esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Analysis Results for Suburban Area  
#### 4.2.1 Interpolation of Spectro-Temporal Profiles  
Figure 4 shows two examples of original and interpolated STPs for the suburban environment, both for the weekend day. First, it can be observed that the original STPs showed higher energy values at low frequency bands (below 500 Hz), due to the presence of RTN. As can be seen in the left-most graph of Figure 4a, a significant increase of the measured energy was observed at 13:00 and 15:00, which was more prominent at high frequencies. This was mainly caused by a rain episode during the weekend, which was confirmed by the ANE labels. After data interpolation, the _rain effect_ was largely removed, as can be seen in the right-most graph of Figure 4a. Figure 4b depicts an example of a missing data period due to an insufficient number of RTN frames (below 40%) within the corresponding audio recording at 11:00. In the interpolated STP (right-most graph),  
Figure 3: Map of the acoustic sensor locations of the WASN deployed across District 9 of Milan (urban environment), adapted from Google Maps®.  
the mean energy curve at this hour was filled through the cubic interpolation of the neighboring data.
# Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
## 1 Introduction
Furthermore, the Random Forest employs Gini impurity [19] in node splitting, which tends to favor majority classes in imbalanced datasets, resulting in poor classification performance for minority classes. To address the challenges posed by high-dimensional data and class imbalance in the Random Forest algorithm, researchers have primarily focused on two approaches: data dimensionality reduction and feature selection. To improve classification accuracy when facing imbalanced datasets, efforts have been made at the data, algorithm, and decision levels. When dealing with high-dimensional datasets, the literature [20; 21; 22; 23] has employed feature selection methods to reduce dataset dimensionality by selecting the most relevant features. However, this approach may inadvertently remove some useful features, potentially leading to the loss of inter-feature relationships. The literature [24] has explored the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), mapping the original feature set into a lower-dimensional space and subsequently applying the reduced data to classifier training, offering a direction for data dimensionality reduction in intrusion-detection scenarios. Moreover, to address the issue of classification results leaning toward majority classes due to a class imbalance, the literature [25] has combined adaptive oversampling, undersampling clustering algorithms, and Gaussian mixture models at the data level and applied them to preprocess the original dataset for classification decision-making. While sampling methods hold advantages in handling imbalanced datasets, strategies that modify data distributions may affect subsequent model construction and hinder the effective extraction of underlying relationships between data. The literature [26] has proposed improvements at the algorithm level by embedding cost information into the model, obtaining cost-sensitive information through expected loss minimization, and applying it to both binary and multi-class classification scenarios, demonstrating superior performance in classification problems. The literature [27; 28] has addressed the issue at the decision level by adjusting classifier decision thresholds, shifting them towards the majority class to reduce the probability of misclassifying minority classes, and effectively mitigating class imbalance. Traditional Random Forest algorithms applied to intrusion detection suffer from excessive computational costs, low classification accuracy, and a tendency to favor minority class results. The current research indicates that to address these issues, optimizing the Random Forest algorithm should consider three key aspects: data dimensionality reduction, algorithm enhancement, and decision evaluation. Therefore, this paper proposes a Random Forest classification model designed for handling high-dimensional and class-imbalanced data. The aim is to address the limitations in network-intrusion detection research, ensuring the security and integrity of computer networks.
