esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Document Image Enhancement Based on Document Degradation Problem
###### Abstract  
Document image enhancement methods are often used to improve the accuracy and efficiency of automated document analysis and recognition tasks such as character recognition. These document images could be degraded or damaged for various reasons including aging, fading handwriting, poor lighting conditions, watermarks, etc. In recent years, with the improvement of computer performance and the continuous development of deep learning, many methods have been proposed to enhance the quality of these document images. In this paper, we review six tasks of document degradation, namely, background texture, page smudging, fading, poor lighting conditions, watermarking, and blurring. We summarize the main models for each degradation problem as well as recent work, such as the binarization model that can be used to deal with the degradation of background textures, lettering smudges. When facing the problem of fading, a model for stroke connectivity can be used, while the other three degradation problems are mostly deep learning models. We discuss the current limitations and challenges of each degradation task and introduce the common public datasets and metrics. We identify several promising research directions and opportunities for future research.  
document image enhancement; document image analysis and recognition; deep learning; convolutional neural network; degradation +
Footnote †: journal: Medical Imaging
# Energy-Efficient AC Electothermal Microfluidic Pumping via Localized External Heating
## 4 Discussion
### On Overcoming Modeling Limitations  
Many computer models used to study alternating-current electrothermal (ACET) systems are based on simplified assumptions. They often use flat, 2D designs, treat fluid and material properties as constant, and separate the interactions between heat, electricity, and fluid flow. These shortcuts can overlook essential effects, such as how heating alters the behavior of fluids and electric fields in small channels. Without more comprehensive comparisons to real experiments, these models can yield results that insufficiently match what happens in actual devices. To improve this, researchers need to develop more comprehensive models that account for the interaction between heat, fluid properties, and the simultaneous effects of electricity, heat, and flow. Using 3D designs and focusing on the most critical areas in the system can yield better results without requiring substantial amounts of computer power. Researchers should also evaluate their models physically, as resources allow, using tools such as high-speed cameras to track tiny flows or infrared sensors to measure heat so they can adjust and confirm that the models accurately match real-life behavior. By overcoming these modeling constraints, ACET research can move toward predictive, scalable design workflows that are better suited for application in biosensing, thermal regulation, and lab-on-chip platforms. Physically paired modeling will not only enable more profound insight into electrothermal flow physics but also accelerate the development of optimized, application-ready microfluidic devices through iterative simulation-experiment integration. A key challenge, among many, will be making physical model pairing less expensive through improved manufacturing capabilities; while this is beyond the scope of the paper, this does appear worth mentioning for thoughts on how to deepen the research.
## 5 Discussion
### Memory Usage Analysis  
In addition to execution time, memory consumption is a critical factor when evaluating the efficiency of association rule mining algorithms, especially for large-scale e-commerce datasets. Figure 5 illustrates the memory usage (in megabytes) of Apriori and FP-Growth at different minimum support thresholds.  
As expected, the FP-Growth algorithm consistently required less memory than Apriori across all configurations. At the lowest support threshold (0.10), Apriori consumed approximately 780 MB compared to 430 MB for FP-Growth, a 45% reduction. This efficiency advantage becomes particularly important in environments with limited memory resources or when dealing with dense transactional data. The memory savings can be attributed to FP-Growth's compact FP-tree structure, which avoids the overhead of generating and storing large volumes of candidate itemsets.  
Furthermore, both algorithms showed decreased memory usage as the support threshold increased owing to the reduction in frequent itemsets. However, FP-Growth maintained a smaller and more consistent memory footprint, reinforcing its suitability for large, complex datasets in commercial applications.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 6 Verifying of DCSS Protocol
### DCSS Protocol Formal Analysis  
The protocol was modelled and simulated using secure, insecure channels and processes representing entities \(UE\), \(SPAAA\) and \(SS\). ProVerif found an attack on the protocol as shown in Figure 4, on a public channel (UE <===> SPAAA <==> SS). We were concerned with federated ID \(FID\), the cache token \(ChT\), share token \(ShT\), and access token \(AcT\) properties. There were attacks on the protocol as a result of a formal analysis based on the adversary vector. The ProVerif results indicate that the secrecy of \(ChT\), \(ShT\), \(AcT\), \(FID\), \(Ack\_1\) and \(Ack\_2\) holds. The authentication event is queried as non-injective and injective agreements between UE and SS, which does not hold, SS may end the protocol assuming it is talking to UE, even though the UE never runs the protocol with SS.  
