esa-sceva/satcom-chunk-collection huggingface dataset:
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design
### Q-Learning-Based Load Balancing
#### 4.3.2 UAV Routing Policy Area  
In a rapidly changing network environment, it is important to narrow and simplify the scope of the problem to be solved in order to design an optimal policy for an effective URN routing through the RL. If a learning algorithm is designed, including ground network routing, the problem to be solved becomes more complicated and the reward through the RL becomes difficult to effectively reflect. Therefore, Q-LBR defines URPA corresponding to two knobs (_URPA_upper_ & _URPA_lower_) when considering the priority of traffic and the existence of a route independently from the ground network routing.  
URPA is a parameter for applying the URN routing policy, and is defined in the following three policy areas to determine whether or not to be the route of an air node relay when a URN is present on the routing path of the GN. URPA sets the boundary for the policy area based on the parameters of _URPA_upper_ and _URPA_lower_ (_URPA_upper_ > _URPA_lower_), as shown in Figure 6, and dynamically changes with time \(t\) based on the action of the Q-learning.  
* Policy Area A: Allow a UAV relay only when there is no ground path with a high-priority packet
* Policy Area B: Allow a UAV relay only when there is no ground path without considering the packet priority.
* Policy Area C: Allow a UAV relay without considering the packet priority or existence of the ground path (allow all traffic)
## 2 Materials and Methods
### Extending WEave for Circular Economy Traceability
#### 2.2.5 Security and Integrity Considerations  
Introducing circular flows raises new trust questions. For instance, how do others trust that the Recycler accurately recorded the recycled output quantity? In our permissioned setting, RC is a known, permissioned participant. The Auditor (AU) can inspect return records and maybe even external certifications of the recycler. If needed, IoT devices or digital scales could feed data to minimize manual error. We might include a mechanism where the auditor or retailer must endorse the RecycleProduct transaction (multi-signature endorsement) to validate that the recycling claim is correct. This could prevent, say, a recycler from falsely inflating recycled content. Such governance can be encoded via Fabric endorsement policies or by requiring an auditor countersign in chaincode logic.  
A key design choice in WEave was to route returns and recycling data through the primary production channel to maintain traceability continuity. While upstream suppliers may not strictly require access to reverse logistics data, such visibility supports a holistic view of material lifecycles within the closed-loop system. Since return and recycling data typically do not expose competitive supplier information, we assume this level of transparency is acceptable. However, if confidentiality concerns arise, reverse flows could be managed through a separate blockchain channel. Notably, visibility into end-of-life outcomes may incentivize upstream actors to design more recyclable products. This mirrors broader industry trends: for instance, Walmart's Scintilla platform provides suppliers with downstream insights to improve operational efficiency across its omnichannel network [28].  
Beyond operational considerations, full-lifecycle traceability has implications for provenance integrity--particularly for high-value or historically significant items. In sectors such as fine art, luxury goods, or museum-quality antiques, the ability to trace an object's complete ownership and transformation history is essential. For example, a piece of furniture with partial gaps in its history--such as disappearing from Paris in 1937 and resurfacing in Munich in 1945, possibly refurbished--raises critical ethical and legal questions. Blockchain-based systems like WEave could, in principle, preserve such chains of custody, enabling more trustworthy provenance records even amid repair, resale, or geographic transfer.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments  
### Datasets  
To evaluate the performance of DSAH, we conducted experiments on two widely used datasets, i.e., MIRFlickr [56] and NUS-WIDE [57] datasets.
