esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Attack Detection in Smart Grid
#### 4.3.3 Data-Driven Detecting Method  
Although the traditional attack detection methods can identify and mitigate some attacks, physical attacks and cyber-physical attacks are not usually considered in smart grid scenarios. In comparison, the anomaly-based method focuses on detecting abnormal behaviour patterns from normal behaviour data and also has several proposed approaches: the data mining method, the information theoretic-based method, and the artificial intelligence (AI)-based method [84]. In this case, anomaly-based IDS has the potential to encounter not only zero-day vulnerabilities but also cyber-physical attacks. Some data-driven attack detection methods are listed in Table 5.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Cite** & **Algorithm** & **Against Attacks** & **Evaluation** \\ \hline
[22] & Transformer + Federated Learning + Pallier Cryptosystem & Stealthy FDI attack & Test on IEEE 14-bus, 118-bus testbed, has more than 90\% accuracy on both weak and strong attacks. \\ \hline
[85] & Jripper, Random Forest (RF), one-R, Naive Bayes & Short circuit faults, Line maintenance, Remote tripping command injection, Relay setting change, FDI attack attack & Classification test on Mississippi State University three-class dataset: no event, attack, natural. RF has 92.1\% accuracy on detecting attack events. \\ \hline
[86] & OCSVM (unsupervised) + Decision Tree; RF (supervised evaluation) & Scanning, replay, and DoS attacks; load breakers and generators with abnormal behaviours (rule-based). & Test on Idaho CPS SCADA (ISAAC) testbed to identify normal, abnormal cyber, and abnormal physical scenarios using PCA anomaly detection method OCSVM has over 98\% accuracy. \\ \hline
[87] & K-means algorithm and DBSCAN clustering & Flooding DoS attacks & Using traffic features: ip source, ip destination, ethernet source, ethernet destination, protocol name, frame numbers, and data bytes to conduct three-class clustering. \\ \hline
[51] & Proposed Autoencoder + Generative Adversarial Network (GAN) & DoS, FDI, replay attacks & Anomaly detection and anomaly classification evaluation with various algorithms on Modbus network flows (generated by Smod), DNP3 network flows (IDS data from Rodofile), and operational data in different scenarios. \\ \hline
[88] & Hierarchical Temporal Memory (HTM) & - & Test on open PMU source data from Lawrence Berkeley National Laboratory’s (LBNL) 7.2 kV distribution grid. Compared with random cut forest, Bayesian change, relative entropy. HTM has over 96\% accuracy. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data-driven attack detecting methods.
## 5 Sensitivity and Comparison Analysis
### Sensitivity Analysis
#### 5.1.2 Loss Attenuation Coefficient  
The loss attenuation coefficient is first divided into six points, as shown in Figure 7 to test the relevance of the weights of the indicators to the benchmarking results. Moreover, regardless of the loss attenuation coefficient, Figure 7 reveals that Singapore, Thailand, and Vietnam have the highest performance, while Myanmar and Laos have the lowest. The remaining countries' standings have altered. Cambodia ranks eighth for coefficients less than or equal to 12.39 and fourth for coefficients equal to 51.47. This suggests that when the coefficient is raised, decision makers are not too concerned with the possibility of boosting Cambodia's status in the ASEAN area.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 2 Secure and Resilient Smart Grid Frameworks
### Secure Framework: NISTIR 7628 R1  
According to NISTIR 7628 [5], the smart grid's operation actors are divided into seven domains: marketing, operations, service provider, bulk generation, transmission, distribution, and customer. All these domains connect and interact with each other via over one-hundred-thirty different logical interfaces, which can be summarised into twenty-two interface categories based on two communication ends and the CIA requirements.  
This work focuses on five main domains: bulk generation, operations, transmission, distribution, and customer. The related logical interface categories can be listed as follows: logical interface categories 1-4 describe the interface between control systems and equipment (e.g., category 37 represents the transmissions between SCADA and transmission substation equipment); logical interface categories 6-7 describe the interface between the control system and the same/different organisations (e.g., the information flow between 29/37 SCADAs and 30 EMS); logical interface category 10 refers to the interface between control systems and non-control/corporate systems (e.g., the information flow between the control system and 46, 47, 17, 5, and 7 non-control/corporate systems); and logical interface categories 11-12 denote the interfaces between the sensors and sensor networks/control system (e.g., the information collected by 12 and the information 12 communicated with control system 37). Figure 1 simplifies the process and the architecture of the smart grid.  