Figure 4: DCSS Protocol Attack Results.  
The event beginSS means that the protocol was completed by SS, UE received message 8 with \(Ack_{2}\) after sending \(Sht\), \(hShT\) in message 7, event beginUE means that the SS received message 3. The protocol parameters are taken as arguments by the events: \(K_{UESS}\) and cache token \(ChT\) and its claims, SS which must verify the cache token and digital signature. If the arguments are true, then acknowledgement is sent otherwise it ends sends an invalid token. We would like to prove the correspondences event (endSS(U, SS, K)) ==> event (beginUE(U, SS, K)).inj-event (endSS(U, SS, K)) ==> inj-event (beginUE(U,SS, K)).  
In ProVerif the direct proof of this correspondence does not hold because message 7 and message 8 sent after query query []; event (endSS()) event(beginUE()) fail to hold due the attacker's knowledge of A'_15173 and kue3a_15172, attacker(A'_15173), attacker(kue3a_15172). The attacker may obtain (hostU[],hostU[],kue3a_15172). attacker((hostU[],hostU[],kue3a_15172)) as explained in attacker trace below. We further attempt to prove and conclude the desired correspondence by noting that an event with the argument \(KUESS\) cannot be executed before \(Ack_{2}\) has been sent, i.e., before the \(Ack_{2}\) is generated by executing the share request message \(ShaTokenReg\) with the share response message \(ShaTokenResp\). One part of correspondence holds with true (basic) while the other does not hold in ProVerif with false (one to one).
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 5 The Performance Analysis of Dual-Hop Multi-Relay System
### Suboptimal Relay Selection Scheme: \(R\)-\(D\) Link  
The second partial relay selection (PRS2) scheme maximizes the instantaneous SNR by selecting the best relay, \(k\), based on CSI of \(R_{l}\)-\(D\) links, i.e.,  
\[k=\arg\max_{l}\{\gamma_{l,d}\}, \tag{34}\]  
and the e2e SNR, \(\gamma_{s2}\), under the PRS2 scheme is given by  
\[\gamma_{s2}=\min\{\gamma_{s,k},\max_{l}\{\gamma_{l,d}\}\}, \tag{35}\]  
where \(\gamma_{s,k}\) is the SNR of the \(S\)-\(R\) link of the selected \(k\) relay. The CDF of \(\gamma_{s2}\) can be expressed by  
\[F_{\gamma_{s2}}(\gamma) =F_{\gamma_{s,k}}(\gamma)+F_{\gamma_{s2}}(\gamma)-F_{\gamma_{s,k} }(\gamma)F_{\gamma_{s2}}(\gamma)\] \[=F_{\gamma_{s,k}}(\gamma)+F_{\gamma_{s2}}(\gamma)\overline{F}_{ \gamma_{s,k}}(\gamma), \tag{36}\]  
where \(F_{\gamma_{s2}}(\gamma)=\prod_{l=1}^{L}F_{\gamma_{l,d}}(\gamma)\), \(F_{\gamma_{s,k}}(\cdot)\) is the CDF of \(\gamma_{s,k}\), and \(\overline{F}_{\gamma_{s,k}}(\cdot)\) is the complementary CDF of \(\gamma_{s,k}\).  
To calculate the performance of the three RS schemes in terms of modulation level selection probability, SE and OP replace \(F_{\gamma_{l}}(\cdot)\) in (20), (23) and (24) by \(F_{\gamma_{sP}}(\cdot)\) for the ORS scheme, by \(F_{\gamma_{s1}}(\cdot)\) for the PRS1 scheme, and by \(F_{\gamma_{s2}}(\cdot)\) for the PRS2 scheme.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 2 Method
### Validation of Geographical Metadata for Gas Flares in Russia  
Alongside the daytime imagery analysis, geographical metadata were also collected from open sources--such as governmental agencies' registries or collaborative mapping projects--to validate the detected gas flares and for later application in the gas flare catalog.  
