esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 4 Results and Discussion  
In Section 4.1, we cover the performance of the model in financial behavior detection and stock price prediction tasks, the verification of its security, the analysis of system overhead, as well as the limitations and prospects of the model. Initially, in the performance analysis section, an in-depth examination of the Merkle-Transformer model's performance in financial behavior detection tasks is conducted. By comparing it with various baseline models, its precision, recall, and F1 score in identifying fraudulent transactions and money laundering activities are assessed. The experimental results demonstrate that the Merkle-Transformer, considering data integrity and security while processing financial sequential data, exhibits commendable performance in detecting financial fraud, particularly excelling in reducing false positives compared to traditional models.  
In Section 4.2, we discuss the current limitations of the Merkle-Transformer model, such as scalability issues when processing extremely large datasets, and ways to further enhance the model's security and reduce overhead. Furthermore, future research directions are proposed, including optimizing the model structure, improving security mechanisms, and exploring potential applications in other financial tasks.
# Design and Optimization of a Compliant Morphing Trailing Edge for High-Lift Generation
###### Abstract  
This work focuses on the design and optimization of a morphing-compliant system developed within the project HERWINGT (Clean Aviation) and aimed at generating high lift during take-off and landing. The device was conceived to replace a conventional flap of a regional aircraft and work in synergy with a droop nose and a flow control system. The architecture is based on a compliant layout, specifically selected to obtain a final morphed shape of the trailing edge of the wing efficient for high-lift purposes and adequately smooth even in cruise clean configuration. At first, the requirements at aircraft level were critically examined and then elaborated to produce the specifications of the morphing device. A layout was then sketched, considering on its potential in approaching the target morphed shape and on its intrinsic criticalities. Starting from this scheme, a simplified FE model was introduced. The scope was to have an efficient predictive tool suited for optimization processes. After having identified the most relevant design parameters (skin thickness distribution, topology of the structure, and actuator interface parameters), the cost function, and the constraints of the problem (structural integrity and stability), a genetic optimization was implemented. Repeating the genetic process starting from different initial populations, some optimized configurations were identified. A trade-off was thus organized on different criteria, such as the lightness of the structure, the load-bearing capability, the force, and the stroke needed by the actuator. The best compromise was finally taken as baseline for the realization of an advanced FE model used to validate the numerical outcomes obtained during the optimization process and as starting point for the next steps planned in the project. The achieved design is characterized by an enhanced aerodynamic performance with the absence of steps and gaps and external track fairings, reduced weight of both the structure and the actuator, reduced maintenance costs due to a simple layout, and smaller take-off and landing distances owing to the high-lift capability and the intrinsic lightness.  
**Keywords:** adaptive structures; FE modeling; stress analysis; genetic optimization
# Designing and Simulating the Control Process of a Feed Pusher Robot Used on a Dairy Farm
## 2 Materials and Methods  
### Technological Conditions for the Robot's Travelling  
The mobile base moves on a feeding table. Inductive sensors guide the robot along a metal belt. If it is supposed that the robot moves off the metal belt during its operation, then the telemetry of the signals from the inductive sensors detects the direction of its deviation from the path. It generates a signal to the robot controller to return it to the travel trajectory.  
Since the feeding table area is often chaotically arranged (with feed heaps made by milking animals), the inductive sensors may not detect the metal stripe when returning to the feeding path, which affects the performance and energy efficiency of the robot. The novelty feature of this solution is the intelligent algorithm. It performs a pre-semantic segmentation of the image from the stereo-vision camera to detect feed and a frame of a milking animals' stall, which provides an additional orientation of the mobile base in case of emergencies and prevents detection of the trajectory in case of contamination by the metal strap.  
Figure 1 shows a preliminary semantic segmentation, with recognition of sub-objects such as feed on the feed table and milking cows' stalls.  
At the designing stage of the pusher robot control system, the expected trajectory of the robot motion was modeled. For autonomous positioning of the designed robot, its motion trajectory should be elementary; it moves in a straight line with minor deviations depending on the amount of feed residue on the feed table (Figure 2).  
The robot starts travelling from the point (S) shown on the motion diagram (Figure 2); at this spot, the robot battery is charged, and the dispenser hopper is filled with feed additives.  
Section (A)--the robot is moving in a straight line. At this moment, the pusher-auger and the dispenser drives are turned off; the drive of the right and left wheels operates at a constant speed.  
