esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Challenges  
In order to detect and classify tunnel traffic, we must find discriminative features between normal traffic and tunnel traffic. Recently, in spite of dramatic progress having been made in detecting encrypted traffic, the detection of tunnel remains difficult (or is inaccurate). Actually, tunnel traffic is often encrypted, and so transferring the source problem into the target problem is beneficial. Due to its concealment and camouflage,which are inherently unique, it poses the following challenges to current researchers. These issues are different from those of encrypted traffic detection [5].  
We must identify whether it is tunnel traffic. A network communication protocol is easy to identify; however, identifying whether it is encapsulated as a tunneling protocol is much more difficult. Additionally, most traffic is encrypted, and the encryption algorithms obscure plaintext features, thereby reducing the randomness of the original message to a great extent. Furthermore, some software can mask tunnel traffic characteristics as normal traffic characteristics by morphing and padding packets, which makes identification more difficult.  
We must identify a passenger protocol for the tunnel. To avoid censorship, tunnel traffic may be mixed between multiple passenger protocols, such as FTP-DNS, DNS-HTTPS, TELNET-HTTP, and SMTP-TLS. To identify each passenger protocol in the specific tunnel, it is necessary to have an accurate command of multilayer protocols so that lower traffic heterogeneity within the same tunnel renders the tasks more complex.  
We must identify applications and behavior in tunnel traffic. Further, there are several specific granularities used to identify different applications and their behaviors in tunnel techniques. These include ongoing applications, service benign/malicious behaviors of the same application, webpages, and content parameters related to the same webpage, etc. A tunnel's traffic is extremely homogeneous, and the application traffic in that tunnel is the same quintuple, so determining its beginning and ending times is difficult, and tunnel noise also makes fine-grained identification difficult.
## 1 Introduction  
In modern cryptographic applications, the most widely used public-key cryptosystems, such as Rivest-Shamir-Adleman (RSA) [1] and elliptic curve cryptography (ECC) [2, 3], are built on the hardness of integer factoring and the elliptic curve discrete logarithm problem(s). The latter offers equivalent security with shorter key lengths, which result in low hardware resource utilization, low power consumption and low bandwidth requirement. Due to these benefits, ECC is now an attractive choice for area- and power-constrained cryptographic applications such as radio frequency identification networks (RFID) [4, 5], wireless sensor nodes (WSNs) [6], etc. Consequently, the ECC is standardized by many organizations, including the National Institute of Standards and Technology (NIST) [7].  
The NIST is an American organization that defines standards to manage and reduce IT infrastructure security risk. In other words, it defines new standards, and provides guidelines and practices that can be used to prevent, detect and respond to cyberattacks.  
The hierarchical model of ECC contains four layers of operations [8]. The highest layer determines the protocol for the desired cryptographic operation, e.g., data encryption or decryption, signature generation or verification, etc. The third layer provides computation of the critical point multiplication operation [9; 10]. It depends on the execution of layer two operations, i.e., point addition and doubling. Execution of each point addition and doubling depends on the computation of finite field operations, i.e., addition, multiplication, squaring and inversion. Despite this, various key lengths for prime \(GF(P)\) and binary \(GF(2^{m})\) fields specified by NIST are commonly known to implement the four-layer model of ECC either as an application running on software or as a hardware accelerator [8; 11; 12]. It is important to note that, for each prime and binary field, NIST specifies five different key lengths. The supported key sizes for the prime field are 192, 224, 256, 384 and 521. Similarly, the offered key lengths for the binary field are 163, 233, 283, 409 and 571. Comparatively, the \(GF(2^{m})\) field is most significant for performance accelerations on hardware platforms such as field-programmable gate array (FPGA) and application-specific integrated circuits (ASIC). Moreover, two types of basis representations, i.e., polynomial and normal, are available in the literature to present the underlying points on the specified ECC curve. The polynomial basis representation is appropriate where the computation of frequent multiplications is concerned, while, for frequent squaring computations, a normal basis representation is more beneficial [13]. Therefore, we target binary fields and the polynomial basis representation for the discussions in this work.
## 4 Discussion
### The Values of the Activation Energy \(E_{r}\) and the Deactivation Energy \(E_{d}\)  
The determined values of the activation energies \(E_{r}\) of 2,4-dichlorophenol degradation by horseradish peroxidase on the nanofibrous membrane were 40.33 \(\pm\) 10.72 kJ/mol and 6.39 \(\pm\) 4.00 kJ/mol, respectively. It should be noted that the lowest value was obtained for the horseradish peroxidase on the nanofibrous membrane. The lower activation energy corresponds to higher reaction rates. The value of the activation energy \(E_{r}\) determined from the Arrhenius equation was 14.63 kJ/mol [30] and 17.7 kJ/mol [16]. [PERSON] and [PERSON] [30] obtained the value of \(E_{r}\) for the reaction of horseradish peroxidase with hydrogen peroxide, as shown by Equation (1a). On the other hand, [PERSON] et al. [16] obtained the value of \(E_{r}\) for horseradish peroxidase, the activity of which was determined using guaiacol as a substrate.  
