esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Towards Single-Cycle Pulse Synthesis
However, unambiguous retrieval of the individual orders is possible. Figure 16b,c show the measured and retrieved PG-XFROG traces of the first upconverted CFWM pulse and its associated temporal shape with 30.6 fs in duration. Pulse compression and synthesis of the generated CFWM beams have also been studied and reported by the [PERSON] (see, e.g., [26]) and [PERSON] [14] research groups. Adding the fields obtained for all measured orders while setting zero relative delay between each CFWM pulse, a synthesized field is obtained. This field has a main central transform-limited pulse with 2.2 fs duration and two smaller pulses, one at each side on the main pulse, separated 25 fs from the main pulse, which corresponds to the pump beat period. Figure 17 shows the synthesized field along with the pulse intensity and evidences the possibility of synthesizing single cycle pulses with this technique. The central pulse carries almost all the energy, which amounts to 5 \(\upmu\)J.  
Figure 16: **(a)** Measured PG-XFROG trace of the synthesized field; **(b)** Measured and **(c)** retrieved PG-XFROG traces of the first frequency upconverted CFWM pulse, and **(d)** corresponding intensity and phase in the time domain. [21]. Figure 17: Total field **(a)** Normalized electric field and **(b)** intensity and phase of the pulses obtained by coherent addition of the retrieved electric fields of the 13 gated CFWM pulses. The main peak is a 1.3-cycle, transform-limited 2.2 fs pulse. [21]. Generation of Ultraviolet (UV), Deep Ultraviolet (DUV), Vacuum Ultraviolet (VUV) and Higher-Order Harmonics by CFWM from a Standard Titanium:Sapphire Laser Amplifier  
Since the materials have usually resonances in the UV, the use of pumps in the visible part of the spectrum means working on the normal dispersion region. Angle phase-matching of upconverted orders in CFWM processes is possible thanks to dispersion. The shorter the wavelength of the resonance in the material the higher the number of upconverted orders that can be obtained, provided that the form of the dispersion curve is adequate. In the previous results we showed how cascaded upconverted orders comfortably reached the UV and DUV up to 209 nm departing from orange and green pump pulses from a dye laser-amplifier. A natural extension of the research in CFWM is trying to reach the UV, DUV, VUV or to generate high order harmonics with more common pump colors, available in many labs, such as the fundamental and the second-harmonic of a Titanium:Sapphire laser.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 6 Results and Discussion
### Intrinsic Parameter Estimation Results  
#### 6.3.1 Estimated Results of Focal Length  
We took the camera focal length when the zoom value was 1, 2,... 19, 20, as with the previous calibration results; its variation trend is shown in Figure 7:  
It can be seen that the focal length of this camera was approximately linear compared to the zoom value; therefore, we can use the linear interpolation method (4) and the quadratic function (5) to establish the model and find which model is better fitted.  
The quadratic function is:  
\[k(z)=-9.2989z^{2}+1614.6z-160.8423 \tag{25}\]  
Here, we will not express the piecewise function calculated by the linear interpolation. Instead, we have taken eight groups of unfitted zoom values and compared the calculated results with the ground truth. The results and errors estimated with the linear interpolation method and the quadratic function model are shown in Table 4. The Zhang method's own error range is less than 0.6%; thus, we can use it as the ground truth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Left camera & \(\mathbf{m}_{l}^{\eta}\) & \(\left(-2.409\right)\) & \(3.105\) & \(82.045\right)^{T}\) \\  & \(\mathbf{m}_{l}^{h}\) & \(\left(-1.403\right.\) & \(-42.201\) & \(79.069\)) \\ \hline Right camera & \(\mathbf{m}_{l}^{\eta}\) & \(\left(-2.523\right)\) & \(3.012\) & \(81.127\) \\  & \(\mathbf{m}_{l}^{h}\) & \(\left(-1.323\right.\) & \(-41.238\) & \(80.529\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rotation radius vector of left and right cameras.  
