esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scalable Deep Network for Graph Clustering
## 3 The Proposed Method
### Problem Formalization  
Graph-structured data can be defined as \(\mathcal{G}=\{\mathcal{V},\mathcal{E},\mathbf{X}\}\), where \(\mathcal{V}=\{v_{1},v_{2},v_{3},\cdots,v_{m}\}\) is a vertex set with \(m\) vertices, \(\mathcal{E}\) is an edge set with \(n\) edges, \(\mathbf{X}=\{\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{x}_{3},\cdots,\mathbf{x}_ {m}\}^{T}\) is a feature matrix (input data). The topology of graph \(\mathcal{G}\) is described by an adjacency matrix (with self-loops) \(\mathbf{\tilde{A}}\), where \(\mathbf{\tilde{A}}=\{a_{ij}\}\). If there there is an edge between \(v_{i}\) and \(v_{j}\), \(a_{ij}=1\), otherwise \(a_{ij}=0\). For non-graph data, we obtain their adjacency matrix \(\mathbf{\tilde{A}}\) by constructing a KNN graph with the Dot-product. We first calculate the similarity between different nodes by \(\mathbf{S}_{ij}=\mathbf{x}_{j}^{T}\mathbf{x}_{i}\), and select \(K\) nodes with the highest similarity for each sample as their neighbors. Degree matrix \(\mathbf{\tilde{D}}=diag(d_{1},d_{2},d_{3},\cdots,d_{n})\in\mathbb{R}^{n\times n}\), where \(d_{i}=\sum_{v_{j}\in\mathcal{V}}a_{ij}\) represents the degree of any node \(v_{i}\).  
Graph clustering is to divide the nodes into \(t\) disjoint clusters \(\mathcal{C}=\{c_{l}\mid l=1,2,3,\cdots,t\}\) according to a selected criterion, and there is \(c_{1}\bigcap c_{2}=\varnothing\). When the node \(v_{i}\) is divided into a certain cluster, it can be expressed as \(v_{i}\in c_{l}\).  
Figure 1: The overall framework of SDN is as above. \(\mathbf{X}\), \(\mathbf{\tilde{X}}\) are input data and reconstructed data, respectively. \(\mathbf{E}^{(l)}\) and \(\mathbf{H}^{(l)}\)are the results of the \(l\)-th layer of the linear encoder in the DNN and Quasi-GNN modules, respectively. Layers with different colors represent \(\mathbf{E}^{(l)}\) of different embeddings learned by the DNN module. The green solid line indicates that the target distribution \(P\) is calculated by the distribution \(Q\), the yellow dotted line represents the dual self-supervision mechanism, and the target distribution \(P\) supervises the training of the DNN module and the Quasi-GNN module at the same time. The solid blue line in the linear propagation layer of the Quasi-GNN module represents the propagation mode.
# Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications
## 3 Results and Discussion
### Challenges in Digitalization of the Food System
#### 3.5.4 Challenges Related to the Nature of the Technologies  
Some challenges arise because of the new, complex, and intrinsic characteristics of the digital technologies. Understanding these challenges will help inventors and researchers to develop user-friendly technologies. The reviewed papers identified many challenges within this category with high emphasis on problems relating to the data generated by digitalization of the food system. Quality challenges with big data, questionability of sustainable integration of the data, challenges in standardization of the data, and possibilities for raw data manipulation were among the data-related challenges identified [44; 45; 60; 74; 80]. The openness of the platform, technological complexities (non-friendly), difficulty in implementation in small and medium agro-food enterprises, difficulties in coordinating with partners of different sizes (agro-business size compatibility), and compatibility and complementarities of the technologies with existing technologies were other challenges mentioned in this category [44; 64; 94; 104].
