esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coplanar Dipole Production Technology
### Description of 3D LDM Printing Technology  
LDM technology (lights-out digital additive manufacturing) uses simultaneous printing of conductive ink (CI) and dielectric ink (DI). By printing with both inks simultaneously while maintaining a high resolution and maximum accuracy, the DragonFly(tm) platform has virtually unlimited design flexibility in multiple applications across various industries. These include communications, RF, medical devices, drones, aerospace, automotive, satellites, in-circuit transformers, antennas, coils, capacitors, etc. [19].  
The 3D printer has two print heads, one for printing AgCite(tm) conductive ink (silver nanoparticles suspended in a solvent) and the other for printing dielectric polymer ink. Both inks have unique and compatible sintering and curing properties. The inks are optimized for use in nanotechnologies [19].  
When printing, the thickness of one printed layer of conductive ink (CI) varies between 1.9 and 3.2 um, and for dielectric ink (DI), 0.27 and 0.44 um. Thus, for one layer of CI, the machine prints about 11 layers of DI (this further depends on the density of the motif and other parameters). However, everything is performed automatically, and DragonFly LDM2 performs an automatic thickness calibration. The time to print one patch antenna using the 3D LDM2 printer is about 5 h. The cost of 3D printing one antenna consists of two items: the price of dielectric ink (DI) is approximately 4.5 EUR, and the price of conductive ink (CI) is 18.5 EUR. It depends on the designed motif and the size of the antenna.  
Therefore, the total cost of the antenna is related to the antenna size. However, if we were to produce, for example, a special antenna consisting of dozens of miniature radiation panels, the production cost would still be the same. Complex antennas that are difficult to produce due to their complex shape are produced using other technologies that are already very costly (for example, the antenna array described in the reference [20]). 3D LDM technology allows for the production of antennas with a precision of 1.9-3.2 um for conductive material and 0.27-0.44 um when printing dielectric material [21]. This makes this technology quite exceptional.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 2 Liquid-Based Antennas
### Basic Design
It was shown that (1) height of water and the diameter of the cylinder greatly affect the resonance frequency and the radiation bandwidth (so by properly choosing a water height to diameter ratio, the fractional bandwidth and radiation efficiency can be maximized); (2) to maximize the fractional bandwidth, the ground plane has to be at least one wavelength long; (3) the position and length of the feed does not affect the radiation characteristics of the antenna if the water conductivity is more than 4 S/m, however for less conductive water, these parameters are important [40]. For the holder of the liquid dielectric based monopole antenna, PVC or other off-the-shelf tubes are typically used which come in standard dimensions. With the help of a 3-D printer it is possible to rapidly manufacture any water holder for the antenna with optimized dimensions. In Reference [41], a low-cost monopole antenna is realized where a 3-D printer was used to fabricate the water holder. Table 1 summarises the performance of the liquid-based monopole antennas reviewed in this subsection. This simple design offers continuous frequency reconfiguration ability for a monopole antenna, while also minimizing the antenna dimensions, with the efficiency of at least 40%.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 5 Applications to the Internet of Things
### Applications in Vehicular Ad Hoc Networks  
A vehicular _ad hoc_ network (VANET) can be seen as a special type of MANET where the nodes are vehicles and the main objective is to prevent adverse circumstances on the roads and achieve more efficient traffic management.  
One of the most important aims in the design of such networks is to resist security attacks [30]. Thus, in the area of VANETs, the proposed scheme can be used to authenticate vehicles in isolated areas (mountain areas, tunnels, _etc._), where no Internet connection is available. Each vehicle can send an authentication message and agree on a shared key to communicate thereafter, following the proposed scheme based on the Diffie-Hellman idea.  
Furthermore, the proposal can be applied to solutions of other problems in VANETs. For example, it can be used to authenticate the information sent from the smart traffic lights to vehicles. Some critical environments require that this information is sent authenticated, so that only legitimate users of the network can decrypt and process it. Thus, for instance, there are proposals like [31] that describes a low-cost solution based on a smart traffic light using a light sensor that provides information in real time about the traffic light color. The solution uses a Bluetooth Low Energy (BLE) module that allows transmitting the state of the traffic light to nearby vehicles, as a beacon notification. This beacon notification can be sent authenticated through the proposal explained in this paper.