For the upstream sector, the main source of geographical metadata was an up-to-date oil and gas field lease information registry maintained by Rosgeolfond (Russian Geological Fund) which specifies the lease ID (which in turn contains information on the regional authority issuing the license), lease name and its owner (subsidiary company) [37], field name and type [38] based on Rosnedra (Russian Federal Agency for Mineral Resources) data, and the oil and gas field map of Russia compiled by \"Mineral\" based on Rosnedra data with the specified geographic boundaries of leases and deposits [39]. Hotspots from the \"high-temperature\" dataset (see Section 2.2) with no available imagery but with a high detection frequency (>10%) found within the boundaries of the oil and gas fields or Rosnedra leases in \"Mineral\" map were considered to be gas flares which are often related to exploration drilling with no oil or gas field registered and mapped yet.  
For the downstream sector, a list of oil refineries in Russia is presented by the Ministry of Energy which only contains the legal address of the refinery [40]. As such, the website \"Energy Base\" was used instead, as it presents specified geographic coordinates of the plants as well as information on the gas processing plants (GPP) and liquified natural gas plants (LNG) [41]. Information on the oil and gas sector in Russia, especially for midstream and downstream facilities located in the urban centers, could usually be found in the collaborative mapping project Wikimapia [42]. Wikimapia can also be used to identify gas flares at the facilities not related to the oil and gas industry.  
More specific information on obtaining the geographical metadata for gas flare catalog can be found in Section S1 of the Supplementary Materials text section.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Data Acquisition  
#### 2.1.1 Ground-Truth Crop Yield Data  
The study area comprises lupin cultivation fields, with a total area ranging from 0.25 ha to 18.03 ha (average area = 7.64 ha), located in the northern region of the Viterbo province, called Alto Lazio or Tuscia, in Italy. As depicted in Figure 1, this area lies north of the volcanic lake Bolsena (42.66\({}^{\circ}\) N, 11.95\({}^{\circ}\) E) at an altitude of approximately 312 m above sea level. The region is characterized by a temperate Mediterranean climate, with an average annual temperature of 15.9 \({}^{\circ}\)C and average annual precipitation totaling 676.4 mm during the 2023-2024 growing season.  
Ground-based measurements taken throughout the harvest season provided the crop yield data used in this investigation in tons per hectare (t/ha). Accurate geolocation and temporal synchronization with satellite data were ensured by spatially referencing the harvested area and aggregating yield values at the field level. These field-measured yield values served as the ground truth for ML model training and testing.
# Geovisualization of Buildings: AI vs. Procedural Modeling
###### Abstract  
Procedural modeling offers significant advantages over traditional methods of geovisualizing 3D building models, particularly in its use of scripts or machine language for model description. This approach is highly suitable for computer processing and allows for the rapid rendering of entire building models and cities, especially when the buildings are not highly diverse, thus fully leveraging the strengths of procedural modeling. The first hypothesis is that buildings in the real world are mostly different and they should still be able to be displayed through procedural modeling procedures, and the second hypothesis is that this can be achieved in several ways. The first hypothesis suggests that real-world buildings, despite their diversity, can still be effectively represented through procedural modeling. The second hypothesis explores various methods to achieve this representation. The first approach involves recognizing the basic characteristics of a building from photographs and creating a model using machine learning. The second approach utilizes artificial intelligence (AI) to generate detailed building models based on comprehensive input data. A script is generated for each building, making reverse procedural modeling in combination with AI an intriguing field of study, which is explored in this research. To validate this method, we compare AI-generated building models with manually derived models created through traditional procedural modeling techniques. The research demonstrates that integrating AI and machine learning techniques with procedural modeling significantly improves the efficiency and accuracy of generating 3D building models. Specifically, the use of convolutional neural networks (CNNs) for image-to-geometry translation, and Generative Adversarial Networks (GANs) for texture generation, showed promising results in creating detailed and realistic 3D structures. This research is significant as it introduces a novel methodology that bridges the gap between traditional procedural modeling and modern AI-driven techniques. It offers a robust solution for automated 3D building modeling, potentially revolutionizing the fields of urban planning and architectural design by enabling more efficient and accurate digital representations of complex building geometries.  