# Optimized Parameter Search Approach for Weight Modification Attack Targeting Deep Learning Models
###### Abstract
In this work, a new approach to the fault injection attack is presented. This version incorporates three steps to attack the targeted model, making the manipulation more efficient. * _Identifying input data clusters_: Similar input data generates similar model behaviors given by the activation of the model's neurons. Therefore, they could share influential weights, which would not be applicable for the other data. Thus, modification of these parameters will affect the targeted cluster. * _Selection of the most-influential weights_: This step chooses some weights to be modified. For this selection, a metric is necessary; some types of adversarial attacks target the gradient of the input data according to the prediction. Thus, the idea is based on introducing a constant adversarial example in the model for a concrete input data subset. * _Modification of the selected weights_: Incorporation of noise in the chosen weights is essential to manipulate the prediction of the targeted model. Noise can be added or subtracted according to the computed gradient, modifying the behavior differently. This decision allows the attacker to focus on varying weights. Figure 1: Schematic representation of a machine learning system. This new version allows more precise manipulation and without misclassifying the non-targeted input data. This attack was tested with a model formed by the convolutional neural network VGG16 and a dense classifier, which deals with a binary classification problem. The rest of the paper is divided as follows: Section 2 presents the literature related to the analyzed attack. Section 3 details the attack generation, highlighting the principal steps to carry out the attack. Section 4 describes the experiments detailing the dataset and the model used for them. The results are given in Section 5, and Section 6 lists the lessons learned and future work.
## 4 Simulation Results and Discussion
### Case 1: Sensitivity Analysis for Criterion Variation Within \(\pm 5\%\)
#### 4.2.6 Sensitivity Analysis  
Figure 8 shows the variation rates of the scores for the Scenario 1, in comparison with the initial case. We observe that the SD-TOPSIS algorithm is the most robust, as it shows the lowest score variation rate of 5.18%, whereas the Entropy-TOPSIS and Importance-TOPSIS exhibit variation rates of 6.85% and 8.06%, respectively. Figure 9 shows the variation rates for the Scenario 2. We observe that the SD-TOPSIS algorithm is still the most robust, as it shows the lowest score variation rate of 7.79%, whereas the Entropy-TOPSIS and Importance-TOPSIS exhibit variation rates of 8.9% and 9.5%, respectively. Figure 10 shows the variation rates for the Scenario 3. We observe that the SD-TOPSIS algorithm is still the most robust, as it shows the lowest score variation rate of 13.88%, whereas the Entropy-TOPSIS and Importance-TOPSIS exhibit variation rates of 16.20% and 14.11%, respectively. Figure 11 shows the variation rates for the Scenario 4. We observe that the SD-TOPSIS algorithm remains the most robust, as it shows the lowest score variation rate of 14.54%, whereas the Entropy-TOPSIS and Importance-TOPSIS exhibit variation rates of 15.07% and 16%, respectively. The SD-TOPSIS algorithm therefore stands as the most robust TOPSIS variant as fluctuations in criteria values have the lowest impact on the variation rate of satellites scores, thus demonstrating greater stability of the SD-TOPSIS.  
Figure 6: Satellites scores for the initial case and the four scenarios using Entropy-TOPSIS, for criterion variation within \(\pm\)5%.  
Figure 7: Satellites scores for the initial case and the four scenarios using Importance-TOPSIS, for criterion variation within \(\pm\)5%.  
Figure 11: Variation rates of the scores for the Scenario 4, for criterion variation within \(\pm\)5%.  
Figure 8: Variation rates of the scores for the Scenario 1, for criterion variation within \(\pm\)5%.  
Figure 10: Variation rates of the scores for the Scenario 3, for criterion variation within \(\pm\)5%.  
Figure 9: Variation rates of the scores for the Scenario 2, for criterion variation within \(\pm\)5%.
## 3 Results and Discussion
### The Energy Characteristics of PSJA Array  
The total kinetic energy of all actuators is calculated using Equation (26), which varies with the actuator number, as shown in Figure 16. In Equation (26), \(k\) is the concrete actuator; and \(n\) is the number of PSJA actuators in the PSJA array. Firstly, the total kinetic energy increases with the number of actuators. Then it begins to decrease, suggesting that the maximum total kinetic energy is not necessarily produced by the array containing more PSJAs and an optimal number of PSJAs exists. The characteristic of total kinetic energy is different from that of the total discharge energy, as shown in Figure 11, which will be investigated below.  
