esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 5 Results and Analysis
### Final Results
Moreover, it is observed that CRL gave us the most significant increase in the overall performance of +2.46% and +1.14% in F1-score over pretrained SdGloVe and SdfastText representations with the CaBiLSTM model. Furthermore, the training accuracy and time of each baseline and proposed model are depicted in Table 7. The results of the proposed CaBiLSTM network on the SiNER test set. Bold font denotes the best results. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Representation** & **Variants** & **Precision\%** & **Recall\%** & **F1-Score\%** \\ \hline \multirow{2}{*}{SdGloVe} & No-attention & 87.92 & 88.27 & 88.16 \\  & Attention & 88.37 & 89.25 & 88.79 \\ \hline \multirow{2}{*}{SdfastText} & No-attention & 89.74 & **90.18** & 89.76 \\  & Attention & **90.27** & **90.64** & **90.11** \\ \hline \multirow{2}{*}{CRL} & No-attention & **90.58** & **90.86** & **90.82** \\  & Attention & **91.43** & **91.76** & **91.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of the proposed CaBiLSTM network on the SiNER test set. Bold font denotes the best results. Figure 3: The performance of the CaBiLSTM model with different dropout rates and context settings (_CWS_) on the SiNER development set. in Figure 4. The lower training accuracy of the SdGloVe (see Figure 4a) model indicates that the model configuration does not capture the complexity of the given training data. The training accuracy of all baseline models is better with SdfastText (see Figure 4b) representations than SdGloVe. However, neural baseline models achieve high training accuracy using task-specific representations (see Figure 4c) as compared to both pretrained SDGloVe and SdfastText representations. Overall, the CaBiLSTM model attains more accuracy in less training time than all the baselines, as depicted in Figure 4d. It can be observed that task-specific joint representation learning as CRL is more beneficial for performance gain. We also present the label-wise F1-score of our CRL-based CaBiLSTM model in Figure 5. This Figure shows that NEs of ORG, LOC, EVENT, and ART get lower F1-scores due to the context-sensitivity and other ambiguities discussed in the earlier section. In contrast, the other NEs, such as NOPR, FAC, and LANGUAGE, get high F1-scores.
# Research Progress and Future Prospects of Key Technologies for Dryland Transplanters
## 3 Research Status of Seedling Picking Mechanism
### Free-Fall Type  
Free-fall seedling extraction technology has unique application scenarios. Compared with conventional plug-seeding cultivation, the nutrient pot seedling cultivation adapted to free-fall extraction exhibits distinct differences: its pot cavity features a frustum-shaped structure with a larger base and smaller top. During extraction, plug seedlings detach from the tray in a nearly vertical posture and directly fall into the seedling delivery device or planting mechanism.  
[PERSON] et al. [128] employed a gravity-based seedling picking method, leveraging coordinated trap and distributor operation to transport seedlings via gravity from the tray through the distributor into the planting unit. A single-row transplanter (Figure 16)  
Figure 14: Double row transplanter [119]. (**a**) A schematic diagram of the seedling picking and throwing system. (**b**) A schematic diagram of the seedling pick-up device. 1. Seedling tray conveying mechanism; 2. seedling pick-up device; 3. control panel; 4. seedling tray frame; 5. seedling separating mechanism; 6. rotating seedlingcup mechanism; 7. 128-hole seedling tray; 8. seedling pick-up end-effector; 9. zero position sensorand induction device; 10. clamping cylinder; 11. lifting cylinder; 12. drive chain; 13. linear guide rail; 14. servo motor.  
Figure 15: Structure drawing of the simplified greenhouse robotic transplanting workcell [120]. (**a**) Mechanism principles: 1, 3, 4, 6, 7, 9. belt pulley; 2, 5, 8. slider; 10. synchronous belt. (**b**) Mechanical structures: 1. transplanting manipulator, 2. seedling gripper; 3. plug seedling; 4. control system; 5. chain conveyor for plug trays; 6. chain conveyor for growth pots; 7. growth pot; 8. working trajectory.  