Section (B)--the robot moves in a straight line along the feed table with slight deviations depending on the degree of spread of the feed by the animals. In the process of moving along section (B), the pusher auger rotates at a speed of 80 to 120 rpm, and the feed additives are dispensed from the dispenser hopper.  
Section (C)--the robot turns to the left, so that the left wheel drive is fixed, and the right wheel drive rotates at a constant speed until the robot reaches the angle of 90\({}^{\circ}\), while the drive of the dispenser and the pusher auger are disconnected.  
Figure 1: Preliminary semantic segmentation Images: 1-feed, 2- frame of milking cows\({}^{\prime}\) stall.  
Figure 2: Schematic diagram of the pusher robot travelling along the cattle farm. 1—pusher robot, 2—robot movement trajectory.  
Section (D)--the robot turns to the right so that the right wheel drive is fixed, and the left wheel drive rotates at a constant speed until the robot reaches the angle of 90\({}^{\circ}\), while the drive of the dispenser and the pusher auger are disconnected.
## 4 Demodulation Method
### Low Complexity Demodulation Methods
#### 4.2.2 TCST Method  
If channel state between tag sensor and reader was more independent, Wi-Fi backscatter system can more effectively transmit information of tag sensor to reader. TCST method selected only subcarriers that had highly independent channel state of tag sensor assigned to the CSI and it was used to demodulate symbol data. In a Wi-Fi backscatter system, channel information between the tag sensor and the receiver can be obtained by transmitting preamble before transmitting data [12]. As in the following Equations (15) and (16), the indexes of symbols having maximum received power and the index of symbol having smallest received power were searched for each antenna from the received preambles in \(j\)-th antenna.  
\[v_{j}=\underset{l}{\arg}\Big{(}\underset{j,l}{\max}\Big{(}P_{j,l}\Big{)}\Big{)} \tag{15}\]  
\[w_{j}=\underset{l}{\arg}\Big{(}\underset{j,l}{\min}\Big{(}P_{j,l}\Big{)}\Big{)} \tag{16}\]  
where \(P_{j,l}\) is the average received power of CSI of the Wi-Fi packet received at the \(j\)-th antenna in the reader during the period of the \(l\)-th preamble backscatter symbol. The \(v_{j}\) and \(w_{j}\) are indexes of preambles which have the largest received power of CSI and the smallest received power of CSI at \(j\)-th antenna. We calculated the difference, \(z_{j}(c)\), in magnitude between the two selected preambles, where \(z_{j}(c)\) can be expressed as  
\[z_{j}(c)=\ \Big{|}Re\Big{\{}p_{j,p_{j}}(c)\Big{\}}-Re\Big{\{}p_{j,w_{j}}(c) \Big{\}}\Big{|}+\ \Big{|}Im\Big{\{}p_{j,p_{j}}(c)\Big{\}}-Im\Big{\{}p_{j,w_{j}}(c)\Big{\}} \Big{|} \tag{17}\]  
where \(p_{j,p_{j}}\) and \(p_{j,w_{j}}\) are the CSI of the Wi-Fi packet received at the \(j\)-th antenna in the reader during the period of the \(v_{j}\)-th preamble backscatter symbol and \(w_{j}\)-th preamble backscatter symbol. Only subcarriers whose \(z_{j}(c)\) was larger than the threshold were used for Euclidean distance calculation in Equations (18) and (19).  
\[e_{j,l,m}(c)=\left\{\begin{array}{cc}\big{|}p_{j,l}(c)-d_{j,m}(c)\big{|}^{2 }&\text{if $z_{j}(c)>threshold$}\\ 0&\text{\emph{Otherwise}}\end{array}\right. \tag{18}\]  
\[\text{q}_{l,m}=\sum_{j=1}^{N}\sum_{c=0}^{C-1}e_{j,l,m}(c) \tag{19}\]  
Data symbol was demodulated to the smallest value in the Euclidean distance between the preamble symbol and the data symbol by using Equation (12).
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 4 Tracking Radar Design
### Radar Block Diagram and Operation
#### 4.3.3 Signal Processor  
A signal processor detects the target's presence and extracts its parameters. The radar uses two processing chains, one for the sum channel, or the main signal processor (SP), and one for the delta channel, or the tracking signal processor (TSP).  
