathing rate, but not vice versa [4]. For these reasons, tidal volume represents an important variable for the evaluation of the respiratory function, and its continuous monitoring may have clinical implications [5, 6].  
Traditional methodologies for the measure of tidal volume rely on spirometry and optoelectronic plethysmography, both requiring a laboratory setting. Spirometry consists in the measurement of the air flow produced during the respiration, when the subject is asked to breath into a mask; thus, it is objective, noninvasive, sensitive to early change, and reproducible [7, 8]. On the contrary, optoelectronic plethysmography consists of a motion analysis system which measures the changes in the chest wall during breathing by modelling the thoraco-abdominal surface [9, 10].  
However, as recently reviewed [11], in the last decade attempts have been made to estimate tidal volume also outside the laboratory settings during either static (e.g., sitting, standing, or lying) or dynamic tasks (e.g., during exercise), with the latter generally providing higher estimation errors. Different wearable technologies have been exploited, including various stretch sensors (optical, resistive, inductive, and pressure/tactile) [12; 13; 14; 15] or acoustic sensors that use the movements of the chest wall or the sound generated by air flow during breathing [16], respectively; in other cases, the estimation of tidal volume was indirectly achieved from the electrocardiogram (ECG) by extracting from it features such as the breathing rate, ECG-derived respiration, and the heart-rate variability [17; 18]. As for these latter methodologies, multiple regression models were created using the features extracted from the ECG acquired using a Holter monitor [18] or an armband device [17] as predictors. In the first case, the estimation was performed during an exercise stress test, whereas in the second during a lab-controlled experiment. To the best of our knowledge, no study has provided an estimation of tidal volume from features such as heart rate and breathing rate directly acquired from a commercially available wearable device during an exercise stress test. This study aims to fill this gap by proposing a multiple regression model based on these features.
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 2 Related Work
### Cluster-Based Attack Detection Methods
#### 2.1.1 K-Means Clustering  
K-means clustering, a widely used clustering algorithm [30], aims to group similar data points together. In the context of financial attack detection, k-means clustering is applied to identify anomalous transaction patterns, such as distinguishing between normal and fraudulent transactions. The fundamental concept of k-means clustering involves minimizing the sum of distances between each point and the centroid of its cluster [31], as shown in Figure 1. First, k initial \"means\" (in this case k = 3) are randomly generated within the data domain (shown in color). Second, k clusters are created by associating every observation with the nearest mean. The partitions here represent the Voronoi diagram generated by the means. Third, the centroid of each of the k clusters becomes the new mean. Finally, steps 2 and 3 are repeated until convergence has been reached.  
Mathematically, this is represented by the following optimization problem [30]:  
\[\min_{\mathbf{S}}\sum_{i=1}^{k}\sum_{\mathbf{x}\in S_{i}}|\mathbf{x}-\mu_{i}|^{2} \tag{1}\]  
Here, \(k\) is the number of clusters, \(\mathbf{S}=S_{1},S_{2},\ldots,S_{k}\) is the set of clusters, \(\mathbf{x}\) represents data points, and \(\mu_{i}\) is the center of cluster \(S_{i}\). The k-means clustering algorithm involves randomly selecting \(k\) cluster centers, assigning each data point to the nearest center, and then updating the cluster centers to the mean of the points in each cluster. This process is repeated until convergence criteria are satisfied [32].
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 4 OS-Based Mitigation Techniques
### Address Space Layout Randomization  
To bypass the NX bit mitigation technique we have used ROP, in which the addresses of ROP gadgets must be known. ASLR randomizes the base addresses of various sections of the process, including the stack, heap, shared libraries and executables [42]. Therefore, the attacker cannot use the same exploit every time to abuse the same vulnerable program, rather has to use an explicit payload for every occurrence of randomized program.  
ASLR is the first protection mechanism implemented in almost all major operating systems. It was first designed and implemented as a patch for Linux Kernel in July 2001 by the Linux PaX project. In June 2005, Linux kernel version 2.6.12 deployed the ASLR as default [43]. OpenBSD version 3.4 was the first operating system that introduced the default support of ASLR in 2003. In March 2011, it was introduced in iOS 4.3. Mac OS X Leopard 10.5 started implementing ASLR for system libraries in 2007 and extended to cover all applications in 2011. Microsoft Windows Vista and its subsequent versions also provide support for ASLR. Many years have been passed; still, ASLR is an effective approach to protect against modern attacks. It is an active research area, and there is still a lot to do with its design and implementation. It has multiple implementations with the difference in their operations and effectiveness [44]. Recently, a novel approach named ASLR-NG (ASLR Next Generation) has also been presented to overcome the weaknesses of traditional ASLR [42].  
In the case of 32-bit systems, only 16 bits are effectively available (or 8 bits in Linux systems with 256 values) for randomization, which is a limitation of ASLR on 32-bit systems, because the 16-bit entropy can be defeated in a few milliseconds using brute force attack [45]. While 64-bit machine provides 40 bits for randomization, that makes brute force attack almost impossible because it could be noticed easily. On 64-bit systems limitation of ASLR realization is that it is vulnerable to memory disclosure and information leakage attacks. The attacker can launch the ROP by revealing a single function address using information leakage attack [46]. The following section describes the similar existing strategy for breaking down the ASLR protection.