was developed, achieving transplanting speeds of 54-69 plants/min in soil tank tests. [PERSON] et al. [129] designed a free-fall seedling picking and planting device, testing it on a walking tractor-driven vegetable transplanter. [PERSON] et al. [130] developed a closed multi-channel air-blowing plug-seedling extractor, using high-speed airflow to blow seedlings into a guide tube for planting, achieving a high extraction success rate. [PERSON] [131; 132] designed a pneumatic down-pressure high-speed extractor with a combined plug tray. Through processes like \"orderly tray supply, continuous seedling feeding, pneumatic down-pressure extraction, and free seedling placement\", it enabled 120 plants/min extraction but with high medium loss. [PERSON] [133] developed a vibration-assisted air-suction device for directional transplanting of Panax notoginseng seedlings. [PERSON] [134] proposed two vegetable transplanting extraction schemes: air-blowing and cylinder-pushing (Figure 17).
## 5 Proposed Framework
### Integration of ZTA and FL
#### 5.4.1 Phase-Specific Security Enforcement  
The integration framework implements comprehensive defense-in-depth protections across all FL stages, with specialized security measures tailored to each phase of the workflow.  
During the Data Collection and Preprocessing phase, continuous authentication is enforced through multiple verification mechanisms. An LSTM-based model with 128 hidden units analyzes API call sequences to detect anomalous patterns, while Intel SGX provides hardware-level attestation for edge devices. Cryptographic tokens are refreshed every 50 milliseconds using ECDSA-P384 signatures to prevent session hijacking. For data quality assurance, slice-specific validation enforces strict ranges (0-100 Mbps for eMBB, 0-10 ms  
Figure 11: Energy–latency tradeoff comparison. Our framework (blue) maintains operation below the critical 5% latency increase threshold (equivalent to 5 ms absolute latency for URLLC, shown as red dashed line), while achieving up to 32% energy savings. The performance of FedAvg ([PERSON] et al., 2020) [8] and centralized baselines are shown for comparison.  
Figure 10: Energy savings under 15% poisoning attacks. Our framework maintains stability while baseline performance degrades.  
for URLLC traffic) complemented by statistical outlier detection using a 3\(\sigma\) threshold to filter corrupted inputs.  
The Local Model Training phase employs dynamic security zoning as detailed in Section 5.1.3, creating three distinct protection tiers. The Core Zone safeguards SMO and RIC components with mutual TLS authentication, while the Controlled Zone manages xApps through RBAC policies enforced via the A1 interface. Suspicious applications are automatically isolated in the Quarantine Zone using Docker containerization. Runtime integrity verification includes pre-execution model hash checks, continuous monitoring of GPU memory access patterns, and strict enforcement of a 10 ms inference deadline to prevent timing-based attacks.  
For Global Model Aggregation, the system implements a multi-layered anomaly detection framework. Each model update undergoes rigorous validation, including \(\ell_{2}\)-norm verification (threshold of 3.0), Kolmogorov-Smirnov distribution analysis (\(p\)-value \(<0.01\)), and temporal consistency checks across training rounds. The robust aggregation process combines several protective measures: trimmed mean filtering discards the most extreme 15% of values to mitigate poisoning attempts, differential privacy guarantees (\(\epsilon=2.0\)) protect participant confidentiality, and ECIES-256 cryptographic verification ensures update authenticity. This comprehensive approach maintains model quality while providing strong security assurances throughout the federation process.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 4 Discussion
