esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 3 Method
### Resampling Operation  
VQ-VAE's powerful reconstruction capability implies that it also has strong generalization capability. During the prediction stage, unseen anomalous images are encoded whose latent codes deviate from the distribution of normal images, and this caused values in index tables to change as well. Our intention is to reconstruct a normal image that most closely matches the corresponding anomalous image. The word \"closest\" is achieved by means that only the areas where anomalies exist are restored, while the normal areas remain unchanged. Since the index arrangement of the index table determines the final latent code to be decoded, the restored image can be reconstructed by updating the value in the index table that does not satisfy the normal image pattern.  
Specifically, as shown in step 3 of Figure 1, an anomalous image \(\tilde{x}\) is pushed into the encoder network and extracted an \(M\times N\) index table \(\tilde{z}\). Then, the trained PixelSNAIL model infers the likelihood on each component of \(\tilde{z}\) in parallel. In this process, PixelSNAIL will estimate the conditional distribution of current component of \(\tilde{z}\) based on arrangement characteristics of the preceding. If the component of \(\tilde{z}\) is assigned an extremely low likelihood, it means that the index does not conform to the pattern of normal images. We set a hyperparameter threshold \(\eta\) to identify these anomalous patterns, and the setting of this threshold is described in detail in the experimental section. When the likelihood of the current component is less than the threshold \(\eta\), we perform a resampling operation in the conditional distribution of that component, generating a new index that is most likely to occur under the semantics of the preceding normal pattern. The entire resampling process is carried out in raster order, ensuring that the pre-sequence arrangement follows the normal images' pattern.  
The resampling operation can be defined by Equation (5), where \(I(m,n)\) means the minimum conditional probability given the bottom half of the 8-neighbor of \(\tilde{z}_{m,n}\), \(1<m,n<M,N\):  
\[\begin{split} z_{m,n}=\begin{cases}\operatorname*{arg\,max}_{k^{ \prime}\in\{1,\ldots,K\}}p(Z_{m,n}=k^{\prime}\mid\tilde{z}_{i,j<m,n})&\text{if }p\left(Z_{m,n}=k\mid\tilde{z}_{i,j<m,n}\right)<\eta \text{ and }I(m,n)<\eta\\ k&\text{others}\end{cases}\end{split} \tag{5}\]  
We assume that anomalous region usually has local continuity, and, based on this assumption, we add a local constraint \(I(m,n)<\eta\) to increase the stability of the resampling. As Figure 2 shows, the probability distribution of the current component (red zone) is conditioned only on previous sequences, lacking the semantic information underneath it. We introduce the likelihood about the bottom half (yellow zone) of the current component's 8-neighbor to avoid noise. Resampling operation is applied when the likelihood of both current component and at least one component in the bottom half is less than the threshold.
## 5 Discussion
Compared to some solutions based on the techniques listed in Table 1, the presented design of CoM2M featuring flexible discovery and presence, ubiquitous messaging, and semantic understanding is well suited for production line-scale intensive data interaction between machines in manufacturing industrial applications. The ICN networking paradigm is a promising technology for future large IoT systems in data distribution and security, which does not concentrate on the performance of a group of machines in a local area industrial network. The presented solution focuses on the interactive messaging and understanding for efficient manufacturing automations, which is a specialised design for production line-scale flexibility and interoperability.
## 3 Results and Discussion
### Mechanical and Piezoresistive Characterization
In fact, considering the S geometry, \(\sigma\) decreased by about one order of magnitude when the porosity increased from 40% to 80%, going from 1.17 \(\times\) 10\({}^{-5}\) to 1.50 \(\times\) 10\({}^{-6}\). Similar results have been obtained when the deformation is 8%. Moreover, the piezoelectrical sensitivity varies significantly with the three geometries, as it is possible to see from the ratio of electrical resistance (R\({}_{0}\)/R\({}_{8}\)), whose values are reported in Table 2.  