Figure 1: NISTIR 7628 smart grid processes.  
The NISTIR 7628 framework provides readers with a better understanding of the smart grid structure, issues, and related security techniques; also, the logic reference model will benefit defence-in-depth security deployment because it defines each interface's security (CIA) requirements.
## 3 VIs as Proxies for Other Canopy Attributes
### VIs and LAI
Arrowweed is an extreme erectophile, with near-vertical leaves often closely expressed to the stem; saltcedar has cylindrical, needle-like leaves, and willow and cottonwood are broad-leafed plants. Field measurements of fPAR, LAI and NDVI show that k in Equation (3) varies from 0.15 for arrowweed to 1.25 for cottonwood, and as a consequence, the LAI required to produce 50% fPAR varies from 4.62 to 0.55. Figure 2 shows LAI, NDVI and ET of these species compared to alfalfa, a common crop plant in irrigation districts along the river. Satellite images typically encompass areas of riparian vegetation as well as agricultural fields. All species had approximately the same LAI, but NDVI values varied by species as predicted by differences in k values, and ET was more closely related to NDVI than to LAI. Clearly, LAI alone is not enough to characterize light-absorption by these species. [PERSON] and [PERSON] [37], reviewing remote sensing applications in ecology, pointed out that the use of LAI in SVAT models is partly historical, because photosynthesis and transpiration were formerly measured mainly at the leaf level then scaled up to canopies. This required an estimate of how many layers of leaves were in the canopy and other details of canopy architecture. Today, the most widely accepted measures of plot-level photosynthesis and transpiration are from flux towers (see **Section 4**). These towers unobtrusively measure moisture and carbon fluxes from canopies to the atmosphere over fetches of several thousand square kilometers. Scaling these plot level measurements to larger landscape units by remote sensing does not require knowledge of LAI. [PERSON] and [PERSON] [37] suggested that the traditional LAI could be replaced by \"effective leaf area index\" as measured by VIs at the top of the canopy.
###### Abstract  
Hydrogen addition can improve the performance and extend the lean burn limit of gasoline engines. Different hydrogen injection strategies lead to different types of hydrogen mixture distribution (HMD), which affects the engine performance. Therefore, the present study experimentally investigated the effects of hydrogen injection strategy on the combustion and emissions of a hydrogen/gasoline dual-fuel port-injection engine under lean-burn conditions. Four different hydrogen injection strategies were explored: hydrogen direct injection (HDI), forming a stratified hydrogen mixture distribution (SHMD); hydrogen intake port injection, forming a premixed hydrogen mixture distribution (PHMD); split hydrogen direct injection (SHDI), forming a partially premixed hydrogen mixture distribution (PPHMD); and no hydrogen addition (NHMD). The results showed that 20% hydrogen addition could extend the lean burn limit from 1.5 to 2.8. With the increase in the excess air ratio, the optimum HMD changed from PPHMD to SHMD. The maximum brake thermal efficiency was obtained with an excess air ratio of 1.5 with PPHMD. The coefficient of variation (COV) with NHMD was higher than that with hydrogen addition, since the hydrogen enhanced the stability of ignition and combustion. The engine presented the lowest emissions with PPHMD. There were almost no carbon monoxide (CO) and nitrogen oxides (NOx) emissions when the excess air ratio was, respectively, more than 1.4 and 2.0.  
Keywords:dual-fuel engine; hydrogen injection strategy; lean burn limit; hydrogen mixture distribution; combustion; emissions +
Footnote †: journal: Applied sciences
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 5 Tests on New Methods for Gamma/Hadron Separation
### Evaluating More Machine Learning Models  
We have implemented and tested several Machine Learning methods using a fast test run with Lazypredict [20] on 42 different models in order to compare them to the benchmark method based on Random Forest.  