## 1 Introduction
The results of these studies have shown that IMERG products are closely related to regional topography and climatic conditions when describing precipitation. For example, [PERSON] et al. [1] showed that topography impacts the performance of IMERG/TMPA data, overestimating precipitation in low-elevation areas and underestimating it in high-altitude areas. [PERSON] et al. [21] found that the accuracy of GPM is related to topography. [PERSON] et al. [17] reported that the eastern and northeastern parts of the UAE have complicated terrain; thus, satellite products perform poorly in these areas. In this context, it is critical to verify the satellite datasets over different terrain and climatic regions. Because GPM products have only been launched in recent years, the terrain of the Tianshan mountainous (TSM) is complex, and there are few comprehensive assessments based on daily-scale data at the subregion level, the application of GPM products in the TSM area has remained limited. Therefore, this study evaluates and compares the estimation accuracy of the two satellite precipitation datasets GPM-IMERG and TRMM 3B42 in the TSM. This assessment uses the China Gauge-based Daily Precipitation Analysis (CGDPA) as the ground reference dataset, which is a set of daily precipitation grid products in China launched by the National Meteorological Information Center (NMIC) of China Meteorological Administration (CMA). It is a comprehensive observational daily precipitation report database. The study period was from April 2014 to March 2015.
# Forecasting of Power Demands Using Deep Learning
## 4 Conclusions  
Power demands forecasting is a challenging topic and important for future planning. In this paper, we introduced the utilization of different deep learning models for future demands forecasting for facility capacity, supply capacity, and power consumption. Different deep learning architectures were studied, namely CNN, RNN, and the hybrid model that combines CNN with RNN. The experimental results show that the CNN model outperformed RNN and hybrid model significantly. Furthermore, we compared the performance of the CNN model with SVM and ANN models. The comparison results showed that CNN performs generally better. The developed CNN model is a short-term power demand forecasting model as it cannot forecast more than one day. The future plan is collecting more training data from the Korea Power Exchange in order to train a more robust forecasting model that can perform mid-term to long-term power demand forecasting.  
T.K. and D.Y.L. and H.T. prepared the dataset, conceived the algorithm, and carried out the experiment and analysis. T.K., D.Y.L., and H.T. wrote the manuscript with support from K.T.C. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
National Research Foundation of Korea: 2020R1A2C2005612; National Research Foundation of Korea: NRF-2017M3C7A1044816  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C2005612), in part by the Brain Research Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2017M3C7A1044816), and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A6A3A01094685).  
The authors declare that they have no competing interests.
## 3 Results
### Time-Domain Performance  
In the following, we also used an eye diagram and a CM noise magnitude (V\({}_{\text{common}}\)) measurement to verify the capability of the proposed filter in the time domain. We measured the eye diagram to verify the proposed suppression filter that maintained good SI for differential signal transmission. First, we excited an 8 Gbps signal to ports 1 and 2 with a \(2^{7}-1\) pseudo-random binary sequence (PRBS), where the amplitude was \(\pm 0.5\) V and the rise time was 50 ps. The eye diagram measurement is shown in Figure 17 from ports 3 and 4.  
Figure 16: S-parameter of measurements and full-wave simulation.  
Figure 15: Photographs of the fabricated prototype with a 5 mm delay line: (**a**) the reference board and (**b**) the proposed filter board.  
Table 5 summarizes the detailed parameters of the eye diagram, including the eye height, width, and timing jitter. As shown by the results, this system still maintained a good transmission quality, although there was a small amount of degradation on the filter board measurement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Eye Height** & **Eye Width** & **Timing Jitter** \\ \hline Reference Board & 807.8 mV & 117.4 ps & 3.125 ps \\ Filter Board & 762.3 mV & 116.8 ps & 2.344 ps \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed parameters of the eye diagram.  
Figure 17: Measurement of the eye diagrams: (**a**) the reference board and (**b**) the proposed suppression filter board.  
Furthermore, we excited CM noise to one of the differential lines with an implemented 5 mm delay line using a two-step function to measure the CM noise magnitude. Figure 18 shows the CM noise magnitude measurements for the reference and the proposed suppression filter boards with a 5 mm delay line. The results showed that the peak voltage was 138 mV and 52 mV, respectively. The measurement results of the CM noise showed that the proposed filter suppressed 62.3% of the magnitude.
