esa-sceva/satcom-chunk-collection huggingface dataset:
# Conjugate Heat Transfer Analysis and Heat Dissipation Design of Nucleic Acid Detector Instrument
## 3 Simulation Process
### Grid Independence  
Mesh independence is an important aspect of the computational fluid dynamics simulation. Although the model used in this study was simplified, it still exhibited a high degree of complexity. The grid independence of the model was verified to ensure the accuracy of the simulation [36]. The average fluid temperature \(T_{ave}\) in the last moment of the simulation was used as the verification index for grid independence. The relative change rate \(\eta\) was defined as:  
\[\eta_{i}=\left|\frac{T_{ave,i}-T_{ave,i-1}}{T_{ave,i-1}}\right|\times 100\% \tag{14}\]  
where \(T_{ave,i}\) is the average value of the simulation of \(T_{ave}\) at the current grid scale and \(T_{ave,i-1}\) is the average value at the previous grid scale. The calculated value of \(T_{ave}\) is shown in Figure 7. When the mesh size was less than 0.008 m, the \(T_{ave}\) was approximately unchanged and \(\eta\) was less than 0.5%. This agrees with the grid independence [37]. The mesh size of the last refinement was used to determine the simulation accuracy. As shown in Figure 8, the maximum element size was 0.005 m and the number of grids was 1,271,934.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Object** & **Type** \\ \hline Fluid and solid contact surfaces & couple wall \\ Fan boundary & polynomial \\ TEC side1 and side2 & temperature wall \\ Heat source on the PCB & values \\ Fluid & air, incompressible \\ Inlet and outlet & pressure inlet/outlet \\ Pressure–velocity couple & simple \\ The discretization of convection–diffusion & second-order upwind \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation settings.  
Figure 7: Grid independence verification.
## 2 Materials and Methods
### Experimental Methods
#### 2.2.3 Compression Strength (Ultimate Stress in Compression Paralled to Grain)  
The ultimate stress in compression (compression strength) parallel to grain was determined according to [37]. The test was performed by means of a ZWICK/ROELL universal testing machine (type BT1 FB050 TN.D30/2007, produced in Ulm Germany), endowed with a device (support) for positioning the specimen and a device for applying the load. The compression test (Figure 2) consisted of measuring the maximum load required to break the specimen under a static load applied parallel to grain. The machine software automatically recorded and displayed all measured data and the calculated value of compression strength. A data correction depending on the real moisture content of tested samples (W, %) was performed in order to assess compression strength at a 12% moisture content (\(\sigma_{c,12}\)).  
\[\sigma_{c,W}=\frac{P_{\text{max}}}{b\times h^{*}}\left[\text{N}/\text{mm}^{2}\right] \tag{10}\]  
where  
\(\sigma_{c}=\) ultimate stress in compression parallel to grain at the actual moisture content (\(W\)) of tested sample, [N/mm\({}^{2}\)];  
\(P_{\text{max}}=\) maximum breaking force of the sample, [N];  
\(b\times h=\) cross section area of the test sample (\(Ra\times T_{\beta}\)), [mm\({}^{2}\)].  
\[\sigma_{c,12}=\sigma_{c,W}\cdot[1+\alpha\cdot(W-12)],\left[\text{N}/\text{mm}^ {2}\right] \tag{11}\]  
where  
\(\alpha=\) correction coefficient of compression strength for 1% MC difference, equal to 0.04  
\(W=\) moisture content of the tested sample [%].  
Figure 2: Determination of compression strength: (**a**) detail of ZwickRoell universal machine equipped with compression device; specimen positioned for testing; (**b**) examples of stress–deformation diagrams automatically registered (different colours represent different samples).