Lazypredict is a Python library used for quickly evaluating and comparing the performance of multiple Machine Learning models. It allows users to easily train and test a large number of models with minimal coding, providing a convenient way to quickly assess the suitability of different models for a particular task.  
Based on these preliminary test results, it appears that Gradient Boosting, LGBM Regressor, and Histogram Gradient Boosting show promising performance for gamma/hadron separation. However, it is important to note that these results are obtained from a limited sample and should be considered as approximate (refer to Table 1). Our primary objective is to improve the Q factor (refer to Section 5.4 for its definition), which heavily relies on impostor rejection. In more comprehensive tests, we have found that methods based on trees and XGB exhibit better performance in achieving this objective.
## 3 Literature Review
### Research Gap  
The discussions on the related works provided above clearly establish the immense potential of Twitter data analytics in healthcare. Two major challenges are evident. First, the current state of research on the topic is limited, both in terms of the scope of the works [10] as well as the investigation into and comparison of the methods for healthcare analytics, including machine learning methods [11]. A number of works exist in the analysis of tweets in the English language; however much more is needed to develop robust analytics methods, tool functionalities, and usability. Further works are needed in languages other than English, particularly cross-language works. There are several challenges that hinder the development of tools for Twitter data analytics in the Arabic language, the greatest being the complexity of the language itself. Research on Twitter data analytics in Arabic has begun to appear in recent years in various application domains (detecting authors' genders [12], detecting traffic related events [18; 20; 38], finding restaurants' reputations [13]) but the progress has been slow. Moreover, some works are available in Modern Standard Arabic (MSA), but in general (not specific to healthcare), the works on Arabic dialects are very limited in number and scope [10; 14]. The three works in Arabic specific to healthcare [15; 16; 17] that we have discussed in the previous section are limited in scope, depth, and/or functionalities. There is no known work in data analytics specifically on Saudi Arabic dialect in healthcare.  
The second challenge related to Twitter data analytics in healthcare concerns the scalability and interoperability of the Twitter data analytics systems. The challenges in this respect include management, integration, and distributed computation of data including the difficulties related to managing the 4V characteristics of big data, i.e., volume, velocity, variety, and veracity. There have been some works on the use of big data platforms in Twitter data analytics in various languages but in different application domains [18; 19; 20; 21; 22; 23; 37; 78; 79]. To the best of our knowledge, no work has been reported that uses big data technologies for data analytics in healthcare using tweets in the Arabic language.  
Healthcare is among the most data-intensive generating and consuming sectors. The use of big data distributed computing technologies is important for scalability and integration with other sources of healthcare information. Motivated by these research gaps, we have attempted the design of a tool that provides healthcare analytics capabilities from tweets in the Arabic language using big data technologies.
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## 2 Dry Eye Disease as an Inflammatory Disorder
### The Case against Antibiotics for Dry Eye Disease  
In relation to DED, antibiotics used to induce gut dysbiosis have resulted dry eye in experimental animal models [16]. Further, germ-free mice have been reported to spontaneously develop Sjogren syndrome-like inflammation and more severe dry eye than conventional mice [17]. Interestingly, restoration of the gut microbiome reversed the dry eye phenotype [17; 44], suggesting compromised innate and adaptive effectors both at the ocular surface and in the gut contribute to increased susceptibility to DED [45]. Locally, a healthy ocular microbiome has been implicated in the homeostatic regulation of the ocular surface while antibiotic disruption of ocular bacterial homeostasis can initiate inflammation [46] and lead to potential deleterious perturbations of commensal microbiome that can cause undesirable side effects resulting from the collapse of the eye's own immunodefense system [47].  
But perhaps a greater issue to be addressed with all these products is the design concept of stimulating or initiating local responses for amelioration rather than addressing the aetiology of DED. As articulated by [PERSON] and colleagues [48]: \"DED remains a disorder of long-term maintenance rather than permanent cure\" with current therapy addressing tear supplementation and localized transitory inhibition of inflammation. Since many patients with MGD suffer despite such treatment [16], how long lasting these treatments are, or whether they are establishing or further contributing to, the vicious cycle of DED [3] are important questions that need to be further addressed.  