# Purely Gain-Coupled Distributed-Feedback Bragg Semiconductor Laser Diode Emitting at 770 nm
## 2 Materials and Methods
For traditional DFB lasers, the coupling strength's real part is usually greater than 1.0 [35]; however, for our device, it is much smaller. Even the refractive-index-coupled strength of multi-longitudinal-mode lasers reached 0.3 [36]. All this shows that the refractive-index-coupled effect does not play an important role in our devices and can be neglected. Since there is no built-in grating or other optical diffraction structure in the cavity, our devices could be considered purely gain-coupled DFB lasers.
# Querying on Federated Sensor Networks
## 3 L-FSN Protocol
### Query's Destination Is Assigned to be Unspecified:  
If destination geographical information is unspecified, queries are initiated to be sent to the entire FSN. L-FSN allows data packets to be routed to upper-tier clusterhead EGs in a distance-sensitive manner. Meanwhile, data is stored at each data forwarding EG on this path. The query packet follows a hierarchical path similar to the data forwarding path until being resolved at the closest clusterhead gateway that stores the relevant data without being propagated to other islands. In addition to distance-sensitive querying, if data need to be collected from the entire FSN, all upmost-tier EGs receive the query. For example, in a campus environment, a mechanic may send a gas leak query. The query follows a path through the mechanic's building's IG to upper-tier clustered EGs in a distance-sensitive manner. As a response from the first EG that stores the relevant data, the mechanic receives the closest gas leak area's geographical location. Moreover, the mechanic may demand to receive all gas leak areas in the campus, and, send a query to entire FSN. In this case, all upmost-tier clusterhead EGs receive the query packet and reply back to the mechanic's query if their underlying island sensor nodes had detected a gas leak event.  
Figure 2: Query and event data message flow among Federated Sensor Network units.  
**Query's Destination Is Assigned to Be Specified:**  
L-FSN queries can be directed to specified islands. L-FSN makes island-aware indexing and advertises data to a selected island or subset of islands instead of advertising to all FSN islands. Based on the destination node's geographical information in a query packet, query forwarding EGs determine the destination EG(s). The destination EG is found to be the one that includes the geographical coordinates of the query destination area. If more than one EG is covering the query destination area, the source EG routes the query packet to each of these EGs. Event data packets follow the reverse path of the query propagation. For example, in a campus, each parking lot can be monitored by sensors, and, parking lots as FSN islands can be federated under an FSN. A driver in a parking lot can send a query for an empty parking spot for the destination parking lot before relocating. In this case, the driver's query is first sent to current parking lot's IG, and then propagated to the next EGs based on the destination parking lot's geographical information. An EG that includes the destination parking lot's geographical coordinates replies back to the driver with the availability of parking spots.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 3 Optimization Algorithms  
This section details the three evolutionary algorithms used in this work: (i) GA, (ii) PSO, (iii) CMA-ES. Our implementations of these algorithms are used to independently optimize the two FOMs defined in Section 2.