### Capability of Earth Observation to Map Coastal Seascapes with Emphasis on Cystoseira s.l. Forests
in shallow reef zones, with canopy cover of \(>\)80% at the 3 m pixel level, enabled accurate delineation and separation from other rocky barrens or other rocky reef assemblages. The classification was performed using a Random Forest algorithm consisting of 100 decision trees grown to maximum depth. This configuration was selected to maximize model flexibility and ensure the separation of spectrally similar classes. Despite the potential for overfitting with fully grown trees, the ensemble nature of the model and the strong accuracy metrics observed suggest robust generalization to the mapping domain. Although the analysis relied on single-date imagery, this does not constitute a major limitation in this context, as the mapped benthic habitats are largely stable over time, not subject to strong seasonal variability, while the key habitat is mapped in the proper period. Future research could further enhance classification performance by exploring refined bathymetric corrections, incorporating additional spectral/temporal features, and testing advanced classification techniques--such as object-based image analysis or deep learning--for better discrimination of complex habitats. Expanding field validation across broader spatial extents would also improve the model's transferability and support large-scale monitoring and conservation planning. The spectral unmixing model exhibited strong performance across most benthic habitat classes, with particularly high accuracy for deep soft bottoms (R\({}^{2}\) = 0.99; RPD = 18.66) and shallow soft bottoms (R\({}^{2}\) = 0.99; RPD = 8.72). These classes also presented the lowest error metrics (MAE \(<\) 0.007), indicating well-defined spectral signatures and minimal confusion with other substrates. _Seagrass_ was mapped with high reliability (R\({}^{2}\) = 0.89; RPD = 3.01), consistent with the known spectral distinctiveness of dense _Seagrass_ meadows. Moderate performance was observed for _Cystoseira_ s.l. (R\({}^{2}\) = 0.82; RPD = 2.37), likely reflecting spectral overlap with adjacent classes and roughness variability in the canopy structure. The lowest accuracy was associated with rocky reefs with other cover (R\({}^{2}\) = 0.72; RPD = 1.87), which may be attributed to greater within-class heterogeneity or shadowing effects. Overall, the combination of a high coefficient of determination, favorable RPD values, and low absolute errors supports the effectiveness of spectral unmixing for benthic habitat mapping while emphasizing the need for enhanced class discrimination in spectrally complex substrates.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 2 Methodology
### RPC Bias Compensation  
The RPCs provided by a satellite data vendor or supplier are often erroneous, resulting in computed image coordinates \((l,s)\) that have biases. This discrepancy arises due to measurement uncertainties from onboard global navigation satellite system (GNSS) receivers, star trackers, and gyroscopes [8]. The error sizes and patterns vary depending on the systems in use. Therefore, the most widely adopted method to compensate for these errors or biases is to employ an affine transform, as illustrated in Equation (2) [2]. According to this equation, the bias-compensated coordinates \((l^{\prime},s^{\prime})\) can be computed using transformation parameters that need to be estimated using GCPs  
\[l^{\prime}=l+A_{0}+A_{1}l+A_{2}s,\;s^{\prime}=s+B_{0}+B_{1}l+B_{2}s \tag{2}\]  
where \(A_{0},A_{1},\ldots,B_{2}\) are for an affine transformation that models shift, drift, and scale to the angular affinity that exists in satellite images.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 4 Simulation Results
### Theoretical Results  
In this subsection, the minimum interleaving distances for CRCSI, RCI and TBI and no interleaving are evaluated, including the interleaving distance \(d_{min}\) and multiplicity \(D_{tot}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Fixed Reception** & **Mobile Reception** \\ \hline Bandwidth & 10 M & 10 M \\ Carrier Frequency & 700 M & 700 M \\ TBS & 76208/45352 & 9912 \\ Channel Type & TDL-B & TDL-B \\ Code Rate & 1/2 & 1/3 \\ \(N_{FFT}\) & 41/472 & 6144 \\ Constellation & 64 QAM & 64 QAM \\ Sub-carrier Spacing & 0.37 kHz & 2.5 kHz \\ RS Overhead & 1/12 & 1/4 \\ Delay Spread & 35 \(\upmu\)s & 20 \(\upmu\)s \\ Speed & - & 30/60/120 kmph \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Schematic diagram of CRCSI.  
For fixed reception, the minimum interleaving distances and multiplicity for \(d_{min}\) of CRCSI, RCI and TBI are calculated, illustrated in Table 2. For TBS 76208, the block size is \(1485\times 15\) and \(2475\times 9\) for TBS 45352. Obviously, it is shown that CRCSI has the maximum \(d_{min}\) for both TBS values. The multiplicity of \(d_{min}\) is reduced over 50 times compared with other methods, which implies that more distance values are distributed over a larger range.  