Figure 7: Variation of focal length with zoom.  
It can be seen from Table 4 that the focal length estimated using the linear interpolation method has an error of less than 4%, which can basically meet the needs of practical applications. Meanwhile, the error in results estimated using the quadratic function is slightly higher than the linear interpolation method when the zoom value is small, and the errors are lower when the zoom value is large.
## 7 Simulations and Results
### Scenario 3: HEMS with ESS and PV
#### 7.3.2 PAR Using RTP and CPP  
In the proposed architecture of the HEMS, an SMSU and EMC were used. Using these units, all the appliances connected to the ESS and PV were optimally controlled in a way to avoid peaks. Therefore, the PAR in the scheduled cases was reduced compared to the unscheduled case. Both for the RTP and CPP tariffs, the PAR for the unscheduled, GA, GWO, and our proposed HGWGA was found to be 4.44, 3.588, 3.369, and 2.730, respectively. The GA, GWO, and our proposed HGWGA reduced the PAR compared to the unscheduled case by 19.18%, 24.13%, and 38.5%, respectively, for both the RTP and CPP signals. Figure 20 presents the PAR value of the unscheduled and scheduled cases.
# Development of an Energy-Efficient Smart Socket Based on STM32F103
## 3 System Development
### Framework of the Hardware System  
Underpinned on the functional requirements of the proposed energy-efficient smart socket, a hardware framework built on STM32F103 microchip was developed. As shown in Figure 1, the developed system is composed of a power supply unit, an infrared signal receiver, a host-slave control operator, a power management unit built on an STM32F103 microchip, a voltage and current detection module and an LCD (Liquid Crystal Display) unit. As discussed in the previous sections, power outlets on the smart socket are developed as main jacks plugged with host devices and auxiliary jacks providing power connections to slave appliances. Instructions sent by the remotes of host devices are received by the infrared signal receiver and transmitted to the power management unit with monitoring voltage and current data provided by the detection module for logic judgment. Where the power management unit takes the role of data processing and logic operation as the central processing unit (CPU) of the developed system. After that, control signals are sent to the host-slave control operator for the power control of jacks. The host-slave control operator applied in this system is composed of relay control units for both the host and slave sockets, which can response to the control signals to power on or power off the outlets on the smart socket. The LCD module was developed in this system to display the real-time monitoring values of voltage and current, as well as the power status of host devices (\"on\" or \"off\"). The power supply unit is a switching power module employed to power the smart socket system in an energy efficient manner. Circuits of the voltage and current monitoring/detecting module are detailed in the following section.
## 3 Experimental Results  
In this section, a series of experiments were conducted using 512 \(\times\) 512 grayscale images to comprehensively evaluate the performance of our proposed scheme. These experiments were conducted on a personal computer equipped with a 12 th Gen Intel(R) Core(TM) i5-12450H CPU @ 2 GHz, 16 GB RAM, NVIDIA GeForce RTX 4060 Laptop GPU (Santa Clara, CA, USA), and Windows 11 operating system, implemented using Microsoft Visual Studio Code 2024. The grayscale images used for the experiments were randomly selected from the BOSSbase image database, as shown in Figure 4. All secret data were generated by a random number generator. To fully demonstrate the effectiveness of our method, a series of feature comparison experiments were conducted between the proposed scheme and other schemes. The verification of effectiveness will comprehensively assess the security performance of the proposed solution from three distinct perspectives. Firstly, it delves into visual quality, conducting a thorough comparison of the Peak Signal-to-Noise Ratios (PSNRs) of the images to discern variations between our solutions and competing alternatives. Secondly, the evaluation scrutinizes image-pixel correlation, aiming to gauge the degree of correlation among pixels within the image. Lastly, the analysis will extend to image histogram examination, offering insights into the efficacy of our method through a comparative assessment of the histograms of the images.  
