esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 4 Results and Discussion
### Prediction Performance of GPR  
The temporal history of example InSAR measurements and GPR predictions is plotted in Figure 8. In these plots, the InSAR measurements from the last four dates were not included in the training of the model and were used as testing data. Figure 8a displays a linear, relatively simple deformation behaviour. The test measurements may be described as displaying a \"normal\" behaviour, because they (blue) were captured within the uncertainty envelope. A more-complex, non-linear deformation behaviour is captured in Figure 8b. In this plot, all test measurements fall significantly outside the uncertainty envelope--displaying an anomalous deformation behaviour.  
Any measurements displaying such anomalous deformation behaviour in the last four temporal points were then clustered. The number of anomalous clusters detected at each date were counted and are summarised in Figure 9. As expected, there was a significant number of anomalous clusters detected in the failure area of the TSF for all dates, also referred to as true positives in the \"T\" column. However, this was at the expense of a much  
Figure 7: The global Root-Mean-Squared Error (RMSE) for different input timescales and prediction horizons. All experiments, except Run-C, predicted at one-step-ahead, and Run-C was at a four-step-ahead prediction horizon.  
larger number of false positives (\"F\" column). This large number of false positives limited the ability of using the GPR uncertainty envelope for localising the problematic parts of the TSF--the failure area.  
Figure 8: Temporal plots of example InSAR measurements and the GPR predictions. (**a**) A typical linear deformation with all test measurements within the uncertainty envelope, therefore displaying a “normal” behaviour. (**b**) An example of anomalous deformation behaviour with the test measurements falling significantly outside the uncertainty envelope.  
Figure 9: Measurements falling outside the GPR confidence interval are clustered and counted. A minimum of four or more anomalous measurements forms a cluster. If these anomalous clusters occur inside the failure area, they are counted as a true positive, “T”, otherwise as a false positive, “F”.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 3 Methods
### Monitoring of Non-Agriculturalization  
#### 3.2.1 Establishment of Change Detection Index  
Cropland non-agriculturalization refers to the conversion of cropland for non-agricultural production and business activities. This study focuses on extracting three typical types of cropland non-agriculturalization: conversion to constructed buildings, conversion to ponds, and conversion to pushed-fill soil. The specific characteristics of these land-use change types are summarized in Table 3. Since SAR is a side-looking active imaging system, the grayscale values of features in SAR images are influenced by their scattering mechanisms [42; 43; 44]. Constructed buildings predominantly exhibit dihedral scattering, resulting in very high signal returns that appear as bright white highlights in SAR grayscale images. Ponds, on the other hand, are dominated by surface scattering, which leads to minimal backscatter and appears as dark areas in the grayscale images. For pushed-fill soil, the backscatter intensity primarily depends on soil moisture and surface roughness, generally leading to relatively low signal returns. When SAR images are synthesized into pseudo-color representations, they become visually distinct: constructed buildings are characterized by bright yellow-green highlights, ponds appear as dark blue patches, and pushed-fill soil is displayed in light blue. Examples of these three typical change types are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Encoding** & **Type of Change** & **Characterization** \\ \hline
01 & Cropland change to & Change in cropland to houses or structures \\  & constructed buildings & Change in cropland to pushed-fill earth for construction or \\
02 & Cropland change to & other uses including types such as pushed-fill, fill, \\  & pushed-fill soil & excavation, etc. \\
03 & Cropland change to ponds & Cropland changes to the lake-pit pond-type water surface \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-agricultural monitoring type.  
Figure 4: Examples of typical change patches: (**a**) optical images at time T1; (**b**) optical images at time T2; (**c**) SAR images at time T1; (**d**) SAR images at time T2. The blue boxes in the images highlight the regions where changes occurred between the two time points. The figure illustrates changes observed in the study area over time using both optical and SAR data.
###### Abstract  
Information-Centric Networking (ICN) typically utilizes DRAM (Dynamic Random Access Memory) to build in-network cache components due to its high data transfer rate and low latency. However, DRAM faces significant limitations in terms of cost and capacity, making it challenging to meet the growing demands for cache scalability required by increasing Internet traffic. Combining high-speed but expensive memory (e.g., DRAM) with large-capacity, low-cost storage (e.g., SSD) to construct a hierarchical cache architecture has emerged as an effective solution to this problem. However, how to perform efficient cache management in such architectures to realize the expected cache performance remains challenging. This paper proposes a cache management scheme for hierarchical cache architectures in ICN, which introduces a differentiated replica replacement policy to accommodate the varying request access patterns at different cache layers, thereby enhancing overall cache performance. Additionally, a probabilistic insertion-based SSD cache admission filtering mechanism is designed to control the SSD write load, addressing the issue of balancing SSD lifespan and space utilization. Extensive simulation results demonstrate that the proposed scheme exhibits superior cache performance and lower SSD write load under various workloads and replica placement strategies, highlighting its broad applicability to different application scenarios. Additionally, it maintains stable performance improvements across different cache capacity settings, further reflecting its good scalability.  
