esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Laser-Induced Surface Damage of KH\({}_{2}\)Po\({}_{4}\) [PERSON]
## 3 Exploration of the Initial Energy Deposition
### The Formation and Evolution of Plasma
#### 3.1.2 Irradiation of Short Pulse Laser  
Although the formation of plasma under short-pulse lasers occurs as in the fs case, a significant difference is that energy deposition is time dependent. The absorption of energy by seed electrons occurs in the later period of short pulse laser irradiation, so the formation of plasma can be controlled by avalanche ionization, which is well confirmed in Figure 7a. Actually, a longer time contributes to effective plasma heating. [PERSON] et al. [69] observed the transient dynamics reaction of early plasma formation of KDP surface during nanosecond laser with the time resolved pump and probe (TRPP) technology, as shown in Figure 7c. The schematic diagram of time resolved pump and probe (TRPP) technology is shown in Figure 8. The plasma is gradually generated due to ionization, as shown in the dark region of Figure 7(c2). Due to the absorption of laser energy by the plasma, a temperature difference will generate between the air and the material, which results in the formation of shock wave in air. It is clear that with the expansion of the plasma, the shock wave becomes more and more significant, which also reflects the deposition process of early energy. The plasma region continuously grows along with the increase of the pump pulse.  
Figure 7: The process of energy deposition and evolution of dielectric under short pulse laser irradiation. **(a)** Curve of the total electron density (N\({}_{t}\)) and multiphoton produced electron density (N\({}_{m}\)) with incident (fluence 10.1 /cm\({}^{2}\)) and transmitted (fluence 9.7 /cm\({}^{2}\)) laser pulse, \(\tau\) = 100 ps [70]; **(b)** Evolution of \(t_{heat}\) of KDP as a function of the fluences \(F_{1\omega}\) and \(F_{3\omega}\), [71]; and **(c)** The transient images of plasma produced by early ionization with the delay between pump and probe pulse of \(-\)2.8 ns to 0.8 ns, the area A on the left side of the red dotted line is air, and B on the right side is KDP [69].  
After the ionization is completed, the plasma formed in the early stage will slowly absorb most of the energy of the incident laser through inverse bremsstrahlung under the irradiation of the subsequent short pulse laser (ps-ns), which makes the plasma in the KDP crystal reach high temperature with a stable form. As shown in Figure 7b, the 1\(\omega\) (1053 nm) radiation is more efficient than the 3\(\omega\) (351 nm) for the plasma heating. The thermal conduction and hydrodynamics will also allow energy to be transferred away from the focus in the duration of input laser pulse [72]. The characteristics of the surrounding material such as absorption will be influenced by the transferred energy. Part of the absorbed and deposited laser energy will be transferred from electron to lattice of material gradually through the electron-phonon coupling technology in the picosecond timescale.
## 4 Experimental Results
### Evaluation of Proposed Method
#### 4.4.3 Visual Results on Nighttime Hazy Images  
As proved in previous sections, our proposed system shows good performance of dehazing on both synthetic and real-world hazy images, where it achieves good quality results in terms of haze removability and quality enhancement. Furthermore, as mentioned above, the proposed method performs well on nighttime hazy images. Figure 16 shows some visual results on nighttime images compared with some previously proposed dehazing methods by [PERSON] et al. [14], [PERSON] et al. [28], and [PERSON] et al. [26].  
As shown in Figure 16e, it is obvious that the proposed system can remove the haze on the input nighttime hazy images and recover the real color and appearance. By contrast, the results achieved by [PERSON] et al. [14], [PERSON] et al. [28], and [PERSON] et al. [26] still suffer from haze particles (see train image results) and real color distortions (see the sky in [PERSON]'s Figure 16d results for the second and third images).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **[PERSON] et al. [14]** & **[PERSON] et al. [28]** & **[PERSON] et al. [26]** & **[PERSON] et al. [40]** & **[PERSON] et al. [35]** & **[PERSON]** \\ \hline
**e** & 1.973 & 1.552 & 1.270 & 2.952 & 3.193 & 2.095 \\ \hline
**r** & 2.813 & 3.001 & 2.287 & 3.077 & 3.982 & 4.190 \\ \hline
**FADE** & 2.172 & 0.903 & 1.816 & 0.534 & 0.510 & 0.289 \\ \hline
**T**(s) & 16.033 & 1.002 & 2.071 & 0.658 & 0.782 & 0.453 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average values of \(e\), \(\bar{r}\), _FADE_, and \(T\) on real-world hazy images of the comparative methods.