\[E_{\text{total}}\;=\sum_{k=1}^{n}E_{\text{kinetic},k} \tag{26}\]  
In the whole working process of the actuator, the energy stored in the capacitor transforms to the kinetic energy indirectly, as shown in Figure 17. The thermodynamic recycle efficiency \(\eta_{\text{t}}\) is defined in Equation (27) follows. The variation of \(\eta_{\text{t}}\) with different actuator number is shown in Figure 18. As the actuator number increases, the energy deposited in an actuator decreases. Based on the research results of [PERSON] et al. [26], the thermodynamic efficiency decreases with the decrease of heating energy. With the increase of actuator number, the heating energy decreases. Therefore, the thermodynamic efficiency decreases when there are more PSJAs. As a result, the total gas kinetic energy doesn't always increase with an increase of actuator number.  
\[\eta_{\text{t}}=\frac{E_{\text{total}}}{Q_{\text{h}}}\varOmega \tag{27}\]  
Figure 16: The total gas kinetic energy versus actuator number.  
Figure 15: The gas kinetic energy versus actuator number.
## 3 EKF-GAPF-Based Method for Multipath Parameter Estimation
### Accurate Identification Based on PF  
#### 3.2.1 Accurate Identification Process  
* Initialize the particle set: The particle set \(\{x_{0}^{i},i=1,2,\ldots,N\}\) is initialized, where \(N\) represents the total number of particles. Since PF uses multiple particles to approximate the state distribution, it is less sensitive to a single initial condition, making it more robust.
* Perform importance sampling: After sampling with EKF, the initial predictions from EKF are employed to obtain predicted measurement values and the measurement noise covariance matrix. A multivariate normal distribution model is then constructed based on these predictions. The initial estimate of the particles suggested by EKF can be expressed as follows: \[X_{K}=\left(X_{k}^{1},X_{k}^{2},\cdots,X_{k}^{N}\right)^{T}\] (14) where \(X_{K}\) follows a normal distribution with mean \(\mu\) and covariance \(R\), that is, \[X_{K}\sim N(\mu,R)\] (15)
* Calculate and normalize weights: Under the assumptions outlined in this model, the particle weights are calculated based on the probability density function of particles and normalized, as shown below: \[D(x)=\frac{1}{\sqrt{(2\pi)^{n}\text{det}(\Sigma)}}\exp\left(-\frac{1}{2}(x- \mu)^{T}\sum^{-1}(x-\mu)\right)\] (16) \[\overset{\sim}{D(i)}=D(i)/\sum_{i=1}^{N}D(i)\] (17) where \(n\) is the dimension of the state vector, \(\mu\) is the predicted measurement value, and \(x\) is the actual measurement value.
* Resample and state estimation: Based on the normalized particle weights, GA is applied to resample the new particle set and obtain the estimated result of the system state.
## 6 Implement and Evaluation
### Scene of LiSEA
#### 6.1.1 Security Operations  
In the realm of business security operations, elucidated in Figure 4, developers employ the embedding client as a conduit to establish a seamless connection between business applications and the LiSEA operating center. Leveraging the operational center's capabilities, business security experts judiciously configure protection policies congruent with prevailing security requisites, all in real-time. This adaptive configuration augments the organization's agility in safeguarding its business landscape. Simultaneously, managerial stakeholders gain real-time insights into the extent of coverage engendered by the business security fortifications. The operational center further emerges as an instrumental conduit for channeling monitoring data to third-party security platforms. This symbiotic interaction bolsters network security experts' capacity to meticulously analyze security events while also bolstering the real-time incident response capabilities of third-party security platforms.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### Machine Learning-Based Robot Dynamic Model
#### 3.2.3 Machine Learning Model  
In addition to traditional mathematical methods, machine learning is another option for obtaining the complete parameters of the arm, especially when the internal parameters are known. Due to the poor accuracy of estimating internal parameters through parameter identification, there has been a gradual emergence in the domestic and foreign literature of the use of machine learning methods to address the issue of nonlinear terms in inverse dynamics, which traditional mathematical methods struggle to solve. It is hoped that by using a large amount of training data, the model can produce better results. This paper will utilize Extreme Gradient Boosting (XGBoost) to develop inverse dynamics models and compare the time consumption and accuracy of torque estimation.  