Mean-Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) defined in Equations (10) and (11) are important indicators relied upon when measuring the accuracy of prediction models. A smaller MSE value implies less difference between the model's prediction and the actual observation, indicating superior predictive performance. PSNR value represents the ratio between the signal and noise, evaluating the similarity between the compressed image or video and the original signal. Additionally, the embedding rate (ER) defined in Equation (12) presents bits per pixel (BPP), measuring the capacity of data embedding, i.e., the ability to hide data in the image.  
\[PSNR=10\times log_{10}\frac{255^{2}}{MSE}. \tag{10}\]  
\[MSE=\frac{\sum_{i=1}^{W}\sum_{j=1}^{H}(C(i,j)-S(i,j))^{2}}{H\times W} \tag{11}\]  
\[BPP=\frac{l}{H\times W} \tag{12}\]  
Here, \(H\times W\) represents the size of the image; \(C(i,j)\) and \(S(i,j)\) respectively represent the pixel values of the cover image and stego image. In addition, \(l\) is used to denote the total amount of embedded data.  
Figure 3: Neighboring 3 \(\times\) 3 blocks on both sides of the diagonal.
## 4 Hybrid Action Space SAC-Based MLLM Task Offloading and Resource Allocation Scheme
### Complexity Analysis of AD-SAC
#### 4.4.2 Space Complexity  
The memory requirements of AD-SAC are primarily determined by the storage of network parameters and the experience replay buffer. Each neural network with \(L\) layers and hidden dimension \(H\) requires approximately \(O(d_{s}H+LH^{2}+Hd_{a})\) parameters, where \(d_{s}\) denotes the state dimension, and \(d_{a}\) represents the corresponding action dimension. With eight networks in total, the parameter storage requires \(O(8(d_{s}H+LH^{2})+4H(d_{\mu}+d_{\
u}))\) memory. The experience replay buffer \(\mathcal{C}\) stores tuples of states, actions, and rewards, consuming \(O(|\mathcal{C}|(2d_{s}+d_{\mu}+d_{\
u}))\) memory, where \(|\mathcal{C}|\) is the buffer capacity. Consequently, the total space complexity is \(O(LH^{2}+|\mathcal{C}|(d_{s}+d_{\mu}+d_{\
u}))\), demonstrating that the algorithm maintains reasonable memory requirements even for large-scale MLLM task offloading scenarios.
## 5 Simulation Experiment Analysis  
### Experimental Settings  
The simulation parameters of the joint signal and target are summarized in Tables 2 and 3. All simulations were performed on a standard laptop with Intel Core i9-12900H CPU 5.0 GHz, 16 GB RAM.  
The number of false targets was set to \(J_{M}=700\), distributed around the real target, to meet the jamming design requirements and make the signal jamming more easy and convenient. The parameter \(n\) in Equation (10) can be set to the smallest positive integer in advance; in this paper, \(n\) was set to 2. To see the jamming effect of our designed joint signal, we also simulated the opponent's radar in the scenario.
## 6 Discussion  
### Multipath Fading Effect  
To evaluate the effect of Path Loss on the calculated \(P_{RX}\), consider a single point of measurement \(P_{m}=(x_{i},y_{i})\). In addition, the _Source_ is assumed to be located at \(P_{j}=(x_{j},y_{j},z_{j})\). If \(P_{m}\) is rotated along the \(z\)-axis, then the relative distance between \(P_{j}\) and \(P_{m}\) varies. An example of such variation is illustrated in Figure 6. The figure is symmetric, which complies with the geometrical configuration. The reflective properties of Earth cause the fluctuations due to changes in the incident angles at various distances.  
Figure 8: The operation efficacy plot of the landing scenario with a single source, located at \((10,0,0.025)\).  
Figure 9: A comparison of \(P_{RX}\) for two angles of approach (for a selected landing scenario).