## 3 Proposed System Model
### Edge Node Model Overview  
Each edge node comprises three types of computational assets, which are: computing, sensing and data storing. Depending on these assets, edge nodes are distinguished from controller nodes. These edge servers get the vehicle information from ordinary nodes and transfer it to the controller nodes, which use IPFS to store this information. Various types of services are supplied by the edge nodes, such as road accidents, traffic blockage, sensory data of smart vehicles and service sharing. Depending on the service given by edge nodes, ordinary nodes give a rating. Depending upon which, the behavior of edge nodes is determined. If the service required by an ordinary node is available with the edge node, then it is transferred by the edge node itself. The edge node communicates with RSU to response against the service request. Edge nodes store frequently used services in cache memory, which improves the overall system's efficiency by reducing the delay in response to the service requests.  
Figure 2: Proposed System Model.
# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 6 Mitigation Strategies for CIA Dimensions in LLM Attacks
### Mitigating Integrity Attacks  
Integrity attacks compromise the reliability of LLM outputs, leading to biased, misleading, or harmful responses. Mitigation strategies aim to reinforce trust in the system's outputs by preventing adversarial manipulation:  
* **Input Validation:** User inputs can be sanitized to remove potentially harmful or adversarial elements, ensuring that only valid prompts are processed. For instance, a university research team successfully blocked adversarial prompts that exploited context-based vulnerabilities by implementing rigorous syntactic checks.
* **Adversarial Training:** Exposing the model to adversarial examples during training enhances its ability to detect and neutralize malicious prompts. For example, Google's BERT was fine-tuned using adversarial examples to prevent manipulative queries from generating biased or deceptive outputs.
* **Bias Mitigation:** Regularly assessing and addressing biases in model outputs helps to maintain ethical and unbiased results. For example, developers of AI recruitment tools have implemented continuous testing with diverse datasets to mitigate cases where adversarial prompts aimed to exacerbate existing gender or racial biases.
* **Context-Free Decoding Mechanisms:** Employing context-limited decoding prevents adversarial inputs from leveraging prior conversational history. Contemporary chat systems such as Microsoft's Azure OpenAI service have begun testing such mechanisms to counter manipulation tactics that rely on sequential prompts.
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 3 Competitive CPM
### Design Criterion
The relationship of the coded modulation systems among \(M\), \(R\), \(B_{99\%}\), \(T_{s}\), and \(\eta\) is defined as:  
\[\eta=\frac{R\log_{2}M}{B_{99\%}T_{s}}=\frac{R}{B_{99\%}T_{b}} \tag{13}\]  
For the given \(\eta\) and \(R\), the \(B_{99\%}T_{b}\) can be attained using (13). The competitive CPM scheme can be determined by the highest \(d_{\min}^{2}\) among these candidate CPM signals with corresponding \(B_{99\%}T_{b}\). For example, \(\eta\) and \(R\) are 0.5 bit/s/Hz and 2/3, respectively, \(B_{99\%}T_{b}\) is approximately computed as 1.33 using (13). Table 1 shows that the 8\(M\)2 RC with \(h\) = 1/2, 8\(M\)1 REC with \(h\) = 2/5, and 4\(M\)2 RC with \(h\) = 2/3 can approximately meet the constraint of \(B_{99\%}T_{b}\), and the \(d_{\min}^{2}\) of the three schemes are 5.56, 4.60, and 3.47, respectively. Consequently, 8\(M\)2 RC with \(h\) = 1/2 is preferred as the competitive CPM scheme due to its highest \(d_{\min}^{2}\) relative to other candidates. The competing CPM schemes for other \(\eta\) and \(R\) can also be selected according to (13) and Table 1.
## 4 Overall Design and System Analysis
### System Implementation
#### 4.3.4 Network Management  
For a large-scale CPNT system, a network management system is essential. The acoustic channel is spatial-temporal varying, and the communication and energy resource are extremely limited. How to choose a reliable transmission protocol, determine the best route, mac protocol, and communication model according to the environment and system status is the key challenge in network design.  
