esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Efficient EMT-Type Model of the MMCs Based on Arm Equivalence
## 5 Simulation Studies
### DC-Side Pole-to-Ground Fault  
At \(t=1.0\) s, a DC-side pole-to-ground permanent fault was applied in the middle of the overhead DC line, as shown in Figure 9. For the AEM and DEM, both MMC1 and MMC2 were blocked at 2 ms after the DC fault occurred. For the AVM, \(S_{1}\) was closed, and \(S_{2}\) was open at 1.002 s. The AC breakers were opened at \(t=1.1\) s. The responses of the three models are shown in Figure 12.  
After the fault occurred, the DC voltage dropped sharply, as in Figure 12b, and the MMCs immediately contributed the fault current to the DC fault location. As shown in Figure 12c, the DC currents rose sharply as the SM capacitor discharge current became the dominant component, resulting in the active power of MM1 increasing, and SM capacitor voltages reducing successively. After blocking the MMCs, the SM capacitor was no longer discharged, thus staying constant, as shown in Figure 12d. Therefore, the fault current from the AC system became the dominant component. After the ACCB is tripped, the residual DC fault currents will gradually decay through the \(R\)-\(L\) damping loop [30; 31]. Due to the large decay time constant in the damping loop, it takes a long time (about 0.4 s) for the fault DC current to decay to zero [30].  
It was observed that the trends of the DC fault currents for three models were similar before blocking the MMCs, since the DC-side equivalent fault circuits were similar [30]. After blocking the MMCs, the proposed AEM very accurately reproduced the transient responses of the DEM. Compared to the AEM and DEM, only the DC voltage was similar to the other models. Since the AC side and DC side of AVM are decoupled, the capacitor \(C_{\text{eq, AVM}}\) is disconnected after blocking the MMCs, leading to the incorrect dynamics, especially for the active power and DC current. This simulation scenario directly reflected that the AVMs have difficulty achieving accurate results under blocked mode.  
Therefore, the proposed AEM can accurately reproduce the dynamic responses in both de-blocked and blocked modes.  
Figure 12: DC fault. (**a**) MMC1 active power; (**b**) DC voltage; (**c**) DC current; (**d**) SMs capacitor voltage; (**e**) Current on the upper arm in phase \(a\); (**f**) AC voltage in phase \(a\) at valve-side.
## 2 Linear Dynamic Analysis with Condensed Localized Mortar Method
### Verification Example for Linear Dynamic Impact and Frictionless Contact Problem  
The problem of ring contacts to the rigid support beam [33] is considered to ensure that the condensed localized mortar method works well in the linear dynamic impact and frictionless contact conditions. Figure 4(a) presents the problem definition.  
Figure 5: Problem definition and deformed configurations of a ring with rigid support.  
As shown in Figure 5a, both side of the rigid support with a size of 24 by 3 is completely fixed and the displacement boundary condition is imposed on the top surface of the ring so that the ring goes down constantly. All the parts are modeled with a 2D linear quadrilateral element for both parts. In the verification example, the elastic ring consists of an inner and outer ring with the same thickness and different materials [33]. In order to ensure that the inner ring is stiffer than the outer ring, the material properties of the inner and outer rings are defined as follows:  
\[\begin{array}{l}E_{\text{inner}}=1E+05,\
u=0.3\\ E_{\text{outer}}=1E+03,\
u=0.3\end{array} \tag{12}\]  
By imposing the condition of pressing on the top side of the ring, as shown in Figure 5a, the finite deformable ring is in contact with a rigid support, and a non-matching interface is then observed on the interface. The contact condition is solved by using the condensed localized mortar method (11).  
A linear dynamic finite element analysis is conducted in 120 steps to solve the verification example. The deformed configurations are presented in Figure 5b-f. The contact area is changed from one spot to one region and then expanded to two regions to distribute the pressure by increasing the contact surface in order to support a given boundary condition as the analysis proceeds. These analysis results demonstrate identical appearances as shown in reference [33].  