For mobile reception, the duration of a subframe is 1 ms. Each subframe corresponding to the 2.5 kHz subcarrier spacing contains two OFDM symbols, where the effective number of subcarriers of the OFDM symbol is 2700, that is, the row number \(M\) is equal to 2700. In the calculation, the unit of time interleaving is the number of subframes, and the length (TIL) is 1, 3, 13 and 39, respectively. At this time, the number of columns corresponding to the block interleaver is 2, 6, 26 and 78, respectively. In order to illustrate the impact of time interleaving, we only consider the \(d_{min}\) performance over the last three TIL values in the following analysis, shown in Tables 3 and 4. It is observed that CRCSI achieves the maximum \(d_{min}\) and smallest \(D_{tot}\) in all three TIL cases.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 2 Autonomic IoT Network  
### Autonomic Computing Concept  
The autonomic property of IoT network comes from the concept of autonomic computing system. This concept began to emerge when the computer system reached a level of complexity that the continuous operation of the system can no longer be handled by humans. This concept seeks to find ways for the computer system to operate without requiring human intervention.  
The term autonomic is popularly used in biological sciences. In the human body, the autonomic nervous system handles involuntary reflexes, which are the body functions that do not require human attention consciously, for example the adjusting the size of the pupil of the eye to light stimulation, the process of digestion in the stomach, the adjustment of breathing rate, and the constriction and dilation of blood vessels. Without the autonomic nervous system, humans will be always busy adjusting the functions in the body against various needs and environmental conditions.  
The autonomic computing concept was first introduced in 2001 by [PERSON] and [PERSON] of IBM [4]. In their article, [PERSON] and [PERSON] explained that the core of the autonomic computing system is the creation of the ability of self-management in the system, with the goal of freeing humans from every detail of the operation and management of the system, while providing a system that operates with the best performance for 24 h per day and seven days per week. This concept was then implemented in web servers and data centers.  
[PERSON] and [PERSON] in [5] mentioned four properties that should be owned by the autonomic computing system, as shown in Figure 1. First, for the self-configuration property, the system performs automatic configuration of the new component so that it will be able to join the system based on the defined policy or rules. Second, for the self-optimization property, the system continuously checking and looking for ways to improve its performance based on defined parameters. Third, for the self-healing property, the system always detects errors and makes the required actions so that it can continue to operate properly. Fourth, for the self-protection property, the system has a self-defense mechanism against possible attacks from the outside and against the impact of the errors that cannot be handled by the self-healing mechanism.
## 4 Experimental Results  
This section begins by introducing the evaluation metrics, followed by describing the utilized FBP datasets. Subsequently, individual fine-tuning and retraining of the employed CNNs with different regression-loss functions (L1 loss, L2 loss, Log-cosh loss, and the proposed ensemble loss functions) are highlighted to assess the model's FBP performance. Finally, a comparison is made between the proposed model's findings and the state-of-the-art approaches.
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 2 Materials and Methods
### Antioxidant Activity Evaluation
#### 2.5.3 2, 2\({}^{\prime}\)-Diphenyl-1-Picrylhydrazyl (DPPPH) Radical Scavenging Microassay  
Antioxidant activity was investigated using DPPH microassay as it was described by [PERSON] et al. with some modifications [31]. Diluted samples and standard solutions (7 \(\mu\)L) were allowed to react with 193 \(\mu\)L of the DPPH radical solution (1.86 \(\times\) 10\({}^{-4}\) mol L\({}^{-1}\) DPPH in ethanol, prepared ex tempore) in 96-well microplate in triplicate. The mixtures were shaken and after incubation (1 h at room temperature); the absorbance was measured at 490 nm on an MTP reader. Trolox was used as a standard (range 0.2-1 mmol L\({}^{-1}\)) for obtaining calibration curve (y = 62.691x - 0.112) with good linearity (r\({}^{2}\) = 0.993). LOD was 0.1 \(\mu\)mol L\({}^{-1}\), and LOQ was 0.4 \(\mu\)mol L\({}^{-1}\). Results were expressed as mM Trolox Equivalents (TE) per gram of dry weight (mM TE g\({}^{-1}\) DW).
