esa-sceva/satcom-chunk-collection huggingface dataset:
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 6 Implementation
### Evaluation  
To evaluate our attacks, we compared our attack with other SGX attacks that mentioned the time that is taken to leak data and we calculated leaked bit per second (bps). Other attacks are summarized in the next section. The Table 3 shows the comparing results. As you can see from the table, in other attacks, they required too many attempts to leak data from the victim enclave and have low bps. They must try many times to leak data since their attacks have noise. Moreover, many times of attempts to leak data can affect bps.  
In MemJam [36], they tried to recover the 128-bit AES key from SGX. Their non-optimized attack needed 2,000,000 observations to recover the key and took 5 min with noise. Since they needed m-arch contention and many times of observations, leaking bps could be low. In CopyCat [9], they tried to recover the 2048-bit RSA key from SGX. On average, they needed 39,400 steps and 20 s to recover the key with noise. CopyCat can leak data faster than MemJam since they do not need any m-arch contention. However, CopyCat's bps is lower than our attack.  
In our attack, when we already found gadgets that are needed to construct the ROP payload, we can extract data with extremely high bps without noise with only one attempt. We tried to leak 8192-bit data by using SGXDump to our implementations. First, in mbedTLS-SGX, we can leak data in only one try without noise and it took 3 s on average. Next, in Graphene-SGX, as in the previous case as, we can leak data in only one try without noise and it took 12 s on average. In the case of Graphene-SGX, it needed a kernel patch to run the Graphene-SGX application, and this may affect bps.
## 3 System Design
### Simulation Scalability and Real-World Relevance  
The simulation is scaled to model 50 users interacting with multiple mining license resources, reflecting enterprise or governmental deployment sizes and addressing known scalability concerns in blockchain systems [8; 37] as depicted in Figure 5. Each user had a unique identity and attributes to simulate role diversity. Each user-resource pair triggers three core operations: registration, policy creation, and access evaluation, as depicted in Table 2, resulting in thousands of transactions across a single simulation run. Despite the load, the architecture requires no fundamental change, consistent with prior findings on Hyperledger Fabric's scalability and transaction throughput [38; 39].  
Transaction latencies remain consistently in the low millisecond range [39; 40], and system error rates are negligible post-initial configuration tuning.  
It is important to note that these simulations were executed on a local, Docker-based deployment of Hyperledger Fabric, which does not reflect the full behavior of geographically distributed or enterprise-scale networks. Performance results such as latency and throughput may vary under real-world conditions due to inter-peer communication delays, consensus timing, and infrastructure variability.  
In addition, the system was tested using synthetic user and policy data designed to mirror realistic mining operations. While this allows for systematic control over experiment variables, it does not fully capture the unpredictability of real-world datasets or user behavior.  
Future extensions may incorporate peer failures, network delays, or adversarial scenarios for stress testing [41; 42].  
In summary, this section outlines a full-stack approach to designing, deploying, and validating a blockchain-based ABAC framework tailored for mineral asset governance. By combining smart contract implementation, synthetic data modeling, and large-scale simulation, the system is evaluated for correctness and scalability. However, we acknowledge that full deployment in operational settings may involve additional complexity not captured by this controlled simulation environment. The architecture's modular design and structured logging enable downstream analytics and future expansion into production-ready applications in compliance-driven environments.
## 3 Nlos and Los Classification Based on Deep Learning Algorithm  
### Data Augmentation by Generative Adversarial Networks  
For the problem of class imbalance, we adopt the generative adversarial networks (GAN) for data augmentation. A typical theoretical GAN network structure is shown in Figure 2.  
