esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 3 Our Method
### Improved Feature Extraction Algorithm
#### 3.1.3 Adaptive Meshing of the Image Pyramid  
To extract feature points using various thresholds within the same image, the image is divided into meshes. Feature points are extracted using lower thresholds on grids with less texture. It lessens the possibility that the feature points are only distributed in certain areas when compared to the extraction of feature points for the entire image. ORB-SLAM2 [11] uses the meshing method. However, it divides the image into multiple \(30\times 30\) grids during feature extraction for each layer of the image. Due to the different scenes and sizes of each image, using a single engineering empirical value of 30 to divide the grid causes the algorithm to be unable to adapt well to the external environment. In this study, we apply an adaptive algorithm to handle the grid division, and we divide each layer of the photo according to its area and the number of feature points it needs to extract. The side lengths of the grid are calculated as follows:  
\[W_{i}=\alpha\sqrt{(width_{i}\times height_{i}/N_{i})}, \tag{4}\]  
where \(W_{i}\) is the side length of the \(i\)-th layer image partition grid. \(width_{i}\) and \(height_{i}\) are the picture's width and height in layer \(i\), respectively, and \(N_{i}\) is the amount of feature points to be extracted from the picture in layer \(i\). \(\alpha\) is the scale factor.  
After obtaining \(W_{i}\), we can calculate the number of columns and rows per layer of the image, as shown below:  
\[\begin{cases}\mathit{Cols}=\lfloor width_{i}/W_{i}\rfloor\\ \mathit{Rows}=\lfloor height_{i}/W_{i}\rfloor\end{cases} \tag{5}\]  
The next step is to traverse the number of rows and columns to extract the picture block's features.
## 4 Edge Cloud-based Next-generation Internet
### Content Access  
Generic content access, with or without terminal virtualization, is another important dimension of the Edge Cloud. Server selection (mapping the appropriate server with the requested content) and traffic engineering (select the optimal route to the mapped server) are two key technologies in content delivery that heavily depend on the infrastructure and current network conditions [26]. The index engine accomplishes server selection and traffic engineering through access of the ISP's topology and overall overlay network condition, although the precise means to accomplish this are not discussed here.  
Although the Infrastructure Provider (InP) (_i.e._, the ISP) implements the physical resources of the Edge Cloud, the CP/SP should have provision for deploying content, applications and index engines as per their needs. Hence, the Edge Cloud implements separate control interfaces for the InP and the CP/SP as well.  
The Edge Cloud thus directly supports user-services and value-added services. It implements support for a number of content-specific services including content search, request, create, add, _etc_.  
Figure 4: User content manipulation architecture using surrogates.
## 5 Performance Evaluation Results and Discussion
### Evaluation Viewpoints  
#### 5.1.1 Reduction in Cellular Traffic  
We confirmed the reduction in cellular traffic compared to the existing method by comparing the total number of cellular packets used for RevAucs. The cellular packets for Bid Invitations Query messages, Bid Invitations messages, and Bids messages were counted. We compared the amount of cellular traffic in terms of the number of 128-byte radio interface packets because such packets are generally used as units for calculating network usage charges [23; 24; 25]. Table 1 shows the size of messages used in our simulation, which assumes that a message is transported in an IPv4/UDP packet and bid invitations and bids are signed by SHA-256 with RSA-2048 (i.e., 256-byte signature).  
To determine whether data were obtained in the proposed method at almost the same location for almost the same reward as in the existing method, we also evaluated the differences in the evaluation scores of successful bids. Hereafter, we refer to the number of data requests for which an evaluation score of a successful bid is different between two simulation scenarios by \"the number of different successful bids\". We measured the ratio of the number of different successful bids between the proposed method and the existing method to a total of 1970 data requests. For example, if the successful bids for a total of three data requests with the proposed methods are A, B, and C, and the ones with the existing method are A, B, and D, then the ratio of the number of different successful bids between the proposed method and the existing method is 1/3 because C and D are different. Simulations were conducted 10 times with different random number seeds for 1000 s with250, 500, and 750 mobile hosts in an environment without misbehaving REPs. We assumed crowded places such as an urban area in choosing the number of mobile hosts.