This result indicates that the condensed localized mortar method used in this study works well even in the linear dynamic impact and frictionless contact conditions, and that the contact regions are changed. Therefore, the interface is treated by the condensed localized mortar method to handle the topology optimization with the impact and frictionless contact conditions.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Evaluation of the Attack Effectiveness
#### 5.4.4 Effectiveness against the Rule Eviction Method  
Both of the previous methods lead to massive packet-in messages because the controller stops installing flow rules for flows, so the packets are still unmatched. Another countermeasure is the eviction strategy. In this method, the OpenFlow switch automatically deletes flow entries to make space for new ones. For the sake of generality, we use  
Figure 16: The ratio of refused and handled packet-in requests when the controller drops the packet-in after the flow table is overflowed.  
Figure 17: The impact of flow table saturation attack on packet loss.  
the eviction algorithm in the widely-used Open vSwitch. We evaluate whether benign background flows can replace the triggered attack flow rules.  
Figure 18 shows the number of flow rules for malicious and benign flows. Since the replacement is performed on the switch side, the flow table utilization is almost always at 100% after the flow table is full. We can see that after overflowing the flow table, the injected malicious flow rules will not be replaced by benign flows. When the number of benign flow rules decreases, the attack flow immediately occupies the freed storage space and keeps the flow table full.  
We also collect the information from the Flow-Removed messages to compare the number of evicted normal flow rules and malicious flow rules. The result is shown in Figure 19. After the flow table is full, more than 82% of the deleted flow rules are benign flow rules. Note that when the flow table is full, the number of attack flow rules is approximately 50% of the flow table capacity. However, benign flow entries are more likely to be deleted when selecting flow entries. This indicates that the eviction strategy is more inclined to keep malicious flow rules rather than benign ones.  
Upon further analysis of the results, we identify the cause of this. OvS implements the Least Recent Used (LRU) eviction method by selecting the entry that **expires soonest** for eviction. In our study, the controller assigns different timeouts for flows based on the packet inter-arrival time instead of using fixed timeout values. When assigned with different idle timeouts, flow rules with large timeouts are less likely to be evicted. During the attack process, we induce controllers to assign larger timeouts for attack flows. In the maintaining  
Figure 19: The ratio of evicted malicious flow rules and benign flow rules in the eviction situation.  
Figure 18: The number of flow rules in the eviction situation.  
phase, we randomly select a value within the timeout range instead of resending the packet just before the flow rule expires. This approach makes it less likely for malicious flow rules to be deleted when selecting flow rules to evict, as they require a long time to expire. This experiment result also validates the effectiveness of triggering large timeouts in the triggering phase and using different packet intervals in the maintaining phase.
## 7 Experimental Setup
### Vision System  
For the vision module, we use a non-local graph convolution (NGC)-based method developed in [35] for action recognition and motion prediction. This implements a spatial-temporal attention mechanism to extract nonlocal spatial-temporal features, enabling more accurate motion prediction over longer horizons than alternative approaches. As this model, referred to as the NGC-model, performs well for the Human 3.6 M, CMU Mocap, and NTU RGB-D datasets (see [35]), we repurpose it to predict the human motion associated with the screwdriver task and classify it into one of six subtasks that constitute the overall activity, namely, 'pick up','move forward', 'take the screw', 'operate screwdriver','move backward,' and 'put down.' This is achieved by retraining the network with a dataset collected for the screwdriver task, consisting of skeletal data and associated subtask labels for three repetitions of the task. In this work, 4372 frames including six different actions are used for training, while 1287 frames are used for testing. Each frame includes 3D joint positions with 32 joints, as well as ground-truth action labels for each frame.  