## 2 Related Work  
Researchers across the globe are researching how to utilize solar energy for sustainable agriculture and irrigation. A group of researchers in Bangladesh have designed a DC-DC buck converter to design an efficient DC solar water pump. It does away with the requirement for batteries and inverters. The DC solar water pump solves energy challenges in irrigation and supports year-round cultivation. The method has also been explored to purify water in rural areas of Bangladesh [10].  
A study was conducted at the Renewable Energy Research Unit in the Saharan region of Algeria, where access to reliable and sustainable water supply is a significant challenge. A group of researchers highlighted the importance of renewable energy in solving water supply problems faced by remote communities. The authors proposed a solar water pump system as a cost-effective solution to meet water demand in remote areas. It details how to choose the right system components, including solar panels, batteries, charge controllers, and pumps. It also discusses the importance of systems optimized for maximum efficiency and performance. They provide an approach to system optimization that includes selecting the appropriate pump, selecting solar panels and batteries, and optimizing the system's hydraulic design. Research shows that optimized solar water pump systems can provide a reliable and sustainable water supply to remote areas. The system has a maximum power of 1.5 kilowatts, which is enough to pump water at a rate of 6-8 cubic meters per hour at a height of 50 m. In summary, this study is a valuable contribution to the design and optimization of solar water pump systems. It highlights the importance of renewable energy in addressing the water supply challenges faced by remote communities and proposes a cost-effective solution to meet their water demand. The proposed system design has the potential to provide a reliable and sustainable water supply to remote areas at a cost-effective price [11].  
Photovoltaic water pumping systems have been a promising area of research over the past fifty years. Various sources examine the current state of photovoltaic water pumping systems. Their primary focus is on system components, factors affecting system performance, performance assessment, system optimization, and integration possibilities with other green technologies [12; 13]. An overview of research and the current situation of photovoltaic water pumping systems is presented, including the components and benefits of PV systems in addition to the factors affecting system performance [14]. The structure of the array of photovoltaic modules and their interaction with the array of photovoltaic modules, electric motors, and pumps with optimization of consumption and improvement of control was discussed [15]. Generally, the motors used for photovoltaic pumps were classified into (i) DC motors, permanent magnet brushed motors and brushless reluctance motors and (ii) AC motors, asynchronous and synchronous, and the appropriate motor selection was based on size, system efficiency, price, energy consumption, and technical condition [16].
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 3 Related Work
In the previous works, we found an increase in the accuracy of a medium-sized VGG13 network on a large image dataset, but we did not study the learned OWA operators and the way they codify information. In this work, we continue exploring new results, focusing on the analysis of the learned OWA operators. For this analysis, we use a simpler network and the CIFAR100 [24] dataset both to reduce the complexity of the problem and the number of variables involved and to allow for a greater number of repetitions of each experiment. Additionally, we compare the learned operators of the OWA layer with several theoretical families of OWA operators. In particular, we use the Stancu OWA operator [22], the Binomial OWA operator [8] and the exponential RIM OWA operator [23]. The Stancu OWA operator is based on the Stancu polynomial and is capable of generating infinitely many sets of weights for a specific orness value, defined by a second parameter \(\alpha\). At the same time, it also generalizes the Binomial OWA operator [8]. We also compare the learned operators to the exponential RIM OWA operator [23], proposed as a MEOWA [37] operator based on a RIM quantifier.
## 3 Results and Discussion
### SEM Morphology  
Figure 4a-e shows the SEM morphology of the composites (EQ-20, EQ-25, EQ-30, EQ-35) embedded with increasing fractions of quartz fibers from 0.20 to 0.35. Fiber-resin adhesion can be seen from these images which was due to the fair adhesion compatibility of quartz fibers with epoxy resin. The fair fiber-resin adhesion and increasing quartz fiber proportion showed a better structural appearance of composites. The fibers in EQ-30 and EQ-35 were tightly packed compared to those in EQ-20 and EQ-25 which shows the even distribution with increasing fiber content in the resin. As the fiber fabric fraction increased, the plies were aligned well and tightly bonded and the chance of fiber pull out was reduced. Fewer voids were also observed in the localized areas in these composites which might be due to early crosslinking there or the trapping of air bubbles. Despite the higher fraction of quartz fibers, it might be possible that during layer-by-layer stacking, the fabric plies were moved slightly from their exact location upon pressing. During compaction, the load was transmitted from the liquid resin to the fiber fabric which finally resulted in a uniform and compact composite structure. Overall, the composite structure was improved with the addition of a small amount of quartz fibers.  
Figure 4: SEM morphology of (**a**) pure epoxy composite, (**b**) 20% quartz fiber, (**c**) 25% quartz fiber, (**d**) 30% quartz fiber, and (**e**) 35% quartz fiber/epoxy composites.  