# Removal of Methylene Blue from Aqueous Solution by Bone Char
## 3 Results and Discussion
The negative \(\Delta G^{0}\) values mean that the adsorption process is spontaneous. Furthermore, the slight increase in \(\Delta G^{0}\) with an increasing temperature implies that the lower temperatures result in more spontaneous adsorption. These findings were in accordance with the experimental results in Figure 2e [4; 34]. Figure 4a shows the reusability of bone char for the removal of methylene blue. The removal rates remain at 45% \(\pm\) 5% during six cycles. The standard deviation of the six data values is 3.18. After the fifth cycle, the removal rate still reaches the level of the first cycle. This result indicates that bone char has excellent adsorption stability for methylene blue and may become potential novel photocatalyst support for the removal of methylene blue. FT-IR spectra of bone char before the adsorption and after the desorption of methylene blue are shown in Figure 4b, exhibiting the characteristic bands coming from PO\({}_{4}\)\({}^{3-}\) ions at 469, 561, 601, 962, and 1020 cm\({}^{-1}\). Peaks at 1632 cm\({}^{-1}\) and 3400 cm\({}^{-1}\) correspond to a small amount of occluded water and O-H broad band, respectively. In addition, peaks at approximately 872, 1414, and 1453 cm\({}^{-1}\) correspond to the carbonate bands of residual carbon in bone char [45]. The peak positions and intensities of the bone char samples have not changed, suggesting the reversible adsorption process.
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 3 Modular Software Architecture Definition
### Class Structures
#### 3.2.2. Instrument Level Classes  
Topside level classes are instantiated in the NI cRIO processor onboard the vehicle, using the same developing environment, tools, and methods from the topside level. The main difference with the latter relies on real-time capability of RIO devices; this specific implementation uses real-time communication channels, called RT-FIFOs. These operate in a similar way to regular LabVIEW queues, with some restrictions on data types, e.g., fixed size arrays and static type variables, but with the benefit of time-controlled data access operations. Strict actor model implementation was compromised, mainly because of these restricted communication channels, yet benefiting in reliability and deterministic message delivery.  
Derived classes at instrument level are shown in Figure 6.  
Altimeter, AHRS, and CTD are associated with physical OEM instruments integrated to the vehicle. SCard refers to an abstract class that implements similar features of Instrument, as described in Section 3.2. This is made to extend modularity to custom hardware and devices in the form of additional SmartCards. At the time of writing this document, three SmartCards were deployed in the vehicle, each one supporting functional watertight enclosures called Boxes; see [54] for details on hardware aspects. Children of Instrument class can also be associated with non-physical entities or instruments. TOPDATA is a derived class that represents topside data to the vehicle computer as an instrument, standardizing the access method to variables originated at the surface level, such as vessel position and heading, joystic  
Figure 6: Topside and instrument levels class structure. The SmartCards (SCard) are classified as instrument since they are monitoring the health status of each box.
