esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie
## 6 Conclusions  
In NDN, packet delivery is based on the content name rather than the destination address. In a forwarding engine, name lookup is faced with some challenges. The content name is variable-length, and its space is unbounded. Additionally, the number of name prefixes is expected to be large compared to that of network prefixes in an IP network.  
In this paper, we proposed a new name lookup scheme that is fast and scalable. Since there is no limit on the number of components and the length of a name string, multiple hash tables are hierarchically constructed in each component level for scalability. Each hash table size can dynamically change so that the scheme is not limited when new name prefixes are inserted. Upon hash collision, a Patricia trie enables a quick search in our scheme, while in separate chaining, every node should be visited in the worst case. Moreover, the key comparison in a Patricia trie occurs only once at a leaf node, whereas a key should be compared on every visited node in separate chaining. The experiment results showed that it provides not only high throughput but controlled memory consumption.  
Conceptualization, J.K.; Formal analysis, J.K. and J.K.; Investigation, M.-C.K. and M.S.S.; Methodology, J.K.; Validation, M.-C.K. and M.S.S.; Writing--original draft, J.K.; Writing--review and editing, J.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 5 Performance Analysis
### Multiple Sources with Different Sink Position
Although the total packets increased in OPP, OPP shows disadvantages with multiple sources and a different sink position. In contrast, RA shows big difference of the total packets of DD regardless of the different MCP. Figure 11 describes the average transmission delay of the each topology. The average transmission delay of RA is smaller than that of OPP for all cases except the topology with cross type of MCP [see Figure 10(b)]. As the error rate increases, the average transmission delay of OPP increases rapidly compared to that of RA. RA shows small difference of average transmission delay even at higher error rate. When the error rate is less than 5%, OPP has small average transmission delay than the case of RA at all types of MCP. However, the average transmission delay of RA slightly increases as the error rate increases. In contrast, the delay of DD increases rapidly according to the increment of the error rate. The result shows that DD is greatly influenced by the instability of the lossy situation. The average transmission delay of the cross type is worst than others as well as other metrics, because the four sources may use different path. Compared with other type, a cross type intermediate node has a different path that creates additional overhead from maintaining multiple paths for each source node. In other types of topology, the path from a source to a sink can be shared by an intermediate node. This behavior of the cross type to maintain multiple paths can be a reason for degrading performance. We verified that RA shows even better performance than DD with different position of multiple sources and one sink. DD suffers from lossy situation a lot more than RA in this environment. DD remains the problem which cannot provide reliable and efficient communication in lossy WSNs. However, RA shows performance improvement regardless of topology, different position and multiple sources. The performance degradation of RA can be seen as little at the experiment with different MCP, but the performance fluctuation according to the error rate is less than the first experiment with one source and one sink. Figure 11: The average transmission delay for each error rate with multiple sources and different sink position (0%, 5%, 10%, 15%, 20%).
# Industrial Dust Explosions. A Brief Review
## 1 Brief Review of Dust Explosion Basics
### Factors Influencing Ignitability and Explosibility of Dust Clouds  
A list of factors influencing the ignitability and explosibility of dust clouds may appear, as follows:  
1. Chemical composition of dust including moisture content.
2. Chemical composition, and initial pressure and temperature of the gas phase in which the dust is dispersed.
3. Distributions of particle sizes and shapes in the dust (determine the specific surface area of the dust in the fully dispersed state).
4. Degree of agglomeration, of dust particles, determining the effective specific surface area available to the combustion process in the dust cloud in the actual industrial situation.
5. Distribution of dust concentration in the actual cloud.
6. Distribution of initial turbulence in the actual cloud.
7. Possibility of generation of explosion-induced turbulence in the still unburnt part of the cloud (location of ignition source an important parameter).
8. Possibility of flame front distortion by other mechanisms than turbulence.
9. Possibility of significant radiative heat transfer (highly dependent on flame temperature, which in turn depends on particle chemistry).  
