esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Implementation and Validation
### Implementation
#### 6.1.2 Planning System  
A planner is a program that receives a domain and a problem as inputs and generates a plan. It starts from a premise that does not fit the real problems in robotics: the world only changes based on the execution of a plan's actions. A planning system has a planner at its core, but it must have more elements:  
* A set of components materializes the actions of the domain. These components are processes that perform an action. These actions receive the arguments set out in the plan and can succeed or fail.
* An executor who takes a plan and orchestrates the activation of the processes that implement the actions. It is also usually responsible for verifying that the conditions of the actions in execution  
Figure 8: Finite state machines with component dependencies per state.  
are valid. If they are not met, or action fails, abort the execution of a plan and ask the planner to replan.
* A knowledge base contains the domain and the problem. The knowledge base accepts queries about the domain and allows queries and updates on the problem. The problem contains the currently valid instances and predicates. Typically, executing an action modifies the knowledge base. The perceptual components of the robot can also synchronize the content of the knowledge base with the perceived state of the real world.  
We have used a customized version of ROSPlan [29] as a planning system in our architecture. Each PDDL action is implemented on a ROS node that implements a ROS action server. These nodes are inactive until the executor in ROSPlan activates it as part of executing a plan.  
Queries to the knowledge base are made using services. Any component that requires access to the planning system accesses it through a set of methods that allow managing the knowledge base and the executor in a simple way from the symbolic layer.  
The planning system is divided into two layers. Knowledge base management and planning is in the symbolic layer, and actions are in the sub-symbolic layer. When an action receives its arguments, it must transform it into concrete information to act. For example, in the execution of (move_to r2d2 kitchen bedroom), the node that implements the move_to action must know the bedroom coordinates to send them to the navigation module. Typically, this translation is configured in a file that is loaded into memory at startup.
# Relationship between Fault Level and System Strength in Future Renewable-Rich Power Grids
## 2 Typical Characteristics of Fault Current of SGs versus PE-Based RESs  
The SGs are considered as the main source feeding the fault level in conventional power systems. Due to their physical behavior, the fault currents fed from SGs are time-decaying currents with a very high initial contribution (i.e., sub-transient current). The initial fault contribution is followed by a lower level of transient contribution before reaching the lowest level of fault current components represented by the steady-state component. Figure 1 shows the typical fault current signature of SG and RESs (i.e., type-four wind). In order to mimic the behavior, SG is usually modelled as a voltage behind and impedance for fault calculations. The impedance, which is mainly characterized by the reactance of the generator, has three different values that vary to represent the above-mentioned components.  
Figure 1: Typical fault current signature of a 50 MVA generation unit. (**a**) SG (**b**) type-four wind [2].  
This behavior is due to the PE interface utilized in their converters. Generally, RESs may have a fast transient that is followed by a limited steady-state fault component in response to the faults and voltage dip conditions [2]. Note that these RESs are usually accompanied by protection schemes that prevent high fault currents from passing through the converter to avoid stressing of the PE switches. In fact, it is not only the protection that affects the fault current contribution of RESs, but also the control strategy, the severity of the fault, and the fault-ride-through (FRT) requirements according to the grid code that is set by the grid operator.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 4 Implementation Results and Comparisons
### Design Validations  
The validation setup consists of an FPGA and a laptop. We programmed the FPGA (after generating the bitstream) to run the SIMMECK block cipher. The data and key bits are transferred using a serial interface (i.e., UART (Universal Asynchronous Receiver Transmitter) protocol). The 32-bit data and 64-bit key are read from the external drive. Next, the script executes the function to transfer the data and key bits. The encrypted data are received from FPGA, which is saved in a file on the external drive. The complete process takes a few seconds to read/write from/on the external drive. It offers robustness as the UART is a stable source of communication. The decryption operation could be performed like encryption. We have created a separate python function for the encryption and decryption.  
The validation setup is illustrated in Figure 7. The left side shows the processing inside the PC. In other words, the PC block in Figure 7 runs the python scripts to load/store data to/from the FPGA. The right side shows the Nexys 4 FPGA, connected to the PC. It is essential to mention that the Nexys 4 device is NIST-recommended to validate block ciphers, and it consists of an Artix-7 FPGA. The red LED on the top-right corner glows when the FPGA device is powered up. The green LED determines the type of operation (encryption/decryption) to be performed on the FPGA. The white LED blinks when the FPGA has received the data and key bits as input using UART. The right bottom pink LED glows when the required encryption/decryption operation is completed successfully. Finally, the left lower pink LED glows when the FPGA transfers encrypted/decrypted data as output using UART to PC.  
Figure 6: Efficiency evaluation with designs of [15; 30] on Spartan-6 FPGA. From left to right, the orange bars with values on top 0.01 and 0.024 are for [15] and [30], respectively. Similarly, a red color bar shows our work.