h  
a _Article_  
A Hierarchical Cache Architecture-Oriented Cache Management Scheme for Information-Centric Networking  
_Future Internet_**2025**, 17, 17, 17  
**Keywords:** hierarchical caching; cache management; cache replacement; SSD; cache admission
# Neurorotrophic Factors in Glaucoma and Innovative Delivery Systems
## 3 Advances in Drug and Gene Delivery Systems for Neuroprotection in Glaucoma
### Neurotrophic Factors Delivery in Glaucoma
The intravitreal GDNF levels were quantified in the vitreous at each time point: 24 h and 1, 4, 6, 8, 12, 18, and 24 weeks after injection. The results demonstrate that intravitreal GDNF concentrations reached levels of 717.1 \(\pm\) 145.1 pg/mL 24 h after the injection, followed by maintained levels of 745.3 \(\pm\) 25.5 pg/mL for the following four weeks. At six weeks post-injection, the intravitreal GDNF concentration was 5.9 \(\pm\) 0.6 pg/mL. After that, a second plateau occurred, with constant levels of 17.4 \(\pm\) 3.7 pg/mL from the eighth week to the end of the assay. Thus, GDNF/vitamin E-loaded PLGA MSs prepared using S/O/W2 technique resulted in providing a sustained controlled release of the neurotrophic factor for up to six months, with significantly higher concentration than basal level [116]. As additional intriguing application of NTF-loaded microspheres, subretinal administration of PLGA microspheres delivering BDNF, has been shown to improve the functional efficacy of transplanted retinal progenitor cells in a rat model of retinal degeneration. Interestingly, the improvement of functional outcomes as evaluated by behavioral and electrophysiology data was not significant with BDNF alone, suggesting a synergistic effect between BDNF and the graft [117]. In an animal model of acute IOP elevation, intravirtually injected PLGA microspheres loaded with BDNF and GDNF were demonstrated to be effective over CNTF microspheres for retinal functional recovery, as evidenced by pupil light reflex (PLR) parameters and electroretinograms (ERG). The authors speculated that intrinsic production of CNTF may have occupied the majority of available receptors, thus preventing any additional therapeutic effect of exogenous CNTF [118]. Although there are no available in vitro or in vivo studies on rhNGF-loaded PLGA microspheres for ocular delivery, encouraging in vitro and in vivo results have been described on the use of rhNGF-loaded PLGA microspheres for targeted delivery to the basal forebrain (BF) in AD. Specifically, rhNGF release kinetics from microspheres after implantation in the BF was sustained for 4-5 weeks, with low initial burst (11.4%) related to the low amount of rhNGF added (1/2000 _w/w_). The encapsulated rhNGF was biologically active and significatively effective at rescuing p75 NTR- and ChAT-positive neurons in the medial septum (MS) and vertical diagonal branch (VDB) [119; 120]. The carriage options thus far described, however, do not avoid the need for repeated injections in the context of life-long treatment, and, therefore, delivery of transfected cells permanently expressing NTFs would firmly represent a promising prospect.