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
###### Abstract
Addressing these challenges requires developing methods that effectively balance label simplicity and accuracy, making weakly supervised bird species recognition more practical and scalable [25]. We address the limitations of existing methods by proposing a novel architecture that incorporates two key modules: a multi-scale feature fusion module and a feature enhancement module. The multi-scale feature fusion module integrates features extracted at different scales within the image. By combining coarse global information with finer local details, the model gains a more comprehensive understanding of the bird's appearance. The feature enhancement module leverages an attention mechanism to dynamically adjust feature response values. This allows the model to selectively amplify informative feature channels by exploiting the relationships between them. Furthermore, the module utilizes correlations within the feature space to enhance features in the target bird region while suppressing irrelevant noise. Finally, we propose a Siamese network [26] that employs contrastive learning to exploit the limited image-level labels for effective feature learning. This network utilizes triplet loss to minimize the distance between feature vectors belonging to the same bird species, while maximizing the distance between feature vectors of different species. This approach encourages the network to focus on extracting features that are common within a bird class while simultaneously learning to differentiate between fine-grained variations that distinguish different bird species. Through these complementary strategies, our proposed architecture aims to achieve high accuracy in weakly supervised bird species recognition. By integrating a multi-scale feature fusion module, a feature enhancement module, and a Siamese network, we present an automated method for bird species recognition. Our approach offers several key advantages over previous works:  
(1) We propose a feature fusion network that integrates features at different scales, enabling the classifier to better distinguish between similar classes. (2) We design a feature enhancement module that selectively enhances effective feature channels and suppresses irrelevant noisy regions. (3) We introduce a siamese network that mines the feature differences between bird species while learning common features among instances of the same class. (4) We validate our method on the CUB200-2011 [27] bird recognition dataset and demonstrate the superior performance of our proposed model through extensive experiments. The structure of this paper is organized as follows. In Section 2, we provide an overview of our proposed method and present a detailed description of each module. Section 3 describes the dataset used in our experiments and reports our experimental results. Extensive experiments and in-depth analyses are presented in Section 4. Finally, in Section 5, we provide our conclusions and discuss future work.
## 4 Evolution of Cadmium Transporters and Their Functions in Cadmium Accumulation
### Function of Cd Transporters
#### 4.2.3 Zips  
ZIP family members are generally involved in Cd uptake and translocation in plants [122]. IRT1 is the first identified member of the ZIP family in Arabidopsis plants and participates in the uptake of Fe\({}^{2+}\), Zn\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Cd\({}^{2+}\) from the soil [123]. Seventeen ZIP transporters have been identified in rice. _OsIRT1_, which is highly homologous to _AtIRT1_, is predominantly expressed in roots and up-regulated by Fe deficiency and Cd exposure [124]. Recently, [PERSON] et al. [125] demonstrated a distinct difference between Arabidopsis and rice plants in terms of their respective expression profiles of ZIP genes in response to Cd stress. In addition, other ZIP transporters, such as OsZIP1 and OsZIP3, have also been shown tobe involved in Cd uptake in rice [125]. It is noteworthy that the involvement of ZIP genes in Cd uptake has also been detected in hyperaccumulators. In _Nocaea caerulescens_, _NcZNT1_, a homolog of _AtZIP4_, mediates low-affinity Cd uptake when expressed in Saccharomyces cerevisiae ZHY3 cells [126]. Recently, it was reported that NcZNT1 is a PM-localized Zn\({}^{2+}\)/Cd\({}^{2+}\) transporter and its promoter is mainly active in the cortical, endodermal, and pericycle cells of _Nocaea caerulescens_[127].
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 3 Determination of Embedded HVDC Operation Point
### System Total-Loss Minimization (STLM) Control
#### 3.1.5 HVDC Operating Point with STLM Control  
In this paper, the optimum operating point of HVDC is calculated by using Equations (4)-(9) as the constraint condition and Equation (3) as the objective function. Using the STLM control, we can establish the active/reactive power operation point of HVDC that minimizes total system loss by using the algorithm of Figure 4 and, in contingencies, apply other control strategies. The explanation of Figure 4 as follows.  
1. The initial active/reactive power operation range is set with reference to the HVDC capacity.
2. The number of branches to be monitored could be reduced using PTDF. These represent branches where the amount of power flow varies greatly with HVDC operating point (Section 3.1.3).
3. The operating area should be adjusted so as not to deviate from the constraints. In the later operating point determination process, the operating range set here should not be exceeded.