Factors \(a\), \(b\), \(c\), and perhaps \(i\) can be regarded as the basic parameters of the explosive dust cloud. However, factors \(d\) to \(h\) are influenced by the actual industrial dust cloud generation process and explosion development.
# Software Fault Localization Based on SALSA Algorithm
## 2 Related Work
### Software Fault Localization Based on Network Analysis Algorithm  
Network analysis algorithms assess the importance and relevance of nodes in a network by analyzing the structure of links and relationships between nodes. Therefore, software fault localization methods based on network analysis algorithms can mine critical information from the network structure of source code or program execution paths, thereby completing the fault localization task. [PERSON] et al. [22] proposed a fault localization technique based on the TrustRank algorithm (TRFL). TRFL combines the internal data dependency relationships of the program to construct a node-weighted program execution network, and uses the TrustRank algorithm to analyze network centrality, thereby re-ranking program entities based on their degree of suspicion. [PERSON] et al. [23] proposed a fault localization method based on the PageRank algorithm and mutation analysis (PRMA). This method first applies the PageRank algorithm to calculate the fault scores of statements, then uses these fault scores to weight the suspicion values of statements to address the tie issue in mutation-based fault localization. Finally, statements are ranked based on their weighted suspicion values. [PERSON] et al. [24] utilized the PageRank algorithm to recalculate the spectral information, taking into account the contributions of different tests, and then applied traditional SBFL techniques on the recalculated spectral information for more effective fault localization. Considering the advantages of network analysis algorithms, we have also employed network analysis algorithms to accomplish the task of fault localization in our research.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model  
With the progress of automotive technology, it helps to cover the limited power output of the car roof PV so that most private cars will be able to run by solar energy equipped with high-efficiency and 3D curved solar panels [1], and will overcome the loss mentioned above. To do so, the conventional IEC 60904 international standard series for PV electrical characterization, now focusing on 2D flat panels, needs to expand to 3D shaped PV modules [1]. We attempt to define 3D solar irradiation and rating to the 3D curved solar panels [24], either through an analytic approach [25] or numerical calculation based on Monte Carlo methods (ray-tracing simulation) [26]. We also consider the cross effect between the 3D curved surface of the PV module and 3D solar irradiance around the module [24].  
The standard PV modules are installed to avoid shadows. However, the car roof PV is not orientated for the utilization of solar energy. The driver's convenience often shades the PV. The relative orientation of the PV on the car to the sun position is not fixed but frequently changes by driving. The PV on the car body and the car roof is curved. It is often shaded by its surfaces. Therefore, the scale of performance is reconstructed.  
The position and height of the shading objects are often difficult to predict. For predicting the total solar energy to a vehicle (either annual or monthly basis) in a specific area, not in a particular driving course, it is convenient to predict the shading influence using rough indicators of the roughness of the land. The value of the annual or monthly solar irradiance value in a specific area is valuable to predict the driving performance of vehicles mounting the PV panel.  
We consider the following car-specific issues:  
1. Greater chance of shading by objects around the car (trees and buildings)
2. Curved surface
3. The orientation angle randomly varies
4. Mismatching loss by partial shading
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Vegetation Index**} & \multicolumn{2}{c}{**Intercept**} & \multicolumn{2}{c}{**Regression Coefficient**} \\ \cline{3-6}  & & **Estimate \(\pm\) SE** & _p_-Value** & **Estimate \(\pm\) SE** & _p_-Value** \\ \hline