In particular, for the systems with G geometry, the R\({}_{0}\)/R\({}_{8}\) values changed from 2.21 \(\pm\) 0.20, and in the case of the G80 structure, to 4.95 \(\pm\) 0.50 for the less porous G40. More significantly, the systems with S geometry showed larger resistance variations with a one-order of magnitude drop when S40 structure is compared with S80 structure (Figure 7c,d). The compression sensitivity of several porous structures was evaluated by measuring the gauge factor (GF) at 8% strain (Figure 7d). All samples displayed GF absolute values above 6, with TPU/GE S40 and TPU/GE S60 displaying a value above 12. This difference is ascribed to the peculiar shape of the S unit cell, which leads to 3D structures with bigger trabeculae, which under deformation give rise to the building up of more effective conductive pathways. To the best of our knowledge, such high values have never been reported for graphene-based polymer porous structures when subjected to compressive strain [47]. It has to be pointed out that GF values are even higher for deformation extents lower than 8%, and then tend to plateau as the maximum strain value is approached (Figure 8). Indeed, most samples displayed GF absolute values ranging from 60 to 20 for deformation extents from 1% to 5%. It is also worth noting that for all porous structures, GF increases by reducing the porosity at a fixed strain. This confirms that a key role is played by the dimension of the trabeculae. The bigger the size of trabeculae, and consequently the larger the number of wrapped TPU particles which can be comp
# Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
## 2 Theoretical Background
### Impact of Elastic Modulus of the Adhesive Layer on Dispersion Characteristics  
Research [28] shows that material deterioration manifests as second and third-order elastic modulus changes. Among these changes, the second-order elastic modulus significantly influences phase velocity matching. In contrast, the third-order elastic modulus has an impact on the nonlinear effects in damage detection, with a relatively minor impact on phase velocity matching. Thus, this study focuses on the phase velocity dispersion curves in the aluminum-acrylic resin-polystyrene composite material, which analyzes the effects of second-order elastic modulus. As shown in Figure 3, when the elastic modulus of the adhesive layer varies within the range of 3.2 to 4.2 GPa, phase velocity dispersion curves shift upwards to the right as the second-order elastic modulus increases. This suggests that the phase velocity increases with an increase in the second-order elastic modulus at the same frequency.  
As depicted in Figure 4, the first six modes are individually extracted and examined to observe the variations in the dispersion curves better. The A0 mode is relatively insensitive to changes in the second elastic modulus of the adhesive layer within the frequency range of 0.42 MHz to 0.7 MHz, presenting only slight variations. The essentially unaffected phase velocities of the S0 and A1 modes by variations in the second elastic modulus of the adhesive layer further shows their stability within their respective frequency ranges. The second elastic modulus phase changes from 0.8 MHz to 1.02 MHz, and above 1.5 MHz,  
Figure 3: Aluminum–acrylic resin–polystyrene dispersion curves of phase velocity with varying second-order elastic modulus of the adhesive layer.  
Figure 2: Aluminum-acrylic resin-polystyrene dispersion curves.  
there is no obvious effect on the phase velocity dispersion curve of the S1 mode. However, at below 0.8 MHz and within the 1.02 MHz to 1.5 MHz range, the changes in phase velocity are greater than in other ranges. The phase velocity dispersion curve of the A2 mode is more sensitive to changes in the second elastic modulus of the adhesive layer within the frequency ranges from the initial frequency to 1.0 MHz and from 1.2 MHz to 1.6 MHz. However, the changes are essentially zero within the frequency range from 1.0 MHz to 1.2 MHz and above 1.6 MHz. For the S2 mode, the phase velocity is significantly affected by variations in the second elastic modulus within the range from the starting frequency to 1.5 MHz. In comparison, beyond 1.5 MHz, the changes gradually approach zero.