4. The operation point that minimizes the system total loss is determined using the PDS. The active/reactive power output determined in this process are respectively input into the active power set value and the AC voltage set value.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 4 Field Experiment Results  
### Comparison of the IoT Tide Gauge and Local Tide Gauges  
Figure 6a,b compares data from the IoT gauge near Sandon Dock (pressures converted to meters of water) and the local tide gauge records for two periods during the transition from spring to neap tides. Whereas Figure 6c shows the raw pressure data prior to conversion to sea-level values for the whole deployment period. The Gladstone Lock tide gauge records 15-min averages, while the Alfred Lock tide gauge records one-minute averages. At the beginning of the experiment, the tidal range close to the spring tide was large, which exposed the pressure sensor at low water; towards the neap tide, it was always underwater.  
Figure 5: Sensor installation at Crosby (Blundellsands). The “Internet of Things” (IoT) tide gauge was attached to the channel marker, and the Nortek acoustic wave and current profiler (AWAC) was located 7 m away and attached with a chain.  
The time lags (of a few minutes) in the tidal curves for Gladstone Lock and Alfred Lock reflect their relative locations along the estuary (Figure 6a,b). As the flood tide propagates up the estuary, it reaches Gladstone Lock first, then Sandon Half-tide Dock and Alfred Lock. This can be seen clearly during the approach of high water on 2 November (02:00), with high water at Alfred Lock occurring last. The differences in tidal height and time lag between Sandon Half-tide Dock and Gladstone Lock are smaller than in the  
Figure 6: Liverpool tidal elevations (Gladstone Lock—red, IoT device—green, Alfred Lock—blue), (a) Spring period: 1 November 17:00-2 November 08:00, 2019, (b) Neap period: 5 November 08:00-23:00, 2019, (c) raw in-water pressures from the Blue Robotics pressure sensor for the whole deployment period.  
corresponding Alfred Lock data, and this is likely because of their locations on the same side of the river/estuary; Figure 3 shows the narrow channel (-940 m at its narrowest part) which connects to an inner much wider part of the estuary (not shown on map), and the corresponding tidal flows are controlled by the cross-section and bed friction [10].  
The cabled pressure sensor configuration worked well, giving comparable sea-level data to the established tide gauges and having sent the data successfully via the Sigfox Cloud to the data processing server. The mean difference between the IoT device and the Gladstone lock data was 0.002 m, the standard deviation was 0.035 m, and the root-mean-square difference was 0.035 m (using all the recorded data and ignoring periods when the in-water pressure sensor is exposed to air, i.e., towards low water during Spring tides); the majority of these differences are likely to be due to the 2.6 km separation between locations. The battery pack also worked well; when coupled with solar panels, it would provide continuous power for the IoT tide gauge, necessary for long-term deployments.
## 5 Challenges and Open Research Directions
### Security Threats  
Despite its decentralized nature, Holochain remains susceptible to various security threats [47]. One significant risk stems from the random selection of transaction approval authorities, which may inadvertently allow an attacker to be part of the network, thereby enabling adversarial control over critical verification processes. This vulnerability can lead to network poisoning, where malicious nodes manipulate data consistency, disrupt consensus, or facilitate fraudulent transactions. The agent-centric model presents risks such as Sybil attacks, where adversaries generate multiple identities to undermine trust mechanisms. The DHT, while enhancing scalability, introduces additional attack vectors, including data tampering, unauthorized access, and eclipse attacks that isolate nodes from accurate network state information. To address these security concerns, future research should focus on the following:  
* Developing secure authority selection mechanisms for transaction approval, incorporating trust metrics, stake-based incentives, and decentralized reputation models to minimize adversarial influence.  
* Implementing decentralized authentication through strong identity verification techniques to prevent identity spoofing and unauthorized access, leveraging cryptographic signatures and decentralized identity frameworks.
* Introducing trust-based or stake-weighted models to restrict the influence of malicious nodes and enhance network resilience.
* Utilizing advanced cryptographic methods such as zero-knowledge proofs and homomorphic encryption to ensure data privacy, integrity, and secure computation within the network.
* Employing real-time ML anomaly detection by training models on Holochain's source chain history to identify suspicious patterns such as Sybil attacks, collusion, and data injection threats.