## 1 Introduction
[12] introduced a combined seismic isolation design approach aimed at damage control for the bridge-track system of high-speed railways. They conducted sensitivity analyses focusing on structural damage, component damage, and seismic isolation design parameters, ultimately optimizing their design framework. [PERSON] et al. [13] investigated the seismic response and energy dissipation of isolated railway bridges by considering uncertainties in pier height, characteristic period, and peak ground acceleration. Their research provides valuable insights for the seismic isolation design of simply supported beam bridges in high-speed railway systems. [PERSON] et al. [14; 15] examined the impact of concrete carbonation-induced rebar corrosion on the seismic performance of existing reinforced concrete bridges. Through sensitivity analyses addressing corrosion levels, the service life of bridges, and failure mechanisms, they assessed the influence of corrosion effects on the seismic capacity of existing reinforced concrete structures, applying their findings to two specific bridge cases. Despite the existing research on the seismic performance of isolation and dissipation bridges that accounts for uncertainties, there remains a lack of targeted classification and consideration of uncertainty factors specific to the established design principles of dissipated continuous beam bridges. Furthermore, studies examining seismic performance under uncertainties often neglect factors related to operational load conditions, particularly the uncertainties surrounding the threshold capacity of shear pins that support the bridge during normal operations and their impact. This paper establishes a detailed numerical model of a continuous beam bridge with seismic isolation. The uncertainty associated with the model was systematically analyzed within two categorical frameworks: the seismic isolation device system and the non-isolated system. A comprehensive review of sixteen structural parameters was conducted, leading to the establishment of a refined numerical model. Employing tornado diagrams and the first-order second-moment method, a systematic analysis of the sensitivity of structural parameters was performed. This analysis identified key uncertain factors that significantly influence the seismic response of bridges designed with seismic isolation. The results enrich the body of research on sensitivity analysis of uncertainty factors in the seismic demand assessment of bridges, thereby providing a solid foundation for the design and calculation of seismic isolation measures in continuous beam bridges. The research framework is shown as Figure 1.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Related Work
#### 2.7.1 Security  
There are only a few studies address the LoRaWAN security [80; 81]. Studies focus on the most important and the least secure stages of the LoRaWAN network protocol which is activation process [82]. [PERSON] et al. [83] penetrate the network to gain network access with sniffing unencrypted join requests in OTAA and replaying these packets to the GW. Also, Join Procedure misusage can lead to the network server to be unavailable for a time period (Denial of Service - DoS). [PERSON] et al. [84] present that DoS attacks can be performed when considered the randomness of the DevNonce. An attack can keep an end-node to out of service by using this approach.  
[PERSON] et al. [85] aim to attach extra information LoRaWAN payloads without decrypting messages using bit-flipping attacks. According to the study, Message Integrity checks can be bypassed using brute-force. Alternatively, a shifting method is presented to prevent bit flipping.  
LoRa itself can be used as a plain radio communication which has no security procedures. [PERSON] and [PERSON] [86] present an architecture for point-to-point LoRa communication.  
A distributed digitalized ledger technology Blockchain has an increasing momentum store any kind of data in a secure manner. Blockchain and IoT integration have been studied and an alternative architecture is presented by [PERSON] and [PERSON] [87]. The authors aim to integrate Blockchain to LoRaWAN infrastructures.  
[PERSON] et al. [88] address PKI (Public Key Infrastructure) issues on resource constrained environments and offer alternative wireless key generation schemes to the problem. According to the results of the study, the presented schema can achieve 128-bit key generation up to 18 bit/s in stationary and 31 bit/s in mobile situations. Authors do not consider how network congestion will affect key generation methods.  
Blockchain technology has become an important player in cybersecurity and traditional centralized methods replaced with decentralized techniques. One of the projections of Blockchain applications is presented by [PERSON] and [PERSON] [89] which study tells us, LoRa enabled IoT data can be stored in a secure distributed system which will ensure distributed denial of service attacks (DDOS).
## 3 Proposed Method
### Underflow and Overflow Management  
In the embedding phase of our steganographic method, special attention is given to managing pixel values at the extremities, particularly those with values between 0 and 255. This precaution is crucial for preventing underflow and overflow scenarios that could jeopardize the accuracy of data extraction and thus affect the integrity of the pixel values.  
Underflow is defined as when an operation attempts to decrease a pixel's value below 0, while overflow occurs when an attempt is made to increase a pixel's value above 255. These conditions can result in erroneous data extraction, as pixel values are supposed to remain within the range from 0 to 255. To address these risks, pixel pairs with extreme values--either (0, 0) or (255, 255)--are intentionally excluded from the data embedding and extraction processes. This approach enhances the system's reliability by avoiding misinterpretations of embedded data and ensuring both the accuracy of the hidden message and the integrity of the pixel values.  
The initial collection of pixel pairs used for data hiding is \(\{(0,\,0),(1,\,1),\ldots,(255,\,255)\}\), with each pair comprising pixels of identical values. To circumvent potential complications arising from utilizing extremities (0, 0) and (255, 255), like generating computed pixel pairs beyond the permissible range, these specific pairs are omitted from the data hiding process. Consequently, the revised set of pixel pairs for use becomes \(\{(1,\,1),(2,\,2),\,\,\ldots,\,(254,\,254)\}\), thereby reducing ambiguity and errors in the data hiding and extraction processes.  
Consider a scenario with a pixel pair of (1, 1) and a data range from 0 to 4; the resulting selection of pixel pairs includes \(\{(1,\,2),(1,\,0),(0,\,1),(1,\,1),(2,\,1)\}\). Through careful selection, potential decoding challenges are averted, leading to a more secure and efficient steganographic procedure. By excluding pixels with extreme values and appropriately adjusting the pixel pairs, decoding issues are effectively mitigated, enhancing the steganography system's reliability and efficiency.