## 3 Results
### Simulation
#### 3.1.2 Simulation Analysis of Non-Smooth Structure Arrangement  
Building on the shear stress peak regions identified in Figure 5, this study further evaluates the relationship between manufacturability and performance gains for different arrangement schemes. Considering machining feasibility constraints for suction surface leading-edge dimple arrays, three comparative schemes were designed: Scheme A (pressure surface trailing edge only), Scheme B (suction surface leading edge only), and Scheme C (dual-region arrangement), all employing a uniform dimple diameter of 0.9 mm (Figure 9). Numerical simulation results reveal the following:  
* **Performance Degradation in Scheme B**: At 150 m\({}^{3}\)/d, Scheme B achieved only a 4.47% head gain, representing a 65.56% reduction compared to Scheme A (12.98%).
* **Marginal Benefits of Dual-Region Optimization**: Scheme C provided merely a 2.55-percentage-point head improvement over Scheme A, with efficiency gain differences below 1.5%.
* **Dominant Role of Pressure Surface Trailing Edge**: Dimples on the pressure surface trailing edge exhibited superior contributions to both head and efficiency enhancements.  
Figure 8: Performance of dimple-type non-smooth impellers under varying flow rates: (**a**) head; (**b**) efficiency.  
These phenomena are attributed to stronger shear layer instabilities at the pressure surface trailing edge, where non-smooth structures more effectively suppress boundary layer separation. For engineering applications, Scheme A is recommended for single-region optimization, achieving over 90% of the maximum performance gain while maintaining practical manufacturability.
###### Abstract  
With the rapid development of ubiquitous data collection and data analysis, data privacy in a recommended system is facing more and more challenges. Differential privacy technology can provide strict privacy protection while reducing the risk of privacy leakage, but it also introduces unwanted noise, which makes the performance of the recommender system worsen. Among different users, the degree of their sensitivity to privacy is usually different. Thus, through considering the impact of users' personalized requirements, the collaborative filtering algorithm can be designed to reduce the amount of unwanted noise. Taking the above assertions into account, we propose a collaborative filtering algorithm based on personalized privacy protection. First, it locally classifies ratings by privacy sensitivity on the user side, then utilizes the random flip mechanism to protect the privacy-sensitive ratings. Then, after the server catches the perturbed rating data, we reconstruct the joint item-item distribution through the Bayesian estimation method. Experimental results show that our proposed algorithm can significantly improve the recommendation performance of recommendation systems while protecting users' privacy.  
recommendation system; differential privacy; collaborative filtering algorithm; privacy leakage +
Footnote †: journal: Medical Support Technology Research Department, Systems Engineering Institute, Academy of Military Sciences, PLA, Tianjin 300161, China 1
## 4 Simulation and Discussion
### Reconstruction Performance versus the Sparsity Level
#### 4.3.1 Reconstruction Success Rates  
Figure 5a,b depict reconstruction success rates versus the sparsity level \(K\) for synthetic and real signals. The horizontal axis represents different \(K\) drawn from [5; 60], and the vertical axis shows the corresponding success rates (in percentages).  
For a small \(K\), high reconstruction success rates are acquired by FRSVssMP and the competing algorithms. As the sparsity level becomes large, success rates obtained decrease considerably, while differences between the success rates of the algorithms become significant. For both synthetic and real signals, FRSVssMP presents a considerable performance advantage over the greedy algorithms in the first category, such as StOMP and CoSaMP, due to the utilization of sparsity variable step-size adaptive. Moreover, FRSVssMP shows a slight superiority over SAMP, AStMP and CBMP on the whole, because it exploits the underlying information of the sparsity level and filters the incorrect atoms. CBMP acquires the second best performance, followed by AStMP, SAMP, CoSaMP, and StOMP.  