## 3 Materials and Methods
### Signal Processing  
The magnetometer signal was subject to noise due to the presence of electromagnetic interference from the environment. To address this, a Savitzky-Golay moving average filter was applied to reduce the noise without compromising the key characteristics of the signal, such as its orientation properties. The magnetometer plays a crucial role in determining the individual's orientation with respect to the Earth's magnetic field, which is essential for fall risk detection. To enhance the detection process, sensor fusion techniques were employed to calculate the angular orientation (roll, pitch, and yaw) of the individual. In this process, a weighted fusion was performed, assigning 60% weight to the gyroscope and 40% to the accelerometer. This configuration was designed to balance the information obtained from both sensors--where the accelerometer provides valuable insights into stability, and the gyroscope captures the rapid changes associated with bodily corrections, which are often  
Figure 2: Evaluation of Mini-BESTest. (**a–d**) Anticipatory Postural Adjustments. Postural control tasks: (**a**) Sitting to standing, (**b**) Stand on tiptoe, (**c**) Left single leg stance, and (**d**) Right single leg stance.(**e–h**) Reactive Postural Control. (**i–k**) Sensory Orientation. (**l–q**) Dynamic Gait.  
indicative of fall risks. In addition, the magnitude of the merged signals was calculated to obtain a new signal that reflected overall changes over the time course. This reduced the complexity of the individual signals while preserving critical information about the individual's movement (See Figure 3).  
For input into the deep learning model, several key signals were selected: gyroscope (xyz), accelerometer (xyz), magnetometer (xyz), angular orientation (roll, pitch, yaw), and the computed magnitude. These signals were packaged and labeled based on whether the subject was classified as a faller or non-faller. After packaging the signals, they were processed using a sliding window approach with a window size of 256 samples and a step size of 64 samples. This approach ensured that the temporal dynamics of the signals were adequately captured, while providing sufficient overlap between windows for robust analysis. Since the duration of each task and experiment varied among participants, the number of segments generated from each sample was variable. This segmentation approach provided the model with more data to learn from, allowing it to capture relevant patterns for fall risk prediction. The increased data helped the model generalize better and recognize individuals with fall risk more accurately. With the signal processing pipeline complete, the data were ready for input into the deep learning model for fall risk classification (See Figure 3).
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Pesticides
#### 5.3.1 Insecticides
Finally, a layer of MIP was fabricated in situ on a glassy carbon electrode by electropolymerization, with dopamine and resorcinol as dual functional monomers (DM) and CYP acting as a template (DMMIP-Ag-N@ZnO/CHAC), as shown in Figure 7. Under the optimal conditions, a calibration curve of current shift versus concentration of CYP was obtained in the range of 2 \(\times\) 10\({}^{-13}\)-8 \(\times\) 10\({}^{-9}\) M, and the developed sensor gave a remarkably low detection limit (LOD) of 6.7 \(\times\) 10\({}^{-14}\) M (S/N = 3) [108]. The sensitivity and selectivity can be improved by combining electrochemiluminescence-cence (ECL) with MIPs. Triazophos were detected using p-aminothiophenol (PATP) as a functional monomer and modified it with luminol on a gold electrode. The results of the detection of triazophos in tap water, reservoir water, and river water showed an LOD of 0.058 nM and a linear range of 0.1-1.0 \(\times\) 10\({}^{3}\) nM [115]. Quantum dots were used as the luminophore and hydrogen peroxide as a co-reactant oxidized or reduced at the electrode in the electrochemical luminescence process, resulting in the emission of radiation [121]. The result from measurement for Cyfluthrin in the seawater sample showed that there was an LOD of 0.12 nM and a linear range of 0.46-2.3 \(\times\) 10\({}^{2}\) nM. Figure 6: Schematic representation of the template extraction-rebinding concept and the production of MIP films on Zr-based metal–organic framework catalyst (Pt-UiO-66)/carbon paste microelectrode [119]. (RSC open access content, further permissions related to this figure should be directed to the RCS, doi:10.1039/d0 an00278j).