Performance of the Vision Module  
As shown by the confusion matrix in Figure 6, the model achieves high prediction accuracy for each subtask (99.0%, 100%, 97.9%, 99.0%, 98.3%, and 99.5%) and an overall accuracy of 98.9%. For motion prediction, the mean per joint position error (MPJPE) is used to quantify the mean deviation between the predicted and actual locations of the human body joints in each frame. As shown in Table 1, the NGC-model achieves the lowest error compared to selected baseline models from the literature (LSTM-3 LR [45], Res-sup [46], Traj-GCN [47] and SPGSN [48]). Table 1 compares the MPJPE of our model and the baselines for all actions under the same conditions, with the prediction horizon of 1000 ms, showing that our model obtains the best performance for all the actions.  
Figure 6: Confusion matrix describing the accuracy of action recognition in the screwdriver usage task.  
This shows that the proposed NGC model can accurately and effectively identify the intended human body movement in real-time. Using this capability, the HRC system can understand the intention of the human being when the human body begins an action, making it feasible for it to provide assistance to the human to complete the task efficiently.
# Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control
## 2 Analysis of Typical Working Conditions of Electric Loader
### The Test of Working Conditions under \"V\" and \"T\" Types  
The vehicle speed not only reflects the dynamic demand of electric loader, but also describes the braking demand of electric loader under different working conditions. This paper takes a self-developed 5-ton electric loader as test object. The 20 loading operations were carried out under the \"V\" and \"T\" types, respectively. Two consecutive sets of data are selected to analyze the vehicle speed change under acceleration and deceleration conditions. As shown in Figure 1, the speed curve of each working cycle includes six working stages: forward with no-load, loading, backward with full-load, forward with full-load, unloading, and backward with no-load. During forward with no load, the digging depth of bucket is ensured by a short time stepping on accelerator pedal. Therefore, a small speed spike will appear when loading. In the \"V\" type, the speed peak will also appear when the sand is unloaded to the top of the sand pile with acceleration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Contents** & **Motion State** & **Stage** & **Contents** & **Motion State** \\ \hline \multirow{3}{*}{1} & Forward with no load & Accelerating & & & \\  & & Uniform speed & 4 & Forward with full-load & Accelerating \\  & & Decelerating & & & Stop \\
2 & Loading & Stop & 5 & Unloading & Stop \\  & & Accelerating & & & \\
3 & Backward with full-load & Uniform speed & 6 & Backward with no load & Uniform speed \\  & & Decelerating & & no load & Decelerating \\ \hline \hline \end{tabular}
\end{table}
Table 1: Working cycle of “V” type for electric loader.  
Figure 1: _Cont._  
Both the speed and displacement of brake pedal are important parameters for braking intention identification, the accelerator pedal displacement is also involved in the deceleration intention identification. If driver releases accelerator pedal and do not step on brake pedal, it is namely a sliding brake. If driver releases accelerator pedal and steps on brake pedal, the system will participate in braking. Therefore, the retraction of accelerator pedal is also taken as a role of braking energy recovery, which can not only improve the energy recovery rate, but also achieve the effect of deceleration or brake in advance, which further ensure the braking safety. In addition, the vehicle speed is also an important input parameter for braking intention and braking strength identifications. When the electric loader is braking, the driver's operation of brake pedal and accelerator pedal is random and unpredictable due to the different driving conditions, the braking intention cannot be formed as an accurate mathematical model [15]. Therefore, fuzzy control method is chosen for the braking intention identification of electric loader in this paper.