As has been established from the above, a large component of DED is inflammatory. Perhaps it is time we move beyond attempts to address only localized ocular inflammatory cascades and expand our search for a dry eye \"cure\" by consulting the chronic inflammatory disease literature. This review posits a case for a more holistic approach to combating signs and symptoms of DED.
## 2 Synthetic DAS Signal Generation Methodology
### Physics-Based Signal Modeling (Flamant-Boussinesq)  
The ground deformation caused by a vehicle wheel's static load is simulated using the Flamant-Boussinesq approximation ([17], Appendix A). This model calculates the displacement field in an elastic, homogeneous, isotropic semi-infinite half-space due to a normal point load on the surface. Given the typically slow speeds of vehicles relative to seismic wave velocities, this quasi-static approximation is considered valid for low-frequency traffic-induced strain.  
The displacement (\(u_{x}\)) parallel to the fiber axis at a point \((x,y,z)\) relative to the load, where \(y\) is the perpendicular distance and \(z\) is the burial depth, is given by the following (derived from Equations (A1) and (A2) in Appendix A):  
\[u_{x}(x,y,z)=\frac{m\cdot g\cdot(1+\
u)}{2\pi E}\frac{x}{r^{2}}\left(\frac{z}{ r}+\frac{2\
u-1}{1+\frac{z}{r}}\right) \tag{1}\]  
where \(m\) is the mass per wheel, \(g\) is gravity, \(E\) is [PERSON]'s modulus, \(\
u\) is [PERSON]'s ratio, and \(r=\sqrt{x^{2}+y^{2}+z^{2}}\) is the distance from the load.  
The strain rate (\(\dot{\epsilon}_{DAS}\)) measured by a DAS system over a gauge length \(L\) is approximated by the finite difference of the time derivative of the displacement at the ends of the gauge length:  
\[\dot{\epsilon}_{DAS}(x,y,z,t)\approx\frac{1}{L}\bigg{[}\dot{u}_{x}\bigg{(}x+ \frac{L}{2},y,z,t\bigg{)}-\dot{u}_{x}\bigg{(}x-\frac{L}{2},y,z,t\bigg{)} \bigg{]} \tag{2}\]  
where \(\dot{u}_{x}\) is the time derivative of \(u_{x}\), calculated numerically from the sequence of displacements as the vehicle moves. The total signal at a given channel (centered at \(x\)) is the superposition of the strain rates induced by all wheels of all vehicles at that time.  
**Assumptions and Limitations:** The model assumes that the vehicle speeds are much smaller that the ground Rayleigh wave deformation velocity, therefore assuming a quasi-static deformation. Assuming dominance of static over dynamic tire-road interactions; see Figure 1 (justified by low-frequency DAS signals, see [7; 11]). Another assumption derived from the Flamant-Boussinesq model is that the terrain is considered a perfectly elastic, homogeneous, and isotropic medium, which is a simplification of real ground conditions. It neglects dynamic effects, layering, and anisotropy. Pathological singularities can occur near the load, especially if the depth \(z\) is very small compared to \(y\) (Appendix A). For the typical depths (0.4 m) and distances in road applications, the model generally produces consistent wavelet shapes (Figure 3). Also, the model inherently assumes uniform fiber sensitivity and coupling along its different sections. Even though, for the case study, constant sensitivity and coupling characteristics were assumed, the methodology accounts for the possibility of including variations across variable-length sections. Variations in real-world installations (i.e., due to burial depth or pavement/soil compaction) can affect the signals and are not studied in the examples; this is a known limitation discussed further in Section 5.
# Performance of Strengthened Accelerated Oscillator Damper for Vibration Control of Bridges
## 2 Topological Configuration and Motion Equations of the SAOD-LS  
### Topological Configuration of SAOD-LS  
In this paper, a generalized SDOF system based on the fundamental configuration of the traditional AOD system is considered, as shown in Figure 1, where the mass \(m_{1}\) is the primary structure, and the mass \(m_{2}\) is the secondary mass. Please note that in this article, the symbols \(m_{1}\) and \(m_{2}\) are used to indicate the primary and secondary mass, respectively. This convention is consistently followed throughout the entire paper. When using a secondary linear spring (LS) in this new configuration, we refer to it as the SAOD-LS.  