## 4 Discussion  
Table 3 shows a comparison of the proposed design with similar works from literature. In terms of RCS reduction, it is evident that the antenna performs almost equally well compared to most of the other designs. Additionally, the proposed antenna is capable of realizing an off-broadside beam radiation functionality. Furthermore, the achieved RCS reduction bandwidth is also superior to other works. In addition, the cavity height is also smaller, making it a low-profile design. Although proposed antenna also has high gain (relative to a conventional patch antenna), the value of gain quoted for other designs is higher as those antennas radiate a broadside beam, and hence do not suffer from scan loss [42]. The second reason for this is their aperture sizes, which are comparatively larger than the proposed design.  
Figure 14: Surface E-field plots. (**a**) Absorber surface for VP incident wave. (**b**) Gradient surface.  
Figure 13: Simulated monostatic RCS reduction for oblique angle incidence of \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), and \(20^{\circ}\). (**a**) VP wave incidence. (**b**) HP wave incidence.
## 4 Results and Discussion
### Impact of Gabion on Room Base Temperature  
The room base temperature of a building is an inherent parameter that reflects the comprehensive regulation of the building envelope under the external environment. To explore the effect of the gabion on the room base temperature, a simulation was carried out by inputting the exterior CHTCs and the weather information of condition I in the DeST software. The thermal characteristics of the envelope are listed in Table 6. The air exchange rate was set as 0.5 ACH. The heating set-point was 20 \({}^{\circ}\)C and there was no heat exchange between rooms because of the same indoor temperature. A 3.5 W/m\({}^{2}\) lighting system was equipped in the room. Moreover, a residential building occupancy of four people with an activity schedule of 18 p.m. to 8 a.m. on workdays is considered. The simulation period was from 15 November to 15 March (i.e., normal heating season) in a typical year. The numerical results are displayed in Figure 9.  
The gabion is effective in increasing room base temperature for the building in cold regions with a mountainous climate. It is observed that the room base temperature of a building with gabions is higher than that of a traditional building during the heating season, while its daily fluctuation range is nearly unchanged. The average room base temperature for the building with and without the gabion structure is 11.21 \({}^{\circ}\)C and 9.18 \({}^{\circ}\)C; the rate of increase is 22%. This increase in the room base temperature indicates an improved thermal insulation performance of the building envelope, which could reduce the energy  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Envelope** & **Thermal Transmittance** \\  & **(W/(m\({}^{2}\)K))** \\ \hline Normal exterior wall & 0.177 \\ \hline Gabion building envelope & 0.171 \\ \hline Roof & 0.393 \\ \hline Window & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Thermal characteristics of envelope.  
Figure 9: Impact of the gabion on room base temperature during heating season.  
consumption for building heating and enhance the adaptive capacity of a building to outdoor environments.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 3 Results  
### Support Vector Machines  
Since there are different kernel functions built into the SVM, the linear kernel and Gaussian kernel function are adopted for the prediction of OMC and MDD to study the effect of different kernel functions on the SVM. The prediction results of OMC and MDD by linear kernel and Gaussian kernel function models are shown in Table 2.  
From Table 2 and Figure 6, it can be seen that the Gaussian kernel function model outperforms the linear kernel in predicting OMC, with RMSE = 1.6308% and \(R^{2}\) = 0.8847. Similarly, the Gaussian kernel function model outperforms the linear kernel in predicting MDD, with RMSE = 0.5039 kN/m\({}^{3}\) and \(R^{2}\) = 0.8831. Therefore, in the prediction of compaction parameters by the SVM, the performance using the Gaussian kernel function is superior.  
It can be observed from the results and previous research that when the data present a complex nonlinear structure, a Gaussian kernel can be used to obtain a good model performance, while a linear kernel can be chosen when the data are linearly differentiable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Kernel Function**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & **RMSE** & \(R^{2}\) & **RMSE** & \(R^{2}\) \\ \hline \multirow{2}{*}{linear kernel} & OMC & 2.1127 & 0.8061 & 1.9083 & 0.8421 \\  & MDD & 0.6908 & 0.7558 & 0.6677 & 0.7947 \\ \multirow{2}{*}{Gaussian kernel} & OMC & 1.4927 & 0.9032 & 1.6308 & 0.8847 \\  & MDD & 0.4391 & 0.9014 & 0.5039 & 0.8831 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different kernel function performance results of SVM.