The obtained values of the deactivation energies \(E_{d}\) for native horseradish peroxidase and on the nanofibrous membrane were 63.35 \(\pm\) 5.45 kJ/mol and 215.00 \(\pm\) 55.04 kJ/mol, respectively (Table 1). The immobilized horseradish peroxidase on the nanofibrous membrane has a high activation energy of deactivation \(E_{d}\) due to thermal stability. The \(E_{d}\) values were not found in the literature for horseradish peroxidase immobilized on the membrane. The lower \(E_{r}\) and higher \(E_{d}\) values for the enzyme on the membrane compared to the \(E_{r}\) and \(E_{d}\) values for the native enzyme may indicate an effective immobilization process on the membrane. The activation energy of deactivation \(E_{d}\) of horseradish peroxidase determined from the half-time \(t_{1/2}\) value at 50 \({}^{\circ}\)C was 64.10 kJ/mol [31]. None of \(E_{r}\) and \(E_{d}\) values was found in the literature for horseradish peroxidase immobilized on the membrane.  
Therefore, the estimated \(E_{r}\) and \(E_{d}\) parameters can be used in the design and modeling of the biotransformation process when using an enzyme membrane with horseradish peroxidase. In the literature, fixed bed flow bioreactors were analyzed [32], in which the value of deactivation energy \(E_{d}\) obtained in a classical reactor study [33] was used.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 5 Experiments and Results
### Results of Improved Bilateral Filtering  
In this experiment, we selected the database [29] which includes 24.png images (typically \(768\times 512\) or similar size) for processing. Gaussian white noise with mean value \(\mathrm{u}=0\) and the standard deviation \(\sigma=0.01\), \(\sigma=0.02\),\(\sigma=0.05\),\(\sigma=0.1\), and \(\sigma=0.2\) was added to these images--an example of which is shown in Figure 5--to degrade their quality such that the performance of the different filtering algorithms could be observed. We note that with the original bilateral filter, edges in the images were lost, as they had all been smoothed. In contrast, Gaussian noise is few perceptible following operation of our algorithm, as shown in Figure 5d. Moreover, the image performance following operation of our algorithm is a better improvement, shown in Figure 5d. The edges in the image are retained after denoising, as shown in Figure 6c.  
For additional verification, we compared the two filtering algorithms with respect to Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity (SSIM) [30], Multi-scale structural similarity (MS-SSIM) [31], Visual Information Fidelity (VIF) [32], Full Refer- ence Visual Information Fidelity (FR-VIF) [33], and visual signal-to-noise ratio (VSNR) [34], shown in Table 1. This MATLAB wrapper code is presented in [35]. In this case, the objective measure scores are directly averaged; we highlight the best result of the two algorithms with boldface. We note that the intensity of noise in the images following processing is higher with the traditional bilateral filtering algorithm than it is with our modified solution, based on a comparison of the values of seven Image Quality Assessment (IQA) measures.  
Figure 5: Comparison of results of bilateral filtering (one image of the database: “kodim08”). (**a**) Original image. (**b**) Image with Gaussian noise (\(\sigma=0.01\)). (**c**) Image processed using original bilateral filtering algorithm. (**d**) Image processed using improved bilateral filtering algorithm.  
Figure 6: Magnified view of the effects of bilateral filtering focusing on the region defined by the red box in Figure 4. (**a**) Image with Gaussian noise. (**b**) Image processed using original bilateral filtering algorithm. (**c**) Image processed using improved bilateral filtering algorithm.
## 3 Data and Methods  
Our analysis is based on tristereo data from the SPOT6 satellite. The three overlapping optical scenes are processed using the ASP version 3.0 [25]. Previously used differential Global Navigation Satellite System (dGNSS) geodetic measurements [10] are also presented in the results section but primarily can be found in the Supplemental Material. The DEM quality and its relation to image texture is explored on the Salar de Pocitos. Finally, landscape curvature is used to compare results of different stereo matching algorithms and processing parameters for geographic applications.  
