esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Process Framework IdMSecMan  
The process framework IdMSecMan collects and aggregates relevant data, such as log files, inventory, user groups, and permissions, taking existing organizational and security-relevant information into account. It combines Vis4 Sec and SABSA. The process, based on the Vis4 Sec approach by [PERSON] et al. [3], provides a framework to acquire, aggregate, visualize, and distribute the gathered information, as visualization provides a useful extension for improvements, to identity management [6] and consists of the following:  
**Initiation:**: Information about the environment, requirements, stakeholders, and planned actions are collected. One possible input for the process run are selected security controls.  
Figure 4: Approach of IdMSecMan.  
Question Phase:This phase is driven by the goal of the iteration; for example, a security incident or an asset. In order to improve the results in a managed and structured way, only one question per iteration is asked. Data Management Phase:Required data and relevant data sources are identified and the data model is utilized. If data is not needed anymore, it is disposed of, while the data sources are refined during the iteration runs. Visualization Phase:Overview representing the data, along with the quality of each source, for the process, entries, and metrics. The visualization provides a quick overview, helping to improve security by providing actionable information for the stakeholders. Interaction Phase:Postponed issues are reviewed and the questions, data sources visualizations, and interactions are refined. After the feedback from the stakeholders is received, a new iteration of the process can start.  
SABSA applies a layered approach to answer the questions of what, why, how, who, where, and when at different business levels. The development process of SABSA follows the lifecycle of a security architecture and, hence, can be mapped to the lifecycle of processes, like authentication. By integrating SABSA into Vis4 Sec, we derived a method to develop security aspects related to identification, authentication, and authorization (IAA), as well as the governance and management of identities in an integral way, which could be continuously improved, helping to prevent situations like the motivating scenario. IdMSecMan answers the organization's internal questions \"Who when why needs identification, authentication, and authorization for what applications, how, and with which policies?\". We describe each phase of IdMSecMan in the following.
## 2 UAV Network Communication Model and Architecture
### Centralized Communication Architecture  
The centralized communication architecture evolved from single UAV systems. It was then gradually developed and deployed for UAV swarms, as shown in Figure 3. The architecture consists of a central node (fixed network infrastructure) connected to all UAVs in the swarm. Each UAV forms a one-to-one relationship with the infrastructure and directly receives control commands from the central station. This type of centralized communication architecture is relatively stable and easy to deploy. It often applies simple routing algorithms. However, this architecture is only suitable when the scale of the UAV swarm and the coverage area of the mission are small, and the tasks to be performed are relatively simple. The coverage area size depends on many factors, such as the UAV's transmit power, antenna gain, path loss pattern, and environmental conditions (in real environments, reflection and attenuation effects may reduce the range). Some control methods of this architectural model include ground control station (GCS) (UAVs receive commands directly from the station), the Leader-Follower model, cloud computing, or military architectures (where UAVs are coordinated from the command center) [27].  
The connections between UAVs require infrastructure for data transmission. The UTI (UAV to the Infrastructure) communication distances are often much larger than the UTU (UAV to UAV) distances, causing longer latency. Considering the high mobility of UAVs and the wide operating range requirements of swarm applications, this type of architecture gradually becomes difficult to meet. If the infrastructure fails or is attacked, the entire network will be paralyzed. Therefore, the disadvantage of the centralized communication architecture is the Single Point of Failure (SPOF). It gradually becomes unreliable in complex requirements.