GAN consists of two main parts: generator and discriminator. The main function of the generator is to generate the sample data. The main function of the discriminator is to distinguish the receive sample data from the \"spurious data\" generated by the generator. The generator and the discriminator are trained at the same time, competing against each other. The loss function of GAN (cross entropy loss function) can be expressed as [23]:  
\[\min_{G}\max_{D}V(D,G)=\mathbb{E}_{x\sim p_{data}(x)}[\log D(x)]+\mathbb{E}_{x \sim p_{z}(z)}[\log\left(1-D(G(z))\right)] \tag{1}\]  
where \(p_{data}(x)\) represents the distribution of real data and \(p_{z}(z)\) represents the distribution of generated \"spurious data\".  
WGAN-GP network is a proposed variant based on the basic GAN network, which can solve the problem of unstable training of the original GAN network. The optimization function of WGAN-GP can be expressed as:  
\[L=\mathbb{E}_{\widetilde{x}\sim\mathbb{P}_{g}}[D(\widetilde{x})]-\mathbb{E}_{ x\sim\mathbb{P}_{p}}[D(x)]+\lambda\mathbb{E}_{\widetilde{x}\sim\mathbb{P}_{ g}}[(\left\|\
abla_{\tilde{x}}D(\tilde{x})\right\|_{2}-1)^{2}] \tag{2}\]  
where \(\widetilde{x}\longleftarrow G(z)\), \(\widehat{x}\longleftarrow\varepsilon x+(1-\widetilde{x})\), \(\epsilon\sim U[0,1],\mathbb{P}\). represent the distribution of \(\cdot\).  
WGAN-GP adopts a new loss function, and the difference before the plus sign is called the Wasserstein distance. It is used to measure the distance or effort required to transform one distribution into another. The formula after the plus sign is called the Gradient penalty, which is a penalty term for the weight of D.  
Figure 1: Frame diagram of our positioning system.  
Figure 2: Structure of a generative adversarial network.  
In GAN networks, the loss function for generators must be reduced, whereas the maximum loss function for discriminators must be optimized. By the max-min game, the generator and discriminator are trained alternately in a loop until the Nash equilibrium point is reached. At this time, after the Gaussian white noise is transformed by the generator, it can be used as a part of the data set for subsequent work to achieve the effect of data augmentation.  
In order to solve the problem of class-imbalance, we train a GAN. We built the generator model and the discriminator model, shown in Figure 3. The coefficient of the penalty term \(\lambda\) is taken as 0.3. During the training, the generator is responsible for generating \"spurious data\" by randomly generating latent data of shape [_batch_size_, 4]. The discriminator receives both \"spurious data\" generated from generator and _batch_size_ samples from the NLOS data.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 4 Hierarchical System Architecture
### Application Control System  
The ACS manages the run-time adaptation of a DSP application. Similar to the ICS, it implements a two-layered hierarchical MAPE loop, where an Application Manager oversees subordinate and decentralized Operator Managers.  
At the lower layer, the _Operator Manager_ (OM) controls the reconfiguration of a single DSP operator and proposes reconfiguration requests to the higher level. The OM uses the _Operator Monitor_ to retrieve the resources usage by the operator as well as its performance in terms of response time. By analyzing this information, the _Local Reconfiguration Manager_ determines if any local reconfiguration action is needed. The available actions are scale-out and scale-in, which respectively increase and reduce the number of replicas per operator. When the OM determines that some adaptation should occur, it issues an operator adaptation request to the higher layer.  
At the higher layer, the _Application Manager_ (AM) is the centralized entity that coordinates the adaptation request aiming to obtain good overall DSP application performance. By means of the _Application Monitor_, it oversees the global application behavior. Then, using the _Global Reconfiguration Manager_, it analyzes the monitored data and the reconfiguration requests received by the multiple OMs. The AM decides which reconfigurations should be granted by using its global policy (Section 7.3). Then, it communicates the adaptation decisions through the _Global Actuator_ to each OM; finally, the latter can execute the operator adaptation actions by means of their local _Reconfiguration Actuator_. We refer the reader to [8] for further details.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Experimental Results  
We conducted experiments on three commonly used datasets, namely, Corel-1k [63], Corel-5k [64], Corel-DB80 [65], and Oxford Building [66], to analyze the performance of the proposed method. All images in the first three datasets are used as query images, and then the retrieval precision/recall of each image, each category, and the total dataset are computed.