The terrestrial network framework follows the OSI architecture, which consists of 7 layers, providing a general principle for the current complex and extensive network system. For an underwater networking system, an agile and adaptive network protocol stack is a prerequisite for efficient networking. SeaLinv is a multi-instance and cross-layer optimization framework [46; 52], as shown in Figure 8. In this framework, the core checks the communication and positioning requirements, the position of the nodes, power consumption, calculation, and storage load, and then selects the appropriate communication model and network protocol, which makes it an appropriate architecture for underwater large-scale networking applications. We designed the network management framework for \(U^{2}CPNT\) based on this architecture.  
In this section, we initially introduce the overall design and then perform the theoretical analysis to support the feasibility of \(U^{2}CPNT\). With the system implementation introduced, the case study will be presented in the following section.  
Figure 6: Hardware architecture which consists of user transducer, hydrophone array, receiving/transmitting circuit, digital signal processing, user interface, power module and etc.  
Figure 7: Software architecture of \(U^{2}CPNT\), which consists of ARM/Linux, DSP/RTOS and ARM-DSP inter-core communication component.
## 3 Error Model and Cross-Identification Algorithm
### Cross-Identification Method Based on LM Algorithm  
The classical industrial robot parameter identification algorithms include the least square algorithm (LS) [30], the extended Kalman filter (EKF) [31], and the Levenberg-Marquardt algorithm (LM) [32]. Among them, the LM algorithm combines the steepest descent method and the Newton method, and can quickly complete the identification task. It has excellent performance in terms of local convergence and anti-disturbance and is most commonly used.
# ScientIST: Biomedical Engineering Experiments Supported by Mobile Devices, Cloud and IoT
## 2 Background
### Jupyter Notebooks  
With the software and hardware components described in the previous sections, we have an end-to-end workflow that includes biomedical data acquisition from a subject (Section 2.3), transmission to a mobile device when specialized sensors are needed (Section 2.2), and recording in a journal-like format with the option to export the raw data (Section 2.1). A final stage of post-processing and analysis of the recorded data are also generally required.  
For that purpose, we consider the storage and sharing of pedagogical materials in the Jupyter Notebooks format. This is a free cloud-based environment, which can also be installed locally on the computer, and that enables the creation of documents incorporating live code, equations, graphics and narrative text.  
We chose to host our Jupyter Notebooks on GitHub, as it is the platform most widely used by the community at large to share software. In particular, the Binder extension [8] integrates directly with GitHub, enabling the creation of a Jupyter environment to run the Python scripts online, without needing to install any software. Other available options include Google Colaboratory (or Colab) [9], or Microsoft Azure Notebooks [10], which allows users to develop and run Jupyter Notebooks using the Microsoft Azure Cloud Services.  
Students can use these tools interactively to create, execute, and easily share their Python scripts to process and extract analytics from the data collected using the GSJ and/or SCB. The Google Colab option has the advantage of enabling direct access to the Google Drive, hence simplifying the process of accessing data recorded with the GSJ app.
# Dynamic Behavior of Track Bridge on Serviced Long-Span Road/Railway Bridge
## 4 Results and Discussions
### Acceleration  
An example of the numerical analysis results and measurements for the vertical acceleration of the rails and track girders is provided in Figure 7.  
Figure 7: Comparison of rail and track girder vertical accelerations (KTX). (**a**) Rail vertical acceleration (100 km/h, numerical analysis); (**b**) Rail vertical acceleration (98 km/h, field measurement); (**c**) Track girder vertical acceleration (100 km/h, numerical analysis); (**d**) Track girder vertical acceleration (98 km/h, field measurement).  
The results of the vertical acceleration analyses of the rails and track girders revealed error ranges between the accelerations measured at a passing speed of approximately 98 km/h on the actual Yeongjong Bridge and the results calculated from the numerical analysis (100 km/h) of 3.36% and 4.2% for the rail and track girder accelerations, respectively. The results demonstrate the appropriateness of the numerical analysis model.  
Using the full-scale model of the Yeongjong Bridge, the vertical and horizontal accelerations of the rail and track girder were calculated with increasing KTX speed (up to 200 km/h). The results are shown in Figure 8.  
