esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Deep Learning Approaches  
Deep learning has demonstrated significant potential for large-scale image processing tasks. As a fundamental deep learning architecture, Multilayer Perceptrons (MLPs) extract complex features through nonlinear transformations across multiple hidden layers. Among various models, convolutional neural networks (CNNs) have emerged as a paradigm due to their exceptional image feature extraction capabilities. Unlike traditional machine learning methods requiring manual feature engineering, CNN automatically learn features directly from raw images, making them particularly suitable for large-scale pavement image analysis--including distress classification, localization, and segmentation. Meanwhile, in recent years, transformer-based deep learning frameworks [36; 37] have also garnered significant attention and are frequently utilized for network enhancements [38].
## 4 A Pilot Study
### Performance of the Proposed Sentiment Analysis System

Table 6: Performance of the proposed system in terms of sentiment analysis at sentence and post levels. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{Emotion} & \multicolumn{6}{c}{Sentiment Polarity (\%)} \\ \cline{2-9}  & Happy & Love & Excitement & Sad & Anger & Fear & None & Average \\ \hline NRC & \multirow{2}{*}{52.85} & \multirow{2}{*}{54.55} & \multirow{2}{*}{46.15} & \multirow{2}{*}{56.25} & \multirow{2}{*}{41.10} & \multirow{2}{*}{43.75} & \multirow{2}{*}{**80.74**} & \multirow{2}{*}{64.84} \\ Emotion & \multirow{2}{*}{52.85} & \multirow{2}{*}{54.55} & \multirow{2}{*}{46.15} & \multirow{2}{*}{56.25} & \multirow{2}{*}{41.10} & \multirow{2}{*}{43.75} & \multirow{2}{*}{**80.74**} & \multirow{2}{*}{64.84} \\ Lexicon & & & & & & & & \\ \hline Overall & **70.04** & **69.58** & **68.87** & **59.52** & **58.95** & **63.77** & 64.83 & **65.59** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the F-measures (FM) computed at the post level results (emotions) obtained from the proposed system and NRC emotion lexicon. \begin{table}
\begin{tabular}{l c} \hline \hline Target & Sentiment Polarity (\%) \\ \hline Attraction & 66.19 \\ Food \& Beverage & 65.39 \\ Accommodation & 66.36 \\ Holiday in general & 64.96 \\ People \& Culture & 66.07 \\ Transport & 65.90 \\ Weather & 66.13 \\ Shopping \& Gift & 62.96 \\ City Facilities & 65.17 \\ None & 66.84 \\ Overall & 65.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F-measures (FM) computed at the post level results (targets) obtained from the proposed system. This might be because of the larger number of positive tweets compared to the negative numbers that make the dataset imbalanced. The correlation between human-annotated results and the results obtained by the proposed machine-operated method at the sentence level is similar to that at the post level. It is worth noting that we further used the sentiment aware nearest neighbour (SANN) model [55] for further experiments. The results obtained from the SANN model were between 3.7% and 13.3% lower than those obtained from the proposed model in this research work.
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 3 Experimental Results and Analysis
### Qualitative Evaluation  
In this section, we present a qualitative evaluation of the proposed algorithm's performance on two distinct datasets: the Middlebury dataset and data captured using the Orbbec Astra S monocular structured light depth camera. The assessment entails a detailed analysis of the algorithm's effectiveness in hole repair, with a focus on specific scenes and comparisons with existing algorithms. Scrutinizing the algorithm's performance on the Middlebury dataset emphasizes subjective quality assessments, while the subsequent exploration of the Orbbec Depth Camera Data highlights the algorithm's adeptness in addressing hardware-specific challenges.