## 2 System Model of RSST-SR-DCSK
### Transmitter Structure of RSST-SR-DCSK
If the transmitted information bit is 10, scrambler 3 is selected, and the working principle of scrambler 3 is to divide the chaotic signal into four segments equally, and mirror invert the signal values in the first and third segments; if the transmitted information bit is 11, scrambler 4 is selected, and the working principle of scrambler 4 is to divide the chaotic signal into two segments evenly, and mirror invert the signal values corresponding to the even positions in the front and back segments; if the transmitted information bit is 11, scrambler 4 is selected, and the working principle of scrambler 4 is to divide the chaotic signal into two segments evenly, and mirror invert the signal values corresponding to the even positions in the front and back segments. The principle of scrambler 4 is to divide a chaotic signal into two segments equally, and interchange the signal values corresponding to the even positions in the two segments. The modulation process of the RSST-SR-DCSK system can be summarized in the following steps: Firstly, a chaotic signal of length \(R\) is generated and split into two paths. On one path, the chaotic signal is scrambled based on the pre-set \(N\)-bit information, resulting in the scrambled chaotic signal. Simultaneously, the other path copies the chaotic signal P times and delays it by \(R\) to generate a delayed chaotic signal of length \(R\), where \(P\) is equal to the pre-set \(M\)-bit information. Next, the delayed chaotic signal is multiplied with the pre-set \(M\)-bit information bit by bit, producing an information signal of length \(\beta\). In the third step, the corresponding scrambling method is chosen based on the pre-set \(N\)-bit information, and this method is used to scramble one path of the chaotic signal, creating the scrambled chaotic signal. Finally, the reference signal is added to the information signal to form the transmission signal, which is then transmitted through the channel from the sender to the receiver. Therefore, the expression for the RSST-SR-DCSK signal transmitted within the \(i\)th frame is  
\[S_{i,k}= \begin{cases}\hat{x}_{i,k}&0\leq i<R\\ b_{k+M-1}x_{i-R,k}&R\leq i\leq(1+P)R\\ x_{i-R,k}\equiv x_{0,k}&mod(R)\end{cases}  \tag{6}\]  
where \(S_{i,k}\) represents the \(i\)-th frame signal; k represents the \(k\)th term in the \(i\)th frame signal; \(mod(R)\) denotes the modulo operation; \(b_{k}\cdots b_{k+M-1}\) represents the pre-set \(M\)-bit information bits. \(x_{0,k}\) represents the \(k\)th term of the chaotic signal in the \(0\)-th frame, and \(x_{i-L,k}\) represents the \(k\)th term of the chaotic signal in the \(i\)th frame after a delay of \(L\). \(\hat{x}_{i,k}^{N}\) represents the \(k\)th term of the reference signal in the \(i\)th frame, and \((1+P)R\) represents the length of the transmitted signal, i.e., the sum of the lengths of the reference signal and the information signal.
# Efficient Use of GPU Memory for Large-Scale Deep Learning Model Training
## 2 Background  
### CUDA Unified Memory  
From CUDA 6.0, NVIDIA provides CUDA Unified Memory, a technology to overcome the limit of single GPU memory size [13; 14; 16]. CUDA Unified Memory is a technology that allows physically separated host memory and device memory to be integrated into one logical address space using virtual memory scheme. Unified Memory works in the following way. First, when the GPU attempts to access data in the CPU memory, a page fault occurs. After that, the host unmaps the page mapped to the CPU memory and migrates the data to the GPU memory. Finally, the page fault is resolved by mapping the page to the GPU memory. By using CUDA Unified Memory, GPU can use data stored in physically different locations without explicitly calling memory copy functions such as _cudaMemcpy()_ and _cudaMemcpyAsync()_. However, since a page fault occurs when accessing data in a physically different location, the ongoing process should be stopped to resolve the page fault and migrate data, so performance may be degraded.
## 3 Materials and Methods
### Experimental Setup  
To simulate the acquisition process of Electrical Capacitance Tomography (ECT) data, a 3D ECT tomography model simulator was developed in MATLAB 2023a, leveraging the EIDORS 3.8 open-source library. A tetrahedral mesh with 5789 vertices and 25350 simplices was employed to represent the 3D ECT sensor model. Simulated capacitance electrodes were differentiated for both 4 \(\times\) 4 and 8 \(\times\) 4 configurations.  
A diverse set of electrical permittivity phantoms was computed using the volume subtraction technique. Subsequently, Finite Element Method (FEM) solutions were obtained, to simulate the electrical field distribution within the phantoms under various Neumann and Dirichlet boundary conditions. The NLECTCM 3D iterative reconstruction algorithm was then applied, to reconstruct the 3D permittivity distribution from the simulated ECT data.  