# Investigation of Self-Driven Profiler with Buoyancy Adjusting System towards Ocean Thermal Energy
## 1 Introduction
The Navis-SL1 system is equipped with two sets of SL1 energy capture and power generation systems. It was tested in Hawaiian waters in 2019 and completed 42 sections from 50 m to 750 m depth [10]. [PERSON] et al. proposed a thermal power generation system that can transform OTE into electrical energy to improve soiling mileage, and this system can be used in ocean observation platforms such as underwater glider, profiler and buoy [11]. [PERSON] et al. investigated an ammonia-water-based ocean thermal energy conversion system using experimental methods, and the values of thermal efficiency under different heating sources and cooling sources were given [12]. [PERSON] et al. designed a thermal-driven volumetric pump for energy harvesting and used it in an underwater glider [13]. [PERSON] researched the phase change process of the thermal glider propelled by ocean thermal energy and analyzed the gliding process when the PCMs were partly melted [14]. [PERSON] established a nonlinear model for the relationship between system pressure and phase change rate and analyzed influencing factors for system pressure and stored energy based on the established nonlinear model [15]. Existing research on buoyancy adjustment systems is mainly for underwater vehicles driven by electric energy. [PERSON] et al. proposed a computer simulation model (CSM) for the preliminary design of an AUV/AUGs variable buoyancy system (VBS) to achieve effective control of buoyancy [16; 17]. [PERSON] et al. proposed an accumulator-based hybrid buoyancy adjustment system for deep-sea gliders, which can effectively use ocean pressure difference energy to achieve buoyancy compensation. Using the Newton-Euler formula and [PERSON]'s law, a mathematical model of DG with ballasted underwater vehicles was established, revealing the relationship between DG's buoyancy and water depth [18]. [PERSON] et al. designed a variable buoyancy system for the AUV to ensure that the AUV can obtain a good posture. Through AMESim's simulation of no-load and load operation of the hydraulic system and the comparison of tests in the lake, it provides a reliable reference for system optimization [19]. However, there are few studies on the driving performance of the thermal profiler's buoyancy adjustment system in the existing research. The previous research studies are mainly focused on heat transfer analysis of the phase process of the thermal engine. However, the static and dynamic characteristics of the thermal engine, which is closely related to the performance of the profiler, have not been studied deeply so far. In this paper, a profiler with the new buoyancy adjustment system, which was driven by OTE, was designed, the characteristics of buoyancy regulation system were calculated using power bonding diagram theory and a lake test was carried out. The results show that the new buoyancy adjustment system with the thermal engine can certainly reduce the consumption of electricity and improve the service life of the underwater platform.
# RF Energy Harvesting Wireless Communications:
## 5 Intermittent Connection Problem in RF-EHWC Networks
### Impact of Intermittent Connections
#### 5.2.2 Unpredictable Changes of Network Topology  
To optimize the network performance in terms of the lifespan and throughput, a routing protocol needs to balance the energy consumptions among sensor nodes. In conventional wireless sensor networks, this can be achieved by carefully scheduling the sleep and active time of each individual according to its concurrent energy level [82], which results in highly controllable topology changes. In RF-EHWC network, however, the downtime of each node enforced by the energy deficiency is out of control in general. How long a node can stay active not only varies with the energy harvest rate but also depends on the temporary traffic load, which is barely predictable ahead of time.  
To handle the above two challenges, the RF-EHWC network calls for the resilient routing protocol, which is capable of tackling the frequent and unpredictable disconnections among RF-EHWC nodes. It is also essential to eliminate or at least significantly reduce the overhead on restoring the network connectivity once the intermittent connections occur.
###### Abstract  
(1) Background: The present pilot in vitro study evaluated, physically and biologically, the effects produced by the wear of the titanium surface using different drill models. (2) Methods: Titanium disks were subjected to wear using four different burs and accordingly divided into the following test groups (\(n=12\) disks per group): Tungsten Burs (TB group), Tungsten Carbide Burs (TCB group), Coarse-Grained Diamond Burs (CGB group), and Fine-Grained Diamond Burs (FGB group). As a control group (CON group), titanium disks with a smooth surface (machined) were used. The samples were subjected to atomic force microscopy (AFM), profilometry analysis, scanning electron microscopy (SEM), and cell viability and adhesion assessments. (3) Results: The values of the measured roughness parameters showed statistical differences among the five groups (\(p=0.0035\) for Ra and \(p=0.0010\) for Rz). All test groups showed an important difference statistically (\(p=0.0032\)) to the CON group for the cell viability and adhesion analysis. The data of cell absorbance at 570 nm were 0.4122 \(\pm\) 0.05 for the CON group, 0.1354 \(\pm\) 0.02 for the TB group, 0.123 \(\pm\) 0.01 for the TCB group, 0.1414 \(\pm\) 0.02 for the CGB group, and 0.1216 \(\pm\) 0.03 for the FGB group. Additionally, the cell count showed the following adherence percentages: 57.6 \(\pm\) 4.6% for the CON group, 22.9 \(\pm\) 3.3% for the TB group, 23.4 \(\pm\) 2.9% for the TCB group, 22.5 \(\pm\) 3.1% for the CGB group, and 23.7 \(\pm\) 3.3% for the FGB group. However, no statistical differences were found among the four test groups analyzed (\(p=0.3916\)). (4) Conclusions: The results showed that the changes produced on the surface by the four different bur models altered the topography characteristics and affected the cell viability and adhesion in comparison with the control group.  