## 4 Experimental Results and Analysis
### Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Test Data: 20\% EXP Data**} & \multicolumn{4}{c}{**Metrics: ACC (\%) \(\uparrow\)**} & \multicolumn{2}{c}{**Avg\_FOM \(\uparrow\)**} \\ \cline{3-10}
**Method** & **Type** & **Training Data** & **H** & **B** & **C** & **O** & **AL** & **Overall** \\ \cline{3-10}  & & & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **Avg\_FOM** \\ \hline \(m_{2}\)[8] & hand-craft & & 31.21 & 24.24 & 16.67 & 32.61 & 6.67 & 25.50 & 0.09 \\ \(m_{2}-m_{3}\)[8] & hand-craft & & 15.38 & 28.57 & 16.67 & 32.61 & 28.96 & 27.48 & 0.10 \\ ANN [22] & machine-learning & & 70.49 & 60.66 & 62.30 & 83.61 & 68.97 & 69.21 & 0.19 \\ SVM-ovr & machine-learning & & 60.66 & 55.74 & 55.74 & 85.25 & 56.90 & 62.91 & 0.16 \\ RNN [11] & deep-learning & & 57.38 & 63.93 & 73.77 & 88.52 & 75.86 & 71.85 & 0.31 \\ LSTM [13] & deep-learning & & 68.86 & 67.21 & 73.77 & **91.80** & 72.41 & 74.83 & 0.39 \\ CNN-LSTM (Ours) & deep-learning & & **98.36** & **81.97** & **88.52** & 90.16 & **100.00** & **91.72** & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison experiment results of previous methods and our CNN-LSTM model under test on multi-type particles with multi-energy scenario. Figure 5: The confusion matrix of (**a**) SVM-ovr, (**b**) LSTM, and (**c**) CNN-LSTM under test on multi-type particles with multi-energy scenario. demonstrate that our method achieves the best performance in this scenario, confirming that our CNN-LSTM network is capable of extracting more discriminative and generalizable features. This also highlights the strong potential of our method for practical applications. Additionally, we observe that when the energy level of the test particles falls between the minimum and maximum energy levels of the training particles (e.g., Training on 2.5 and 5 MeV, test on 3.75 MeV) can achieve a better performance. This may be because, in this case, the amplitude of the test waveform lies within the range of the training waveforms, increasing the similarity of the feature vectors. This phenomenon also provides valuable guidance for model training in practical applications.
## Data and Methodology  
As mentioned before, in this work, a general methodology able to investigate the temporal variations of the SUHI as a whole and its connection with LC changes was implemented through the GEE platform. The main novelty of this approach lies indeed in its easy applicability to the analysis of a huge amount of data, thanks to the Big Data processing capabilities of GEE.  
Specifically, a large-scale spatio-temporal analysis was carried out in order to investigate the possible connections between the Land Surface Temperature (LST) trends and Land Use/Land Cover (LULC) changes.  
The study was performed through the joint use of GEE and CE, focusing the analysis on six different U.S. Metropolitan Areas (MAs) (Figure 2), characterized by different climate conditions and the availability of a long time series of EO data: Atlanta (Georgia), Boston (Massachusetts), Chicago (Illinois), Houston (Texas), Phoenix (Arizona) and San Francisco (California).  
In the last few decades, all these cities have experienced a significant urban expansion, which has profoundly modified the main features of their territories. Residential suburbs, commercial and industrial areas have replaced forests and/or agricultural hinterlands surrounding the traditional downtown urban centers: soils that were once permeable and wet have been transformed into waterproof and dry surfaces. Such growth in the peri-urban fringe has resulted in a loss of cultivated areas and natural open spaces: the low values of albedo, vegetative cover and moisture availability in combination with the presence of high levels of anthropogenic heating can thus strengthen the UHI intensity. The selected U.S. MAs therefore represent valuable test sites to assess the effectiveness of the proposed methodology, hereafter described.
# New Insights into the Biology of the Emerging Tembusu Virus
## 4 Clinical Features and Pathogenicity
### Detection and Diagnostics  
Since the first isolation of TMUV, several techniques have been developed to study the virus or detect it for diagnostic purposes. The development of diagnostic tools was particularly motivated to prevent large outbreaks in farms and preserve the poultry industry from economic losses. Prevention of outbreaks in countries producing and exporting a large amount of poultry is a major issue, especially for China as it was the first producer of ducks in the world.  
