esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation and Implementation
Concerning some additional observations, there are no significant differences regarding the performance of the deep learning models between the standard and the balanced undersampled datasets. Once again, the deep learning models perform very satisfactorily and there are no significant differences among them (DCNv2 performs slightly better). Furthermore, it is worth mentioning that traditional techniques perform better on the undersampled datasets (although they cannot outperform the deep learning techniques). Finally, the ensembler was slightly better at any case as expected. Step 3 Development/application implementation  
The last step after the offline experiments, as it is discussed above, is the development (to use the selected model in real-world applications). The objective is to verify the technical feasibility and show a possible implementation approach (could be different implementation approaches based on the developers' expertise). A mobile application for android devices has been implemented for this purpose. JAVA programming language was chosen for the implementation ([[https://www.java.com/en/](https://www.java.com/en/)]([https://www.java.com/en/](https://www.java.com/en/))) (accessed on 28 September 2021). The application displays a test advertisement (or not) to the user. When the user launches the application, the client (JAVA in mobile) first collects and then sends to the application's server the relevant input data (time, application category, ad position, device, connection type, etc.--all the variables that are contained in the first dataset). Then, the server (Python Django) collects these values and starts the calculation process. First, it (a) runs the model based on the input data values and calculates the result, and (b) sends the result back to the client. To build the server, Python and Django framework have been used [27]. In more detail, the ensembler was chosen as the prediction model since it had the best results. The six deep-learning models that were trained offline in the AVAZU dataset run sequentially and calculate the predicted probability based on the input. Then the average is used for the final prediction. The server returns the result/response to the client and based on the probability, the client shows (or not) a test advertisement from the network to the current user (if the probability is higher or lower than 0.5). It should be mentioned that Google Ads is used as an advertisement provider to fetch and display a test advertisement to the user [30]. The UML Activity diagram of Figure 5 displays the flow regarding the development step. Figure 4: Log-loss scores for the balanced undersampled Avazu and Digix datasets.
## 4 Analysis and Result  
Twelve features extracted from the FRF graph in Figure 3 were used to construct a basic data set using PCA. As a result of the PCA, the feature value of the largest variance was the moment of real value (PC1), and the feature value of the second largest variance was the moment of imaginary value (PC2). Figure 7 shows two-dimensional scatter plots with PCs on the \(x\)-axis (PC1) and \(y\)-axis (PC2). The PCA result revealed that the normal data were distributed over a fairly wide range. This is judged to be an error that occurs because the porcelain insulators are used under various environmental conditions. Data of the broken discs were found to be outside the distribution range of the normal data, but some internal defect data were located close to the distribution range of normal data. Therefore, it is necessary to set the distribution range between normal data and porcelain damage data for these distribution results.
# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
## 3 Saturable Reactor Core Looseness Experiment  
### Experiment Platform  
The iron core looseness experiment platform is shown in Figure 2. The platform consists of three parts, a power circuit, a saturable reactor, and vibration acquisition equipment. The structural diagram of the experimental platform is shown in Figure 3. The power circuit provides power, control and protection for the entire experiment platform. It includes a power supply, control switch and H-bridge module. The power supply provides the required DC and AC power for the entire experimental platform. The control switch is responsible for controlling the start and stop activities of the experiment and also has the function of protection. The H-bridge module mainly includes IGBT devices to achieve rectification and provide voltage excitation to the saturable reactor. The saturable reactor is the experimental object. The iron core in it will be artificially set to loosen during the experiment, which is to simulate iron core failure. The vibration acquisition device includes a vibration sensor, optical fiber, acquisition board, upper computer, etc. The sensor is adhered to the surface of the saturable reactor. The collected vibration signals will be uploaded through optical fibers. In the entire experimental platform, the power circuit emits voltage excitation that simulates the two pulse voltages generated by the switch instantaneously. Equivalent stray capacitance is used to regulate the voltage peaks and simulate the actual operating conditions. The vibration signal generated by the saturable reactor is received through a sensor, transmitted through optical fibers, processed by the acquisition board and finally input into the upper computer for analysis.
