esa-sceva/satcom-chunk-collection huggingface dataset:
# Class Retrieval of Detected Adversarial Attacks
## 4 Results
### Parameters Investigating  
Our class retrieval algorithm has two important parameters, number of iterations (\(NbIter\)) and number of classes (\(NbClass\)). \(NbIter\) parameter does not have much effect on the algorithm according to our measurements, and it gave the same results after the first iteration, as we can observe in Figure 3. \(NbClass\) parameter is essential to the algorithm as we have to conduct (\(NbClass-1\)) counter attacks. Statistically, the accuracy will drop when having a large number of classes preventing a correct retrieval. We conducted an extensive investigation over the CIFAR10 dataset, evaluating the success rate of adversarial attacks having different number of classes, from four to ten classes. We used AlexNet architecture to train a classifier for the randomly selected classes (4 to 10 classes), and we then calculated the average of the success rate of our class retrieval for each classifier (evaluating the retrieval of 10k adversarial attacks) to create a plot, Figure 8, illustrating the relationship between the accuracy of our algorithm and \(NbClass\) parameter. We can see that the algorithm is functioning effectively with a relatively high number of classes, but the accuracy will eventually decrease when processing a huge number of classes. Although a high number of classes is a limitation of our method due to time consumption and statistical probability, in most practical applications, only a few output classes are used.  
Figure 7: The figure depicts the success rate of our adversarial retrieval on ImageNet dataset, where each cell represents the accuracy of the retrieval in a specific setup investigating 1000 cases selecting 10 random classes for each hundred trials.  
Another parameter which can effect our method is the size of the input space. While low-resolution images, e.g., MNIST, are difficult to attack with small bounded unnoticeable perturbation, high-resolution images, e.g., ImageNet, are easy to attack because of their high dimensionality, rendering a complicated but vulnerable decision boundary curvature which can be easily compromised by slightly modifying the high number of pixels.  
As we mentioned before, we assumed the existence of an optimal detector which can identify any adversarial attacks, and we implemented our experiments in light of this assumption. Unfortunately, practical detectors may result in false positive and false negative samples. False negative samples will not be processed by our method but false positive samples will, yielding bad classification and reducing the success rate of our method. In practice, the success rate of our method will drop slightly due to false positive detected samples, but we hope that with the rapid development of this field, a near optimal detector will be available soon.
## 5 Conclusions  
To realize the reliable in-orbit operation of satellites and improve the fault diagnosis capabilities of their systems in the design stage, a fault diagnosability evaluation method based on information geometry is proposed in this paper. The Fisher information distance is used to realize the quantitative evaluation of the fault diagnosability of a satellite's attitude determination system. The proposed fault diagnosability evaluation method is independent of specific fault diagnosis schemes, fault nonlinearities, and system interferences, and it is suitable for multi-fault situations. The designed diagnosability indexes have explicit and intuitive geometric significance, and they solve the problem of the asymmetry of the fault isolability indexes designed with the traditional diagnosability algorithm based on the distance similarity. Fault information is expressed \"geometrically\" through the geodesics of the fault manifold; this may inspire efficient fault detection, diagnosis, design, and optimization methods.  
Methodology, validation, and writing--original draft preparation, R.Q.; writing--review, editing, and supervision, Y.C. and B.J.; funding support, B.J. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62003162) and the Fundamental Research Funds for the Central Universities (NZ2020003).  
Not applicable.  
The authors declare no conflict of interest.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### Deployment Phase
#### 4.5.3 Model Integration  
There are three straightforward approaches of providing an application with the overvalue of an ML model, as briefly demonstrated concerning the framework _ML.NET_ by [PERSON] et al. in [45]. Next to deploying a model inside containers and connecting them via Remote Procedure Call (RPC) to the serving system (the application respectively), the direct integration of the model execution into the application decreases custom engineering effort during deployment. Another approach is to white-box the model, where different models are represented as Directed Acyclic graph (DAG) and registered inside a serving system which is accessible via RPC or REST calls. The exchangeability of models decreases with integrating its execution within an application's source code. At the same time, the central deployment via REST endpoints will increase the application's dependability on network connectivity and service availability.