The main signal processor (SP):  
* Time stamps the input signal;
* Translates the input signal into the frequency domain by using a large short-time Fourier transform;
* Detects the presence of the target through a continuous false alarm rate (CFAR) algorithm;
* Processes the detected target's frequency domain data in order to extract the target's range, doppler, and signal-to-noise ratio (\(SNR\)).  
The main signal processor (SP) target's data are sent to the main processing unit (MPU) for storage, TDM formatting, and display.  
The intermediary extracted data INT (detected target's frequency, its associated complex value, and the corresponding \(SNR\)) are sent to the tracking signal processor (TSP) to assist in the processing of the delta channel signal. The INT data are sent to the TSP every half-modulation period.  
The tracking signal processor (TSP):  
* Time stamps the input signal;* Translates the input signal into the frequency domain by using a large short-time Fourier transform;
* Processes the delta channel frequency domain data in order to extract the complex delta channel signal corresponding to the target;
* Uses the INT data to detect if a tracking lock condition is present and to normalize the azimuth and elevation antenna-positioning error data;
* Converts the azimuth and elevation antenna-positioning error data into analog signals, as required by the antenna 2 control unit (ACU).
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 2 The Development of Neural Networks
### Recurrent Neural Network (RNN)  
Different from CNN, RNN introduces the dimension of \"time\", which is suitable for processing time-series-type data. Because the network itself has a memory ability, it can learn data types with correlation before and after. Recurrent neural networks have a strong model fitting ability for serialized data and are widely used in the field of natural language processing (NLP), including image classification, image acquisition, machine translation, video processing, sentiment analysis, and text similarity calculation. The specific structure is as follows: the recurrent neural network will store and remember the previous information in the hidden layer and then input it into the current calculation of the hidden layer unit.  
Figure 4 shows the typical structure of an RNN, which is similar to but different from the traditional deep neural network (DNN). The similarity lies in that the network models of DNN and RNN are fully connected from the input layer to the hidden layer and then to the output layer, and the network propagation is also sequential. The difference is that the internal nodes of the hidden layer of the RNN are no longer independent of each other but have messages passing to each other. The input of the hidden layer can be composed of the output of the input layer and the output of the hidden layer at a previous time, which indicates that the nodes in the hidden layer are self-connected. It can also be composed of the output of the input layer, the output of the hidden layer at the previous moment, and the state of the previous hidden layer, which indicates that the nodes in the hidden layer are not only self-connected but also interconnected.  
Figure 4: Typical structure of an RNN.  
Although the RNN solves the problems that the CNN cannot handle, it still has some shortcomings. Therefore, there are many deformed networks of RNN, among which one of the most commonly used networks is the long short-term network (LSTM). The input data of such networks is not limited to images or text, and the problem solved is not limited to translation or text comprehension. Numerical data can also be analyzed using the LSTM. For example, in predictive maintenance applications of factory machines, LSTM can be used to analyze machine vibration signals to predict whether the machine is faulty. In medicine, the LSTM can help in reading through thousands of pieces of literature and can find information related to specific cancers, such as tumor location, tumor size, number of stages, and even treatment policy or survival rate. It can also be combined with image recognition to provide keywords of lesions in order to assist doctors in writing pathological reports.  
In addition to the DNN, CNN, and RNN, there is also an emerging network called reinforcement learning, among which the generative adversarial network (GAN) is a distinctive network.