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
###### Abstract  
The combination of light detection and ranging (LiDAR) sensors and unmanned aerial vehicle (UAV) platforms have garnered considerable interest in recent years because of the wide range of applications performed through the generation of point clouds, such as surveying, building layouts and infrastructure inspection. The attributed benefits include a shorter execution time and higher accuracy when surveying and georeferencing infrastructure and building projects. This study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to collect topography data and propose a procedure for obtaining a georeferenced point cloud that can be configured according to the user's needs. A structure was designed and built to mount the LiDAR system components to the UAV. Survey tests were performed to determine the system's accuracy. An open-source ROS package was used to acquire data and generate point clouds. The results were compared against a photogrammetric survey, denoting a mean squared error of 17.1 cm in survey measurement reliability and 76.6 cm in georeferencing reliability. Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure in which a presentation scale of 1/2000 or more is required, due to the accuracy obtained in the work presented.  
drones; LiDAR; UAV; point cloud; 3D surveys; photogrammetry Article
###### Abstract  
This paper proposes a Dynamic Hybrid Binary Particle Swarm Optimization (DH-BPSO) algorithm to improve the bandwidth of an inverted-F antenna (IFA). The proposed algorithm improves upon the existing Artificial Immune System (AIS) algorithm by including a weighting factor that dynamically changes throughout the optimization. DH-BPSO activates or deactivates a 12 \(\times\) 2 grid of parasitic patches incorporated between the IFA and ground plane. The DH-BPSO optimized and conventional IFAs are fabricated and compared while maintaining the same antenna volume. The measurement results show that the optimized IFAs have characteristics of 58.6% wider bandwidths and 5.8% higher antenna gain for various ground clearance lengths at Long Term Evolution (LTE) 700 MHz band compared to the conventional IFAs.  
dynamic hybrid particle swarm optimization; inverted-F antenna; parasitic patches; antenna optimization; bandwidth enhancement
## 2 System Description
### Organic Rankine Cycle Modeling  
A typical ORC is composed of four components, namely, an evaporator, condenser, turbine, and pump. The liquid organic fluid is firstly heated to a superheated state by absorbing the wasted heat in the exhaust gas of the engine via the evaporator. The superheated fluid then drives the turbine to generate power. For the cycle to repeat, the superheated fluid is condensed to the liquid phase and pumped again to the evaporator. The mathematical model of the ORC system is detailed below.  
The energy balance through the evaporator is presented in Equation (3).  
\[Q_{Eup}=\dot{m}_{exh}(h_{exh,in}-h_{exh,out})=\dot{m}_{wf}\left(h_{uf,out}-h_{ wf,in}\right) \tag{3}\]  
Similarly, the energy rejected through the condenser can be obtained using Equation (4).  
\[Q_{cond}=m_{wf}\left(h_{wf,in}-h_{uf,out}\right) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Engine Point** & **P1** & **P2** & **P3** \\ \hline Speed [rev/min] & 750 & 1200 & 2200 \\ Torque [Nm] & 234 & 1388 & 1500 \\ BSFC [g/KWh] & 277 & 188 & 221 \\ Mass flow rate of exhaust gases [kg/s] & 0.310 & 0.380 & 0.500 \\ Temperature of exhaust gases [K] & 688 & 679 & 810 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Engine properties at different engine operating conditions.  
Figure 1: The proposed system along with T-s diagram of the thermodynamic cycle.  
To ensure a liquid state at the outlet of the condenser (pump inlet), a sub-cooling of 2 \({}^{\circ}\)C is assumed. A 65% pump efficiency \(\eta_{pump}\) is assumed. Therefore, the power consumed by the pump can be obtained from Equation (5).  
\[P_{pump}=\frac{\dot{m}_{inf}\Big{(}h_{s,wf,out}-h_{wf,in}\Big{)}}{\eta_{pump}} \tag{5}\]  
The cycle thermal efficiency can be obtained using the following (6). The numerator of Equation (6) is simply the cycle network.  
\[\eta_{thermal}=\frac{W_{output}-W_{pump}}{Q_{Eusp}} \tag{6}\]  
It is worth mentioning that NOVEC 649 is used as the working fluid in the cycle because it is non-flammable and has a near-zero GWP. Moreover, NOVEC649 produces lower back pressure at the evaporator outlet and lower turbine rotational speed.