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 6 Conclusions  
More and more remote-sensing technologies use big data sets that are comprised of different spatial scales and/or formats of files. New methods are needed to handle this information [84]. Many studies focused on, e.g., urban land use classification, and today consider features excerpted from high-spatial resolution remote-sensing images, as well as social media data [85]. This is a way to include another source of big data sets into traditional remote-sensing LU/LC analysis.  
The rationale behind the RS algorithm application as a data fusion tool for spectral bands aimed at land use--land cover studies arose from experiences of geography linked to geospatial technologies and remote sensing. It was not aimed at proving the correctness of a mathematical expression, also due to the limited mathematical competence of the author. However, there is operationalization of such proof in the form of a spreadsheet, which is able to calculate both outcomes and reverse procedures (with limited values of parameters, because of the range of integer calculations). The RS algorithm can be classified as operating at the pixel level fusion, i.e., fusion is performed on a pixel-by-pixel basis.  
Some further steps concerning the precise classification of LU/LC can be developed after the development of the dedicated tools able to handle both visualizations as well as statistics of the RS algorithm outcomes.  
There are also some other geographical problems to which the RS algorithm can be applied. The challenges are related to handling the set of huge positive integers, and awareness that these numbers are in fact descriptors of a real-world multidimensional view and should be treated, at least partly, as a nominal scale. However, the simplest way of thinking about the RS algorithm in the case study above concerning remote-sensing is to treat it as a compression algorithm: one can have one compound file instead of nine separate ones.  
This research received no external funding.  
The authors declare no conflict of interest.
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
###### Abstract  
Monitoring the morphological evolution of a river-mouth bar is of both practical and scientific importance. A large amount of sediment is transported from a river to surrounding littoral cells via a deltaic bar after an extreme weather event. However, it is often not feasible to capture drastic morphological changes in the short term with conventional bathymetric surveys. This paper presents a depth-inversion method based on unmanned aerial vehicle technology to estimate two-dimensional bathymetry from video-sensed swell propagation. The estimation algorithm is tested over four cases with varying wave and bathymetric conditions and is validated with transect survey data. The test results suggest that the method can estimate deltaic-bar topography in front of a river mouth with a root-mean-square error of \(<\)0.5 m. The applicable range is limited by wave breaking in the inner bar and up to a depth of \(\sim\)8 m, where swell intensity signals become ambiguous. A comparison of the different cases shows that the method works better under calm weather conditions with dominant swells propagating from non-local sources. Significant morphological changes of a river-mouth bar due to a powerful typhoon are successfully detected by observations right before and after the event.  
Unmanned Aerial Vehicle; bathymetry; river mouth; delta Article Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results
### Ablation Experiments  
Ablation experiments were conducted on the Lowship dataset using the YOLO11n model as the baseline to assess each module's effectiveness in ship detection tasks. All ablation experiments employed consistent hyperparameter settings, and the results are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision/\%** & **Recall/\%** & **mAP@50/\%** & **mAP@50:90/\%** \\ \hline Faster R-CNN & 63.4 & 59.9 & 61.2 & - \\ SSD & 70.5 & 66.3 & 67.6 & - \\ YOLOV3-tiny & 82.4 & 77.4 & 85.0 & 50.3 \\ YOLOV5 & 87.7 & 81.2 & 89.3 & 56.8 \\ YOLOV8n & 93.7 & 86.0 & 90.7 & 60.2 \\ YOLOV9t & 89.5 & 82.7 & 88.5 & 57.9 \\ YOLOV10n & 90.3 & 86.5 & 90.4 & 60.5 \\ YOLO-SAR (ours) & 96.4 & 93.6 & 94.5 & 65.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results for the real dataset.  
Figure 10: Sample images from the real dataset (multi-category vessel samples captured in the evening from the real-world ship image dataset collected in the open waters of the Yu River, Guilin, Guangxi, China).  