**Year 1** & NDVI & \(-\)0.34 \(\pm\) 0.06 & \(<\)0.001 & 4.44 \(\pm\) 0.10 & \(<\)0.001 \\  & NDARE & \(-\)0.96 \(\pm\) 0.06 & \(<\)0.001 & 10.82 \(\pm\) 0.13 & \(<\)0.001 \\  & EVI & \(-\)0.77 \(\pm\) 0.06 & \(<\)0.001 & 8.54 \(\pm\) 0.13 & \(<\)0.001 \\  & CIr & \(-\)0.84 \(\pm\) 0.06 & \(<\)0.001 & 3.36 \(\pm\) 0.03 & \(<\)0.001 \\  & VREI & \(-\)0.72 \(\pm\) 0.06 & \(<\)0.001 & 33.19 \(\pm\) 0.36 & \(<\)0.001 \\  & BI & 1.41 \(\pm\) 0.06 & \(<\)0.001 & \(-\)16.46 \(\pm\) 0.12 & \(<\)0.001 \\ \hline
**Year 2** & NDVI & \(-\)0.84 \(\pm\) 0.07 & \(<\)0.001 & 6.61 \(\pm\) 0.05 & \(<\)0.001 \\  & NDARE & \(-\)0.94 \(\pm\) 0.07 & \(<\)0.001 & 9.79 \(\pm\) 0.07 & \(<\)0.001 \\  & EVI & \(-\)0.66 \(\pm\) 0.07 & \(<\)0.001 & 6.71 \(\pm\) 0.05 & \(<\)0.001 \\  & CIr & \(-\)0.75 \(\pm\) 0.07 & \(<\)0.001 & 2.76 \(\pm\) 0.02 & \(<\)0.001 \\  & VREI & \(-\)0.57 \(\pm\) 0.07 & \(<\)0.001 & 22.23 \(\pm\) 0.17 & \(<\)0.001 \\  & BI & 0.71 \(\pm\) 0.07 & \(<\)0.001 & \(-\)7.56 \(\pm\) 0.05 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Linear mixed-effect regression models of vegetation indices and live weight change of grazing cattle calculated from Sentinel-2 satellite imagery in both experimental years. Figure 4: Observed and predicted live weight changes of breeding cows from satellite imagery, rainfall and day of the year in extensive conditions over the 2-year trial period. The assessment of the model variable importance suggested that Clr was the most important predictor of the LWC in the random forest model (Figure 5). This was followed by the BI index and day of the year. Rainfall and VREI shared similar importance as predictors, followed by the month of the year. Less important predictors of the LWC were NDRE and NDVI, suggesting benefits of certain VIs compared to others in the ability to predict the LWC in extensive grazing conditions.
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 3 System Architecture  
The system is designed to detect ambulances on the road using a camera and a YOLOv8 model and to send a signal to open the road for ambulance vehicles. The system aims to improve the efficiency and safety of emergency services by reducing traffic congestion and response time. The system consists of four layers: the data acquisition layer, ambulance detection layer, monitoring layer, and cloud layer (see Figure 1).
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 3 Results and Discussion
### Rheological Performance Testing of CAA-CS Polysaccharide Hydrogel  
In the initial stage of hydrogel formation, the energy storage modulus (G\({}^{\prime}\)) in the system is lower than the loss modulus (G\({}^{\prime\prime}\)), indicating that the system remains in a solution state. As the reaction progresses, the acetoacetyl groups on CAA react more fully with the amino groups in chitosan, leading to an increase in the cross-linking degree of the three-dimensional network. Consequently, G\({}^{\prime}\) of the gel increases, and the formation of enamine bonds causes G\({}^{\prime}\) to rise faster than G\({}^{\prime\prime}\). Thus, when G\({}^{\prime}\) exceeds G\({}^{\prime\prime}\), the system exhibits solid-like behavior. The intersection point where G\({}^{\prime}\) equals G\({}^{\prime\prime}\) (G\({}^{\prime}\) = G\({}^{\prime\prime}\)) is defined as the gel point, and the corresponding time is referred to as the gel time as shown in Figure 5a-c. The gel time can be controlled by adjusting the concentration of CAA. Higher CAA concentrations result in faster gelation. For instance, as shown in Figure 5d, when the CAA concentration is 2 wt%, the gel time is 186 s, and increasing the CAA concentration to 2.5 wt% reduces the gel time to 169 s. The gel time decreases with increasing CAA concentration, while G\({}^{\prime}\) increases. When both CAA and chitosan concentrations are 2 wt%, the gel time is 186 s, and the energy storage modulus is 1486 Pa. Appropriate gel times and energy storage moduli facilitate the thorough mixing of the hydrogel precursor solution with drugs and ensure proper formation and injectability of the hydrogel. The energy storage modulus (G\({}^{\prime}\)) and loss modulus (G\({}^{\prime\prime}\)) of the hydrogel increase with the CAA mass fraction in Figure 5e. Additionally, G\({}^{\prime}\) consistently exceeds G\({}^{\prime\prime}\), and G\({}^{\prime\prime}\) remains at a low level, indicating that the hydrogel exhibits viscoelastic properties similar to those of a solid. As the CAA content increases, the number of acetoacetyl groups providing cross-linking sites also increases, forming a denser cross-linking structure. This results in enhanced gel strength within a certain range of CAA concentrations. In Figure 5f, the viscosity of the hydrogel as a function of angular frequency is shown. Similarly to most polymer materials, the viscosity of the hydrogel gradually decreases with increasing angular frequency, exhibiting shear-thinning behavior, which facilitates the injection of the hydrogel.