# Novel Materials for Heavy Metal Removal in Capacitive Deionization
## 3 Materials for Heavy Metal Removing Using CDI
### Heteroatom Doped Carbon
The specific capacitance of N-6-G is 239.7 F g\({}^{-1}\), which is larger than N-5-G (195.2 F g\({}^{-1}\)) in Na\({}_{2}\)SO\({}_{4}\) solution, while the Pb\({}^{2+}\) electrosorption capacity of N-5-G is 2.43 mg m\({}^{-2}\), which is three times that of N-6-G (0.73 mg m\({}^{-2}\)). The removal efficiency of Pb\({}^{2+}\) was found to be 95.8% for N-5-G. Three main types of nitrogen defects exist in the N-doped carbon structure: graphitic nitrogen, pyridinic nitrogen, and pyrrolic nitrogen. The final two kinds are proven to be more electroactive for CDI than graphitic nitrogen because they are prone to donor electrons [109]. Graphitic nitrogen and pyridine nitrogen oxide have shown merit in capacitance. [PERSON] et al. revealed that pyridinic nitrogen groups tend to prefer H\({}^{+}\) and Na\({}^{+}\), while pyrrolic nitrogen exhibits efficient Pb\({}^{2+}\) sorption. This conclusion bridges the matric structure of N-doped carbon with CDI performance for the removal of heavy metal ions. P and N have the same number of valence electrons, meaning that P-doped carbon materials are also electron-rich. Moreover, the diameter of P is much larger than that of C; thus, P-doping results in the greater local structural distortion of the hexagonal carbon framework and also protrudes out of the graphene plane. The reasons why P-doped carbon materials overcome the steric hindrance effects encountered in N-doped carbon materials were indicated in [110]. Interestingly, multi-heteroatom co-doped carbon materials can produce double functional groups or diatomic synergies, likely resulting in improved electrical conductivity and the enhanced wettability of the carbon materials. The doping of nitrogen atoms into graphitic networks is seen as one of the best approaches to producing n-type conductive materials with improved conductivity [109].
# Optimization Method of the Clamping Force for Large Cabin Parts
## 3 Calculation and Optimization Method of the Clamping Force
### Gradient Flow Optimization Method for the Clamping Force  
The linear constraints in Equation (12) are called affine constraints. Because matrix \(\mathbf{P}\) is a symmetric positive definite matrix, Equation (5) can be transformed into a convex optimization problem which describes matrix \(\mathbf{P}\) with affine constraints in a smooth Riemannian manifold. According to Equations (6) and (12), the convex optimization problem can be expressed as below:  
\[\left\{\begin{array}{cc}\min&\phi\\ \text{S.T}&\mathbf{P}_{i}\succ 0\\ &\mathbf{A}\cdot\text{vec}(\mathbf{P})=\mathbf{q}\end{array}\right. \tag{13}\]  
The gradient flow method can solve the convex optimization problem with affine constraints. The target function of the convex optimization can be expressed as:  
\[\phi=\text{tr}(W_{\text{p}}\cdot\mathbf{I}\cdot\mathbf{P}+W_{\text{i}}\cdot\mathbf{I} \cdot\mathbf{P}^{-1}) \tag{14}\]  
in which \(W_{\text{p}}\) and \(W_{\text{i}}\) are weight coefficients. The former is used to control the magnitude of the normal clamping force, and the latter is used to control the distance between the clamping force vector and the edge of the friction cone. \(\mathbf{I}\) is the identity matrix and \(\text{tr}()\) is defined as the trace operation of the matrix. A linear constrained gradient flow using the Riemann metric can be expressed as:  
\[\text{vec}(\dot{\mathbf{P}})=-\text{grad}(\phi)=(\mathbf{I}-\mathbf{A}^{+}\mathbf{A})\cdot \text{vec}(\mathbf{P}^{-1}\cdot W_{\text{i}}\cdot\mathbf{I}\cdot\mathbf{P}^{-1}-W_{\text{ p}}\cdot\mathbf{I}) \tag{15}\]  
in which \(\mathbf{A}^{+}=\mathbf{A}^{\text{T}}\cdot(\mathbf{A}\cdot\mathbf{A}^{\text{T}})^{-1}\) is the Moore-Penrose generalized inverse matrix of matrix \(\mathbf{A}\). In order to start the iterative calculation, it is also necessary to discretize the linear constrained gradient flow, which can be expressed as:  
\[\text{vec}(\mathbf{P}_{k+1})=\text{vec}(\mathbf{P}_{k})+\alpha_{k}(\mathbf{I}-\mathbf{A}^{+} \mathbf{A})\cdot\text{vec}(\mathbf{P}^{-1}\cdot W_{\text{i}}\cdot\mathbf{I}\cdot\mathbf{P}^{- 1}-W_{\text{p}}\cdot\mathbf{I}) \tag{16}\]  
in which \(\alpha_{k}\) is the iteration step size. Adopting the heuristic iteration step size can reduce the number of iterations. Here, the heuristic iteration step size is chosen as follows:  
\[\alpha_{k}=\frac{0.2}{\text{max}\Big{(}\Big{|}\text{vec}(\dot{\mathbf{P}}_{k}) \Big{|}\Big{)}} \tag{17}\]  
Then, as long as the initial optimization clamping force satisfying each constraint is determined and the convergence error is selected for iterative calculation, the final convergence value obtained after the iteration is the optimal clamping force value of each clamping mechanism. The calculation process of the gradient flow optimization method is shown in Figure 6.