One terminal of \(k_{1}\) and \(c_{1}\) is linked to \(m_{1}\), while the other terminal is connected to the ground. One terminal of \(k_{2}\) and \(c_{2}\) is linked to \(m_{1}\), while the other terminal is linked to \(m_{2}\). The transmission device is connected to the ground, with a large radius attached to one end of \(m_{1}\) and a small radius connected to one end of \(m_{2}\). The transmission ratio, denoted as \(r\), is defined as the ratio of the big radius to the small radius. It is important to mention that, based on the connection method of the transmission device shown in Figure 1, the movement directions of \(m_{1}\) and \(m_{2}\) are in opposing directions. The transmission device \(G\), composed of a securely grounded rack-and-pinion system, serves to transform the linear motion of the rack into rotary motion for the pinion. By varying the radii of the pinions, the relative displacement between the input and output terminals can be adjusted by a factor of \(r\) (i.e., the gear ratio of \(G\)) [40]. As a result, \(m_{1}\) can transmit the amplified displacement, velocity, and acceleration to \(m_{2}\) through this device. Additionally, \(k_{2}\) and \(c_{2}\) contribute to the system's functionality by absorbing and dissipating energy. According to the principle of conservation of energy, the energy gained by the secondary structure directly correlates to the energy lost by the building [39]. This interaction helps to reduce the oscillation amplitude of \(m_{1}\).
# Novel Common-Mode Current Suppression Method in Transformerless PV Grid-Connected System
## 1 Introduction
Furthermore, it is easy to produce high frequency resonance in the CM circuit by using the single CM internal loop method so that the large CM current of the resonant frequency will be produced. In this paper, the CM equivalent circuit is used to analyze the high frequency components of the CM currents. For the high frequency component in CM currents, a method of constructing a dual CM internal loop to suppress the high frequency component of CM currents is proposed. This method does not change the original topology, and the parameter tuning is simple. The simulation results show that the high frequency components of the CM currents can be effectively suppressed by using the proposed method. Finally, the experimental results show that the proposed method has a significant effect on suppressing the high frequency components in the CM currents.
## 4 Discussion
### Limitations  
Despite the promising results, several important limitations of our study warrant consideration. First, while our data include reports from three different Italian laboratories, this regional focus may limit generalizability to laboratories with substantially different reporting standards, particularly those in countries with different units of measurement or reference range practices. International validation would be necessary before global implementation.  
Second, although we included some pediatric cases (such as Case #5), our study is primarily focused on adult populations. Pediatric laboratory interpretation presents unique challenges due to rapidly changing developmental reference ranges and age-specific physiological considerations. Further research specifically targeting pediatric laboratory interpretation is needed.  
Third, there exists a potential risk of patients misinterpreting AI-generated explanations as diagnostic advice, despite our careful design to avoid diagnostic language. In our patient feedback, 7% of participants indicated they might rely on the AI interpretation without physician consultation for future results, highlighting the need for clear communication about the system's role as a supplement to, not replacement for, clinical consultation. Implementation would require careful user interface design with prominent disclaimers about the non-diagnostic nature of the interpretations.  
Fourth, the perfect accuracy rate observed in our study may not be sustained in real-world implementation at scale, especially as laboratory methodologies evolve and new tests emerge. Ongoing monitoring and periodic revalidation would be essential for maintaining quality.  
Finally, our evaluation focused on technical accuracy and patient comprehension but did not assess potential impacts on healthcare utilization or clinical outcomes. Longitudinal studies would be needed to determine whether improved laboratory result comprehension translates to measurable health benefits.  
Regarding methodological aspects, we have implemented a three-stage training process: (1) initial familiarization with laboratory terminology and reference ranges, (2) supervised interpretation of increasingly complex cases, and (3) validation against expert interpretations. For values outside the training scope, we specifically engineered the system to acknowledge limitations with statements like \"This parameter requires further evaluation by your healthcare provider as it falls outside common interpretation frameworks\" rather than attempting speculative interpretations.