# Traffic-Aware Optimization of Task Offloading and Content
This approach guarantees a safer and more efficient urban mobility experience, especially in the context of the rapidly evolving IoV technology and the advances brought about by 5G. Specifically, this work makes the following main contributions:  
* By preprocessing the dataset, we enhance its adaptability to ensure alignment with the operational requirements of the open-source prediction tool. The open-source prediction tool FOST supports mainstream deep learning models such as RNN, MLP, and GNN for predicting the preprocessed BikeNYC dataset. Its fusion module automatically selects and integrates predictions generated by different models, enhancing the overall robustness and accuracy of predictions. * We use the predicted traffic stream and an enhanced multi-objective evolutionary algorithm (MOEA/D) to decompose the multitask offloading and content caching problem into individual optimization problems. This decomposition helps us to obtain a set of Pareto optimal solutions, which is achieved through the Tchebycheff weight aggregation approach. * Finally, we chose the BikeNYC dataset for traffic flow prediction. It is a multimodal dataset rich in spatiotemporal data, making it ideal for this purpose. We conducted comprehensive simulation experiments to evaluate the TOCC algorithm's performance, which effectively reduced execution time and energy consumption, as shown by the results.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 4 UE Receiver PHY for CS
### Boundary Search  
The aim of this block is to generate various time reference tick signals to locate the starting samples of the OFDM symbol, slot, sub-frame, and frame. These are critical for MSI reception and uplink synchronization. Such signals are also needed in 5G repeaters to accurately extract the data samples for re-transmission.  
Since the SS signals are transmitted at fixed OFDM symbols and the DMRS search block has already identified the index of the detected SS signal, we can directly infer the OFDM symbol number for a detected _Symbol_tick_ by PSS search and SS Block extraction blocks. Since each slot, sub-frame, and frame consists of a certain number of OFDM symbols, corresponding ticks are appropriately generated as shown in Figure 10. For instance, the detection of \(SS_{i}=2\) corresponds to OFDM symbol number of 8 for PSS which lies in slot 0 of the even frame. Using this information, _Slot_tick_ is detected which goes high after \(N_{slot\_edge}=6\) OFDM symbols from the detected PSS. Similarly, the detection of \(SS_{i}=3\) corresponds to the OFDM symbol number of 16 for PSS which lies in slot 1 of the even frame. Using this information, _Slot_tick_ is detected which goes high after \(N_{slot\_edge}=12\) OFDM symbols from the detected PSS. Thereafter, _Slot_tick_ is generated after the interval of every 14 OFDM symbols. For every two low-to-high transitions of the _Slot_tick_, _Sub-frame_tick_ is generated and for every 10 low-to-high transitions of the _Sub-frame_tick_, _Frame_tick_ is generated.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Trajectory Planning  
The goal of this phase is to generate and evaluate a set of trajectories for the robotic gripper to follow after grasping a tube. A cost is computed for each trajectory which reflects an estimate of how likely executing it will lead to a case of entanglement. This cost is computed via simulation.  
Definition 12: A **trajectory** of a virtual tube is defined by an ordered list of poses, known as **waypoints**, that represent translations and rotations of the tube relative to its initial position in the bin. The first waypoint of any trajectory is always a relative pose with no translation or rotation.  
In this article, the waypoints are simplified to only contain a relative position, since all trajectories are a set of translations.
# Multi-Criteria Decision-Making Techniques for Optimal In-House Storage Location Selection
## 4 Results and Discussion
To compare the results with the ARAS method, the authors applied the CoCoSo method, and the results are presented below (Tables 5-7). The rank of the storage location obtained by the mentioned method is presented in Figure 3.  