# Research of Tri-State Optical Signal Detectors for Ternary Optical Computers
## 2 Materials and Methods
### Design of Three-State Optical Signal Detectors
#### 2.3.2 Parallel Design of 576-Bit LCD Pixel Decoding Circuit  
The tri-state optical signal detector is composed of 576 one-bit pixel decoding circuits connected in parallel, as shown in Figure 6. In Figure 6, there is a percentage that represents the ratio of the actual resistance of the adjustable resistor to the maximum resistance, where 50% means that the resistance of this adjustable resistor is half of the maximum resistance. In the actual circuit, we will adjust this ratio as needed.  
Figure 4: Data transcoding logic.  
Figure 5: Decoding circuit for one LCD pixel.  
The parallel circuit can be divided into three arrays: the photoreceptor array, voltage divider array, and latch array. The photoreceptor array consists of 576 parallel-connected photosensitive sensors. The voltage divider array is responsible for the conversion of the electrical signals acquired by the photoreceptor array, while the input active terminal OE and the output active terminal LE of all latches in the latch array are connected in parallel, which can allow the complete synchronization of the operation timing. Considering the workload of this experiment, only the decoder is discussed here to obtain the valid photoelectric conversion information, and the output part of the latching array is omitted in Figure 6. Due to the large number of components in the decoder, a layered design was carried out for the physical design.
## 2 Thermal Evaluation of Overhead Electrical Lines
### Dynamic Line Rating
#### 2.2.3 Steady-State Dynamic Ampacity  
When discussing DLR, it is important to specify two concepts related to the dynamic ampacity of overhead electrical conductors: steady-state dynamic ampacity (steady-state DLR) and transient dynamic ampacity (transient DLR).  
The current size at which the conductor temperature stabilizes at a certain value is referred to as the steady-state dynamic ampacity. In this calculation, it is assumed that all operating parameters are constant (do not change over time), meaning the conductor is in thermal equilibrium. Therefore, this calculation is associated with the stable temperature of the conductor before or after the initiation or termination of a transient event caused by the change in one operating parameter or the change in multiple operating parameters simultaneously. The steady-state solution often focuses on the opposite question, i.e., determining the stable conductor temperature with a known current flowing through the conductor and constant climatic conditions [15].
# Water Resistant Composite Membranes for Carbon Dioxide Separation from Methane
## 1 Introduction
For a single gas permeation measurement, there is no concentration polarization on either the feed or permeate sides, and hence the total resistance to flow is related to only to the respective thickness of the different layers and their respective permeabilities:  
\[\text{R}_{\text{T}}=\text{R}_{\text{L1}}+\text{R}_{\text{L2}}=\frac{\text{I}_ {\text{L1}}}{\text{P}_{\text{L1}}}+\frac{\text{I}_{\text{L2}}}{\text{P}_{\text {L2}}} \tag{3}\]  
Hence, the resistance and the permeability of the composite membrane can be determined based on the permeabilities and thicknesses of the respective layers. Under mixed gas conditions, concentration polarization can be avoided on the feed and permeate sides of the membrane if operated at low stage cuts, and hence the resistance and permeability of the composite membrane can be determined from the performance of the individual layers of the composite structure. The permeability of the gas through a glassy polymeric membrane can be described through the solution-diffusion mechanism, which explains the transport by diffusion-solubility through the membrane. The sorption (S\({}_{\text{A}}\)) of a gas within the polymeric membranes to the polymeric matrix through Henry's Law and to the micro-voids within the glassy polymeric structure as Langmuir adsorption [16]:  