## 5 SDN-based VANETs Security Challenges
### Attacks on the Resource Consumption of the Control Plane  
The greater part of the SDN-based VANET models proposed in the literature [40; 56; 67; 68; 69; 70] have been planned without security as a top priority. Specifically, they are helpless against control plane asset utilization which is a significant shortcoming in the SDN. The attack is activated when there are numerous solicitations to the control layer from the information layer. In SDN-based VANETs, the control layer is formed by various RSU-SDN controllers that uphold stream rules, and afterward control the system productively. In any case, this control mode can cause major issues specifically because of numerous solicitations forwarded to the control layer. In SDN-based VANETs, like models, the RSU-SDN controller [40] bolsters less solicitations than expected. For example, in certain circumstances, arranged bundles in a few RSUs should hold up until vehicles erase old stream rules. At long last, the effect of the attack, which devours assets of the control layer by the quantity of stream, is taken care of, as is the information plane by the quantity of stream rule passages.  
Potential assistance to the control layer asset utilization in SDN-based VANET structures could be an appropriation of present arrangements in the SDN [71; 72; 73; 74]. [PERSON] et al. [71] developed a method to retain both the control and information layer in any event when there is an information to control plane immersion attack. Specifically, they embraced the information movement idea. Likewise, they utilized the information plane store idea so as to diminish counterfeit bundles by recognizing them from ordinary ones. In a normal SDN-based VANET design, the two modules are included in the controller level. In another study [73], [PERSON] et al.developed line switch, an answer dependent on two ideas identified with likelihood and boycotting. The arrangement gives both strength against SYN (SYN is a short form of synchronize) flooding immersion attacks and security from support immersion. In a study by [PERSON] et al. [74], flood defender depends on three procedures, namely table less designing, bundle separating, and stream rule the board. The principle objective of this arrangement is to lessen the data transmission sticking and spare the memory space of switches. Nonetheless, receiving these arrangements with no alteration probably will not be useful for the SDN-based VANETs because of the qualities (highest portability, dynamic system topologies, and asset requirements) of SDN-based VANET information plane components that are altogether different from the information plane components of SDN-based VANETs.
# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 2 CEEMDAN Denoising Technology  
### CEEMDAN Principles  
The CEEMDAN decomposition method was proposed based on the Ensemble Empirical Mode Decomposition (EEMD), primarily targeting the decomposition of nonlinear and non-stationary signals. Performing Empirical Mode Decomposition (EMD) multiple times provides better modal separation with relatively lower computational costs.  
The main process and calculation formula of signal decomposition are as follows:  
Step 1: Add Gaussian white noise \(\delta_{i}(t)\) to signal \(X(t)\) to be decomposed and construct sequences \(X_{i}(t)\), \(i=1\), \(2\), \(3\), \(\ldots\), \(n\).  
\[X_{i}(t)=X(t)+\varepsilon\delta_{i}(t) \tag{1}\]  
where \(\varepsilon\) denotes the weighting coefficient of the Gaussian white noise.  
Step 2: Perform EMD decomposition on the sequence to be decomposed, and take the average of the first modal component from the EMD decomposition to obtain the first IMF (Intrinsic Mode Function) of the CEEMDAN decomposition.  
\[IMF_{1}(t)=\frac{1}{n}\sum_{i=1}^{n}IMF_{1}^{i}(t) \tag{2}\]  
\[r_{1}(t)=x(t)-IMF_{i}(t) \tag{3}\]where \(IMF_{1}(t)\) denotes the first-order IMF (Intrinsic Mode Function) obtained from the CEEMDAN decomposition, and \(r_{1}(t)\) denotes the first-order residual signal.  
Step 3: After adding noise to the decomposed \(k-1\)th order margin signal, calculate the \(k\)th-order modal components.  
\[IMF_{k}(t)=\frac{1}{n}\sum\limits_{i=1}^{n}E_{1}(r_{k-1}(t)+\epsilon_{k-1}E_{ k-1}(\delta_{i}(t))) \tag{4}\]  
\[r_{k}(t)=r_{k-1}(t)-IMF_{k}(t) \tag{5}\]  
where \(IMF_{k}(t)\) denotes the \(k-1\)th order IMF (Intrinsic Mode Function) obtained from the CEEMDAN decomposition, and \(E_{k-1}(t)\) denotes the \(k\)-1 th order modal components.  