## 6 Prospects and Discussion
### Use Cases  
The presented script has the potential to be applied in a multitude of situations and applications. Primarily, however, three distinct groups can directly benefit from its implementation. The IT department may utilise this script to assist colleagues who are uncertain about the contents of an email attachment. Typically, the analysis ceases after the email body has been evaluated for indications of potential deception, such as a hasty request for action, including a password change, or grammatical errors. As large language models become increasingly sophisticated, there is an increased risk of their misuse to perpetrate social engineering attacks. These involve the creation of a convincing email in any language supported by the model. Safety measures cannot be built into the model to prevent such emails being crafted, as they can always be circumvented by faking a legitimate purpose. In such cases, an IT employee can verify the document and gain valuable insight into its legitimacy. To further expand this case, the potential for an automated mail service, as outlined in the future work Section 6.3.2, provides a promising avenue for exploration.  
In consideration of the potential for creating verbose output, as previously outlined in Section 4.4, the scope for utilisation may be expanded to encompass the facilitation of malware analysis, specifically within the context of static analysis, which serves as the foundation for dynamic analysis. The capacity to adapt the prompt in any manner renders it adaptable to an extensive range of requirements, combining multiple tools in a single entity. This adaptability is particularly advantageous when extending its applicability to a broader array of file types, as detailed in Section 6.3.1.
## 4 VM-Based Intrusion Detection Mechanism
### System Components
#### 4.2.1 Memory Integrity Monitor  
Memory integrity monitor is a software module that keeps monitoring the internal state (i.e., the integrity of memory) of an emulated device during runtime. Specifically, it periodically obtains a copy of memory snapshot of the running VM, which contains an instance of an execution code and a runtime data at that time. Then it checks whether the integrity of the memory is maintained or not by a hash computation.  
To obtain the snapshot, a memory integrity monitor makes use of a guest management interface which is offered by a VMM for virtual machine introspection. The guest management interface allows to monitor the low-level details of a running VM by viewing its entire memory content, trapping on hardware events, and accessing the CPU registers. This interface has a variety of implementations according to the types of VMM. For instance, in general purpose VMMs such as Xen, KVM, and QEMU, the interface is implemented as an introspection library (e.g., Libvmi [20]). In Dynamips, which is designed for emulating Cisco networking devices, a debugger such as GDB can be used as a guest management interface.  
Figure 2: Example of a remote code attack detection.  
After obtaining a memory snapshot for the emulated device, the memory integrity monitor generates a hash value of the snapshot. The hash is computed by cryptographic hashing algorithms such as SHA-256. Then, it is compared to a previously computed hash for a former snapshot. The memory integrity monitor will be informed of an intrusion if there is a difference between two hashes, since it indicates the content of the captured memory has been altered.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 5 Privacy Preserving Federated Learning  
In the proposed PPFL system, each client participating in the training process encrypts local model parameters trained with local data, using its own private key to protect thetrained local model parameters. Thereafter, the clients transmit the encrypted local model parameters to the CS. The CS updates the global model parameters with the local model parameters encrypted with different keys by exploiting the partial homomorphic decryption capabilities of CPs. As a result, the proposed PPFL algorithm ensures data confidentiality between the CS and the clients, as well as data confidentiality among the clients because each client has its own private key and does not send the private key to other third parties. The detailed procedure of the proposed PPFL is described in the following subsections.
## 2 Materials and Methods
### Samples' Characterization Techniques  
The PAN and PAN-biochar electrospun fibers were analyzed through a combination of the following techniques.  
* The Environmental Scanning Electron Microscope (ESEM, FEI Quanta-200) was used for microstructural characterizations. Scanning electron microscope imaging uses a focused electron beam to scan the sample surface. The signals that originate from the electron-surface interactions provide the information regarding the sample micromorphology. ESEM was used in high-vacuum mode, applying the secondary electron detector, Everhart-Thornley type (ETD), to obtain micro-morphological data on sample surfaces.
* Energy-dispersive X-ray spectroscopy (X-EDS) was applied to carry out semi-quantitative chemical analysis. This analytical technique is often provided by the ESEM apparatus due to back-scattered electron imaging. X-EDS provides a chemical characterization of the sample, analyzing the specific electromagnetic emission spectrum, which identifies each element. Data were acquired and elaborated through X-EDS Oxford INCA-350 software. More spectra were acquired for each sample to obtain a significant statistical analysis. Chemical information about elemental composition was expressed as weight elemental percentage.