Figure 1: The study area in northwestern Argentina: (**A**) topography from Copernicus 30 m DEM [32]. The eastern area of the map shows steep east-to-west gradients in topography to the drainage boundary of the hyperarid, internally drained Altiplano-Puna Plateau (shown in white), where elevation is \(\geq\)3000 m. SPOT6 panchromatic (PAN) area shows the stereo–data extent; (**B**) zoom on the SPOT6 data area centered on the Salar de Pocitos dry paleolake bed (salt flat) with steep surrounding mountains and a \(\sim\)2500 m elevation gradient between the salar (lowest elevation) and highest volcanic mountain (Nevado Queva). Slope map indicating the flat salar and steep mountains is derived from the Copernicus 30 m DEM; (**C**) photograph identified in (**B**), demonstrating the flat salar (photo taken on low–slope alluvial fan, white area in middle ground is salt flat), and the smooth, vegetation–free topography of the surrounding mountains characteristic of the study area, with localized bedrock outcrops and incised valleys.
## 1 Introduction
Can conventional channel estimation methods and ML-based solutions be applied to RIS and NTN systems? 3. How can RISs and NTNs be integrated into 6G technologies? 4. How can energy efficiency and sustainability principles be applied to RISs and NTNs? 5. What are the future perspectives and challenges faced by RIS and NTN technologies? The details of the different topics covered in this paper are shown in Figure 2. The remainder of the paper is organized as follows. Section 2 discusses the basic principles of RISs, beamforming optimization techniques for RIS-HAP systems, and applications of  
Figure 1: High-altitude platform (HAP) system architecture integrating ARIS for enhanced wireless communication for multiple users [13]. RISs in NTNs. In Section 3, ML-based channel estimation solutions in RIS-HAP systems are elaborated. RIS integration with other communication technologies is presented in Section 4. Section 5 focuses on energy efficiency and sustainability issues in RIS-HAP systems. Section 6 explores the intersection of ML and RISs in wireless communication networks and discusses future perspectives and challenges in implementing RIS-based NTN systems. Finally, Section 7 concludes with a summary of the key findings.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 4 The Fundamentals of Clustering
### General Framework
#### 4.1.1 Cluster Formation  
This phase aims to reduce the workload of the CHs near the SNs by ensuring that each cluster has few members. Each member node is assigned to the nearest CH, based on the Received Signal Strength Indication (RSSI) [26]. The membership of each node within a cluster is determined by the received local data that the CH sends to nodes within its radius [27,28].
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 3 Classifier Models
### K-Nearest Neighbors  
The K-Nearest Neighbors (KNN) algorithm classifies a new data point based on the \(K\) closest training samples using a distance metric, most commonly the Euclidean distance [28]. KNN is a non-generalizing learning method, as it stores all training data and it does not involve an explicit training phase.  
The distance between two points \(\mathbf{x}\) and \(\mathbf{y}\) is defined as  
\[d(\mathbf{x},\mathbf{y})=\left(\sum_{i=1}^{n}x_{i}-y_{i}^{p}\right)^{1/p},\]  
which generalizes several metrics, including Euclidean (\(p=2\)), Manhattan (\(p=1\)), and Minkowski distances.  
The value of \(K\) influences the model behavior: small values make it sensitive to noise, while larger values provide smoother predictions but may overlook local patterns. Once the \(K\) nearest neighbors are identified, the predicted class \(\hat{y}\) for a query point \(\mathbf{x}\) is given by a majority vote:  
\[\hat{y}=\arg\max_{c}\sum_{i=1}^{K}\mathbb{I}(y_{i}=c),\]  
where \(\mathbb{I}(y_{i}=c)\) is the indicator function, which is equal to \(1\) if \(y_{i}=c\), and \(0\) otherwise. The class with the most votes is assigned to the query point.
# A Low-Cost and Portable Smart Instrumentation for Detecting Colorectal Cancer Cells
###### Abstract  
Fluorescence imaging is a well-known method for monitoring fluorescence emitted from the subject of interest and provides important insights about cell dynamics and molecules in mammalian cells. Currently, many solutions exist for measuring fluorescence, but the application methods are complex and the costs are high. This paper describes the design and development of a low-cost, smart and portable fluorimeter for the detection of colorectal cancer cell expressing IRFP702. A flashlight is used as a light source, which emits light in the visible range and acts as an excitation source, while a photodiode is used as a detector. It also uses a longpass filter to only allow the wavelength of interest to pass from the cultured cell. It eliminates the need of both the dichroic mirror and excitation filter, which makes the developed device low cost, compact and portable as well as lightweight. The custom-built sample chamber is black in color to minimize interference and is printed with a 3D printer to accommodate the detector circuitry. An established colorectal cancer cell line (human colorectal carcinoma (HCT116)) was cultured in the laboratory environment. A near-infrared fluorescent protein IRFP702 was expressed in the colorectal cancer cells that were used to test the proof-of-concept. The fluorescent cancer cells were first tested with a commercial imaging system (Odyssey(r) CLx) and then with the developed prototype to validate the result in a preclinical setting. The developed fluorimeter is versatile as it can also be used to detect multiple types of cancer cells by simply replacing the filters based on the fluorophore.  