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### The Mechanical Behavior of Stabilized and Unstabilized Soil
#### 3.3.3 The Impact of WMP on the UCS  
Research on WMP-stabilized clay shows that the compressive strength improves as more powder is added, up to a concentration of X5 (60%). However, a slight decrease in compressive strength was observed at X6 (75%). With this in mind, it was determined that a 60% marble powder amendment provides the strongest soil amendment. In one study by [PERSON] [44], clayey soils were stabilized with marble powder, and the maximum UCS value was obtained at a lower dose level of 5%. Another study by [PERSON] [39] found that the highest UCS was achieved in samples treated with 20% waste marble powder. In yet another experiment by [PERSON] et al., a 50% dose of WMP achieved the highest strength in stabilizing soil with fine [69]. Therefore, these and related studies indicate that the continuous addition of WMP raises the UCS of soil with fine up to a specific dose.  
It should be noted that the effectiveness of marble powder as a stabilizing agent may vary based on several factors. These factors include the type of clayey soil being stabilized and the type and quality of waste marble powder used. Other factors that may affect the stabilization process include the regional origin of the marble powder, its impurity levels, vein density, and color. Therefore, it is essential to carefully consider these variables when determining the optimal dosage of marble powder needed to achieve the desired stabilization results [70].  
The use of WMP as an additive in clay soil has been found to have both positive and negative effects on the properties of the soil. The addition of marble dust reduces the soil's clay content and increases the additive's proportion. A minor pozzolanic reaction occurs when water is present, forming a stable calcium silicate hydrate. This reaction enhances the soil's unconfined compressive strength (UCS) and gains significant strength. This finding is consistent with the results of a similar study conducted by [PERSON] et al. [71]. However, when marble dust is used to substitute clay soil at a certain point, the cohesiveness and density of the mixtures decrease [72]. As such, it can lead to a decline in soil strength when the percentage of marble dust exceeds 60 percent, as observed in this study. Therefore, using marble dust as an additive in clay soil must be carefully controlled to ensure that the desired properties are achieved without compromising the strength or cohesiveness of the soil [73]. It is essential to conduct thorough testing and analysis to establish the most effective and efficient use of marble powder in specific soil stabilization applications. Still, adding WMP can increase fine-grained soils' strength and stability.
###### Abstract  
Localization is one of the hottest research topics in Underwater Wireless Sensor Networks (UWSNs), since many important applications of UWSNs, e.g., event sensing, target tracking and monitoring, require location information of sensor nodes. Nowadays, a large number of localization algorithms have been proposed for UWSNs. How to improve location accuracy are well studied. However, few of them take location reliability or security into consideration. In this paper, we propose a Collaborative Secure Localization algorithm based on Trust model (CSLT) for UWSNs to ensure location security. Based on the trust model, the secure localization process can be divided into the following five sub-processes: trust evaluation of anchor nodes, initial localization of unknown nodes, trust evaluation of reference nodes, selection of reference node, and secondary localization of unknown node. Simulation results demonstrate that the proposed CSLT algorithm performs better than the compared related works in terms of location security, average localization accuracy and localization ratio.  
underwater wireless sensor networks; trust evaluation; collaborative secure localization
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
###### Abstract  
Medical imaging plays a crucial role in modern healthcare, providing essential information for accurate diagnosis and treatment planning. The Digital Imaging and Communications in Medicine (DICOM) standard has revolutionized the storage, transmission, and sharing of medical images and related data. Despite its advantages, implementation and deployment of the DICOM protocol often suffers from incomplete understanding, leading to vulnerabilities within the healthcare ecosystem. This research paper presents an implementation of DICOM communication and the development of a practical demonstration for simulation purposes The simulation can be used for conducting cybersecurity tests in the context of DICOM communication. Overall, the simulation provides a digital environment that can help in retrieving valuable insights into the practical aspects of DICOM communication and PACS integration, serving as a valuable resource for medical imaging professionals, researchers, and developers. These research results provide practical insights, and the DICOM simulation can be used in realistic contexts to showcase a variety of security scenarios.  