## 5 Discussion  
In the results section, the three-phase voltage and current waveforms of the STPC were verified through both simulation and experimental testing. Beyond performance under typical low-voltage load conditions, it is also important to consider fault scenarios that may occur in real PDS.  
Because the STPC is installed on overhead distribution lines, it may be exposed to environmental hazards such as strong winds, tree falls, and vehicle collisions. In such cases, the STPC must disconnect immediately to protect adjacent PDS assets and loads. To support this, a Molded Case Circuit Breaker (MCCB) is embedded in the enclosure as a protective device.  
Another consideration is economic feasibility. Unlike conventional transformers that use iron cores and coils, the STPC includes power semiconductor devices, LC filters, and cooling systems. While this enables advanced control and voltage regulation, it also introduces potential complexity in fault diagnosis and maintenance. Therefore, in real-world applications--especially in areas with limited three-phase access or wildfire risks--it is essential to perform a cost-benefit analysis when considering the STPC installation versus conventional infrastructure.
## 4 Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D)
### Implementation of Zero Trust Methodologies on Artificial Intelligence Models/Features  
Applying ZT principles directly to the AI components is a critical, self-referential aspect of the AZTRM-D framework. Granting implicit trust to any system, including an AI, is inherently risky [67]. Therefore, each AI model and automation process is treated as an independent entity that must be authenticated and authorized before it can interact with data or other systems [26].  
This is achieved through several layers of control. First, stringent IAM protocols assign specific, limited roles to each AI process. For example, an AI model does not use static, long-lived credentials. Instead, it authenticates using a short-lived cryptographic token (e.g., a JWT or SPIFFE certificate) that is automatically rotated by a central secrets manager. This token is presented each time the AI process requests access to a data source or another service. An AI model designed to analyze network traffic has no permission to access code repositories. This is enforced through a Policy Enforcement Point that validates the AI's token and checks its associated role-based access control (RBAC) policy. The policy for the network-traffic AI would explicitly grant'read-only' access to a specific network log stream and deny all other actions, such as write access or access to the Git Server API.  
Second, all data inputs to AI systems undergo integrity validation to ensure they have not been tampered with or poisoned [72]. Finally, human oversight remains a crucial backstop. While AI can automate risk assessment and suggest responses, the final decision to, for example, shut down a critical production system, rests with a human operator who can evaluate the broader context, aligning with the principles of responsible AI [73]. This ensures that the automation serves, rather than supplants, human judgment in critical security decisions.
## 3 Experimental Setting and Results
### Experimental Results  
#### 3.3.1 Qualitative Analysis  
In this section, we show the qualitative image search results on the UCMD dataset [37]. The proposed TOCEH and the other seven state-of-the-art methods separately map the image content information into 64-, 128- and 256-bit binary code. The images with minimal Hamming distance to the query sample are returned as retrieval results and the false images are marked with red rectangles, as shown in Figures 7-9.  