## 2 Materials and Methods
### Techniques for Explainable AI (XAI)
#### 2.4.2 Gradient-Weighted Class Activation Mapping (Grad-CAM)  
Gradient-weighted Class Activation Mapping (Grad-CAM) serves as a technique in deep learning that facilitates the visualization and interpretation of the reasoning behind decisions made by Convolutional Neural Networks (CNNs) [84; 85; 86; 87; 88; 89; 90; 91; 92]. By leveraging the gradients of the target class heading into the last convolutional layer, Grad-CAM produces a localization map. This map accentuates the critical regions within an input image that play a pivotal role in the model's predictive process.  
The Grad-CAM technique involves several key steps:  
* Forward pass--the input image is processed through the CNN to extract feature maps from the last convolutional layer.
* Gradient calculation--the gradients of the classification score with respect to the feature maps are computed. These gradients indicate the sensitivity of the output value of the target class to changes in the feature map values.
* Weight averaging--the gradients are averaged to obtain weights, which signify the importance of each feature map channel for the target class.
* Weighted combination--a weighted combination of the feature maps is performed using these weights, resulting in an initial coarse localization map.
* ReLU activation--the ReLU activation function is applied to the weighted combination to retain only positive influences, highlighting the regions in the image that positively impact the target class score.
* Upsampling--the coarse localization map is upsampled to match the dimensions of the input image, producing the final Grad-CAM heatmap.  
This heatmap can be overlaid on the original image to visualize which parts of the image the network focused on during its prediction.  
In our research, we utilized Grad-CAM technology with a customized VGG16 network, originally developed for classifying drilled holes in melamine-coated chipboard. Our objective was to identify the specific areas the CNN model focuses on when making predictions. This approach helped us delve deeper into the model's rationale and improved the clarity of our automated tool condition monitoring system.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 5 Proposed Method
### Quotient Distance-Angle Histogram  
The selection and computation of shape descriptors have a great impact on the matching quality. [PERSON] constructed the quotient distance histogram (QDH) in topological space to describe the topological properties [39]. After being applied to binary images, the value of each interval in the histogram is given by the following equation:  
\[d^{\prime}(i,j)=\left\{\begin{array}{cc}d_{1}(i,j)&d_{1}(i,j)<\xi\\ \infty&\text{otherwise}\end{array}\right. \tag{1}\]  
\[d^{*}=In(d^{\prime}) \tag{2}\]  
\[qd(i,j)=d^{*}(i,j)/d_{1}(i,j) \tag{3}\]  
\[h(k)=\sum_{i=1}^{n}\sum_{j=i+1}^{n}I(qd(i,j)\in B(k)) \tag{4}\]  
where \(i\) and \(j\) are pixel points on the image, \(d_{1}(i,j)\) denotes the Euclidean distance, \(d^{\prime}(i,j)\) denotes the distance between two points in the image, and \(\xi\) denotes the tolerance. Points with a distance greater than this tolerance are considered to have an infinite distance. This tolerance assists the algorithm in disregarding minor changes, facilitating image abstraction and simplification. \(In\) denotes the inner-distance operation, \(d^{*}(i,j)\) denotes the inner distance, and \(qd(i,j)\) denotes the quotient distance, which is used as the vote for constructing the histogram. This intuition is reasonable because the inner distance contains rich structural information and the quotient of the inner distance and Euclidean distances is scale invariant. \(n\) is the count of pixels in the structure, \(I\) is the indicator function, and \(B(k)\) is the range of the \(k\)-th interval.  
The QDH algorithm can describe topological features to some extent. However, the retrieval effect of the QDH algorithm in practical application is greatly affected by the discrete features of pixels, and it is also unable to distinguish between holes and concave unclosed shapes. Therefore, in this paper, two improvements are proposed to extract the QDAH feature to characterize the topological perception.