[PERSON] et al. identified viral particles by electron microscopy in the transformed chicken B lymphocyte cell line BK3 (LSCC-BK3) infected with TMUV [5]. Although virus isolation and electron microscopy remain the gold standard, they are not suitable for diagnostic purposes. Isolation of TMUV is time-consuming, and implementation requires a dedicated laboratory with costly equipment and high-skilled trained personnel, making isolation difficult outside research laboratories. Moreover, fresh specimens must be used to obtain viable viruses. Finally, TMUV isolation from cells or embryos is mainly carried out in laboratories for basic research.  
Nowadays, common methods for diagnosis of TMUV include serological detection by enzyme-linked immunosorbent assay (ELISA), based on NS1 protein detection, and the plaque reduction neutralization test (PRNT) [81; 82]. The spatial organization of the E protein has been well described in DENV, WNV and JEV, showing a high degree of similarity between flaviviruses [83; 84; 85]. Since this glycoprotein is exposed on the surface of the virion, it is responsible for its immunogenicity [86; 87]. Indeed, the E protein has been widely used to produce mono- or poly-clonal antibodies. Recently, a new neutralizing antibody (IG2) cross-reacting with JEV, WNV and ZIKV and targeting a minimal epitope located in domain II of the E protein was reported to provide a large protection against TMUV in mice, suggesting a potential usage in detection and a valuable candidate for diagnostic and therapeutic purposes [88]. Reverse transcriptase coupled with polymerase chain reaction (RT-PCR) is commonly used to evaluate the presence of viruses in biological fluids. Several approaches using multiplex PCR targeting avian viruses were developed to detect TMUV [89; 90; 91]. Finally, a new and promising detection method using reverse transcriptase-PCR coupled with mass spectrometry detection has been developed to screen the presence of specific duck viruses [92]. With this technique, various viral infections of interest to duck farming can be simultaneously identified with a low detection threshold.
# A Ku-Band Bi-Directional Transmit and Receive IC in 0.13 \(\upmu\)m CMOS Technology
## 2 Design of Ku-Band Bi-Directional Transmit and Receive IC  
Figure 1 shows a block diagram of a Ku-band bi-directional CMOS transmit and receive IC. The transmit and receive IC is composed of a bi-directional amplifier, a 6-bitphase shifter, and a 6-bit digital step attenuator. The phase shifter and attenuator are composed of DPDT switches. Since the series transistors in the switches determine the insertion losses of the phase shifter or the attenuator, the number of the cascaded series transistors should be reduced. The phase shifter or the attenuator with DPDT switches can reduce the number of the series transistors compared to the control circuits only with SPDT switches, which results in improving the insertion loss. In this design, the phase shifter and the attenuator are split with bi-directional amplifiers to improve linearity. A serial peripheral interface (SPI) is implemented to control the state registers of the phase and attenuation, and a low dropout (LDO) voltage regulator is also integrated to provide the stable DC bias.
# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 3 Methodology  
An important consideration, although often over looked part of the calculation of thermal comfort, is the metabolic rate. The metabolic rate is highly dependent on the activity of the person (see e.g., [29]). It is proposed here to recognise actions using a deep learning trained computer vision system. This is illustrated in Figure 2.  
The action recognition system uses both appearance information in the form of RGB from digital video together with depth information. This type of modality has become quite popular for computer vision applications, as it provides distance information as well as the appearance. Applications, such as activity recognition, have become considerably more viable since the advent of the RGB-D cameras, such as Microsoft's Kinect and Asus Xtion. Video data are particularly important because they can provide detailed information regarding the movement of a person that is important in applications, such as activity recognition. Therefore, a 3D model of the appearance and depth information is used where the appearance information is taken over time to form a 3D stream of appearance information. Similarly for the depth information, the depth information is combined from multiple sequences into Depth Motion Maps (DMM)s [30]. The DMMs are extended to include multiple appearance information, similar to e.g., [31]. The important features in the 3D depth and appearance streams are then learned by a series of fine-tuned, pre-trained 3D Convolutional Neural Networks (CNN)s. More details on this action recognition technique can be found in [32].  