\[\text{S}_{\text{A}}=\text{k}_{\text{DA}}+\frac{C^{\prime}{}_{\text{HA}}\text{b }_{\text{A}}}{(1+\text{b}_{\text{A}}f_{A})} \tag{4}\]  
where k\({}_{\text{DA}}\) is the Henry's law constant, C\({}^{\prime}{}_{\text{HA}}\) the maximum adsorption capacity and b\({}_{\text{A}}\) the Langmuir affinity constant. The diffusion of gas species adsorbed in the micro-void region is partially immobilized. To account for this a parameter F\({}_{\text{A}}\) is introduced to describe the fraction of penetrant in the micro-void region that is mobile [18]:  
\[\text{S}_{\text{A}}=\text{k}_{\text{DA}}+\frac{\text{F}_{\text{A}}\text{C}^{ \prime}{}_{\text{HA}}\text{b}_{\text{A}}}{(1+\text{b}_{\text{A}}f_{A})} \tag{5}\]When binary gases are present the competitive sorption occurs in the micro-void region and the solubility of gas A is reduced. Hence, the mobile solubility of gas A in the membrane becomes [19]:  
\[\mathrm{S_{A}=k_{DA}+\frac{F_{A}C^{\prime}_{\mathrm{HA}}b_{A}}{(1+b_{A}f_{A}+b_{ B}f_{B})}} \tag{6}\]  
where \(b_{B}\) is the Langmuir affinity constant of the competing gas B. The corresponding average permeability for gas A (P\({}_{A}\)) through the membrane can therefore be calculated by the integration of the diffusivity and solubility at the feed (\(f_{A0}\)) and permeate (\(f_{A1}\)) sides of the membrane, where D\({}_{A}\) is the diffusivity of gas A:  
\[\mathrm{P_{A}=\int_{f_{A0}}^{f_{A1}}D_{A}\cdot S_{A}\ d}f \tag{7}\]
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 4 Analysis
### Attacks for Forging Signatures  
We now considered the possibility of forging a given signature without knowing the private keys \(k\) and \(p\). In order to forge the signature of a message \(m\), the adversary must simulate the two signature parts \(s_{1}\) and \(s_{2}\); because he/she does not know either \(k\) or \(r_{2}\) or \(p\), he/she must randomly generate two numbers \(s^{\prime}_{1}=random_{1}\), \(s^{\prime}_{2}=random_{2}\) or use random numbers \(k^{\prime}\), \(r2^{\prime}\), and \(p^{\prime}\) as secret keys, then calculate \(s^{\prime}_{1}=m^{k^{\prime}}\mod n^{\prime}\) and \(s^{\prime}_{2}=m^{r_{2}\times p^{\prime}}\mod n^{\prime}\). Of course, the adversary can encrypt any message \(m\), because he/she knows the public key of the victim, where \(c=Enc_{\mathit{victim}_{pk}}(m)\). The digital signature to be sent to the receiver is \(sig^{\prime}_{m}\), where \(sig^{\prime}_{m}=(s^{\prime}_{1},s^{\prime}_{2})\).  
In the verifying step, the receiver will calculate \(x\) using the decrypted message \(m\) and victim public key \(pk_{s}\) as follows: \(x=m^{pk_{s}}\mod n^{\prime}\). To generate \(s^{r\times p}_{2}\), the receiver should calculate \(s^{r_{1}}_{2}\).  
We have now \(x=m^{k_{s}+r\times p}=m^{k_{s}}\times m^{r\times p}\) and \(y=s^{\prime}_{1}\times s^{\prime\prime_{1}}_{2}\) which implies \(y=m^{k^{\prime}}\times(m^{r_{2}\times p^{\prime}})^{r_{1}}\). Thus, \(y=m^{k^{\prime}}\times m^{r_{1}\times r_{2}^{\prime}\times p^{\prime}}\). We can clearly see that \(m^{k^{\prime}}\
eq m^{k}\), while \(k^{\prime}\
eq k\), \(m^{r_{1}\times r_{2}^{\prime}\times p^{\prime}}\
eq m^{r_{1}\times r_{2}\times p}\), while \(r_{2}^{\prime}\
eq r_{2}\) and \(p^{\prime}\
eq p\).  
The adversary can easily compute \(xm^{pk}\), then obtain two numbers \(\alpha\) and \(\beta\) where \(x=\alpha\times\beta\). \(\alpha\) simulates \(s_{1}\), and \(\beta\) simulates \(s^{r_{1}}_{2}\); knowing that \(r_{1}\) is public, the adversary's issue is to find a number \(\gamma\) that verifies \(\gamma^{r_{1}}\mod n^{\prime}=\beta\). This is equivalent to the DLP.  
Therefore, to forge the proposed signature scheme, the adversary has to resolve the DLP.