In this case, the CoCoSo method confirmed Location A as the best way to store goods. Location C is the second best, followed by Location B and D, respectively. In both cases, Location A is shown as the best possible solution for storing goods in a brewing company. \begin{table}
\begin{tabular}{c c c c c} \hline  & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) \\ \hline Location A & 0.3333 & 1.0000 & 1.0000 & 1.0000 \\ Location B & 0.6667 & 0.0000 & 0.3333 & 0.7143 \\ Location C & 0.0000 & 0.6400 & 0.6667 & 0.6286 \\ Location D & 1.0000 & 0.3200 & 0.0000 & 0.0000 \\ Criteria Weights & 0.2857 & 0.2551 & 0.2142 & 0.2448 \\ \hline \end{tabular}
\end{table} 
Table 6: Normalized Decision-Making Martix if the Input Data. Figure 3: Rank of the storage locations obtained by the CoCoSo method. Figure 2: Rank of the Storage Locations obtained by the ARAS method. A comparative analysis of the ARAS and CoCoSo methods is performed to check the results' reliability. The results are depicted in Figure 4.  
The results of the comparative analysis reveal that both methods ranked Location A as the best solution for in-storage goods in a brewing company. According to the ARAS method, the locations are ranked as A \(>\) C \(>\) D \(>\) B. On the other hand, the CoCoSo method ranks the locations as follows: A \(>\) C \(>\) B \(>\) D. The only difference is in the locations D and B.
# One-Dimensional Fluid-Structure Interaction Models in Presurized Fluid-Filled Pipes: A Review
## 1 Introduction  
The first scientific contributions to the field of Fluid-Structure Interaction (FSI) in transient pipe flow took place in the 19 th century when authors like [1; 2] realized the need for considering both fluid compressibility and pipe-wall distensibility as interacting mechanisms. Classical water-hammer theory is also based on this principle. Since then, many researchers have added their contributions in a step-wise manner, building up and shaping the theory of hydraulic transients in pipe flow.  
Software in the field of hydraulic transients is developed to cope with water-hammer waves, which are pressure waves associated with a sudden momentum change of fluid flow embedded in a closed-conduit. FSI models deal with the original principle of classical water-hammer theory, i.e., the consideration of water-hammer waves as a result of the relation between fluid and pipe deformations. A milestone was presented in [3] with a PhD thesis entitled _'An extension of the theory of water-hammer'_. The basis of one-dimensional (1D) FSI was established, pipe vibration modes were described and the basic formulation for straight pipes was presented. [PERSON]'s work triggered the FSI research on the two-way coupling between fluid dynamics and structural mechanics. Contributions by [4; 5; 6; 7; 8; 9; 10; 11; 12] developed and completed the theory for all the basic degrees-of-freedom (DOF) of pipe-systems.  
Some historical reviews on hydraulic transients in pipe flow are given by [13; 14; 15; 16; 17; 18]. The developments in water-hammer research before the 20 th century are well summarized by [19]. In addition, [PERSON]. [20; 21] presented in-depth reviews that served, at that time, as vision papers. More recently, [PERSON]. [22] presented a complete state-of-the-art review focusing on both historic and most recent research and practice covering most of the water-hammer research topics. Surveys more specific in the field of Fluid-Structure Interaction are given by [8; 11; 23] and, more recently, by [24]. The aim of the current review is to report the most significant contributions carried out in water-hammer research related to Fluid-Structure Interaction in 1D hydraulic transients modelling, giving emphasis on the time-domain analyses and focusing on the most recent research. A novel classification of FSI models based on pipe-degrees-of-freedom is presented.  
The paper starts with the basic definitions and background theory that frame the research of FSI in water-hammer modelling. Numerical and experimental research is documented following the physics-based classification of pipe degrees-of-freedom. Finally, insights of engineering applications of Fluid-Structure Interaction developments in pipe flow are pointed out.