## 3 Results and Discussion
### The Volatile Compound Profile of Cream and Sour Cream Samples
In our study, the stored sour cream samples containing the essential oils of clove buds and juniper berries had a higher amount of hexanal than the stored sour cream samples without additives. On the other hand, essential oil-enriched sour cream samples that were fermented and stored for 21 days had an increased content of dimethyl sulfide. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline
**Component** & **IK\({}^{1}\)** & **Ion\({}^{2}\)** & **Cream** & **Sour** & \multicolumn{4}{c}{**Sour**} & \multicolumn{4}{c}{**Lameon Pool**} & \multicolumn{4}{c}{**Crow**} & **Data** & \multicolumn{4}{c}{**Jumpic Energy**} \\ \cline{4-13}  & & & & & & & & & & & & & & & & & & & \\  
Short-chain fatty acids, such as acetic and butyric (butanoic) acid, were present in all samples fermented with _B. animalis_ subsp. _lactis_ Bb-12. The levels of both acids did not decrease during the storage of samples for 21 days. These acids can be produced by the lipolysis of milk fat, or they can be specifically formed by the action of LAB or from the deamination of amino acids as products of lactose metabolism or even lipid oxidation. The representative _Bifidobacterium_ species used in this study is mainly responsible for producing acetate through the fermentation pathway. In contrast, lactobacillus can produce end products, such as pyruvate by carbohydrate fermentation, during the glycolytic metabolic pathway. The nonfermented samples did not have these acids, which confirms the significant influence of the fermentation process on their formation. The highest increase in the content of acetic and butanoic acids was found in sour cream samples enriched with essential oils at the lowest concentration (12.5%). Volatile organic acids, including acetic and butanoic acids, are important components determining the overall palatability of fermented milk beverages. A significant increase in the content of butyric acid during the storage of products may result in undesirable changes in the smell and taste, characteristic of rancidity. The addition of essential oils to cream subjected to fermentation resulted in the formation of the identified volatile components, which were part of the chemical composition of the used essential oils. Sour cream samples containing lemon peel essential oils contained \(\beta\)-pinene, limonene, and \(\gamma\)-terpinene. On the other hand, samples enriched with clove bud essential oils contained eugenol, eugenol acetate, humulene, and caryophyllene, and those supplemented with juniper berry essential oils contained three terpen, namely sabinene, \(\beta\)-pinene, and myrcene. The content of these compounds in sour cream samples stored for 21 days increased in proportion to the concentration of essential oils added.
## 4 Results
### Model Validation and Comparative Analysis
#### 4.1.1 Physiological Process Representation Advantage  
The demonstrated superiority of the DSSAT framework over purely data-driven approaches (Table 7) stems fundamentally from its explicit modeling of biophysical processes governing plant-environment interactions. Unlike machine learning methods that rely on statistical correlations within training datasets, DSSAT embeds first principles of crop physiology through differential equations describing photosynthetic carbon assimilation, nitrogen translocation dynamics, and source-sink relationships. This mechanistic foundation enables accurate simulation of delayed fertilization effects--a critical capability where neural networks (BP RMSE = 519.57 kg/ha) fail catastrophically due to their inherent assumption of instantaneous input-output relationships. The model's soil-plant-atmosphere continuum module explicitly resolves nitrogen diffusion gradients and root uptake kinetics, capturing threshold behaviors in nutrient response curves that tree-based methods (XGBoost R\({}^{2}\) = \(-\)1.637) cannot extrapolate beyond observed fertilization ranges.  
The temporal integration capacity of DSSAT proves particularly crucial for modeling phenological development under drip irrigation regimes. By coupling daily solar radiation and temperature inputs with cultivar-specific thermal time accumulation algorithms, the framework maintains phase-locked synchronization between simulated and observed growth stages--a temporal coherence unattainable by pattern recognition approaches (random forest RMSE = 409.35 kg/ha). This synchronization ensures proper alignment of nitrogen demand peaks with fertilization events, avoiding the temporal misallocation errors prevalent in KNN predictions (R\({}^{2}\) = \(-\)0.052). Furthermore, DSSAT's dynamic partitioning of assimilates between vegetative and reproductive organs incorporates hormonal regulation mechanisms that machine learning architectures cannot represent without explicit biochemical knowledge.  
The model's predictive fidelity (R\({}^{2}\) = 0.871) ultimately arises from its hierarchical organization of biological processes. Soil nitrogen mineralization rates modulate root uptake through Michaelis-Menten kinetics, while canopy photosynthesis follows Farquhar biochemical models constrained by Rubisco activity. These process-based constraints prevent nonsensical extrapolations exhibited by data-driven methods in low-probability scenarios (negative R\({}^{2}\) values), effectively regularizing the solution space through biophysical laws. The tight coupling between nitrogen transport models and crop demand functions generates physically consistent response surfaces, enabling reliable optimization despite the high-dimensional, non-convex parameter landscape that confounds pure machine learning approaches.