titanium surface; implantoplasty; cell viability and adhesion; peri-implantitis; titanium instrumentation; fibroblast cells +
Footnote †: journal: Applied Sciences
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
## 1 Introduction
In this way, the legitimate receiver can perfectly cancel the AN while the eavesdropper will suffer from the interference induced by the AN. To estimate the secrecy performance, we derive the secrecy rate of the proposed scheme. The rest of this paper is organized as follows. In Section 2, we describe the basic concept of the conventional PLS scheme with SM by using a simple system model. Section 3 presents the proposed scheme, starting with the representation of the operational principle in brief. Afterwards, it proposes a method to generate AN, which can be perfectly canceled only at the legitimate receiver, and then presents the optimum antenna selection method by considering imperfect CSI. In Section 4, we derive the mathematical expressions of the secrecy rate of the proposed scheme. The numerical results and discussions are presented in Section 5. Finally, Section 6 draws the conclusions. Notation: Bold lower case letters represent vectors, while bold upper case letters denote matrices. \((\cdot)^{H}\), \(|\cdot|\), and \(\|\cdot\|\) denote the Hermitian transpose, modulus operator, and Frobenius norm operations, respectively. \((\cdot)\) is the binomial coefficient. \(\mathbb{C}^{m\times n}\) stands for the complex space of \(m\times n\) dimensions.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 6 Limitations  
Our method has a few limitations. In Section 4.4, we saw that the terrain influences our method's performance. If the terrain is relatively unchanging (e.g., in the case of an ocean or desert), the method's performance is not optimal. In such terrain conditions, the changes between the frames are almost imperceptible, and as a result, the method will be unable to differentiate between frames. It should be noted that for the use case presented in this article, where we are concerned with the protection of a delivery drone in an urban area, this does not pose a limitation, because for the route of a drone traveling over urban terrain, there is variation between frames.  
Another limitation is associated with the ambient light. In Section 4.5, we saw that when the light decreases, the efficacy of our method decreases, to the point that it is not effective in conditions of total darkness. We hypothesize that using a camera with night vision capabilities will increase the method's efficacy in such conditions.  
Another limitation is the altitude. In Section 4.2, we saw that when the altitude is low, the efficacy of our method decreases. Drone altitudes under 50 m can affect the efficacy of our method, since the camera's FOV will be narrowed to a view that makes it difficult to use the frames. We hypothesize that using a camera with a wide FOV will increase the method's efficacy.
###### Abstract  
We report results of galvanomagnetic and terahertz time-domain spectroscopy measurements on thin films of Bi\({}_{1-x}\)Sb\({}_{x}\) on polyimide and mica substrates with various antimony concentrations (\(x\) from 0 to 15 %) and film thickness (70, 150 nm). The resistivity, Hall coefficient and magnetoresistivity of the films were measured experimentally in the magnetic field of 0.65 T at room temperature. Mobility and concentration of electrons and holes in the film plane were calculated using the transport coefficients. The terahertz time-domain spectroscopy is used to measure the complex conductivity and permittivity of Bi\({}_{1-x}\)Sb\({}_{x}\) thin films on the dielectric substrates in the frequency range from 0.2 to 1 THz. The plasma frequency, relaxation time, DC conductivity and effective carrier mass were extracted from these data and evaluated as functions of the Sb concentration for different film thickness and substrate. We observed that the film magnetoresistivity decreases with increasing the Sb concentration and for most of the films the Hall coefficient is negative and depends on the external factors insignificantly. We show that the mobility of charge carriers weakly depends on Sb concentration, which confirms the assertion about the scattering of carriers on themselves and not on defects in the structure. It was revealed that film static and dynamic resistivity (conductivity) as well as dielectric permittivity depend on Sb content and the film thickness. The results may be used for development of various thermoelectric, electronic and optical devices, such as THz detectors or components which can control the properties of THz radiation.  
semimetals; thin films; bismuth-antimony; galvanomagnetic properties; terahertz time-domain spectroscopy; solid solutions +
Footnote †: journal: Applied and Solid State Communications
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 3 Compact Public-Key Cryptosystem with KDM Security  
In this section, we will describe the construction of a public-key scheme based on the variant of the RLWE problem. At first, we introduce the RLWE\({}^{*}\) problem by applying the invertible technique, and then give the new version by scaling the noise. After that, to ensure that the secret chooses form error distribution, a useful transformation is given to obtain the RLWE\({}^{*}\) assumption-Hermite normal form. Finally, we construct a compact public-key scheme, analyze its correctness, and prove its key-dependent message security.