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 4 Architecture and Implementation
### Key Generation/Distribution  
For the generation of the shared key the Elliptic Curve Diffie Hellman algorithm was implemented. The exchange takes place during the initial connection between two devices. In SCADA systems the communication is usually initiated by the client/master, thus these devices are the ones to begin the exchange. Let us assume that we have Device 1 and Device 2 which wish to establish as secure communication channel. Based on the ECDH algorithm, Device 1 and Device 2 choose random private numbers d1 and d2, correspondingly. Subsequently, a key is computed by each device who multiply the private random number d (d1 or d2) with the public base point G of the elliptic curve. The resulting K1 = d1*G and K2 = d2*G keys are also points on the Elliptic Curve and, thus, they consist of two parts or Cartesian coordinates. However, in our implementation only one coordinate is used for the generation of the shared key. Therefore, only the first coordinate is exchanged.  
The keys are multiple precision integers based on the _GNU Multiple Precision Arithmetic Library_ ([[https://gmplib.org/](https://gmplib.org/)]([https://gmplib.org/](https://gmplib.org/))). To be received correctly, the keys are converted to a character array. Afterwards the device exchanges keys with another device over an insecure channel based on the Elliptic Curve Diffie-Hellman key exchange protocol. The keys could be intercepted at this point; however, the eavesdropper can only obtain the values K1, K2 which are exchanged over the channel. To find the value of d, the eavesdropper would have to solve the Elliptic Curve Discrete Logarithm Problem (ECDLP) which is is hard, computationally (the best-known algorithms require exponential time). After the exchange of the values, the devices compute the shared key by multiplying S = d1*K2 and S = d2*K1. These values are equal since d1*K2 = d1*(d2*G) and d2*K1 = d2*(d1*G). As we stated above, an eavesdropper can know only the K1, K2 values and, thus, it is not computationally feasible to find out the shared key. For implementation purposes, the value S, which is  
Figure 4: Time-sequence diagram for the library operations.  
received as a character string is, again, converted to a multiple precision integer based on the GNU library.  
Finally, each device stores the computed shared key in the temporary session structure which contains all the information of the Modbus session. The structure has been modified to include a GNU large integer array. The stored key is valid only for the duration of a single Modbus communication session. The key is recomputed each time a new communication section is required [30; 31; 32; 33].
# A Survey of Machine Learning Models in Renewable Energy Predictions
## 1 Introduction  
### Machine Learning  
In the past decades, machine-learning techniques have been widely applied to many fields associated with data-driven problems. Machine-learning techniques include many interdisciplinary areas, such as statistics, mathematics, artificial neural networks, data mining, optimization, and artificial intelligence. Machine-learning techniques try to seek the relations between input data and output data with or without mathematical forms of problems. After the machine-learning models are well-trained by the training dataset, decision makers can obtain satisfying forecasting output values by feeding the forecasting input data into the well-trained models. The data pre-processing procedure plays an essential role in machine learning and can improve the performance of machine learning efficiently [1]. Basically, machine-learning technology mainly uses three learning methods: namely, supervised learning, unsupervised learning, and reinforcement learning. Supervised learning takes advantage of labeled data in the training phase. Unsupervised learning is to automatically categorize input data into clusters by certain criteria for training data that has not been labeled in advanced. Thus, the number of clusters generally depends on the clustering criteria used. Reinforcement learning is learning through interaction with the external environment to obtain feedback in order to maximize the expected benefits. By ways of three basic learning principles, many theoretical mechanisms and applications have been proposed [2]. Due to the rapid development of information technology in hardware and software, deep learning, a sub-field of machine learning, has been booming recently. Deep learning is able torealize characteristic nonlinear attributes and high-level invariant data configurations and, therefore, has been applied in many fields to obtain satisfying performances [3]. Additionally, some studies have focused on forecasting renewable energy by using a single machine-learning model [4]. However, due to the diversified datasets, time steps, prediction ranges, settings, and performance indicators, it is difficult to improve the forecasting performance by using a single machine-learning model. Therefore, in order to improve the prediction performance, some studies developed hybrid machine-learning models or overall prediction methods in renewable-energy predictions. Recently, support-vector machines and deep-learning methods are very popular in the field of machine learning [5].