## 5 Results and Analysis
### Factors Influencing Bearing Stiffness  
The relationship between the variation in bearing stiffness and gas film thickness across different throttle hole diameters is depicted in the data shown for a configuration for which the length of the rectangular orifice array \(a\) measures 40 mm and with a gas supply pressure of 0.5 Mpa, as per Figure 17. For throttle hole diameters ranging from 0.4 mm to 1 mm, there is an observed increment in stiffness corresponding to the increase in gas film thickness. Conversely, when the throttle hole diameter is set to 0.2 mm, the stiffness demonstrates growth followed by decline as the gas film thickness increases, peaking at a gas film thickness of 14 um. The reduced throttle hole diameter correlates with a marked rise in bearing stiffness, showcasing that micro-porous bearings are more stable. In the context of practical bearing designs, aiming for a gas film thickness in the vicinity of 14 um is advisable to secure higher stiffness and thus achieve enhanced stability in the bearings.  
The dependence of bearing stiffness on air film thickness across varying supply air pressures is illustrated in Figure 18. This configuration employs a rectangular orifice array with a length of 40 mm and an orifice diameter of 0.2 mm. An initial rise in stiffness is observed as the air film gap enlarges, followed by a subsequent decline, with each condition reaching a peak stiffness at approximately 14 um. Furthermore, it is noted that escalation of supply air pressure contributes to augmentation of the stiffness of the bearing.  
Variations in bearing stiffness in relation to air film thickness under assorted orifice arrangements are delineated in Figure 19. This is for orifices with a diameter of 0.2 mm and subjected to a supply air pressure of 0.5 Mpa. The data suggest that optimal stiffness of the bearing is achieved with a rectangular orifice array of 40 mm in length, where maximum stiffness is attained at an air film thickness of 14 um.  
Figure 16: Influence of the length \(a\) of the rectangular orifice array on the maximum velocity of the airflow within the air film gap.  
Figure 17: Influence of the throttle hole diameter on stiffness.
###### Abstract
The performance of these algorithms was improved by leveraging chaos mapping for initialization and utilizing the global search capabilities of the Artificial Fish Swarm Algorithm. [PERSON] et al. [26] employed the Harris Hawks Optimization (HHO) algorithm, leveraging its dynamic exploration and exploitation mechanism with adaptive switching to facilitate rapid satellite selection in multi-GNSS systems. [PERSON] et al. [27] developed a dual-objective model combining Geometric Dilution of Precision (GDOP) and satellite selection number using the Imperialist Competitive Algorithm (ICA), enhancing decision-making flexibility by incorporating prior constraints on elevation and azimuth angles. Although these algorithms improve computational efficiency and reduce complexity, intelligent optimization-based satellite selection algorithms typically encounter issues such as extensive initial parameter configurations, susceptibility to local optima, and reliance on a singular satellite selection criterion. [PERSON] et al. [28] reduced the computational complexity and enhanced the efficiency of satellite selection by incorporating an adaptive convergence factor into the Grey Wolf Optimizer (GWO) algorithm. The GWO algorithm is particularly well-suited for the application of satellite selection algorithms in complex environments, such as LEO satellite constellations, owing to its advantages of low computational complexity, ease of implementation, and minimal parameter adjustments. For the above issues, this paper proposes a Multi-Strategy Fusion Grey Wolf Optimization (MSFGWO) algorithm, which is designed to enhance dynamic adaptability and computational efficiency in the process of selecting LEO satellites. By incorporating geometric configuration optimization, computational efficiency enhancement, and visible satellite time maximization, the limitations of traditional single-index optimization are effectively overcome. This approach not only reduces the hardware requirements for receivers but also improves the real-time performance, stability, and accuracy of navigation and positioning in complex environments.