* Fourier-transform infrared spectroscopy (FT-IR): measuring the absorption and emission infrared spectrum of the samples, it is possible to identify compounds, gaining fundamental information about the chemical bonds. The instrument used in this work was FT-IR (Bruker Tensor 27 IR). IR analysis was conducted at ACI-LUH, Hannover.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 3 Gap Analysis  
To see whether the existing model can be updated using new empirical data, the ITU data set is analyzed for gaps in its coverage. The raw data used in this paper is obtained from the Radio Noise Data Bank of ITU-R Study Group 3. This analysis is restricted to the outdoor man-made noise measurements contained in the database. Currently, as of 1 October 2024, the ITU Radio Noise Data Bank contains a total of 90,205 such measurements. Each measurement is characterized by its observation frequency, country, location, environment type, measurement year, and time-of day. Although the total number of measurements is large, it will be shown that the data set is scarce in this multi-dimensional space. In this section we will analyze the dataset. The impact of data gaps will be dicussed in Section 4.
## 4 Simulation and Evaluation
### Jitter Evaluation  
The jitter in our experiments is calculated using the following Equation (5). It is calculated according to the D-ITG definition of the jitter. The average of the direct jitter of network packets is computed as well as printed to a file known as \"jitter.dat\". The results are displayed through opening the log analysis using D-ITG [39].  
Figure 11 shows jitter results in milliseconds (ms) on the y-axis for the proposed method [25] and [26]. We can see from Figure 11 that the jitter in the proposed approach is small as compared to the other two approaches which do not use the proactive mechanism during handover. Moreover, the proposed approach takes advantage of SDN's central management. Hence, the status of the network is visible to the controller resulting in a lower jitter during the handover scenarios for the proposed scheme. In addition, the jitter can be seen as higher in the other categories using more controllers for [25] and [26] during the handovers.  
\[AvgJitter=\frac{\sum_{i}^{n}\lvert D_{i}\rvert}{n} \tag{5}\]  
Figure 10: Comparison of the packet loss ratio for the proposed approach with ([PERSON] 2017) [25] as well as ([PERSON], 2017) [26].
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 3 IM-OM Circles and Frequency-Bounded Mismatch Envelope  
As shown in Figure 1, a two-port network such as an LNA is characterized by four reflection coefficients (\(\Gamma_{S}\), \(\Gamma_{IN}\), \(\Gamma_{L}\) and \(\Gamma_{OUT}\)). These coefficients determine the amount of matching at each terminal that can be calculated using IM and OM expressions. Interestingly, the reflection coefficients are interdependent and can be expressed as functions of one another.  
\(\Gamma_{s}\) controls output reflection coefficients (\(\Gamma_{OUT}\)) through the following relation:  
\[\Gamma_{OUT}=\frac{(S_{22}-\Gamma_{S}.\Delta)}{(1-\Gamma_{S}.S_{11})}=f([S], \Gamma_{S}) \tag{1}\]  
Similarly, \(\Gamma_{L}\) influences the input reflection coefficient (\(\Gamma_{in}\)) as:  
\[\Gamma_{in}=\frac{(S_{11}-\Gamma_{L}.\Delta)}{(1-\Gamma_{L}.S_{22})}=f([S], \Gamma_{L}) \tag{2}\]  
From these equations, it follows that knowing one reflection coefficient from each side of the device is sufficient to determine all four, assuming the known values are independent.  