The results of the action recognition system are used here as inputs for the simulation of different action sequences. Each recognised action is then mapped to metabolic rate.  
Figure 2: An illustration of the steps in the computer vision based action recognition system.  
Following that, the metabolic rate forms an input variable to the PMV calculation. The resulting PMV value is then fed to a fuzzy controller. Figure 1 shows the proposed system.
# An Efficient and Robust Topology Optimization Method for Thermoelastically Damped Microresonators
## 2 Statement of the Topology Optimization Problem  
The objective of this topology optimization method is to enhance the quality factor of thermoelastic microresonators with the phase-lag heat conduction law. As per the definition of the phase-lag heat conduction model, the dissipation resulting from the non-Fourier form of the entropy increases because of the irreversible process related to both internal energy and heat flux and can be converted into a time-dependent lag form [32]. The introduction of higher-order time-dependent terms significantly increases the complexity of solving the system equations.  
In an optimal design method, the search direction, search domain, efficiency, and convergence are the crucial properties [33]. The search direction determines the ability to obtain optimal solutions to a design problem, while the search domain influences the speed of the method. Moreover, the optimization method must be convergent to obtain an appropriate solution.  
In this section, we propose a modified evolutionary structural optimization method to determine the search direction of the design method.
# Selection of Key Frames for 3D Reconstruction in Real Time
## 2 Related Work
The MP4 files are encoded using the MPEG format and are structured in containers that either contain information or more containers. Recording videos with 30 frames per second produces many frames in a short time and motion blur due to camera motion. This led to the overall problem of redundant and low-quality frames. However, what if one could use the IMU to recognize suitable frames? By recording IMU data while recording, we can use the angular velocity and acceleration of the camera to make a statement about the quality of the images and determine if the camera has moved. If the camera does not move while recording, identical images are produced. If the camera moves too much, images suffer from motion blur. For this purpose, a calibration procedure firstly defines the requirements for which frames are suitable for further processing. The requirements are defined in terms of quality and motion in the frames itself. Quality is defined as the intensity change in a frame. By means of the 2D discrete Fourier transformation (2D-DFT), one can detect those intensity changes. Such intensity changes in a frame depend on the sharpness of edges. If the sharpness decreases, one can detect lower intensity changes, which leads to blurred areas. The second requirement refers to motion in frame-to-frame correspondences. The underlying assumption is that consecutive frames with little motion result in a high number of redundant frames. By calculating the optical flow via the Lucas-Kanade (LK) method [9], we provided a threshold that stated that the camera was not moving. We draw a direct connection between the current IMU state and the defined requirements of quality and motions in the frames. This leads to an automated way of labeling the inertial data that denotes a frame as _good_ or _bad_. This paper aims to classify frames by a random forest classifier (RF) to identify suitable frames due to inertial data in real time. The result is an efficient and automated way to classify suitable frames. It is not necessary to perform complex and computationally demanding image-processing algorithms.
# Secured Real-Time Machine Communication Protocol
## 6 Results and Analysis
### Local Network Placement  
As presented in Figures 7-9, the test results for connecting, reading, and writing operations in a local network are comparable to those executed on the local loopback interface. ModBus outperforms SRMCP in connection time due to its simplicity, with an average connection time of 3.83 ms compared to SRMCP's 7.78 ms. However, SRMCP shows a slight advantage in both reading multiple variables and writing a single variable, with an average reading time of 0.22 ms and writing time of 0.32 ms faster than ModBus. Despite these differences, both ModBus and SRMCP can complete read and write operations in under 2 ms, making them well-suited for the 20 ms cycle typical of fast tasks on most PLCs. In contrast, the OPC UA exhibits the poorest performance, with an average connection time of 33.35 ms, a reading time of 18.42 ms, and a writing time of 18.49 ms. These values render the OPC UA unsuitable for fast tasks on most PLCs, potentially hindering the  
Figure 6: Testing results for writing a variable over a local loopback address.  
adoption of the IEC 61499 standard, which requires horizontal PLC communication during scheduled tasks.  