# Robot Bionic Vision Technologies: A Review
## 2 Literature Review
### Common Bionic Vision Techniques
#### 2.5.2 Structured Light  
The basic principle of this technology is that the main hardware includes a projector and camera. The projector actively emits (therefore called active measurement) invisible infrared light onto the surface of the measured object, then captures pictures of the object through one or more cameras, collects structured light images, sends the data to a calculation unit, and calculates and obtains position and depth information through the triangulation principle to realize 3D reconstruction. Therefore, structured light is used to determine the structure of the object. There are many projection patterns, such as the sinusoidal fringe phase-shift method, binary-coded gray code, and phase-shift method + gray code. The advantages of this method include mature technology, low power consumption, low cost, active projection, suitability for weak lighting, high precision within a close range (within 1 m), and millimeter-level accuracy. A major disadvantage is poor long-distance accuracy. With the extension of the distance, the projection pattern becomes larger, and the accuracy worsens. It is not suitable for strong outdoor light, which can easily interfere with projection light. Representative products include Kinect, Primesense, and Intel Realsense.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 5 Discussion
### Application of Electronic Nose  
Results of studies using the electronic nose to discriminate between uninfected and fungal infected seeds are presented for the first time in this proof of concept study, and thus a limited number of biological replicates were used. We wanted to see if this direction of research was worthwhile pursuing. We used dead (killed by fungi) seeds and seeds without fungal growth after incubation, but the uninfected seeds also showed low germination rates which is typical for fir seeds, especially those produced outside the area typical for silver fir occurrence in Poland. Silver fir is normally found in the northwest and southern parts of the country [85], and the seeds for this study were from the northeast. However, it is important to study the seeds from the northeast because foresters hope to replace Norway spruce with silver fir in the Bialowieza Forest, a remnant of the ancient forest  
Figure 12: Distribution of modeling feature extracted from the sensor response characteristics for measured samples categories. Data for selected sensors at the selected moment of measurements are indicated in the grid labels. The box spans the 1 st to 3 rd quantile of data. The whiskers span 1.5 IQR (IQR-inter quantile range). The horizontal line inside the box represents the median, and the \(\times\) inside boxes represents the mean.  
which covered all of northern Europe. Near the Bialowieza Forest, Norway spruce trees have been dying as a result of a bark beetle outbreak of unprecedented scale enhanced by climate change, which has killed the largest Norway spruce trees [39]. In our experiments, we used one of the leading commercially available electronic devices for measuring volatile compounds (electronic nose PEN3), and we follow the recommended measurement protocols. The results showed discrimination between the infected and non-infected seeds but with high variability in the results which indicates that the protocol and equipment are not currently suitable for field or practical use. Further research is needed, especially in the construction of special devices with sensors targeted to the chemical components in the types of biological samples studied, and improved methods of data analysis.
###### Abstract  
A new methodology to map Italian coastal areas at risk of flooding is presented. This approach relies on detailed projections of the future sea level from a high-resolution, three-dimensional model of the Mediterranean Sea circulation, on the best available digital terrain model of the Italian coasts, and on the most advanced satellite-derived data of ground motion, provided by the European Ground Motion Service of Copernicus. To obtain a reliable understanding of coastal evolution, future sea level projections and estimates of the future vertical ground motion based on the currently available data were combined and spread over the digital terrain model, using a GIS-based approach specifically developed for this work. The coastal plains of Piombino-Follonica and Marina di Campo (Tuscany Region), Alghero-Fertilia (Sardinia), and Rome and Latina-Sabaudia (Lazio Region) were selected as test cases for the new approach. These coastal stretches are important for the ecosystems and the economic activities they host and are relatively stable areas from a geological point of view. Flood maps were constructed for these areas, for the reference periods 2010-2040, 2040-2070, and 2040-2099. Where possible, the new maps were compared with previous results, highlighting differences that are mainly due to the more refined and resolved sea-level projection and to the detailed Copernicus ground motion data. Coastal flooding was simulated by using the \"bathtub\" approach without considering the morphodynamic processes induced by waves and currents during the inundation process. The inundation zone was represented by the water level raised on a coastal DTM, selecting all vulnerable areas that were below the predicted new water level. Consequent risk was related to the exposed asset.  
relative sea level rise (RSLR); MED 16; EGMS and LC/LU; coastal flooding maps; climate change +
Footnote †: journal: remote sensing  
Acticle  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Evolution of Coastal Environments under Inundation Scenarios Using an Oceanographic Model and Remote Sensing Data  
Sensing Data.Remote Sens.20241625999.[[https://doi.org/10.3390/rs16142599](https://doi.org/10.3390/rs16142599)]([https://doi.org/10.3390/rs16142599](https://doi.org/10.3390/rs16142599))