Step 4: Repeat the above steps until the residual signals cannot be further decomposed and the final residual signal is monotonic. If the total number of final modal components is \(N\), then the formula for the final residual signal is as follows:  
\[R(t)=X(t)-\sum\limits_{n=1}^{N}IMF_{n}(t) \tag{6}\]
## 2 Enhancement Methods for High-Resolution SAR Ship Wake Processing
### High-Resolution Spaceborne SAR Wake Imaging
In the imaging process of high-resolution spaceborne SAR, sliding spotlight mode is commonly employed, where the radar antenna scans in the opposite direction of the flight path, continuously approaching a virtual rotation point. Taking the example with 0.5 m resolution, the synthetic aperture time reaches 4 s, and the antenna rotation angle in the azimuth direction exceeds 3\({}^{\circ}\), causing variations in the sea surface backscattering coefficient of over 2 dB [23]. Therefore, under such conditions, the sea surface backscattering coefficient influenced by antenna direction and surface fluctuations exhibits certain temporal variations. In this scenario, the anisotropic scattering behavior of the sea surface background due to random motion results in the phenomenon of random noise in the sea surface background. The unique motion of scattering points caused by wake formation leads to azimuthal pixel movement and decreased resolution. Consequently, in high-resolution SAR ocean images with long synthetic aperture times, the wave texture appears blurred, and the signal-to-noise ratio is relatively low [24]. The movement of wave texture on the sea surface primarily involves the propagation of wave phase and energy. However, the water body itself undergoes simple reciprocating motion and does not propagate with the waves. Therefore, features such as narrow V-shaped wakes and turbulent wakes formed by vessels remain stable in position and persist for a longer duration in the image, unaffected by various motions' effects on imaging offsets. Thus, from a fundamental perspective of high-resolution spaceborne SAR imaging, the processing of raw echo data with different imaging times and azimuth angles can enhance the typical characteristics of wakes.
## 9 Discussion, Contributions, and Future Directions
### Overview of the Proposed Architecture  
The proposed architecture prioritizes real-time data extraction from industrial controllers, integrating both native protocols (for direct access to legacy devices) and standardized solutions such as OPC UA. This approach ensures high flexibility, allowing for simultaneous support of modern IIoT components and older, profibus-based systems. By combining streaming analytics, event-driven processing, and edge computing, the system can optimize data flow in real time while reducing network load. Furthermore, advanced error detection and adaptive sampling improve data integrity and responsiveness. A modular design facilitates scalability and future upgrades, ensuring that the architecture can evolve in tandem with emerging industrial standards.
# DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
## 2 PRESENT Encryption  
The PRESENT block cipher has two main parts: round key generation using _generateRoundKeys()_ and data encryption. Round keys are generated on the fly and subsequently used in each round. The _generateRoundKeys()_ function generates 31 different round keys for each round _\"i\"_ using the provided single secret key. Here, _\"i\"_ is the round number and its value ranges between \(i\leq 32\). However, in the first encryption round, the round key is extracted directly from the secret key, followed by the use of 31 round keys from _generateRoundKeys()_. The PRESENT block cipher has 64 bits of plaintext, which are referred to as STATE, \([S_{63},S_{62},S_{61}\ldots\ldots S_{0}]\).  
The encryption begins by performing an XoR operation between STATE and the first-round key in _AddRoundKey()_. This step is succeeded by the application of nonlinear layer _sBoxLayer()_, a substitution layer. Subsequently, a linear permutation layer _pLayer()_ is applied to complete the first round of encryption. This process is repeated for 31 rounds, and after this, the output of the 31 st round undergoes an additional key whitening round, generating the ciphertext using the last round key. The pseudocode of the encryption process is presented below in C language as Algorithm 1. The complete encryption and decryption process of the PRESENT block cipher is presented in Figure 4, depicting that the decryption is the exact reversal of the encryption process.  
```
1:generateRoundKeys()
2:for\(i=1\) to 31 do
3:addRoundKey(STATE,\(K_{i}\))
4:sBoxLayer(STATE)
5:pLayer(STATE)
6:endfor
7:addRoundKey(STATE, \(K_{31}\))
```  
**Algorithm 1** Pseudocode of PRESENT block cipher Encryption  
Figure 3: Biological DNA replication process.
