esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 3 WAMS Operation and Dynamic Fault Detection
networks in developing countries, where aging infrastructure and inconsistent grid performance remain persistent challenges. Through the integration of WAMS into these networks, utilities can develop data-driven strategies to enhance system resilience, optimize grid operations, and facilitate a smoother transition toward modernized, renewable energy-integrated power grids. As illustrated in earlier sections, the issue of communication is central to the operational effectiveness of WAMS. The SCADA network, which is the most popular system, utilizes LAN systems to provide situational awareness [8]. On the other hand, WAMS aims to circumvent the insufficiencies of SCADA systems and provide more achievable situational awareness that relates real-time information from the power system network to the control room and from the control room relay instructions to automated infrastructure like isolators, circuit breakers, and CTs to observe and control the entire power network [14; 21]. The PMUs scattered and deployed across different locations on the power system network must be interconnected to fulfill their responsibilities [14]. WAMS communication infrastructure, therefore, plays a crucial role in transforming a conventional grid into a smart grid network. [PERSON] and [PERSON] (2018) [45] analyzed the challenges and efficiencies in deploying wireless communication infrastructure in WAMS. [PERSON] et al. analyzed a WAMS architecture communication system and found that the precision of the communication system was determined by the distance of the PMUs from areas and, over time, the quality of the PMU placement on these energy infrastructures [1]. Generally, for the purpose of communication, the telecommunication infrastructure has been designed to be wireless to truly achieve wide-area operation. GPS transmission is the most significant data transmission infrastructure. The C37.118 standard format is divided into four packets, which can a transmit data frame, configuration frame, and header frame [1]. The data frame consists of the PMU-measured real-time phasor data in binary. The configuration frame is the subset of the available data in the data frame, which indicates the real-time data in the PMU [1; 42]. These data can be transmitted only on a communication jacket in a way that synchronizes the PMU data. The GPS provides the communication infrastructure that synchronized the phasor measurements using PMUs placed in buses to complete the observability and controllability of the grid. [PERSON] et al. designed a communication infrastructure that deploys several wireless technologies to test their efficiency in monitoring and controlling WAMS operations. These infrastructures include wireless LAN, Wi-Fi, Wi-Max, GPRS, and GSM infrastructure [55]. Different communication infrastructures are selected to address the specific requirements of a given energy grid.
# Structural Correlation Based Method for Image Forgery Classification and Localization
###### Abstract  
In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.  
image forgery; copy-move; splicing; classification; localization +
Footnote †: journal:  
copy-move images which were composed using that authentic image as the host image. The algorithm also determines the authentic image based on structure and luminance similarity between images and assigns it as the centroid of the cluster. The cluster centroid is used to classify the image forgeries and localize the tampered regions. The experimental results show that the proposed method outperforms state-of-the-art techniques in image forgery classification and localization accuracy. In addition, we distinguish the source and target regions in copy-move tampering localization.  
The further part of this paper is organized as follows. Section 2 provides a brief review of image splicing and copy-move detection and localization methods. In Section 3, we present the image retrieval algorithm based on HE and BOF. The proposed image clustering algorithm is introduced in Section 4. Section 5 presents the image forgery detection and localization. The experimental results are discussed in Section 6. Finally, Section 7 concludes the paper.
## 1 Introduction
Moreover, it has been shown that the improvement of the adsorption capacity and the chemical stability of magnetic nanoparticles can be enhanced by their surface stabilization by incorporating functional groups such as amine, carboxylic and phosphoric acids, or its coverage by dispersing into activated carbon, graphene, and carbon nanotubes to form composite materials [8; 11; 16; 17; 24; 25; 26]. Sodium alginate (A) is a linear biopolymer. It has been extracted from brown algae and consists of edible polysaccharides with homopolymeric blocks of 1-4 linked d-mannuronated and L-guluronate. As adsorbent, sodium alginate is effective for the removal of several heavy metal ions. Its adsorptive ability comes from its structure, which presents many carboxylic functional groups [27]. This biopolymer is environmentally benign, biodegradable, hydrophilic and abundant in nature [5; 7]. Because of these advantages, alginate has been usually used as beads to encapsulate several adsorbents, including magnetite nanoparticles [28], activated carbon [29], carbon nanotubes [30], graphene oxide [31], titania nanoparticles [32] and clays [33] in order to improve their dispersion, stability and affinity towards several compounds in aqueous solution. It is known that a certain amount of the materials used in the adsorption processes could be lost when they are used in powder form after water treatment, generating another environmental problem and huge quantities of waste. Likewise, these powdered materials cannot be used in a fixed-bed column because of the regeneration costs and the clogging of the reactor [34]. In general, the encapsulation of the adsorbent in alginate beads is an excellent solution to overcome this problem. In this context, the purpose of this study is to prepare an adsorbent material by encapsulating functionalized maghemite \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) and carbon nanotube (f-CNT) in alginate beads (A) and used them for methylene blue uptake.