# Extraction of Creation-Time for Recovered Files on Windows FAT32 File System
## 2 File Allocation Behavior of Windows FAT32 File System
### Allocation of Content Clusters to Store File Contents  
The Windows FAT32 file allocator searches for available clusters linearly from the beginning of the data area. The Windows FAT32 file allocator searches according to the next fit algorithm that skips available spaces smaller than the size of a file being stored. The reference to the cluster lastly allocated is stored in the FSL_Nxt_Free field of the FSIINFO structure, which exists in the reserved area of the FAT32 file system. The Windows FAT32 file allocator searches for available space from the FSL_Nxt_Free reference. If some files located before the FSL_Nxt_Free reference are deleted, the value of the FSI_Nxt_Free field is updated with the lowest index among clusters allocated to the deleted files. We refer to the file allocation pattern of Windows FAT32 file system as deletion-backward next fit algorithm.  
Figure 3: Structure of directory entry.  
Figure 2: Structure of the FAT32 file system.  
Figure 4 shows an example of the deletion-backward next fit pattern. In Figure 4a, files A-E are allocated sequentially and next files B and D are deleted. Then the FSI_Nxt_Free pointer is changed from 16 to 4, where 16 is the cluster index behind the lastly allocated cluster and 4 is the lowest index among clusters of deleted files. In Figure 4b, another file F with a size of three clusters is additionally allocated. The Windows FAT32 file allocator searches three clusters continuously available from cluster 4 pointed by the FSI_Nxt_Free reference. Cluster 4 and cluster 5 are skipped because their continuously available size is smaller than the file size. Cluster 9, cluster 10 and cluster 11 are allocated to file F because their continuous available size is equal to or larger than the file size.  
If the search of the file allocator reaches the end of the data area, the search is wrapped around and restarted at the beginning of the data area. File fragmentations occur only after the file allocator reaches the end of the data area, because the Windows FAT32 file allocator searches continuously available clusters before reaching the end of the data area. If there exist data behind the tail of allocated clusters, the file allocator already reached the end of the data area in the past [25; 26]. That is, if there is no data behind the tail of allocated clusters, the file allocator never reaches the end of the data area and thus there is no file fragmentation. Because the start point of file allocation moves back whenever deleting a file, the file allocator reaches the end of the data area when the space of the data area is fully occupied.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 3 Experiments
### Robustness Test  
It is observed in Table 2 that most of the methods are vulnerable to the regeneration attack, whereas our proposed scheme performs robustly under all the three regeneration attacks. Figure 4a visualizes the noise distribution introduced by the diffusion attack in latent space. It is seen that its distribution closely resembles the noise distribution used in training, and hence, the impact of this attack on our proposed scheme is negligible. Benefiting from this embedding process, the proposed method performs better in terms of generalization against regeneration attacks compared to those based on noise layer training. To verify the effectiveness of the spread spectrum scheme, the radar chart in Figure 4b compares the robustness of using QIM [31] as the embedding scheme, with the quantization step set to 50. The chart highlights the superiority of the spread spectrum watermarking in latent space.  
To verify the effectiveness of the proposed under image editing attack, Instruct-Pix2 Pix [32] and UltraEdit [33] are applied. The former is an instruction-driven image editing framework, enabling precise image modifications with user-specified commands, while the latter allows high-quality regional editing through the guidance of a mask. Figure 5 shows the editing styles imposed on the images, including local content replacement, background change and human action modification. These kinds of editing can  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Common Attacks**} & \multicolumn{6}{c}{**Regeneration Attacks**} & \multicolumn{2}{c}{**Image Editing**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **B** & **C** & **D** & **P** & **U** \\ \hline \multicolumn{13}{c}{COCO} \\ \hline HDDeN & 0.310  
modify the content of images easily and disrupt the embedded watermark information. The editing pipelines are controlled by the Text CFG (Classifier-free Guidance) and the Image CFG, which are the parameters for adjusting the relevance of the generated image to the prompt. In our experiment, the text and image CFG are set to 7.5 and 1.5, respectively. As demonstrated by the results shown in Table 2, the proposed method performs robustly to all kinds of image editing except image cropping. Although Stegastamp also shows very competitive results in terms of robustness to the attacks launched by generative models, our scheme excels a lot in terms of imperceptibility.