Figure 3: FTIR analysis of quartz fiber (0.20, 0.25, 0.30, 0.35)/epoxy composite.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 4 Results and Discussion
### Correlation Analysis
Figure 4: Distribution of adjacent pixels in the original images and corresponding ciphered images: (**a**) Horizontal plain image and cipher image of Lenna, (**b**) vertical plain image and cipher image of Lenna, (**c**) diagonal plain image and cipher image of Lenna, (**d**) horizontal plain image and cipher image of [PERSON], (**e**) vertical plain image and cipher image of [PERSON], (**f**) diagonal plain image and cipher image of [PERSON], (**g**) horizontal plain image and cipher image of Peppers, (**h**) vertical plain image and cipher image of Peppers, (**i**) diagonal plain image and cipher image of Peppers, (**j**) horizontal plain image and cipher image of Airplane, (**k**) vertical plain image and cipher image of Airplane. Airplane, (**l**) diagonal plain image and cipher image of Airplane. strong similarity across the measured orientations. Specifically, these coefficients range from 0.9106% to 0.9740%, indicating a high degree of correlation in the spatial distribution of pixel intensities in these orientations. Conversely, the encrypted versions of these images exhibit markedly negative or low positive correlation coefficients, ranging from \(-\)0.0144 to 0.0034. These values indicate a significant reduction in similarity or a complete dissimilarity in the spatial distribution of pixel intensities post-encryption across all orientations. This dramatic shift in correlation coefficients from the original to the encrypted images underscores the effectiveness of the encryption process in dispersing the pixel intensity patterns, thereby obfuscating the visual and structural content of the images. The analysis of such correlation coefficients is crucial in assessing the strength and effectiveness of image encryption techniques. High correlation coefficients in original images reflect natural patterns and similarities, while significantly lower or negative coefficients in encrypted images indicate successful disruption of these patterns, an essential feature for securing image data against unauthorized access or interpretation.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 3 Methods  
In this study, a set of combined semi-empirical approaches was constructed based entirely on three theoretical models (i.e., the PROSAIL model, the AIEM model, and the MIMICS model) for the retrieval of SM in winter wheat fields. The illustration is shown in Figure 2. In the first step, the LAI was estimated using the Sentinel-2-derived NDVI. Then, the LAI was transformed into the input parameters of the MIMICS model, and an improved ratio model for calculating the soil backscattering coefficient was developed based on a regression analysis of the simulation data set of the MIMICS model, while the soil backscattering coefficient was parameterized as a nonlinear function of SM and soil surface roughness through a statistical analysis of the data set simulated by the AIEM model. In the last step, the SM was calculated by using the Newton iteration method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Satellite Platform** & **Product Type** &
\begin{tabular}{c} **Relative Orbit** \\ **Number** \\ \end{tabular} \\ \hline
3 April 2019 & S2B & MSIL2A & R075 \\
5 April 2019 & S2A & MSIL2A & R032 \\
10 April 2019 & S2B & MSIL2A & R032 \\
15 April 2019 & S2A & MSIL2A & R032 \\
28 April 2019 & S2A & MSIL2A & R075 \\
30 April 2019 & S2B & MSIL2A & R032 \\
3 May 2019 & S2B & MSIL2A & R075 \\
10 May 2019 & S2B & MSIL2A & R032 \\
13 May 2019 & S2B & MSIL2A & R075 \\
15 May 2019 & S2A & MSIL2A & R032 \\
20 May 2019 & S2B & MSIL2A & R032 \\
23 May 2019 & S2B & MSIL2A & R075 \\
25 May 2019 & S2A & MSIL2A & R032 \\
28 May 2019 & S2A & MSIL2A & R075 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some specific information on the Sentinel-2 optical data.  
Figure 2: Flowchart of the SM retrieval process.
# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 5 Discussion  
In this study, we use deep learning methods for fingerprint epidermal gender feature extraction. The results show that the combination of our proposed feature extractor DDC-ResNet and CNN classifier outperforms other combinations, with an average accuracy of 96.50%. In addition, the study found that most males were more correct in their predictions than females, with an average accuracy rate of 87.330% for males and 87.205% for females. The highest accuracy rate for males was 2.04% higher than that of females. In particular, for the combination, the difference was more than 10%, indicating that the male fingerprint epidermis is richer in gender-related features than the female. In the study of the fingers, we found that the right ring finger had the highest accuracy rate of 92.455%. Among the five pairs of fingers, the ring finger performed significantly better than the other fingers and had a higher average accuracy rate for the right hand than the left hand, with an average accuracy rate of 87.872% for the right hand and 86.418% for the left hand. Most studies of manually extracted features have focused on the ridges of the fingerprint epidermis. This study visualized the heat map through the grad-cam technique and found that the concentration was mainly on or around the center of the fingerprint, as shown in Figure 6. with the concentration area tending to be more of a continuous section, and it can be said that the basis of recognition is the complete ridge feature, thus, also confirming the reason for the manual extraction of features and also validating the interpretability of this paper. This work not only comprehensively explores the efficiency of the proposed method, but also provides a way to observe fingerprint identification features much more closely. These specific features will be quantified in the future to further explore the impact on fingerprint recognition further.