## 5 Discussion  
Statistical techniques, like linear regression, ARIMA, ARCH, and NARX modeling, have been traditionally employed in time series forecasting [166]. Strong assumptions on data are necessary for the construction of such models (e.g., data are generated by linear, time invariant processes, possibly with added Gaussian noise). These assumptions do not hold in many practical situations, and by using these traditional statistical techniques on time series from which we actually know very little about their true data-generating process, we incur the risk of generating inaccurate forecasts. Comparatively, ANN models offer a more flexible modeling strategy, with fewer assumptions on data-generating mechanisms, and produce accurate forecasts.  
Generally speaking, the aim of time series forecasting is to predict future values with accuracy and simplicity. However, a large fraction of the ANN architectures reviewed in this article are more complex than others. But this fact seems to contradict the principle of Occam's razor, which maintains that the simplest solution is usually the best. In machine learning literature, Occam's razor is used for two different principles [167]:  
1. First razor: Starting from the fact that simplicity is desirable in itself, the simpler model should be preferred between two models with the same generalization error.
2. Second razor: Starting from the fact that you are likely to have a smaller generalization error in the simpler model, it should be preferred between two models with the same error in the training set.  
[PERSON] [167] argued that, in the first razor, simplicity is only a proxy for comprehensibility. Nevertheless, his paper shows that, contrary to the second razor's claim, greater simplicity does not necessarily lead to greater accuracy. _If we accept the fact that the most accurate models will not always be simple or easily understandable, we should allow an explicit trade-off between the two_[167].  
[PERSON]'s razor is largely controversial. However, a simple and easy-to-understand method is needed to calculate point forecasts from machine learning time series models based on proven techniques.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Clustering Algorithms
#### 3.5.2 Partitioned Algorithms  
Partitioned clustering is a common technique that divides the data into K distinct point sets, each of which has homogeneous points, by selecting the appropriate scoring function and minimizing the distance between each end and the cluster centroid of each cluster [152; 153]. The evaluation function is the most critical aspect of partitioned clustering. However, some elements of the method are pretty much like general algorithms. Partitioned clustering is suitable for nonwishing the cluster in the small-scale database to identify the collection (each cluster class regarded as one cluster). The K-means algorithm is one of the most common flat clustering algorithms and is one of the most well-known partitional clustering methods. [PERSON] coined the term 'K-means' in 1967 [154; 155]. [PERSON] (1957) was the first to offer the standard method as a pulse-code modulation approach. The K-means algorithm's purpose is based on the input parameters \(K\), which split the dataset into \(K\) clusters. First, we select \(K\) objects as initial cluster centres, compute the distance between each cluster centre and each object, assign it to the nearest cluster and update the cluster averages. This process continues until the criterion function is satisfied [156].  
The K-means algorithm has a time complexity is \(O\) (\(knl\)), where (\(k\)) refers to the number of clusters, (\(n\)) refers to the number of objects, and (\(I\)) refers to the number of iterations(which depending on the stopping condition, can typically be seen as being included by a limited number). The cluster centroids and (\(kn\)) similarities between all objects and all clusters must be calculated in each iteration [157].  
The K-means algorithm requires specifying the number of clusters (\(K\)) as input, and therefore, determining the optimal number is critical. However, the process can be performed whilst varying numbers of clusters and clustering with the best results documented (for example, measured by the objective function). A conventional partitioning technique allows for cluster merging and splitting, and the conclusion should theoretically have the most significant number of clusters [24].  
K-medoids [151] is a partition clustering algorithm with significant similarities to the K-means clustering algorithm. Nonetheless, K-medoids differs from K-means because the centre of a cluster is an actual data object with K-medoids. K-means requires calculating the mean vector for the data objects in a cluster. Thus, the K-means algorithm can only be applied to a Euclidean feature space. The K-means++ algorithm [158] is an improvement on the original K-means algorithm, which uses randomized seeding approaches to attain higher accuracy and less complexity.
## 3 Simulation Results
### The Effect of the Remanence on the Trajectory of Particles  
Figure 7 shows the trajectories of magnetic particles with diameter of 1 \(\upmu\)m under the action of the magnet with different remanences, taking a moving time of 300 s. It was found that the trajectory of the particles did not change with the increasing remanence when the releasing height was 5 mm. However, when the releasing heights were 10 mm and 15 mm, as shown in Figure 7b,c, the changes in remanence caused an obvious difference in the trajectory of the particles. The higher the particle releasing position, the greater the deviation in the trajectory of the particles caused by the change in remanence.  