Figure 4: Performance of the initial sparsity level estimation approach. **(a)** The initial estimation of sparsity level in different experiments; **(b)** The estimated sparsity level vs. iterations.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 3 Implementation of the Selected Balancing Methods  
Following the comparative analysis presented in the previous section, two topologies were selected for implementation and experimental evaluation in a four-cell battery balancing circuit: delta-structured switched capacitor equalizer (DSSCE) and single flyback converter-based equalizer (SFCBE). This section details the implementation of these two balancing methods, explaining their operating principles and design considerations.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 2 Related Works  
In this section, we first describe some of the existing and planned satellite constellations for IoT. At present, satellites are widely recognized as an essential part of the IoT. They form communication satellite constellation, which realizes applications in data acquisition, system monitoring, tracking and positioning, and message transmission. Next, some new concept satellites proposed in recent years, and their characteristics are summarized. These new concept satellites represent some of the current trends in satellites.
## 6 Limitations and Future Work  
In this section, we summarize several limitations and the future of WDPSs. Generally, a hybrid isolated WDPS is developed with an aim to achieve maximum generation benefits from RE resources. However, the penetration of RE resources has certain limitations and varies from system to system. Based on this concept, the conventional DPP used in this study has minimum operational limits of 30% of the full load. This means the maximum variable load demand is 0.7 pu irrespective of the wind generation. Moreover, the simplified model of the DFIG and converters in the VSWT restricts its usage in carrying out studies within the time horizon that covers primary frequency control actions. This means that it is not useful for other types of analysis, such as voltage control, short circuit, protections, and harmonics. Furthermore, the SPBA also has certain limits, solely depending on the number of controllers tuned at a time, because the increased number of controllers significantly enhances the number of variables, which increases the complexity and computational time. Regarding future directions, we recommend including the energy storage system with the aim of providing the frequency regulations. Moreover, the consideration of flexible alternative current transmission system (FACTS) devices can be an option to increase system stability and reliability. Finally, the hybrid system can be tested because hardware in a loop (HIL) in certain real-world environments, like OPAL-RT, can be valuable.
## 4 Performance Analysis
### Execution Time  
Figure 5 displays the efficiency of the proposed and current methods for different file sizes, such as 20 MB, 40 MB, 60 MB, and 80 MB, compared in terms of execution times. The findings are evaluated and compared to other methods such as DES, RSA, and AES. As a consequence of the results, it was discovered that the suggested approach outperforms the alternate methodologies in terms of performance.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Energy transmission & 70 m \\ \hline Energy sampling & 30 \\ \hline Energy amplitude & 0.0123 m \\ \hline Energy aggregate & 5 kWh \\ \hline Primary user & 150 \\ \hline Secondary user & 150 \\ \hline Number of packets & Random of 10–15 \\ \hline Square area size & 1000 \(\times\) 1000 m\({}^{2}\) \\ \hline Velocity & [10,40] m/s \\ \hline \end{tabular}
\end{table}
Table 1: Simulation specifications of the suggested system.  
Figure 4: Comparison of security level (%) for various file sizes for existing vs. proposed method.
## 3 Results and Analysis
### Failure Process
#### 3.1.3 Group Three (AFCB1-AFCB4)  
(1) AFCB1: As the loading remains at a relatively low level, nothing visible is noticed. When it reaches 175 kN, the joints of fly-ash blocks and clay bricks in the center start to crack vertically and then gradually extend at both ends as the loading increases to 200 kN. At this point, the clay bricks crack slightly, while the fly-ash blocks show no visible change. Under a loading of 250 kN, through cracks are noticed at the brick-block joints. As the ends of the vertical cracks on the clay bricks meet, some bricks crash. Afterward, the loading continues, during which exfoliation takes place; cracks at the top and bottom parts of the structure are widened and the fly ash blocks break in half. As the loading increases to 298 kN, the clay bricks at both sides of the fly ash blocks start to show wearing of the edges and corners, the clay bricks nearly crash and the blocks bend. Therefore, the specimen yields. Please refer to Figure 7a for the failure state of AFCB1.  