# Leveraging Social Network Analysis for Crowdsourced Software Engineering Research
## 3 Data and Methods
### Data Preparation  
After collecting the data on publications, we prepared our data repository, which contained information on the collected publications (metadata). The data included the publication ID, title, authors, author affiliations, year of publication, author keywords, citation count, topic within the broad field of software engineering (e.g., software testing or coding), and type of publication (e.g., conference paper, journal article, book, or thesis). Data in our repository were then formatted for completion by filling in any missing data. Some authors had different names in different publications; for example, some of them included their middle name in some of their papers and not in other papers; therefore, authors' names were consolidated, as this is critical for accurate linkage. We standardized keywords to remove different spellings of the same keyword, and the co-occurrence relationships between two keywords were identified. We retrieved the citation count of each work and manually checked that the publications from our repository cited this work. We identified citation relationships between each pair of publications. A publication category was assigned for each publication by reading the abstract and content when necessary. We defined publication categories based on how CSE was applied in the various software engineering phases [1]. Missing information regarding an author, an institution, or a country was found and added. We defined the collaborative relationships between each pair of authors, institutions, and countries, and the number of publications was calculated for each.  
After that, we built two lists for each network, a node list and an edge list, by using the data repository. Each row in the node list represents a node in the network, and each row in the edge list represents the link between two nodes in the network. The node list contains the ID, label, and attributes of the nodes. The edge list contains the source node, target node, type of relationship (directed or undirected), and/or weight of relationship.  
In a co-authorship network, the nodes represent authors and the links represent collaborations between authors of CSE publications. The nodes in an institutional co-authorship network represent authors' institutions, and the links represent co-authorship collaborations between institutions. The country co-authorship network nodes represent authors' countries, and the links represent CSE co-authorship collaborations between countries. In the CSE citation network, each node represents a CSE publication, and the link is directed because it represents the citation relationship direction between two publications.  
After the social network lists had been prepared, we converted them into comma-separated values (CSV) files and imported them into the Gephi platform as input data for visualizing the CSE networks. Gephi [30] is an open source software that allows for network visualization and analysis as well as the calculation of network measures.
## 5 Experiments  
### Experimental Setup  
To evaluate the practical performance of the proposed method, a line array of 31 loudspeakers (HiVi BIS) with spacing of 0.038 m, shown in Figure 10a, has been built. The experimental setup is shown in Figure 10b. The array was installed in an anechoic chamber and placed on top of a turntable. An omni-directional microphone (BSWA MPA201) was placed at 3 m from the geometrical center of the array. Both the microphone and the line array were placed at a height of 2 m from the floor.  
The transfer functions between loudspeakers in the array and the microphone have been measured. The speaker is excited by linear swept sine signal in the frequency range of 250-4k Hz with resolution of 5 Hz, and an AP2720 audio analyzer is used to measure the transfer functions from the loudspeaker to the microphone position. The transfer functions measured at every 5\({}^{\circ}\) from 0\({}^{\circ}\) to 180\({}^{\circ}\) by using the turntable have been undertaken to calculate the measured beampattern by different beamforming methods.
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 2 Models and Methods
### Improved YOLOv8
#### 2.2.3 Shared Convolution Detection Head (LSCDH)  
The original YOLOv8n network uses three detection heads to detect targets of different sizes. However, as the model deepens, its performance in handling small targets diminishes. This study introduces a newly designed Lightweight Shared Convolutional Detection Head (LSCDH) to enhance the network's precision and speed in multi-scale target detection.  
The overall structure of the LSCDH detection head is illustrated in Figure 4. Firstly, the feature output information from the YOLOv8 network is compressed by replacing the 3 \(\times\) 3 convolutional kernels with 1 \(\times\) 1 convolutional kernels, effectively improving the training speed of the network. Secondly, to integrate feature information from different branches, Group Normalization (GroupNorm) [18] is applied for feature fusion, enhancing the model's generalization ability. Next, two shared convolutions (Conv_GN) are consecutively applied to process the feature maps of different scale output by YOLOv8, achieving parameter sharing. GroupNorm is used to normalize the feature information, followed by an activation function to enhance the network's ability to handle non-linear expressions. The resulting feature maps are divided into three branches based on their scales. Subsequently, each branch splits the output into regression and classification outputs according to the position and category of the predicted objects. After the regression output passes through the scale layer, the feature map output dimensions are restored to the same dimensions as the original input. The normalized weights are then added to the features of each channel, allowing the better fusion of low-level feature details with high-level information, ensuring the effectiveness and adaptability of different scales.