According to the rail and track girder acceleration results, the predicted vertical accelerations of the rail and track girder were similar to those measured in the 20-98 km/h speed range. In addition, when the KTX speed increased to 200 km/m, both the rail vertical and horizontal accelerations increased by approximately 2.59 times, whereas the track girder vertical and horizontal accelerations increased by approximately 2.4 and 6.1 times, respectively.
## 7 Security Analysis
### Informal Security Analysis
#### 7.1.6 Mutual Authentication  
After receiving the authentication request message \(\{M_{1},M_{2},M_{3}\}\) from the \(SD_{i}\), \(UC_{j}\) checks whether \(M_{3}^{*}\stackrel{{?}}{{=}}M_{3}\). If \(M_{3}^{*}\stackrel{{?}}{{=}}M_{3}\) is valid, \(UC_{j}\) authenticates \(SD_{i}\) successfully. After receiving the authentication message \(\{M_{4},M_{5},M_{6}\}\) from the \(UC_{j}\), \(SD_{i}\) also checks whether \(M_{6}^{*}\stackrel{{?}}{{=}}M_{6}\), and then \(SD_{i}\) authenticates \(UC_{j}\). Therefore, our protocol ensures secure mutual authentication between \(SD_{i}\) and \(UC_{j}\) because \(U_{ma}\) cannot generate correct authentication messages.
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
## 3 Jamming Attack Models in WSN
Supposing a legitimate node has signals to transmit, it cannot change from listening state to send state because of the presence of a constant stream of signals in the channel. Deceptive jammers are difficult to detect and tend to be more effective when compared with constant jammers due to their impersonation features that makes them look like a legitimate transmission. _Random jammer_: Random jammer attacks differ from both constant and deceptive attacks as they conserve their energy by alternating between jamming and sleep states. During the jamming process, the attacking node jams for a predetermined time before turning off its radio and switching to sleep mode. After a while, it reactivates the jamming process from the sleep mode and continually follows that sequence. During the jamming mode, it exhibits either the constant or deceptive jamming feature, while during the sleep mode, it does not dissipate energy, therefore reducing its rate of power consumption. Reactive jammerAll the three previous jamming strategies discussed are active jammers, as they attempt to block the communication channel, regardless of the traffic pattern. An alternative to active jammers is the reactive jammer that presents a smarter and more power efficient approach [7]. Reactive jammers function by continually sensing the communication channel to detect when signals are being transmitted. On detecting a signal transmission in the channel, they start to transmit radio signals to cause collisions. Reactive jammers minimize the rate of power consumption and are very difficult to detect. The power they use when listening to a channel is less than that needed for jamming. Having described the four common jamming strategies in WSNs, it has been observed that the different forms of DoS jamming attacks are automated and follow a seemingly similar and predictable pattern with regards to the packet IAT. This can therefore be tracked using a statistical process control, EWMA, to detect the mean shifts in the jamming event intensity. In this work, we focus mainly on reactive, constant and periodic jamming attacks in WSNs.
## 2 Islanding Detection Methods
### Remote Methods  
The communication between the main grid and the DGs inverters serves as the foundation for remote islanding detection methods. The primary benefit of this approach is that there are no NDZ and the system's power quality is not compromised. However, this method is relatively considered an expensive and complicated technique. The remote islanding detection methods are presented below.
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 2 Methodology  
### Experimental Study  
The results from laboratory experiments were used for comparing the two numerical models. In this study, a laboratory experiment with the setting as presented in Figure 1 was conducted to investigate the hydraulic properties of a scour hole developed in a sand bed channel. This physical study was performed by using a 12.5 m long and 0.6 m wide channel made of two parallel vertical glasses. The input water runs into the channel controlled by a hydraulic pump, and a volumetric flow rate (\(Q\)) of 0.35 m\({}^{3}\)/s was maintained. A tail gate was set at the end of the channel to control the water depth of 0.144 m. The detail conditions of the experiment are as shown in Table 1, where \(h_{0}\) denotes the water height (m) and \(d_{50}\) presents the mean particle size by weight (mm). The inlet flow velocity \(u\) (m/s)can be calculated as the division of the volumetric flow rate \(Q\) (m\({}^{3}\)/s) and the area of the inlet boundary, \(A\) (m\({}^{2}\)):  
\[u=\frac{Q}{A} \tag{1}\]  
The reference length \(D\) (m) of the water height at the inlet (\(h_{0}\)) was used to calculate the Reynolds number as:  
\[Re=\frac{uD}{\
u} \tag{2}\]  
and \(\
u=1\times 10^{-6}\) is the kinematic viscosity of water (m\({}^{2}\)/s).  