procedural modeling; geovisualization; building +
Footnote †: journal: Applied Sciences
###### Abstract  
It is commonly known that for characteristics, such as long-distance, high-sensitivity, and full-scale monitoring, phase-sensitive optical time-domain reflectometry (\(\Phi\)-OTDR) has developed rapidly in many fields, especially with the arrival of 5G. Nevertheless, there are still some problems obstructing the application for practical environments. First, the fading effect leads to some results falling into the dead zone, which cannot be demodulated effectively. Second, because of the high sensitivity, the \(\Phi\)-OTDR system is easy to be interfered with by strong noise in practical environments. Third, the large volume of data caused by the fast responses require a lot of calculations. All the above problems hinder the performance of \(\Phi\)-OTDR in practical applications. This paper proposes an integration method based on a complete ensemble empirical mode decomposition with adaptive noise and Kullback-Leibler divergence (CEEMDAN-KL) and an adaptive moving neighbor binary pattern (AMNBP) to enhance the performance of \(\Phi\)-OTDR. CEEMDAN-KL improved the signal characteristics in low signal-to-noise ratio (SNR) conditions. AMNBP optimized the location and recognition via a high calculation efficiency. Experimental results show that the average recognition rate of four kinds of events reached 94.03% and the calculation efficiency increased by 20.0%, which show the excellent performance of \(\Phi\)-OTDR regarding location and recognition in practical environments.  
fading effect suppression; \(\Phi\)-OTDR; feature enhancement; CEEMDAN-KL; AMNBP
## 2 Electromagnetic Wave Propagation Behavior and the Principle of the Reflected Voltage
### Reflection Coefficient and Power Reflection Coefficient  
We used the reflection coefficient method to calculate the relative dielectric constants of the concrete surface and concrete materials and obtained the changes of the reflection coefficient and the power reflection coefficient of electromagnetic waves in different media.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Medium**} & **Conductivity \(\sigma\)** & **Relative Dielectric Constant (\(\varepsilon_{r}\))** & **Velocity v (m/ns)** \\ \hline Air & 0 & 1 & 0.3 \\ Concrete & 1 & 4–10 & 0.09–0.15 \\ Iron & 1010 & 300 & 0.017 \\ Steel Bar & \(0.6\times 10^{7}\) & 2600 & - \\ Iron oxide & - & 14.2 & - \\ (Corrosion products) & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dielectric Constant.  
Figure 2: First Fresnel Energy Wave Band.  
where \(R_{mn}\) is the reflection coefficient in the interface of the medium, \(\epsilon_{m}\) is the relative dielectric constant of medium 1, and \(\epsilon_{n}\) is the relative dielectric constant of medium 2.  
The power reflection coefficient, \(P_{r}\), is the square of the reflection coefficient. Its value represents the intensity of electromagnetic wave reflection in the medium interface, as shown in Equation (6):  
\[P_{r}={R_{mn}}^{2}=\left|\frac{\sqrt{\epsilon_{m}}-\sqrt{\epsilon_{n}}}{\sqrt{ \epsilon_{m}}+\sqrt{\epsilon_{n}}}\right|^{2} \tag{6}\]
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 5 Results
### Application Architecture  
This subsection presents a comparative evaluation of monolithic and microservice architectures tested under varying user loads (100, 1000, and 10,000 users) using the Locust tool (an open-source performance/load testing tool). Key metrics, including the user count, average response time (ms), throughput (requests per second), and total requests, were analyzed to assess the scalability and efficiency of each architecture.
## 3 Results  
### DEM Generation over Site A--Seogwipo City  
An interferometric pair with the shortest temporal baseline and the highest perpendicular baseline assessed the DEM based on Sentinel-1 SAR data over Seogwipo. Figure 7 shows the interferometric phase generated and the corresponding coherence to check the quality of phase information. The interferometric fringes are clearly shown in the lowlands towards the coast, which is a good indication of topographic information, but are not visible in the vicinity of Mt. Halla. The dominant grassy and vegetated nature of the AOI in the vicinity of Mt. Halla caused considerable loss of coherence due to strong temporal decorrelation (i.e., surface change between the 12-day time interval). The mean coherence of the whole AOI is below 0.30. Figure 8 shows the InSAR-derived DEM.  
Figure 9 shows the comparison to the reference model (SRTM DEM). Figure 10 shows the elevation profiles along sections A-A' and B-B'. Although the InSAR-derived DEM and the SRTM DEM appear to be almost similar to each other, the elevation profiles derived from InSAR show more fluctuation compared to the smooth SRTM elevation profiles. More importantly, the InSAR result shows that better elevation measurements can be obtained from plain areas compared to hilly areas and vegetated areas. It underestimates the elevation value towards the mountain peak; the absolute elevation difference can be as high as 250 m. For the lowlands, the absolute elevation difference between InSAR-DEM and SRTM DEM can be as low as 1 m.  