# Adaptive Ransomware Detection Using Similarity-Preserving Hashing
## 1 Introduction
Typically, this is implemented by storing the hash for each file in the system and continuously comparing the stored hashes with the current hashes. If there is a mismatch, then it is logged, and an alert is issued after a certain threshold of changes indicating a malicious change in the file system. Using the same idea to detect changes in files due to ransomware is tempting, but it entails a major drawback. Ransomware changes the file, so it should be detected via a FIM; however, hashing the whole file is time-consuming. This means that a long time will pass before the FIM issues an alert, causing a lot of files to be lost due to the ransomware. The authors of [15] proposed a crypto-ransomware detection method that relies on selective hashing. In this method, only small parts of the file are hashed to detect changes. By definition, encrypting a file changes the whole file and produces data that appear to be random. This idea reduces the hashing time substantially, making the behavioral detection of ransomware time-efficient. However, this method presents a drawback, as a threshold is set to issue an alert. The threshold is defined as the number of files that have changed. While this threshold can be efficient for small files, it fails for large files, as, even though a large amount of data are encrypted within a single file, it is still counted as one file. A false negative may occur due to counting the number of files. In this paper, we address the weakness in [15] by dividing each file into partitions, applying selective hashing for each partition, and setting the threshold as the number of partitions, instead of the number of files. The main contributions of this paper can be summarized in the following points:  
1. An adaptive hashing methodology is tailored for efficient behavioral ransomware detection that overcomes the security weakness in [15] while still achieving the same performance. By adaptive, we mean that the user can configure two main parameters, partition size and threshold value, according to the security requirements of the environment used. 2. A software implementation of the newly proposed methodology is presented along with a performance analysis and a comparison with the previous implementation. The new adaptive hashing ransomware detection methods outperform the previous implementation in two main metrics: the detection time and the percentage of saved files. For example, for 50 files of size \(\sim\)100 MB each, 92% of the files are saved within 0.75s of scanning. The rest of the paper is organized as follows: In Section 2, the literature is reviewed and discussed. In Section 3, the main methodology and implementation are presented. The results are presented and discussed in Section 4. Finally, the concluding remarks and future research directions are discussed in Section 5.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.6 Alex Net  
AlexNet was a seminal accomplishment in deep learning, particularly for image classification problems. It is widely regarded as the pioneering model that sparked the deep learning revolution, having been developed by [PERSON], [PERSON], and [PERSON]. AlexNet utilized a novel design and training procedures that established the groundwork for contemporary convolutional neural networks (CNNs).  
During the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, the AlexNet model rose to prominence. The goal of its creation was to outperform standard computer vision approaches and pave the way for deep learning models that can solve large-scale picture categorization tasks. AlexNet's designers made crucial design decisions, such as using deep convolutional layers, rectified linear unit (ReLU) activation functions, dropout regularization, and data augmentation approaches. These elements all led to the model's remarkable success.  
The hallmark of AlexNet's architecture is its numerous layers, which include convolutional, pooling, and fully connected layers. Convolutional layers collect picture characteristics via filters, while pooling layers minimize spatial dimensions to improve computing efficiency. Rectified linear units (ReLUs) are used as activation functions in AlexNet, which aid the model's capacity to understand complicated nonlinear correlations within the data. Dropout layers reduce overfitting and hence improve generalization.  
AlexNet is used for a variety of computer vision applications, with picture categorization being its core strength. Because of the model's depth and creative architecture, it has achieved a cutting-edge performance on various benchmark datasets, including the ImageNet dataset. Because of its success, deeper and more complicated CNN architectures have been developed.  
AlexNet's key characteristics are its eight-layer-deep architecture, novel use of ReLU activation functions for quicker training, and introduction of GPU acceleration for efficient learning. Its capacity to capture detailed visual patterns and identify items across several categories highlights its versatility. In the framework of the agricultural Guard System, AlexNet's picture classification expertise is used to identify intrusions in agricultural fields, whether they be animals or people. Its solid performance, learned from a large quantity of data, improves the system's capacity to effectively preserve agricultural land.
## 3 Design and Implementation
### Implementation of the Second Version of the System  
In the second version of the system (Figure 2a), we added a third sensor that was sensitive to skin temperature bio-signals, in order to collect a wider range of physiological reactions and thus get closer to the users' real anxiety state. Moreover, we tried to improve user convenience by keeping both hands as free as possible. Therefore, we embedded the skin temperature (SKT) (Figure 2b) and GSR (Figure 2c) sensors on the surface of a bracelet. We also changed the heart rate fingertip sensor and replaced it with a clip attached to the ear lobe (Figure 2d). This is part of the Grove ear clip kit which calculates the values of interbeat interval time series (IBIs) resulting in heart rate values (HR). In addition, a low pass filter and moving average filter were applied to the GSR and skin temperature values to remove the high frequency components. All collected bio-signals, namely the GSR, SKT  
Figure 1: First version of the biofeedback device.  
and HR, were added to bio-signal vectors, which were then transferred to the biofeedback classification application.  