## The Function of Remote Sensing in Support of Environmental Policy  
**[PERSON] 1,2,*, [PERSON] 2, [PERSON] 2, [PERSON] 2, [PERSON] 3, [PERSON] 2, [PERSON] 4 and [PERSON] 5**  
1 International Livestock Research Institute (ILRI), Nairobi, Kenya  
2 ITC, University of Twente, Enschede, The Netherlands; E-Mails: [PERSON]RESS] (Y.G.); [PERSON]L_ADDRESS] (N.K.); [PERSON]_ADDRESS] (A.G.); [EMAIL_ADDRESS] (J.F.)  
3 NIAES, Ecosystem Research Group, Tsukuba, Japan; E-Mail: [EMAIL_ADDRESS]  
4 Frogs International Environmental Management and Consultancy, The Hague, The Netherlands; E-Mail: [EMAIL_ADDRESS]  
5 Ministry of Environment, Ulan Bataar, Mongolia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +254-20-422-4831.  
_Received: 20 May 2010; in revised form: 13 June 2010 / Accepted: 17 June 2010 / Published: 12 July 2010_  
Limited awareness of environmental remote sensing's potential ability to support environmental policy development constrains the technology's utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of \"remote sensing and policy\" revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 _versus_ 8.8% yr\({}^{-1}\)), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance.  
keywords:earth observation; remote sensing; environment; policy; governance
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 5 Guarding against Load Altering Attacks
### Transactive Energy Framework  
To enable the real time power exchanges between the the flexible loads and the power distribution system, the operation of the distribution system is divided into successive time intervals of _short_ length \(T\); e.g., \(T=10\) milliseconds. Each time interval is referred to as a _transaction cycle_. At the beginning of a transaction cycle, power consumers indicate their energy demands or their energy offers by submitting price bids to the distribution market. A flexible load that can increase its power consumption submits a negative price bid to indicates its willingness for purchasing energy. The absolute value of this price bid reflects the highest rate at which the power consumer is still willing to pay for energy. On the other hand, a flexible load that can reduce its power consumption submits a positive price bid to the market to indicates an energy offer. This price bid reflects the least rate at which the flexible load expects to receive payment for its energy offer to the market.  
In each transaction cycle, the power market operator collects all the price bids and clears the market by solving a linear optimization problem that maximizes the social welfare [46]:  
\[\text{Social Welfare}=\sum_{k=1}^{N}-\left|\text{Bid}_{k}\right|\Delta P_{k}, \tag{8}\]  
where \(\text{Bid}_{k}\) is the price bid submitted to the distribution market by the power consumer at bus \(k\). Also, \(\Delta P_{k}\) is the amount of energy exchange at bus \(k\), expressed in terms of additional power injection to bus \(k\). Once the market is cleared, the dispatch instructions are sent to the flexible loads. Since each transaction cycle has a short duration \(T\), the maximum energy that a power consumer can sell or purchase in a transaction cycle is upper bounded by \(P_{max}T\), where \(P_{max}=0.01\) Mega-Watts. Such an upper bound on the energy purchases ensures that the power consumers can follow the dispatch instructions before the start time of the next transaction cycle.  
We note that, in clearing the distribution market the power system operator considers the physical constraints in the operation of the distribution system, including the limited capacity of the distribution lines and the safe range of voltage magnitudes. As a result, power flow equations are included in the social maximization problem that is solved in the distribution market; see [27]. However, since the energy transactions in each transaction cycle are small amounts upper bounded by \(P_{max}T\) the nonlinear power flow equations are _accurately_ approximated by the following linear differential power flow equation [46]:  
\[\Delta P_{k}=\sum_{i=1}^{N}\frac{\partial P_{k}}{\partial V_{i}}\Delta V_{i}+ \frac{\partial P_{k}}{\partial\theta_{i}}\Delta\theta_{i}. \tag{9}\]
## 3 Results and Discussion
### Optimization of Interface Defect Density  
The effect of the interface defect density on the _PCE_ of PSC was investigated for the absorber/ETL and absorber/HTL interface with interface defect densities of \(10^{13}\) to \(10^{20}\) cm\({}^{-3}\) (Figure 12). As shown in Figure 12a, the _PCE_ steadily decreases as the interface defect density between the absorber and ETL layer increases. The interface defect density decreases from 18.45% to 8.26%. As shown in Figure 12b, when increasing the interface defect density of the absorber/HTL up to \(10^{14}\) cm\({}^{-3}\), the _PCE_ also remains almost constant (18%). The _PCE_ then decreases, reaching 17.28% at \(10^{18}\) cm\({}^{-3}\). The decrease in _PCE_ when increasing the interface defect density was attributed to the increase in the number of traps [55].  