For reproducing the riverbed protection part, the experimental channel bed was set up with two different parts as shown in Figure 1. A 4.5 m long acrylic bed was used to produce a smooth condition at the upstream, while the downstream was of 8 m length filled with sand. In this work, the mean particle size by weight (\(d_{50}\)) was to 1.2 mm. This laboratory experiment took up to 12 days to perform until it reached the equilibrium state, defined as there being no further increase in the scour hole. At the final stage, an Acoustic Doppler Velocimeter (ADV) system was used to measure the water flow properties and bed profile.
## 3 Results
### Framework Compatibility
#### 3.1.3 Intel Card Compatibility  
Intel ARC A-series cards do not support double-precision floating-point (FP64) calculations, which is a direct result of the technical specification [73], which does not specify performance for this type of operation. Intel's documentation for the PyTorch [74] and TensorFlow [75] frameworks in the \"Known Issues\" section also indicates the lack of compatibility with FP64. The lack of support for this type of data prevents the use of neural network models that use FP64 in their structure or during the training process. To confirm this incompatibility, tests were carried out on all frameworks used. The test results confirmed the lack of support, and an attempt to perform calculations on FP64 data ended in an error. Due to this compatibility limitation, the Intel card was awarded one point in all compatibility-related evaluation criteria. The results presented in Table 1 indicate the significantly lower compatibility of the Intel card compared to the Nvidia card.  
The use of the Intel graphics card in the PyTorch, TensorFlow, and JAX frameworks requires partial modification of the code intended for training on Nvidia cards, which is related to the need to use dedicated extensions, such as IPEX (Intel PyTorch Extension) in the case of PyTorch [74]. Direct execution of code prepared for Nvidia graphics cards on Intel cards without applying these extensions is not possible in the mentioned frameworks. However, code using the ONNX framework can run on Intel cards without making changes,provided that the appropriate device is specified during initialization. On the other hand, the OpenCV library, built with OpenCL and OpenVINO extensions, only requires you to indicate the right device when running a neural network.
## 2 Materials and Methods
### One-Dimensional CNN  
One-dimensional CNN is a variant of a neural network, used for processing one-sequence data. Unlike the two-dimensional convolution used in traditional image processing, 1D CNN is primarily used for processing time series data, text data, or other one-dimensional data. The basic structure of a 1D CNN is similar to that of a traditional CNN, including convolutional pooling and fully connected layers. The basic workflow of the 1D CNN is as follows: 1. Input Layer: One-dimensional sequence data are received as input. 2. Convolutional Layer: Features are extracted using one-dimensional convolution. The convolutional layer convolves the input data with a set of learnable convolutional kernels to produce a series of new feature maps. Each convolutional kernel captures different local patterns and features. 3. Activation Function: Activation functions are applied after the convolutional layer to introduce nonlinearity and enhance the power of the network. 4. Pooling Layer: The dimensionality of the feature maps are reduced through pooling operations, reducing computation and extracting the main features. Common pooling operations include max pooling and average pooling. 5. Fully Connected Layer: The output of the pooling layer is connected to one or more fully connected layers. Linear transformations are performed using weight matrices, and activation functions are applied. Fully connected layers were used to learn high-level representations of the input data and perform classification tasks. 6. Output Layer: Depending on the specific task, the output layer can consist of one or more neurons. For example, in classification tasks, the output layer can contain multiple neurons with a soft max-activation function. One-dimensional CNN has some advantages in processing one-dimensional sequence data. It can automatically capture local patterns and relationships within a sequence, making it suitable for tasks such as time series analysis, text classification, and speech recognition. By stacking multiple convolutional layers and pooling layers, 1D CNN can extract features at different levels, enabling a better understanding and processing of complex sequence data.