fluorescence imaging; fluorimeter; colorectal cancer; IRFP702; fluorescence microscopy 2019 9 3510 10.3390/appr9173510
## 4 Discussion
### Limitations and Future Studies  
No kinematic or kinetic parameters being collected in this study and the exact time of perturbation in the cycle while participants were walking was not controlled, which should be addressed in future research. Moreover, previous physical condition (such as ankle joint mobility/muscle tone for the muscles surrounding the ankle joint and generalized joint hypermobility) was not considered for our healthy physical education students. However, having a random presentation of the perturbation mimics the reality of sudden unexpected events that challenge balance even in athletic populations.  
Future studies could also benefit from further assessing parameters that can distribute between genders such as generalized joint hypermobility; and, the effect of perturbation type, magnitude, and direction on the extent and the degree of retention of single leg and double leg responses (in symmetrical and asymmetrical positions) in both healthy and injured populations [17].
## 4 Results Analysis and Discussion  
### Single-Channel Deposition  
#### 4.1.1 Influence of Powder Feeding Rate on the Shape of Single-Channel Deposition  
For the simulation calculation of single-layer single-channel deposition molding, 500 W of laser power, 10 mm/s of scanning speed, and feeding rates of 9.6 g/min, 12.0 g/min, 14.4 g/min, and 16.8 g/min of powder were employed. Figure 4 depicts the simulation results when the geometry of the sedimentary layer achieves a steady state after a length of time. It can be observed that the deposited height and breadth grow with increasing powder feeding rate when the laser power and scanning speed are constant.  
The specimens of the above single-channel forming experiments were measured under an ultra-deep field microscope. Shape and dimensions of the sedimentary layer under different powder feeding rates were obtained, as shown in Figure 5. Figure 6 compares the simulation findings to the experimental measurement data, and it can be observed that the estimated values for the breadth and height of the deposition corresponding to various powder feeding rates are greater than the experimental results. This implies that the simulation findings of the sediment layer shape include a certain amount of systematic inaccuracy, which is mostly attributable to the departure of the proposed powder feeding equation based on the normal distribution from the experimental powder flow concentration distribution. Through calculation, the relative error in height increased from 3.67% to 6.80% as the powder feeding rate increased; while the relative error in width was 7.29% at the highest when the powder feeding rate was 16.8 g/min and 5.40% at the lowest when the powder feeding rate was 14.4 g/min. The maximum relative error in height and width is less than 8%, and the numerical simulation results of the deposition channel height and width values are essentially comparable with the experimental data, demonstrating the validity of the numerical model developed in this paper.  
Figure 4: Simulation results of powder deposition channel shape under different powder feeding rates.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 2 LSTM and SSA Fundamentals
### SSA Principles
The parameter \(\beta\) is a step control parameter, which is a random number with a normal distribution having a mean of 0 and a variance of 1. \(K\) takes values in the range [\(-1\), 1] as a random number. \(f_{g}\) and \(f_{w}\) denote the current global best and worst fitness values, respectively. Additionally, \(\varepsilon\) is a small constant used to prevent zero-division errors.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 6 Conclusions  
This paper presents an information compression scheme for target tracking. Comparing the proposed CTCI method to the TCAT and DCTTP methods, the simulation results show that the proposed CTCI scheme can adaptively control the estimation performance and closely reflect the relationship between the distortion of tracking results and energy consumption. Considering the system parameters (e.g., transmission range, the number of sensor nodes, compressing bound, the number of sub-cluster size and the number of sampling points), at the slight cost in data size and energy consumption, the tracking accuracy of CTCI is superior to those of TCAT (improved by 20%) and DCTTP (improved by 100%). Therefore, many trade-offs between the system parameters need to be considered in each scheme. In future work, we will explore a more effective and robust compression scheme, evaluate the compression complexity and quality and adjust the tactics of the two-level clustering to further reduce the data size, maintain the desired estimation quality and achieve energy conservation.  
This research is supported by the Ministry of Science and Technology of Taiwan under Grant No. MOST-104-2221-E-005-034.  
[PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] contributed reagents/materials/analysis tools. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 4 Experimental Results and Discussion  
In order to analyze the potential of the MFFPN proposed in this paper, we compared it with existing feature pyramids on a constructed spatial small target dataset. We also conducted a comprehensive ablation experiment to compare the effects of different modules on the detection performance of spatially small targets. The parameter configuration of the experimental environment is shown in Table 1.