This application uses the same machine learning technique as the first version of the presented system in order to convert bio-signals into real-time anxiety levels and store all measurements on an online database for record-keeping.  
It also has a graphical interface that appears either on screen or embedded in a web environment. The graphical part of the application displays the anxiety level detected by the biofeedback system as a visualized response on the computer screen, where a user can see his/her personal code and recognize his/her anxiety states. During the measurement, once high anxiety is reached, the application encourages the user to use diaphragmatic breathing and projects pictures to relax him/her. Furthermore, the application informs the user about whether the sensors are working properly (with a green color next to the sensor name) or not (with a red color next to the sensor name).  
The tutor can monitor the students' stress level in real time through a monitor user interface.  
The convergent validity of the biofeedback system in relation to self-report measures of anxiety was quite satisfactory. Moreover, a relevant research showed that students become more anxious or even stressed when they realize that their performance is declining [16].
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 2 Fundamental Trust Model Concepts
### Honesty  
Relying on other devices for information when seeking the reputation of device \(y\), device \(x\) might be misinformed and form the wrong perception about \(y\). This is due to dishonest recommenders that try to pollute the environment by intentionally giving bogus reputation reports. Ideally, we want to prevent dishonest devices from contributing to the computation of reputation. The definition of honesty used in this article is adopted from [18; 19]:  
_A recommender is said to be honest if the information, pertaining to a specific entity within a specific context at a given time, received from entity is the same information that entity believes in._  
When device \(x\) gives out information, \(x\) fetches this information from a data structure that \(x\) maintains. Hence, by \"believes in\", we mean the information that is stored in \(x\)'s data structure. Honesty is a critical factor in any trust model. In modeling trust, we can not assume that devices are honest and therefore we need a mechanism to identify and prevent dishonest devices from polluting the recommendation network. Hence, the goal is to come up with a mechanism to compute honesty and use this measure to weed out and prevent dishonest recommenders from influencing the recommendation network.
## 3 Controller Parameters Tuning
### Optimization of Inertia Weight and Learning Coefficients  
The inertia weight \(\omega\) affects the optimization ability of the G-PSO algorithm. When the particles are aggregated, a large \(\omega\) is set to ensure the search ability of the particles, and when the particles disperse, \(\omega\) should be small [30]. Therefore, this paper adopts an adaptive adjustment method, as follows:  
\[\omega=\left\{\begin{array}{l}\omega_{\min}+\frac{(\omega_{\max}-\omega_{ \min})(f_{i}-f_{\min})}{f_{arg}-f_{\min}},f_{i}\leq f_{arg}\\ \omega_{\max},f_{i}>f_{arg}\end{array}\right. \tag{17}\]  
where \(f_{i}\) is the fitness value of the \(i\)-th particle, \(\omega_{\max}\) and \(\omega_{\min}\) are the maximum and minimum inertia weight, and \(f_{\min}\) and \(f_{arg}\) are the minimum and average fitness of all particles, respectively.  
The cognitive and social coefficients \(c_{p}\) and \(c_{g}\) are used to determine the influence of individual and population experience on particle trajectories. The optimization results may not converge or fall into a local optimum with unsuitable values of \(c_{p}\) and \(c_{g}\)[31]. The following equation shows the dynamic learning coefficients of the proposed G-PSO algorithm:  
\[\begin{array}{l}c_{p}=c_{p2}-(c_{p2}-c_{p1})\frac{t}{t_{\max}}+k\cdot\sin(i \cdot\pi)\\ c_{g}=c_{g1}+(c_{g2}-c_{g1})\frac{t}{t_{\max}}+k\cdot\sin(i\cdot\pi)\end{array} \tag{18}\]  
where \(c_{p1}\), \(c_{p2}\), \(c_{g1}\), \(c_{g2}\) are the maximum and minimum values of \(c_{p}\) and \(c_{g}\), respectively, and \(k\) is a positive coefficient.
## 3 Experimental Results and Analysis
### Experimental Environment and Background  
This experiment uses AMESim/Simulink join joint simulation. The main features of AMESim/Simulink joint simulation are:  
1. In the process of establishing the simulation model, the two software of AMESim and Simulink are independent of each other and establish their own models respectively. The two simulation models communicate through the S function.  
2. Co-simulation works through two different working interfaces. The modeling of the mechanical part is performed in AMESim. The modeling of the control system is completed in Simulink. The user establishes relevant models in different simulation environments according to their own needs and work requirements.  