Figure 6: Trajectories of magnetic particles with different diameters released at different releasing positions. (**a**) Releasing position 5 mm from the bottom; (**b**) releasing position 10 mm from the bottom; (**c**) releasing position 15 mm from the bottom. The remanence of the magnet is 0.5 T.  
As can be seen from the above simulation results, the particle diameter and remanence of the magnet exhibited different effects on the movement of the particles, and different releasing heights also seemed to change the trajectory of the particles. The relevant mechanism will be further analyzed in the following discussion section.
## 4 Simulations on the Trapping System
### Thermal Simulations  
#### 4.2.1 RF Power Dissipation  
Each pair of electrodes can be schematized as a capacitor having a complicated shape and containing a dielectric medium (ceramics of the lateral supports) disposed only around their cylindrical extrusions. As a result, an equivalent series resistance (ESR) can be associated with each electrode, the value of which can be estimated as:  
\[ESR\ =\ \frac{\tan(\delta)}{\omega_{RF}\,C}\]  
where C is the lossless electrode capacitance and \(\tan(\delta)\) is the loss tangent, which arises from the phasor representation of the equivalent circuit parameters. For Shapal Hi-M Soft, its value is \(\tan(\delta)\approx 10^{-3}\). The reactive term of the \(ESR\) is responsible for the heating of the parts composing the trap by the Joule  
Figure 7: Stability diagram of the Paul trap, characterized by a coupling angle of 45\({}^{\circ}\) between the RF electrodes and endcaps’ axes. The black thick dots represent the \(\{q,a\}\) pairs for which the Paul trap (described by a system of coupled Mathieu equations) is stable, whereas the small gray dots indicate values of parameters for which the trap is unstable. The blue curves, calculated with the multiple-scale perturbation theory, enclose a stability region that is wider than the numerically estimated one, yet well describing the stability behavior for small values of \(q\). The green area is the stability region of the corresponding uncoupled system.  
effect. The electrodes' \(ESR\) is calculated by using a COMSOL Multiphysics simulation to estimate the corresponding capacitances \(C\). In the case of the blade electrodes, with \(\omega_{RF}=2\pi\cdot 3.5\) MHz and an RMS amplitude of \(200\,\mathrm{V}/\sqrt{2}\), we found an overall dissipated power of \(0.69\,\mathrm{mW}\) for the upper blades and \(0.74\,\mathrm{mW}\) for the lower ones. These values can be employed for simulating the trap heating due to the RF power losses in stationary conditions, using as the only constraint a fixed temperature for the external side of the flange (\(293\,\mathrm{K}\)). The results showed a small temperature increase on the order of tens of mK, with the hot spots mainly localized at the interfaces between the electrodes and the lateral supports. Regarding the thermal perturbation on the optical cavity, the simulation showed at most a vertical thermal expansion of \(0.4\,\mathrm{nm}\) for the ceramic lateral supports, while along the directions in the horizontal plane, there were no appreciable expansions.
## 3 Systematic Odometry Error Evaluation and Correction  
The procedure proposed in this paper to systematically evaluate and correct the systematic odometry errors of the omnidirectional mobile robot APR-02 is based on the definition of 36 individual calibration trajectories which together depict a flower-shaped figure, on the measurement of the odometry and ground truth trajectory in each calibration trajectory, and on the application of several strategies to iterative adjustment of the effective value of the kinematic parameters to match the odometry and the ground truth trajectories registered in these 36 calibration trajectories. The implementation of the 36 trajectories that define the flower-shaped figure is proposed as a representative test-bench of the infinite trajectories that can perform this omnidirectional mobile robot. In this paper, each calibration trajectory has been repeated 10 times; with a total of 360 registered trajectories. Five repetitions will be used to calibrate the effective value of the kinematic parameters and Five repetitions will be used to validate the results.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 4 Differential Privacy
### Mechanisms of Differential Privacy
#### 4.1.2 Gaussian Mechanism  
The Gaussian mechanism is an alternative to the Laplace mechanism used to inject noise into the results of a function to ensuring privacy while preserving data utility in differential privacy. Because of its bell-shaped distribution, the Gaussian mechanism [37] smooths out noise and is frequently chosen when there is a need for more precise control over noise distribution or when sensitivity (\(\sigma\)) is high.  