For visualization purposes, a custom Matlab code was developed, utilizing the Visualization ToolKit (VTK) library. This code facilitated the 3D rendering and analysis of both the original phantoms and their reconstructed counterparts.  
To enhance the quality and interpretability of the 3D ECT reconstructions, a Graph Convolutional Network (GCN) pipeline was integrated. This pipeline operated on graph-based representations of the 3D images, leveraging the inherent connectivity and spatial relationships between the voxels.  
The computational demands of the NLECTCM algorithm and the AI routines were addressed by using the Supermicro SYS-6029P HPC Server equiped with ten Nvidia A6000 graphic cards, dual Xeon Platinum 8368 CPUs and 1 TB RAM (Supermicro, San Jose, CA, USA). This hardware enabled efficient parallelization of the calculations, accelerating both the raster-type and graph-based 3D ECT reconstruction processes.
## 4 Experimental Results and Analysis
### Tool Formal Security Validation
#### 4.4.3 ProVerif-Based Security Analysis  
Taking into consideration that ProVerif is a formal verification tool designed for symbolic cryptographic analysis, unlike AVISPA, which uses a model-checking approach and ProVerif relies on first-order logic reasoning to detect vulnerabilities in cryptographic protocols, we used both of these, with specific parameterizations, adapted to our specific case.  
Our ProVerif model encapsulates the parameterized ECC-based key exchange, integrating modular arithmetic operations and contraction transformations within the logical structure of the verification system.  
Output of Query Execution in ProVerif  
To rigorously assess the security assurances provided by our authentication protocol, we conducted an automated verification using the ProVerif tool. Below, we present the resulting output from the query execution within ProVerif, which substantiates the protocol's resilience against specific potential threats.  
$ proverif protocol.pv Parsing... Processing... Resolution... Query attacker: secret. RESULT not attacker(secret[]) is true.  
Query attacker: session_key. RESULT not attacker(session_key[]) is true.  
ProVerif confirms that the authentication protocol maintains the confidentiality of the session key and other sensitive data. Execution trace: 1. Message M1 securely transmitted. 2. Protocol correctly verifies integrity and authenticity. 3. No vulnerabilities detected.  
Analysis: The results indicate that the security mechanisms successfully prevent unauthorized access to both secret values and session keys. The protocol is resilient against potential attacks and maintains strong confidentiality guarantees.  
The above output illustrates the security evaluation, where ProVerif confirms that all protocol components function securely and effectively. The following properties were examined:  
* Key Secrecy--Ensuring that an attacker cannot infer any part of the secret key \(K_{AB}\).
* Message Integrity--Verifying that messages exchanged between devices are not tampered with.
* Resistance to Known Cryptographic Attacks--Validating that the protocol remains robust under active and passive adversary models.  
The verification results from ProVerif aligned with those obtained via AVISPA, further reinforcing that the modular-enhanced ECC protocol with contractions remains a viable cryptographic solution for constrained IoT networks.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 4 Security Analysis
### Forward Security and Post Security  
This paper proposes an access control scheme based on CP-ABE, which can guarantee the forward and backward security of the NDN cached data for newly added and revoked consumers. Forward security denotes the revoked user unable decrypt novel ciphertext that needs to be revoked for decryption. Backward security denotes newly added users can also decrypt previously ciphertexts. Forward security denotes the revoked user cannot decrypt the new content of the ciphertext that demands to be revoked for decryption. Backward security denotes newly added users can also decrypt previously public the content of the ciphertext, and if it has enough attributes, it uses the previous public key encryption. When the consumer is revoked, the NDN cache node will delete its proxy key. Therefore, user who is revoked is unable to decrypt the ciphertext by utilizing its proxy key to ensure the forward security of the system's cached data. When a whole new consumer adds system, relevant  
Figure 4: Relationship between NDN routers and proxy servers and applications.  
ciphertext information is re-encrypted so that it can also decrypt the ciphertext, ensuring backward security of the cached data.