3. The user can directly establish the system simulation model without a mathematical model, which can make the modeling process more concise. Through co-simulation, the system simulation model can be operated more effectively.  
4. The emergence of co-simulation greatly reduces the workload of simulation, improves the efficiency of simulation and obtains more accurate data.  
In the joint simulation of AMESim and Simulink, AMESim and Simulink co-simulation have two modes: standard mode and co-simulation mode. The joint simulation process of AMESim and Simulink is shown in Figure 2.  
The physical model and mathematical model of the electro-hydraulic servo synchronization system are established in AMESim and Simulink respectively. To connect the two, one more output function module needs to be added in AMESim. Through this module, the physical model is processed to generate the S function. It can be used by Simulink's S-Function module. Through this module, the two can be combined to achieve the joint modeling and simulation of AMESim and Simulink. During simulation, the output displacement of the cylinder is transmitted to the co-simulation interface through the displacement sensor installed on the cylinder in AMESim. After processing, the S function is generated and input to Simulink. After the operation of the fuzzy sliding mode controller, the proportional servo valve spool is controlled. The moving signal is fed back to AMESim and the speeds of the master and slave cylinders are adjusted to achieve synchronized movement of the twin cylinders.
## 6 Movement Track Positioning Model
### Modeling Route  
In order to describe the movement trajectory of the target point when collecting data, it is first necessary to determine the position of the target point at each moment according to the collected distance data. Therefore, it is necessary to use the classification model established in chapter 5 to judge the collected data, first determine whether the data is disturbed during the collection, and then put the normal data and abnormal data into the two positioning models in chapter 3. The group is positioned to the position of the target point to obtain the movement trajectory of the target point. In summary, the main technical route of the movement track positioning model modeling process is shown in Figure 14 below:
## 3 Dataset and Experimental Setup
### Normalization and Classification Setup  
Due to limited available compute, to reduce the computational demands of the problem we sub-sample all available information streams to 8 Hz. Then, the data of each participant are normalized based on their afternoon baseline recording (see Section 3.1). We choose afternoon baseline over the morning one, as it led to slightly better overall performance during experimentation. The normalization formula is shown in Equation (4).  
\[x_{i,j}^{c}=\frac{x_{i,j}-mean(x_{afternoon\_baseline})}{std(x_{afternoon\_baseline })} \tag{4}\]  
where \(x_{i,j}^{c}\) is the normalized feature value \(x_{i}\) of feature x and j is the participant ID.  
Finally, consecutive samples are grouped into batches of 64 by using an eight second, non-overlapping windowing approach. As a result, all of our models provide one prediction every 8 s. For all classification experiments, we apply a 10-fold cross validation scheme, using at each fold 20% of the users for testing and the rest of the users for training.
# Towards Understanding Neural Machine Translation with Attention Heads' Importance
## 4 Linguistic Knowledge  
In this section, we analyze how the attention head learns linguistic information at various granularities, which better reveals how NMT makes decisions. Through various linguistic knowledge granularities--POS, dependency relations, and syntax trees--we explore how NMT models process word information, inter-word relations, multi-word information, and syntactic structure information. We strive to elucidate model decisions that are beyond human comprehension by leveraging linguistic knowledge that is understandable to humans [32].  
We masked several important and unimportant attention heads according to the attention heads' importance to identify the various linguistic knowledge that these attention heads had acquired. Three steps comprised our investigations. In the first step, the reference translation was annotated by StanfordCoreNLP 4.5.4. StanfordCoreNLP is an open-source natural language processing toolkit that integrates various NLP tools, enabling comprehensive linguistic analysis. Therefore, we chose it for our experiments. The accuracy of the reference translation and the translation generated from the complete model are compared in the second step. In the third step, we compare the accuracy of the translation generated by the pruning model and the reference translation. We believe that when the important attention heads are masked, the performance of the model is not as good as the complete model, leading to a decrease in the accurate translation rates of various types of linguistic information and the occurrence of negative accuracy. This experiment helps us explore which key linguistic information the model has learned and which pieces of linguistic information are more important. On the other hand, when the less important attention heads are masked, it may have a positive effect on some linguistic information,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pearson** & \(KL_{Norm}\) & \(KL_{Softmax}\) \\ \hline None & 0.85586148 & 0.00288859 & 0.01695689 \\ Verb & 0.81776615 & 0.00448712 & 0.01009835 \\ Adjective & 0.6634828 & 0.12858195 & 0.05381574 \\ Adverb & 0.59999701 & 0.12642623 & 0.07041694 \\ Preposition & 0.61585583 & 0.10229620 & 0.12294885 \\ Determiner & 0.36217017 & 0.11767329 & 0.34239231 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The correlation between attention head importance and attention head POS contribution.  
providing new insights for model pruning. The following is a detailed explanation of specific linguistic knowledge.