Figure 8: InSAR-derived DEM using Sentinel-1 SAR data processed through SNAP.  
Figure 7: Quality check of InSAR-derived DEM using (**a**) interferometric phase and (**b**) interferometric coherence.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 5 Simulation Study and Results
### Simulation Setup  
The simulations were run in MATLAB/Simscape using a variable-step ode45 solver. The Simscape model, consisting of the hybrid-compliant system mounted on the servicer CubeSat and the space debris, were developed for the simulations. During the simulations, the positions and velocities of the masses under the impact and their interpenetration were calculated. This was fed back to a contact model and a force was generated, pushing away the masses under impact. The contact time \(t_{c}\) was calculated based on the impact force.  
The developed contact model uses the visco-elastic theory. According to this theory, a compliant surface under impact can be modelled by a combination of lumped parameter elements, i.e., springs and dampers. This study calculated the contact force between the bodies under impact using the Kelvin-Voight model [47]. Assuming that the impact is close to an elastic (no damping), the impact force is given by:  
\[F_{i}=k_{i}(x_{c}-x_{d}), \tag{18}\]  
where \(x_{c}\) is the position of the mechanism's tip and \(x_{d}\) is the point on the debris that comes into contact with the mechanism's tip. In this study, stiffness \(k_{i}\) was equal to 10,000 N/m [48; 49] and, thus, the contact was assimilated to a very stiff spring, activated when \(x_{c}\) is greater than \(x_{d}\).  
The CubeSat-ADR system has a small relative velocity set to 10 mm/s with respect to the debris. The CoM's initial position \(x_{s}\), of mass \(m_{s}\), equals zero before impact. The initial position of \(x_{c}\) equals \(l_{m}\) (\(l_{m}\) is defined in each experiment). The debris' initial position relative to the ADR system's tip, denoted by \(x_{d}-x_{c}\), was set equal to 10 cm without  
Figure 14: CubeSat-ADR system as two masses connected by (**a**) a passive and active compliance in series, (**b**) a hybrid compliance, equivalent to (**a**).  
loss of generality since, in the simulation, the ADR system approaches the debris with a constant velocity \(v_{\rm s}\). Equivalent systems' point masses \(m_{\rm s}\), \(m_{\rm c}\), and \(m_{e}\) (when applicable) are 12.0012 kg, 0.024 kg, and 0.016 kg, respectively.
## 3 Crafting the Future: Unveiling 6G's Pinnacle Features, and
### Key Features of 6G
#### 3.2.4 Security Versus Spectral Effectiveness  
The trade-off between security and spectral efficiency is challenging. Potential solutions include designing more efficient encryption algorithms, leveraging PHY security technologies, and using AI algorithms for early security warnings.
## 3 Results
### Off-Target Drift Losses
#### 3.4.2 Aerial Drift Losses  
Mean aerial deposition for modified irrigation micro-emitter i.e., T1 (0.7 \(\pm\) 0.1 ng cm\({}^{-2}\)) was significantly lower than hollow cone nozzle SSCDS treatment, i.e., T2 (3.2 \(\pm\) 0.4 ng cm\({}^{-2}\)) (Table 8). Additionally, the percent of applied active ingredient lost to the aerial drift was negligible for both treatments (0.02, and 0.08% for T1 and T2, respectively). Similar aerial deposition trends were observed at 3 and 6 m downwind (Figure 9a). Additionally, the deposition evaluated at various sampling heights (i.e., 3.3, 3.6, and 3.9 m above ground level) for treatment T1 (1.0 \(\pm\) 0.5, 0.6 \(\pm\) 0.3, 0.4 \(\pm\) 0.2 ng cm\({}^{-2}\), respectively) was significantly lower than T2 (3.9 \(\pm\) 1.0, 2.8 \(\pm\) 0.9, 2.9 \(\pm\) 0.9 ng cm\({}^{-2}\), respectively) (Figure 9b). However, the sampling height did not significantly affect the aerial deposition for a particular SSCDS treatment. Analysis of coverage samplers indicated that there was negligible mean aerial coverage (<0.1%) for both the treatments.