The mismatch at each port can be computed using the equations of OM and IM as follows [12]:  
\[OM=\left|\frac{\Gamma_{L}-{\Gamma_{out}}^{*}}{1-\Gamma_{L}.\Gamma_{OUT}}\right| \tag{3}\]  
\[OM=f(\Gamma_{L},\Gamma_{OUT})=f(\Gamma_{S},[S],\Gamma_{L}) \tag{4}\]  
And  
\[IM=\left|\frac{\Gamma_{S}-{\Gamma_{in}}^{*}}{1-\Gamma_{S}.\Gamma_{in}}\right| \tag{5}\]  
\[IM=f(\Gamma_{S},\Gamma_{in})=f(\Gamma_{S},[S],\Gamma_{L}) \tag{6}\]  
From Equations (1)-(6), it is evident that IM and OM are mutually dependent. That is, knowledge of one can inform the other, given the appropriate device parameters. _[PERSON] et al._[12] first formalised these relationships and introduced a set of equations to graphically represent mismatch through IM-OM circles. In this work, we will be summarizing the behaviour of these circles and further extend the concept to derive a frequency-bounded mismatch region on the Smith chart.
## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### 4.1.8 Stone  
As the field of sensor networks has seen developing very fast in recent years, this project [53] focuses on the energy efficient storage for the sensors. In this case, sensor platforms are equipped with a finite energy source. Thus significant research is focused on optimizing the energy consumption of node resources such as computation, communication and storage. Since new generation of sensor platforms has tracked technology trends in computation and communication components, the project deals with the storage subsystem that has undergone little change. All generations of the Mica motes provide limited storage of a less than a megabyte, at an energy cost equivalent to or greater than that of communication. The high energy cost of storage has raised questions about the rationale for using in-network storage-based data management techniques for sensor networks; though a lot of current research assumes the presence of such an energy efficient storage subsystem. There are few key questions that the project aims to deal, (i) to find out the most energy-efficient storage platform for sensor networks and to ascertain the implications of an ultra-low power storage subsystem on sensor network design and (ii) to design a storage frame-work that allows sensor network researchers/developers to use ultra-low power storage in an efficient and useful manner.
## 2 Materials and Methods
### Experimental Setup and Data Sets  
Two rounds of data collection were performed during the two months of the seabass identification experiment. Images were taken of the whole fish out of water (lateral view of the left fish side, Figure 1). Each fish was caught in the cultivation tank, anesthetized in a bucket using Phenoxyethanol, and moved to a green background (Figure 1) for imaging. Natural light conditions were used during data collection. Ten images of each individual were taken using a Nikon D90 digital camera for each round of data collection. Each fish was moved and rotated on the green background to simulate different object-camerapositions and angles. The fish head was oriented to the left for all images. There was no obvious pattern on the fish (both sea bass and carp). The main pattern was a lateral line for seabas. For some fish, this line was interrupted (Figure 1, top). Some fish were also scratched on the body. These scratches were visible as lines on the body (Figure 1, middle). Parts of the tail and fins were sometimes missing due to fish cannibalism (Figure 1, bottom). Pictures were saved in the RAW format. The manual camera mode was used to control the focus, shutter, and ISO. The resolution of each picture was 4288 \(\times\) 2848 pixels, with 12 bits/pixel and 3 color channels.  
In the first round of data collection, all 300 fish were photographed, and this set was labelled as the short-term seabas (STS) database. The 32 randomly-selected fish were PIT-tagged and separated into RAS. The rest of the fish were returned to the tanks. All fish recovered after the experiment.  
After two months, the second round of data collection was performed with 32 fish. The procedure was the same as that for the short-term experiment (STS).  
Common Carp  
Four rounds of data collection were performed over the four-month experiment for carp identification. Images were taken of the whole fish out of water (lateral view of the left fish side, Figure 1). Each fish was caught in the cultivation tank, anesthetized in a bucket using clave oil, and moved to the green background (Figure 2) for imaging. A single-layer white-fabric photographic tent with controlled illumination was used for photography. The fish heads were oriented to the left for all images.  