Figure 8: Testing results for reading multiple variables over a local network.  
Figure 7: Testing results for local network connection time.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 2 MANET Routing Protocols: Background and a Review of the Literature
### MANET Routing Protocols Considered
The protocol aims to have a much simpler design that helps to lower the system requirements from the nodes and simplify its implementation. Path accumulation is another feature that this routing protocol has inherited from the DSR (dynamic source routing) protocol. These accumulated paths provide information that the nodes are processing route discovery packets. From there, these routes that have been discovered are learned and used to route packets. The major difference between DYMO and its predecessor, AODV, is that the latter can only generate route table entries for the destination node and next hop node. DYMO,on the other hand, stores routes newly discovered for each intermediate hop. What this means, essentially, is that instead of skipping intermediate nodes as AODV does, DYMO learns the routes to discover new paths to the destination nodes. * BATMAN protocol: Due to the shortcomings of OLSR, the BATMAN protocol was introduced [17; 18]. The weaknesses included routes regularly going up and down due to routing tables being unnecessarily flushed due to loops. In BATMAN, all the nodes connected broadcast HELLO packets, also known as originator messages, at periodic stages to their neighbors. These messages consist of their unique address and sequence number, and each neighbor changes the sending address to its address and re-broadcasts messages from there. On receiving the message, the originator makes a bidirectional link to confirm that the link detected can be used in both directions [31]. The currency of the message is then checked against the sequence number. This protocol does not maintain the full route to its destination. Instead, each node along the route only maintains information about the next link, which can be used to find the best route. BATMAN's objective is to maximize the probability of delivering a message and it does not attempt to check the quality of each detected link, it just checks to ensure that the link is there. The links are then compared in terms of the total of the originator messages that were received within the current window.
## 2 Related Work
Thus, these nodes will never be presented data packets for forwarding. This type of node can never act as a service provider in a MANET, making it difficult for existing watchdog mechanisms that rely on feedback to compute the direct reputation of nodes. When we compared some of the above-mentioned related works to our proposed model, one significant difference is in the monitoring technique that we employ in detecting misbehaving nodes. For instance, the watchdog monitoring techniques employed in [7; 8; 29; 31; 38; 39; 40; 41; 42; 44] have the ability to detect when a node forwards a packet by listening to the traffic of the forwarding node. When it overhears the packet being forwarded, it uses the information to compute the corresponding reputation or trust values. Although, the watchdog mechanism is effective at detecting successful packet dropping and forwarding activities of nodes in MANETs, the watchdog mechanism has its own limitations. The watchdog mechanism is prone to ambiguous collisions, receiver collisions and limited transmission power, which may lead to false detection. More significantly, the watchdog monitoring technique will not be able to monitor and detect the network activities of selfish nodes that do not participate in the route discovery processes for other nodes. Nodes exhibiting this type of selfish behaviour will not be presented packets to forward for other nodes, which ensures that they will not act as service providers for other nodes in the network. However, the limitation of the watchdog mechanism in reference to monitoring the network activities of nodes exhibiting totally selfish behaviours is addressed by the monitoring scheme employed in this work. In our proposed work, a node relies only on first-hand information in computing the reputation of its neighbours. It captures packets through promiscuous listening of transmissions carried out by all of its neighbours and on packets it received from the target nodes that are being monitored. It employs a novel technique to analyse and filter each received and captured packet to identify if the node being monitored is the originator or just a forwarder. A full description of the monitoring technique is described in Section 4.1.