XGBoost is a model based on the improvement of Gradient Boosting Decision Tree (GBDT) [9]. Both XGBoost and GBDT belong to the ensemble learning algorithm known as Boosting. It utilizes multiple classifiers to make predictions on the dataset and combines the results of these classifiers through iterative calculations to obtain the final output. As both are boosting models, XGBoost inherits the advantages of GBDT. GBDT finds the optimal solution in the previous training and continues to split new trees based on this, thereby improving the overall accuracy of the model. The difference between XGBoost and GBDT is that GBDT uses the negative gradient of the loss function to construct the tree for the current round, while XGBoost utilizes the second-order approximation of the loss function to expedite the descent of the loss function and achieve a faster iteration  
Figure 3: Illustration of the frictional force regression curve.  
speed. XGBoost also organizes all features before training and stores each feature point in separate memory blocks. This allows for the simultaneous traversal of leaf nodes and data features for splitting points when building a new tree. As a result, parallelization is achieved, leading to improved training speed.  
Due to the multi-link nature of the six-axis robotic arm, the torque output of each motor in the arm's axes is influenced by the angle and angular velocity of any given axis. Therefore, the dynamic model established in this paper, as shown in Figure 4, consists of six independent models for each axis, with 12 inputs and 1 output. The inputs for the robotic arm are the angles and angular velocities of each axis' motors, while the output is the corresponding torque of the motor for each axis.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 4. Rank the Failure Modes  
Following the assessment conducted in Step 3, it is now possible to calculate an aggregated index, which takes into account occurrence, severity and detectability. This index, i.e., RPN (Risk Priority Number) aims at prioritizing the need for updated design requirements necessary to reduce risks and increase system's reliability. Depending on the values, the failure modes can be thus interpreted as negligible (low RPN), to be investigated (moderate RPN), or to be necessary managed (high RPN). The RPN is calculated as the product of the indexes and the relative thresholds are defined in Table 8.
## 5 Experiments
### Surrey Audio-Visual Expressed Emotion (SAVEE)  
Developed at the University of Surrey, the SAVEE dataset consists of high-quality voice recordings, specifically from native English-speaking males [16]. While this dataset is somewhat limited in terms of gender diversity, it offers recordings that span seven categories of emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral.
## 3 Proposed Methods
### Image Generation Model  
As shown in Figure 2, an image generation model based on ACGAN [37] is introduced. It is believed that artificially generating a portion of images used to supplement the supervised part of the restricted samples can improve the classification performance of the student model. This model consists of three parts: a generator, a discriminator, and a classifier. First, random noise \(Z\) and randomly given label information are inputted into the generator to generate fake images. The generated fake images and actual samples are then provided to the discriminator and classifier. The discriminator needs to discriminate the authenticity of these images, and the classifier needs to classify them accurately to improve the model's discrimination and classification performance. The loss function of the discriminator is defined as Equation (5).  
\[\mathrm{L(D)}=\mathrm{BCE}(\mathrm{D(G(Z))},0)+\mathrm{BCE}(\mathrm{D(x)},1)+ \mathrm{CE}(\mathrm{C(G(Z))},y^{\prime})+\mathrm{CE}(\mathrm{C(x)},y) \tag{5}\]  
The loss function of the classifier is defined as Equation (6).  
\[\mathrm{L(C)}=\mathrm{CE}(\mathrm{C(G(Z))},y^{\prime})+\mathrm{CE}(\mathrm{x},\ y) \tag{6}\]  
The loss function of the generator is defined as Equation (7).  
\[\mathrm{L(G)}=\mathrm{BCE}(\mathrm{D(G(Z))},1)+\mathrm{CE}(\mathrm{C(G(Z))},y \prime) \tag{7}\]  
Here, D, G, and C represent the discriminator, generator, and classifier, respectively, x and y are the actual training sample with its label, \(Z\) is the random noise, and y\({}^{\prime}\) is the corresponding label of the generated artificial image. BCE is the binary cross-entropy, and CE is the cross-entropy. To obtain good performance, these generated artificial images are fed into student model training simultaneously with actual samples.  