From the RS image retrieval results, we intuitively know that TOCEH owns the best retrieval results. When encoding RS image content as a 64-bit binary code in Figure 6, TOCEH and TBH [10] return two false positive images. Correspondingly, the number of false images retrieved by the other six methods is larger than two. Furthermore, the false RS images' ranking position in TOCEH is higher than that in TBH [10], which gives TOCEH a larger mAP value. In Figure 7, the length of the binary code is 128. One RS image is incorrectly returned by TOCEH, TBH [10], DCH [11] and PRH [23], and the false image has a relatively higher ranking position in TOCEH. As the number of binary bits increases to 256, only TOCEH and TBH [10] retrieve no false image, as shown in Figure 8.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### POA and PDOA  
POA ranging techniques estimate the distance by measuring the phase of the carrier signal [51]. It is also called received signal phase (RSP). There is a number of POA measurements that have been used in RFID-based localization systems. The POA-based approach was introduced to increase accuracy and decrease disturbances due to multipath propagation in passive RFID 2D localization system [79]. The results of the estimated POA existed in an unlimited number of paths due to the 2\(\pi\) uncertainty in phase estimations. By means of the frequency-stepped continuous-waveform principle, the distance of the propagation path can compute definitely for a high bandwidth system. The POA techniques can be used integrated with different techniques such as TOF, TDOA, and RSSI to increase their performance. However, POA-based approaches may need LoS for high accuracy.  
The ranging measurement based on PDOA uses the phase difference of the propagation path between the anchor nodes or the reader to the tag to calculate its distance [80]. It is also mostly used in RFID and wireless sensor networks (WSNs) system. The phase errors can be small due to the very small signal bandwidth. Unfortunately, unavoidable ambiguities can occur during the evaluation of the true distance due to the multipath effects and a 2 \(\pi\) phase periodicity [81].
## 1 Introduction
A multitude of reactive powders obtained from natural sources, industrial by-products and alkaline activators can be used for their production. The nature and content of the alkaline activators as well as the variability in the composition and reactivity of the raw materials impact the development of the mechanical and the fresh state properties, the shrinkage behavior and the durability of the produced mortars or concrete. When amorphous aluminosilicate reactive powders, such as metakaolin and fly ash, are used, strong alkaline activator solutions and mild temperatures are necessary to activate the powders. The so-obtained binders are geopolymers. They can be considered as the amorphous analogue of zeolites and are characterized by a higher initial alkalinity, the formation of an amorphous sodium aluminosilicate hydrate gel (N-A-S-H) and the absence of lime as reaction product [21; 22; 23]. They can find application in structures exposed to high temperatures or chemical attacks. When granulated blast furnace slag (GGBFS) is used, milder process conditions can be adopted and the main hydration product is a calcium aluminosilicate hydrate (C-A-S-H) gel. Finally, several works have investigated the hybrid or blended alkali-activated materials (AAM), which are a combination of the previously described systems and could provide improved mechanical and durability properties and a better tuning of the fresh properties [24; 25]. Recently, some authors have investigated the use of steel fiber-reinforced geopolymer matrix (S-FRGM) composites for external strengthening applications [26]. The initial highly alkaline environment, required for the geopolymerization to start, resulted in having a detrimental effect on composite performance by weakening the interaction between matrix and fibers, when not carefully tuned. The aim of this work is to overcome the issues observed when geopolymeric mortars are used in fiber-reinforced geopolymer matrix composite when still using a binder with improved resistance to fire and high temperatures, a higher resistance to sulfate attack than the one provided by Ordinary Portland Cement (OPC) and lime matrices [27; 28; 29; 30; 31; 32; 33]. The mortar, based on hybrid alkali-activated binder and containing only industrial by-products as reactive powders, was characterized by measuring drying shrinkage, capillary water absorption, compressive and flexural strengths and analyzed using X-Ray diffractometry (XRD), Fourier-transform infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA). The mortar was then used as matrix to manufacture basalt fabric-reinforced alkali-activated matrix (FRAAM) composites. A commercial balanced, coated, bidirectional fabric made out of basalt fibers was used as reinforcement. Composites were characterized through direct shear tests carried out on brickwork substrates using an externally bonded FRAAM composite strips and tensile tests. Their mechanical behavior was compared with that exhibited by an available commercial system based on the same basalt textile [14].