# Natural Photosensitizers in Clinical Trials
## 5 Reported Toxicity and Adverse Side Effects of Natural Photosensitizers
\begin{table}
\begin{tabular}{c c c c} \hline
**Photosensitizer** & **Toxicity** & **Side Effects** & **Article** \\ \hline \multirow{6}{*}{Curcumine} & & Mild sensation of warmth, mild erythema on & \multirow{6}{*}{} \\  & No serious adverse effects in clinical trials & & both sides (resolving up to 2 h), mild \\  & No adverse effects on healthy tissues in & pigment deposition on both cheeks after two & \\  & clinical trials & treatments in both experimental and control & [35; 36; 37; 38; 39; 40; 41] \\  & & clinical trials & groups in two patients (this pigmentation \\  & & resolved spontaneously after 1-month & \\  & & follow-up) & \\ \hline \multirow{2}{*}{Hypericin} & & Mild local skin lesions and injection site & \multirow{2}{*}{[54; 55]} \\  & No serious side effects in clinical trials & & reactions, burning and pain & \\  & & during irradiation & \\ \hline Riboflavin & No described side effects in clinical trials & No described side effects in clinical trials & [63; 64; 65] \\ \hline Phyocyanin & No described side effects in clinical trials & No described side effects in clinical trials & [74] \\ \hline Antrachinones & No described side effects in clinical trials & No described side effects in clinical trials & [82] \\ \hline Furanocoumarins & No data available & No data available & [138; 139] \\ \hline \multirow{6}{*}{Pheophorbide _a_} & Conflicting data in in vitro studies: (1) & \multirow{6}{*}{} \\  & low sensitivity to Pa-PDT of WRL-68 & & \\  & liver cells; (2) cytotoxic effects of Pa-PDT & No serious side effects in in vivo & \\  & in four primary cultures of normal & mouse studies & [96; 97; 98; 140] \\  & mammary epithelial cells; (3) cytotoxic & & \\  & effect of Pa-PDT of neutrophils & & \\ \hline \multirow{6}{*}{Alkaloids} & Less than 10\% mortality of healthy & \multirow{6}{*}{} \\  & HaCaT keratinocytes after bereberin & & \\ \cline{1-1}  & administration without irradiation and & No data available & [103] \\ \cline{1-1}  & about 53\% mortality of these cells after & & \\ \cline{1-1}  & in vitro irradiation & & \\ \hline \multirow{6}{*}{Chlorophyllin} & No in vitro cytotoxicity against RAW & \multirow{6}{*}{} \\ \cline{1-1}  & 264.7 macrophages both in the dark and & & No data available & [115] \\ \cline{1-1}  & after illumination & & \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of toxicity and side effects induced by photodynamic therapy with natural photosensitizers.
###### Abstract
[PERSON] et al. [9] studied the vibration suppression of a beam with an NES and analyzed the influence of NES parameters. Some scholars used an NES to suppress the vortex-induced vibration of slender structures, which achieved a good vibration suppression effect. [PERSON] et al. [10] simulated the vortex-induced vibration response of a rigid pipe under wind loads by using the wake oscillator model, and the results show that the structural vibration is minimized through adjustment of the mass ratio of an NES. [PERSON] et al. [11] used CFD to analyze the vortex-induced vibration of pipes with an NES and calculated the response of pipes considering a two-degrees-of-freedom system. The results show that the vibration of steel pipes is significantly reduced after adding an NES. [PERSON] et al. [12] studied a rotating NES inside the pipe in which the displacement of the NES was not limited, and obvious vibration reduction effects were obtained. Current research shows that when an NES is used to suppress the vortex-induced vibration of elastic structures, the response of structures is complex due to the nonlinearity of an NES, and even amplitude jumps and unstable motion will occur for certain parameters. In addition, the NES's parameters have a significant influence on the motion forms and vibration suppression effects [13]. In this paper, an NES is used to suppress the wind-vortex-induced vibration of a deep-water jacket pipe, and the nonlinear response of the system was analyzed. The design parameter of an NES was suggested according to the results of the nonlinear analysis.