## 4 Experiments and Results
### Baseline Methods  
In this work, we compared the DNN-MHAT model with five state-of-the-art DNN models that have been developed to detect the polarity of sentiment classification as listed below:  
* IWV [15]: This model has been proposed for sentiment analysis, which comprises three convolution layers, a max-pooling layer, and a fully connected layer.
* SS-BED [13]: This model uses two equal LSTM layers on two various word embedding matrices for learning emotions and representations of semantic features. Then, LSTM output layers are fed as the input of a fully connected layer with one hidden layer for predicting sentiment categories.
* ARC [45]: This model applies a bidirectional GRU layer to the word vectors, and the outputs are fed into the attention layer. The attention Layer outputs are fed into a CNN layer followed by a max-pooling process and a fully connected layer.
* AC-BiLSTM [41]: This model uses a one-dimension CNN layer on the word embedding layer to extract local features, BiLSTM to extract long dependencies and an attention mechanism to focus on significant text domains.
* ABCDM [44]: This model first uses GloVe embedding as the weights to the embedding layer, then two bidirectional GRU and LSTM layers for extracting past and future contexts and an attention mechanism to focus on various words. Finally, convolution and pooling mechanisms are applied to extract local features' static positions and reduce feature dimensions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Dimension (\(d\)) & 100 \\ Hidden unites Bi-LSTM & 128 \\ Word of comments for reviews & 100 \\ Word of comments for tweets & 45 \\ Convolutional layer & 64 \\ Window\_size & 7 \\ Head (\(m\)) & 8 \\ Dropout (\(\rho\)) & 0.5 \\ Regularization coefficient (\({}^{n}\)) & 0.001 \\ Batch\_size & 64 \\ Activation function & Relu \\ Patience & 10 \\ Kernel-size & 4 \\ Pool & 2 \\ Optimizer & Adam \\ Loss function & Binary cross-entropy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings.  
In Tables 3-6, DNN-MHAT achieved good performance in terms of accuracy, as 0.32%, 0.47%, 0.43%, and 0.38% on Kindle, Electronics, CD, and App datasets, respectively. For the F1 scale, the improvements are 0.55%, 0.63%, 0.77%, and 0.48% for the positive class and 0.38%, 0.30%, 0.50%, and 0.65% for the negative class on Kindle, Electronics, CD, and App datasets, respectively. As indicated above for accuracy and F1 scale, our DNN-MHAT outperformed the other five methods. It can be seen that these improvements were mainly derived from (i) handling long dependencies in text utilizing bidirectional LSTM layers, (ii) employing local features of varying lengths by applying CNN layers of different sizes, and (iii) assigning weights to words in the review according to their significance achieved from the multi-head attention (MHAT) mechanism layer.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 2 Pathogens That Cause Foodborne Illness  
Pathogenic microbes, including bacteria, viruses, fungi and parasites, are responsible for causing foodborne illnesses [74; 75]. Among foodborne pathogens, bacteria are the most common ones, causing a number of illnesses in both humans and animals [22; 76; 77]. In food, there are several common bacteria that are encountered such as _Salmonella_ species, _Sligella_ species, _Listeria monocytogenes_, _Bacillus_ species, _Yersinia_ species, _Campylobacter_ species, _Clostridium botulinum_, _Clostridium perfringens_, _Eclerichia coli_, _Staphylococcus aureus_ and _Vibrio cholera_[78]. A number of viruses have been identified as significant foodborne pathogens, which are capable of causing foodborne diseases [79]. These pathogens include hepatitis virus and norovirus (NoV) [76; 80]. The presence of various parasitic species has also been found to pose a danger to the safety of food, including _Toxoplasma gondii_[81; 82], _Gadia lamblia_[82], _Entamoeba histolytica_[82] and _Ascaris lumbricoids_[83]. Furthermore, there are a number of different species of fungi which may cause foodborne illnesses as well, including _Aspergillus flavus_, _Aspergillus chracus_, _Fusarium_ species, _Penicillium patulum_, _Penicillium citrinum_ and _Claviecps purpurea_[84; 85].