medical imaging; DICOM; PACS; eHealth; simulation +
Footnote †: journal:  
0000-0002-4569-2230][PERSON]  
0000-0002-0002-3330][PERSON]  
0000-0002-0002-3330][PERSON]  
0000-0002-3330-3300][PERSON]  
0000-0002-3330-3300][PERSON]  
0000-0002-3330-3300-3300-3300-3300-3300-33000-3300-3300-3  
and even produced a 3D model of a single individual's anatomy [17]. These incidents underscore the urgency of implementing robust security protocols, including encryption, authentication, network segmentation, and regular security audits, to mitigate risks and protect patient privacy within the DICOM ecosystem.  
Despite the indispensability of DICOM and PACS, there is often a lack of comprehensive understanding and implementation in healthcare organizations [18; 19]. This lack of comprehension and implementation can lead to various security challenges and vulnerabilities [20; 21]. The existing literature highlights the security vulnerabilities in these systems, such as unencrypted communications, weak authentication mechanisms, and inadequate access controls [15; 22; 23; 24]. Nevertheless, healthcare organizations often fail to implement robust security measures or conduct comprehensive security testing, leaving them vulnerable to cyberattacks [25; 26].  
This research paper proposes a DICOM simulation with the aim of improving understanding of the DICOM network protocol. The simulation serves as a practical demonstration that can enhance comprehension and implementation. By simulating data transfer using the DICOM protocol from a healthcare modality, the practical aspects of DICOM communication and PACS integration can be further investigated. The simulation involves the replication of the network layer for DICOM transfers and establishing the necessary connections to a PACS server.
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging  
Wideband synthetic aperture radar (SAR) systems are known to produce high resolution images of target scenes. High resolution in the range direction is obtained using a wideband signal while high resolution in the azimuth direction is achieved by coherently processing the Doppler modulated returns as the antenna is scanned over a large synthesized aperture [64]. In this section, we show that a harmonic SAR can be used to form high resolution images of nonlinear targets and also be used to suppress linear clutter plaguing traditional linear SAR systems. Two different SAR data collection techniques have been implemented to create SAR images: a moving aperture, and a fixed aperture.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 5 Experimental Results, Analysis, and Discussion
### Cluster Lifetime  
A life cycle for a cluster is the cumulative time from creation to disposition for the cluster. When the clustering algorithm is executed, the FANETS node with the most appropriate value is elected as CH and takes over the cluster's task. We can track the life cycle of the FANET's nodes with a measurement parameter with two parts, a stable period and an irregular period. However, in our case, we mainly use the time between the first node death and the last node death, which is known as the unstable period. FANETS are mainly used for environmental monitoring as a wide range of sensor nodes are deployed over a wide distribution of area. If a large number of nodes dies, the collected information would not be able to reach its destination. The time from the FND to the last death node (LND) indicates that the period is uncertain. These strategies can be employed in environmental surveillance and disaster management since a broad area is needed to place sensor nodes. The wide range means that specific data obtained cannot effectively evaluate the surrounding factors in the case of the death of many nodes.  
This research also analyzes network life using FND and shows several clear advantages while evaluating our proposed methodology compared to the two protocols. As shown in Figure 8, the FND has 1710, 2022, and 2618 rounds and LND has 2343, 2786, and 3990 under three protocols. Compared with LEACH and LEACH-C, our proposed algorithm increased FND by 53% compared with LEACH and 30% compared to LEACH-C.
## 3 Message Transmission and Notification
### Hive  
The HIVE cloud, developed by SWARM Technologies, serves to store messages and facilitate communication between devices and satellites. It includes an account system that assigns a unique number to each device in use. An API Gateway provides IDs for every device, enabling connection to the AWS API Gateway. Each device, equipped with a built-in GPS, can be tracked on a map featured on the HIVE cloud website.