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 4 System Architecture and Security Model
### Architecture  
The architecture of our location-sharing system consists of four entities: users, base stations, the social network server, and the location server. Figure 1(a),1(b) shows the system architecture of our scheme and the workflow of location query, respectively.  
1. The entity of users, with mobile terminals, can send a request for location-sharing with nearby friends or strangers. Within a specified query distance, an issuing user can know nearby friends or strangers' locations if some pre-set access control policies are satisfied. Users can communicate with base stations and online social network server directly via their mobile devices. One user only has a unique identity in the online social network server.
2. Base station (\(B\)) is an entity that has a certain computation and acts as a connecting thread. Users can communicate with servers through base stations.
3. The social network server (\(SNS\)) is an entity that stores users' social network topology, in the form of social network graph \(G\ =\ (V,E)\). \(V\) is a set of vertices which represent users' identities and \(E\) is a set of edges that indicate the friends' relationships. If two vertices are connected by an edge, the two corresponding users have a social relationship (i.e., they are friends). In addition, the social network server is also responsible for user registration and provides social network services based on the requirements of users' queries.  
Figure 2: System architecture and workflow of location query.  
The location server (\(LS\)) is an entity that stores the location information of users and dummies. In addition, it also takes charge of providing location-related services such as computing and comparing the Euclidean distance of two locations based on their coordinates.
# Frequency-Dependent Schroeder Allpass Filters
## 2 Schroeder Allpass Filters
### General Allpass Filters  
With the previously introduced notation, it is straightforward to define the general allpass filter \(A(z)\) as  
\[A(z)=\frac{a_{m}+\cdots+a_{1}z^{-m+1}+z^{m}}{1+a_{1}z^{-1}+\cdots+a_{m}z^{-m} }=\frac{\overset{\leftarrow}{D}(z)}{D(z)}, \tag{8}\]  
where \(D(z)=\sum_{i=0}^{m}a_{i}z^{-i}\) and \(a_{0}=1\). It can be seen easily that \(A(z)\) has a magnitude response with an allpass characteristic  
\[|A(e^{i\omega})|=\left|\frac{\overset{\leftarrow}{D}(e^{i\omega})}{D(e^{i \omega})}\right|=\left|\frac{e^{-i\omega m}D(e^{-i\omega})}{D(e^{i\omega})} \right|=\left|\frac{(D(e^{i\omega}))^{*}}{D(e^{i\omega})}\right|=1. \tag{9}\]  
Because of the form (8), the poles \(p_{i}\) and zeros \(q_{i}\) of \(A(z)\) are reciprocal, i.e., \(p_{i}{}^{-1}=q_{i}^{*}\) for all \(i\). The phase response of the allpass filter is  
\[\arg(A(e^{i\omega}))=-m\omega+2\arg(D(e^{i\omega})), \tag{10}\]  
where the group delay is  
\[\tau_{A}(\omega)=-\frac{\text{d}\arg(A(e^{i\omega}))}{\text{d}\omega}=m-2 \frac{\text{d}\arg(D(e^{i\omega}))}{\text{d}\omega}. \tag{11}\]  
The group delay of a single pole, single zero allpass filter \(A_{i}(z)=\frac{a_{i}+z^{-1}}{1+a_{i}z^{-1}}\) is  
\[\tau_{A_{i}}(\omega)=\frac{1-\alpha_{i}^{2}}{1+2a_{i}\cos\omega+\alpha_{i}^{2}}. \tag{12}\]  
Any allpass filter can be factorized into first-order allpass filters, i.e., \(A(z)=\prod_{i=1}^{m}A_{i}(z)\). Therefore, the total group delay is the sum of the group delay of the first-order components, i.e., \(\tau_{A}(\omega)=\sum_{i=1}^{m}\tau_{A_{i}}(\omega)\). Next, we review the classic [PERSON] allpass filter.