## 3 In Situ Sensors Used in the L-PBF Processes
### Acoustic Sensors  
Acoustic emission (AE) is a non-destructive evaluation method that has been employed for defect detection of manufactured parts for decades [75]. It has recently been applied for online monitoring of the welding process due to its ability to characterize and detect weld defects in real time [76]. Acoustic emission is \"the generation of an elastic wave by the rapid change in the stress state of some regions in the material\" [77]. This change can be related to microcracks formation, pores, evaporation dynamics, and temperature gradients [78]. These sensors can be fixed within the build plate to collect structure-borne acoustic emissions or along the build chamber walls to collect air-borne acoustic emissions, as shown in Figure 6[79]. The working principle of the AE sensor used can be based on piezoelectric transducer (PZTs) or optical fiber sensors [80]. Generally, piezoceramic transducers are used to detect structure-borne acoustic emissions, while regular membrane microphones are used to detect airborne acoustic emissions. There is higher damping in the air than solid. Therefore, a broader frequency spectrum can be measured in solids [79].  
Figure 5: Optical micrographs showing some cracks formation in a B4C/Ti64 composite [70]. (**d**) at low energy density and (**e**) at higher energy density.
## 4 Fault-Tolerant Control of Three-Level T-Type Inverter  
Most applications have particularly high requirements for equipment reliability. Accordingly, the reliability of inverter systems can be improved by equipping such systems with fault-tolerant control mechanisms that can enable them to maintain a stable output voltage. Figure 9 presents the architecture of the three-level T-type inverter proposed in this study. Compared with conventional three-level T-type inverters, the proposed inverter includes two additional insulated gate bipolar transistors and 3 three-pole ac semiconductor switches (Triode AC, TRIAC). The spare leg comprises the insulated gate bipolar transistors (IGBT) \(S_{x}{}^{+}\) and \(S_{x}{}^{-}\), which can be used to replace those in the faulty phase to maintain circuit operation. The 3 three-pole ac semiconductor switches \(T_{a}\), \(T_{b}\), and \(T_{c}\) connect the spare legs and \(a\)-, \(b\)-, and \(c\)-legs. When a fault occurs, the spare leg is connected with the faulty leg so that the spare leg can replace the function of the faulty leg, thus achieving fault tolerance.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 6 Implementation
### Testbed #2: Maximum Range Coverage  
With the goal of assessing the suitability of the ZolertiaTM RE-Mote as a device for LPWAN applications, a test was performed in the localities of _Prats i Sampsor_, and _Savanastre_ (Catalan Pyrenees) to determine its maximum range coverage in line-of-sight condition when using the 868 MHz frequency band. Two ZolertiaTM RE-Mote units were used--both of them running the HARE protocol stack: one acting as a GW placed in a static position, and the other as an STA trying to associate to the network from an increasingly greater distance. Selected data rate was \(r=50\) kbps and transmission power was set to its maximum value (\(P_{\text{TX}}=14\) dBm).  
Under these conditions, the maximum distance at which the STA was able to associate to the network created by the GW was \(\sim\)850 m. Figure 11 shows the test locations of the STA, and Figure 12 represents the RSSI received at the GW in function of the distance \(P_{\text{RX}}(d)\), according to the propagation model obtained by using the least square approximation method and expressed as:  
\[P_{\text{RX}}(d)=-29.47-2.45\cdot 10\,log_{10}(d). \tag{3}\]  
Figure 11: Positions and altitudes of the device acting as STA in Testbed #2.  
Figure 12: Least square approximation of the simple pathloss propagation model at 868 MHz.