Figure 1: Examples of seabas images. (**Top**) example of the interrupted lateral line. (**Middle**) example of a fish with scratches visible on its body. (**Bottom**) example of a missing upper-tail part.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 2 Materials and Methods
### Superpixel Segmentation and Feature Extraction  
#### 2.1.1 Superpixel Segmentation  
An image is a kind of regular data in Euclidean space that can also be regarded as a special kind of graph structure data. However, using each pixel as a node when training a GNN requires huge computing resources. With the ability to aggregate similar pixels to form a more representative large 'element', superpixel segmentation can greatly reduce the number of nodes and the effect of speckles. Additionally, superpixels preserve most object information and adhere well to boundaries [33]. Therefore, the proposed method takes the superpixel as the basic classification unit. Available algorithms include FNEA, simple linear iterative clustering (SLIC) [31], improved SLIC method [34] and mixture-based methods [35].  
The SLIC algorithm clusters pixels in the combined five-dimensional color (the L, a, b values of the CIELAB color space) and image plane space (x, y coordinates of the pixels) to cluster pixels. It includes three steps, initializing the center of clustering, local k-means clustering, and postprocessing. Due to its ease of use and effectiveness, it can be adapted to segment scenes such as ponds and lakes. The improved SLIC method modifies the SLIC clustering function to adapt the characteristics of polarimetric statistical measures and renews the initialization method to produce robust cluster centers. It is a better choice in complex scenes such as floods. In the following study areas of this paper, the improved version was adopted. Note that over-segmentation is required to obtain object boundaries accurately.
## 3 Results and Discussion
### Integrated Biomarkers Response (IBR)  
The score values of the various studied parameters in the blood of diazinon-exposed _Clarias batrachus_ have been represented as star plots in Figure 6a-c, for a period of 15, 30, and 45 days, respectively. The score values for parameters such as Hb, TLC, TEC, MCH, K+, SGR, GH, T\({}_{3}\), T\({}_{4}\), and protein decreased with the increasing concentration of the toxicant. A decreasing trend was observed for all the exposure periods. The score values for HSI and glucose increased with an increase in dose concentrations. A similar increasing trend was observed with periods of toxicant exposure. The IBR values for different dose concentrations and periods were calculated using the score values and represented graphically in Figure 7. The IBR values for the control and experimental group for 15 days of exposure was estimated to be 36.93, 32.67 (T\({}_{1}\)), and 33.26 (T\({}_{2}\)). IBR values for 30 days of exposure to diazinon were calculated to be 40.12 (control), 35.75 (T\({}_{1}\)), and 36.15 (T\({}_{2}\)). Similarly, IBR values for 45 days were 38.79 (control), 36.19 (T\({}_{1}\)), and 36.62 (T\({}_{2}\)), respectively. These marked depictions of relative toxicity using changes of each endpoint on the star plot reflect a major advantage of IBR as a technique in toxicity assessments. The visual feature of IBR highlights its strength as a species-specific and toxicant-specific technique that affords a ready qualitative assessment across parameters and groups [107]. The overall effects of various xenobiotics in different groups of animals, including invertebrates, have been demonstrated using this technique [108; 109]. Aside from the simplification of complex biological responses into a single index and predefined quality class, the IBR chart also simplifies relative toxicity under exposure gradients [54; 110].
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 4 Goal-Oriented Requirements Analysis Approach for IoT Software Systems
### Application Example: IoT System for Monitoring Air Quality
#### 4.2.3 Acceptance Criteria  
* Air Quality Sensors: The application should be able to connect and recognize multiple IoT-based air quality sensors. Sensors must measure critical parameters, such as particulate matter, CO\({}_{2}\), humidity, and temperature.
* Real-Time Visualization: The application should provide an intuitive interface for real-time visualization of the data collected by the sensors. Air quality levels should be clearly represented.
* Customized Alerts: It should be able to set custom thresholds for each parameter and receive real-time alerts if the levels exceed these thresholds.  
* Data History: The application must store and display a history of air quality data to enable long-term analysis. Users should be able to access detailed reports on trends and changes in air quality.
* Integration with Smart Systems: The application should be compatible with smart home systems, such as thermostats and air purifiers, to respond automatically to air quality data.