Comparing the range of change in Figure 12a,b, it can be seen that the change in the interface defect density between the absorber and the ETL has a greater effect on the _PCE_ than that in the interface defect density between the absorber and the HTL. As shown in Figure 1a, upon irradiation, carriers started to be generated at the interface between absorber/ETL before those at the absorber/HTL interface. Thus, the change in the interface defect density between the absorber/ETL layer has a greater effect on the loss rate. This seems to have led to a rapid decrease in _PCE_[75].  
Figure 11: Effect of PAL defect density on the _PCE_.
# Fabrication of Self-Healable Magnetic Nanocomposites via Diels - Alder Click Chemistry
###### Abstract  
In this study, we report a novel approach to fabricate an organic/inorganic magnetic hybrid system capable of self-healing, wherein a polycaprolactone-poly(furfurylyl glycidyl ether) copolymer (PCLF) serving as the structure template was first synthesized, followed by the incorporation of iron oxide nanoparticles-decorated multiwalled carbon nanotubes (IONPs-MWCNTs) and 1,1\({}^{\prime}\)-(methylenedi-4,1-phenylene)bismaleimide (BMI) into the polymer matrix to form a covalently crosslinked hybrid network via a Diels\(-\)Alder (DA) reaction. For this system, the reactive combination of diene and dienophile from furan/maleimide, MWCNT/furan, and MWCNT/maleimide could facilely induce multiple DA reactions that imparted a versatile route to efficiently introduce IONPs-MWCNTs into the organic polymer hosts, resulting in a uniform distribution of IONPs-MWCNTs that led to a hybrid system with superparamagnetic properties. Beside the magnetic behavior, such material synergistically exhibited a superior ability for healing scratch defects via a retro-DA reaction. Therefore, this crosslinked PCLF/BMI/IONPs-MWCNTs hybrid system which exhibits multifunctional properties including superparamagnetic behavior and self-repairability can serve as an intelligent material for developing advanced electromagnetic applications.  
self-healing; Diels-Alder reaction; nanocomposites; copolymer; MWCNT; iron oxide nanoparticles +
Footnote †: journal: Applied Physics Letters
## 2 Methodology and System Architecture
### IoT Platform Configuration  
The IBM Watson IoT Platform is a hub for connecting devices, gateways, and applications for IoT solutions. REST and MQTT protocols are supported within this platform for applications, devices, gateways, event processing, and administrative tasks. This platform bridges the gap between field devices and data analytic services of IBM services or online user applications. The easy interfacing through the MQTT protocol as well as the user-friendly platform sufficed for this application.  
In the _Devices_ tab shown in Figure 5, devices can be added and managed. Each device that connects to the IoT platform must be configured with a device ID, type, and authentication token. External devices are connected to the platform according to their parameters.  
Figure 4: The proposed CAD model of the drilling unit.  
In the _Boards_ tab, real-time data can be visualized by creating cards with many different visualization options. In the _Apps_ tab, Application Programming Interface (API) keys can be added and generated for different user applications that can connect to the IoT platform and subscribe to devices' topics or publish commands to devices.  
In this paper, the following connection information for the MQTT client was used:  
* Messaging Address: 110.messaging.internetofthings.ibmcloud.com, where 110 is the name of the organization
* Security Port: connection type non-secure, protocol MQTT, port number 1883;
* MQTT Client: device;
* Device Authentication: token authentication;
* QoS: at most once (QoS0).  
The data are sent to the platform along with a topic and a property describing the data.