(2) AFCB2: Apart from the improvement in split limit, loading limit and ductility, the failure process of AFCB2 bears resemblance to that of AFCB1.  
(3) In comparison with AFCB2, AFCB3 and AFCB4 show great improvement in ductility, split limit and loading limit, as the cracks now densely spread on the surface. It can be said that the steel pipe embedded largely contributes to better force-bearing performance of the integrated brick-block masonry structure. Please see Figure 7c,d for the failure states of AFCB3 and AFCB4.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 4 A Proposed New Secured Unclonable Medical Watermarking Scheme  
The key idea of the proposed approach is to embed SUC in each medical image device to make them physical unclonable. A medical image generator with an embedded SUC, in particular, becomes a clone-resistant medical image generator. Figure 6 illustrates a sample comparison between a traditional medical device without an SUC identity and a medical device with an embedded SUC.  
Figure 5: SUC-based generic identification protocol. Adapted from [32].  
Figure 6: The proposed concept of making medical images clone-resistant by embedding the SUC technique.  
The proposed clone-resistant medical image generator (CRMIG) produces a clone-resistant watermarked image as follows: After generating the original image, a watermark (WM) is generated and then it is signed by using one SUC input-output challenge pair as a one-time ticket. The resulting signed watermark (Z) is embedded in the original image as a one-time watermark signature. The resulting MIS attains the following security features:  
1. Medical images are not repeatable and are non-replaceable.
2. Medical images are non-splice-able.
3. Non-repudiation.
4. Provably unique medical images.
5. Integrity guarantee.
6. Authentication.  
The proposed CRMIG counteracts all expected splicing and cloning attacks as SUC provides a medical image generator with a unique signature which is non-repeatable and unclonable.
## 2 Materials and Methods
### Data-Collecting Sensors Used in the Project: GNSS, Meteorrones, In Situ Sensors, Sentinel
#### 2.2.3 In Situ Meteorological and Soil Sensors  
The MAGDA project deployed metIS hubs as part of its in situ sensor network to enhance localized environmental monitoring in agricultural areas. These hubs were specifically designed to provide high-frequency meteorological and soil measurements and were deployed across the demonstration sites. The metIS hubs integrate various sensor configurations, including air temperature and humidity sensors, anemometers, rain gauges, soil moisture probes, and leaf wetness sensors, making them a versatile solution for both meteorological and agronomic monitoring.  
The hubs were deployed in two configurations: as standard weather stations, and as in-crop sensors placed directly within cultivated fields to provide more representative environmental data. The weather station configuration included wind speed, rainfall, air temperature, humidity, and soil temperature and moisture sensors, while the in-crop configuration focused on leaf wetness duration and soil properties relevant to precision agriculture.  
Data from the metIS hubs were transmitted every 15 min via cellular networks, initially using 2G connectivity, which was later upgraded to LTE-M to improve data transmission reliability, particularly in rural areas where network coverage was initially problematic.  
These data were processed and stored in the CAP 2020 infrastructure and made accessible to the MAGDA forecasting system through a structured API, ensuring seamless integration with numerical weather prediction (NWP) and hydrological models.  
The validation of the metIS hub measurements was conducted by comparing their outputs with data from existing meteorological station networks, Sentinel satellite observations, and GNSS-Reflectometry (GNSS-R) soil moisture retrievals. This multi-source validation framework ensured that the hubs provided accurate and reliable environmental data, thus enhancing the representation of small-scale meteorological and soil moisture variations essential for agricultural decision-making.  
The modular and easy-to-deploy nature of the metIS hubs allowed MAGDA project partners to install them efficiently across different demonstration sites, reducing setup costs while significantly enhancing localized meteorological monitoring capabilities. Their integration into the broader observation network, alongside GNSS, Meteorones, and Sentinel, further strengthened the multi-scale, high-resolution monitoring strategy adopted within the project, providing valuable input for improved weather forecasting, hydrological modeling, and agricultural risk assessment.