Figure 5: The general framework of the semi-supervised model.
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 4 The Proposed Protocol
### Ring Manager and Token-Based Mechanism  
In this section, we describe the basic mechanism of our approach that allows us to provide the sender with anonymity against a global passive adversary.  
We assume the presence of a _ring manager_ (RM) that partitions the nodes of the network in several _rings_.  
The ring manager selects the nodes forming a ring in such a way that the background knowledge does not allow a possible adversary to have more information than the uniform distribution of senders. In other words, given a ring, any node of the ring is potentially a sender (with no probability bias). This is achieved by selecting, for a given ring, hosts belonging to the same, even large, geographical region.  
A ring is a sequence of \(k\) nodes such that each node has exactly a _preceding_ (_prec_, for short) and a _next_ node. In our setting, each node only knows its prec and its next node. Several messages, called _tokens_, move through the ring. There are two kinds of tokens. The first type is used in the set-up phase. The second type is used in the succeeding communication phase. The detail will be discussed next. Tokens are filled by senders to deliver their messages to a proxy node, which, once a Tor circuit is established, sends them to the destination host. To obtain that any eavesdropper is unable to distinguish an empty token from a filled token, each node encrypts the token with a symmetric key shared with its next node.  
RM maintains, along with the next node, the public keys and the network addresses of each node of the network. For each ring, each belonging node receives from RM the set of the public keys of the other nodes of the ring, and among these keys, the information about which is the public key associated with the next node in the ring. In this paper, we assume that RM is a centralized entity.
## 2 Methodology
### Meshing and Boundary Conditions  
As the polyhedral mesh is recognized [30; 31] to be the most economical in terms of computational resources, being able to adapt well to complex shapes and produce a cell count approximately one quarter that of the tetrahedral mesh, all the HPs domains were discretized with this type of grid.  
For instance, a picture of the central region mesh of S2 on a mid-plane is provided in Figure 6. The mesh was generated using Ansys Fluent Meshing 22 R2.  
Prismatic cells were generated for the boundary layers with a uniform method that sets a fixed first layer height, chosen to achieve a near-wall \(y^{+}\approx 1\), remembering that the following:  
\[y^{+}=\frac{\rho yu_{\tau}}{\mu} \tag{5}\]  
Figure 6: Mesh on the mid-plane and detail of the boundary layer discretization for sample S2.  
with \(u_{\tau}=\sqrt{\tau_{w}/\rho}\). The requirement is commonly recommended when employing the EWT [25] to adequately capture the viscous sub-layer in cases like transitional flows, separations, and heat transfer.  
Flow rates measured by a flowmeter were used to set a mass-inlet along with a pressure-outlet boundary at the vessel ends, specifying the hydraulic diameter and a standard value of 5% turbulence intensity at the entrance and a no-slip condition at the walls. Gravity was accounted for.  
The SIMPLE algorithm was implemented, with the PRESTO! scheme for pressure and second-order upwind spatial discretization for momentum, turbulent kinetic energy, and dissipation rate.
## 4 Discussion
It should be taken into account that an overestimation in the contrast value could result in a loss of important information for medical diagnosis in the final scatter-corrected image. Results in Table 11 show that the contrast is overestimated in 7 out of 31 cases in the single-energy model, in 14 cases in the 1-output dual-energy model, and 11 cases in the 2-output dual-energy model. Nevertheless, the difference with the ground-truth value is, on average, just \(1.11\%\) for the single-energy method, \(1.48\%\) for the 1-output dual-energy network, and \(1.01\%\) for the 2-output dual-energy model. Thus, the overestimation is not significant and will not jeopardize the image information. This work has been focused on the dataset generation, comparison of different inputs and outputs, and evaluation of the performance from the point of view of robustness. We have not performed extensive optimization of the hyperparameters of the NNs as we have used hyperparameters similar to the ones of the reference work of the MultiResUNet architecture [30].