**Definition 3**.: _Given a function (\(f:O\rightarrow\mathbb{R}\)) that operates on a dataset (\(O\)), the Gaussian mechanism perturbs the output of \(f(O)\) to achieve \(\epsilon\)-differential privacy. The perturbed output (\(P(O)\)) is defined as follows:_  
\[P(O)=f(O)+\mathcal{N}(0,\sigma^{2}) \tag{7}\]  
where \(f(O)\) is the exact result of the \(f\) function on dataset \(O\). \(\mathcal{N}(\sigma^{2})\) represents the noise that is evaluated through a Gaussian distribution. \(\sigma\) refers to a parameter that is evaluated  
Figure 1: Laplace distribution.  
according to the sensitivity of the \(f\) function. It measures how much the output of \(f(O)\) can change when one element of \(O\) is altered. Here, \(s\) is the sensitivity of the function, and log represents the natural logarithm [38].  
\[\sigma^{2}=\frac{2s^{2}\log\left(\frac{125}{\delta}\right)}{\epsilon^{2}} \tag{8}\]  
The privacy parameter that regulates the quantity of generated noise is \(\epsilon\). This mechanism balances privacy and utility by controlling the \(\epsilon\) and \(\sigma\) parameters. Larger \(\epsilon\) and smaller \(\sigma\) values provide weaker privacy guarantees, as they add less noise while providing higher utility. In the same way, smaller \(\epsilon\) and larger \(\sigma\) values add more noise, strengthening privacy but potentially reducing utility.
## 3 Field Measurements
### Field-Measurement Results
#### 3.2.2 Track Settlement Measurement System  
The track settlement was measured in the HSR T1 and HSR T2 sections, and the measurement was conducted over a total of 20 m (10 each at 2 m) in each section. Figure 12 shows the measured results of the sections HSR T1 and HSR T2 [17].  
To examine the characteristics of the settlement behavior of the tracks due to tunnel excavation, an RM sensor was installed, and initial values were set before the onset of the tunnel excavation work. All of the data were collected during the tunnel excavation process.  
In the case of the HSR T1 section, the maximum displacement was measured to be \(\sim\)5 mm, and the points 3, 4, and 9 showed a large displacement difference.  
Figure 12: Track-settlement measurements (HSR T1). (**a**) Points 1–5; (**b**) Points 6–10.  
Conversely, there was no significant displacement at the center of the tunnel excavation, which corresponds to points 5 and 6, thus indicating that the tunnel excavation had an effect on the ends of the measurement section.  
The results show that drastic settlement behavior occurred in the early stage of tunnel excavation, and the settlement during the process of tunnel excavation was mostly maintained at a constant level. In addition, it was confirmed that the settlement was gradually restored to its initial value at the stage near the completion of the tunnel excavation.
## 3 System Architecture
### Edge Node Design  
To retrofit the existing mechanical counters, we aimed to develop a low-cost edge node with specific features that would work effectively under resource-constrained conditions. As our approach is focused on environments where high-performance hardware is not available, we designed the system to balance accuracy and efficiency. The developed node contains the following features:  
* A 5-megapixel camera to capture the mechanical counter image;
* A backlight LED that facilitates camera usage even in low-light conditions;
* A low-power MCU with sufficient storage and computing capabilities to capture images, store them locally, and run the ML training + inference on-spot;
* A communication module enabling connectivity service in difficult places (closed spaces, basements, etc.) without having to rely on the existence of the local communication infrastructure such as WiFi.  
The existing off-the-shelf devices fail to satisfy all of the aforementioned requirements. Therefore, we designed the edge node based on the ESP32-CAM module [47]. In addition to WiFi and Bluetooth Classic/BLE interfaces embedded within the ESP32, we included a cellular module supporting 4G services such as NB-IoT and LTE-M, as well as EGPRS fallback for areas where such new services have still not been deployed. Relying on the existing and widely deployed MNO infrastructure, we thereby strive to achieve a nearly ubiquitous connectivity. The main components and the fabricated prototype are shown in Figure 4.
## 5 Future Trends, Challenges, and Opportunities
### Source Code Sharing  
The availability of a dataset for ML training is important for testing different ML algorithms and constructing models for RIS-assisted communications systems. Sharing source code with the community motivates researchers to strengthen the design of ML models. Researchers are also motivated to reproduce the experiments and results using the available source code. Having reproducible results is important for scientific research since it ensures the findings can be validated and verified.  
Source code sharing encourages the collaboration between researchers to enhance ML models and the performance of the systems. Collaboration allows the scientific communityto contribute improvements, suggest modifications, and lead to more robust and reliable solutions [110]. It brings different perspectives and expertise to the benchmarks. The educational value of sharing the source code resides in the insight gains and best-practice benchmarks for researchers, students, and practitioners, who can go deep into implementation details and novel techniques about ML methods in this area. The accelerated progress is ensured by having open-source initiatives to accelerate the innovation of techniques to enhance RIS-assisted wireless systems [111]. It leads to a faster development of new algorithms, models, and methodologies.