As shown in Table 3, each standalone modification to the detection framework effectively boosts its accuracy. Compared to the baseline YOLO11n, the integration of the Self-Calibrated Illumination Network (SCINet) increases the [EMAIL_ADDRESS] by 0.8 percentage points. This addition effectively addresses multi-scale perception deficits and uneven feature contrast through cascaded low-illumination enhancement. Introducing receptive-field attention convolution (RFAConv) further increases [EMAIL_ADDRESS] by 0.6 percentage points, indicating that the model not only captures global context more effectively but also adaptively reweights multi-scale features. Additionally, incorporating the Adaptively Spatial Feature Fusion (ASFF) module in the head results in a 1.1 percentage point increase in [EMAIL_ADDRESS], demonstrating the great capacity to adaptively flexible multi-scale features and automatically balance information across different levels through learnable weights. While all three modifications contribute to improved detection performance, they do incur a corresponding increase in parameter count and computational overhead.  
The combined-methods experiments show that merging SCINet and RFAConv modules improve the [EMAIL_ADDRESS] by 1.5% and the [EMAIL_ADDRESS]:0.95 by 1.6%, further enhancing detection performance compared to single-module upgrades. Additionally, the combination of SCINet module with ASF module results in an increase of 2.0 percentage points in [EMAIL_ADDRESS]. Finally, the integration of all three enhancements yields significant improvements in precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95, with increases of 2.6, 3.8, 2.7, and 2.4 percentage points, respectively. These findings validate that our core YOLO SAR framework achieves a noteworthy enhancement in detection accuracy.
# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 2 Single Improved Prediction Model
### Improved Least Squares Support Vector Machine Error Prediction Model  
#### 2.2.1 Improved Least Squares Support Vector Machine  
Because of the quadratic programming problem under the constraint of inequality in the support vector machine, the calculation amount is large. In order to improve the calculation speed, [PERSON] et al. [34] proposed LSSVM in 1999, and converted the inequality constraint into an equality constraint. The complex convex quadratic programming problem is transformed into a linear equation solution. Its optimization objective function is as follows:  
\[\begin{split}\min\{\frac{1}{2}\mathbf{w}\cdot\mathbf{w}+\frac{C }{2}\sum\limits_{i=1}^{n}\varepsilon_{i}^{2}\}\\ s.t.y_{i}=\mathbf{w}\cdot\varphi(\mathbf{x}_{i})+b+\varepsilon_{i },i=1,2\cdots,n\end{split} \tag{8}\]  
where \(\mathbf{w}\) is the weight coefficient vector, \(C\) and \(\mathbf{b}\) are constants, and \(\varepsilon_{i}\) is the error.  
In order to solve the minimization problem, the Lagrangian function method is used.  
\[L(\mathbf{w},b,\varepsilon_{i},\alpha)=\frac{1}{2}\mathbf{w}\cdot\mathbf{w}+ \frac{C}{2}\sum\limits_{i=1}^{n}\varepsilon_{i}^{2}-\sum\limits_{i=1}^{n} \alpha_{i}[\mathbf{w}\cdot\varphi(\mathbf{x}_{i})+b+\varepsilon_{i}-y_{i}] \tag{9}\]  
where \(\alpha\) is Lagrangian factor.  
Then we need to take the derivative of these parameters:  
\[\begin{split}\frac{\partial L}{\partial\mathbf{w}}=\mathbf{w}- \sum\limits_{i=1}^{n}\alpha_{i}\varphi(x_{i})=0\\ \frac{\partial L}{\partial b}=\sum\limits_{i=1}^{n}\alpha_{i}=0\\ \frac{\partial L}{\partial\varepsilon_{i}}=C\varepsilon_{i}- \alpha_{i}=0\\ \frac{\partial L}{\partial\varepsilon_{i}}=\mathbf{w}\cdot\varphi (\mathbf{x}_{i})+b+\varepsilon_{i}-y_{i}=0\end{split} \tag{10}\]  
Figure 1: The flow chart of grey wolf optimization algorithm.  
The LSSVM model can be obtained by solving the above equation, which can be expressed as  
\[f(x)=\sum_{i=1}^{n}\alpha_{i}K(x_{i},x)+b \tag{11}\]  
where \(K(x_{i},x)\) is the kernel function.  
This paper implements the optimization process of the LSSVM using the IMGWO algorithm.
## 3 Multipath Routing in Wireless Sensor Networks  
Due to the limited capacity of a multi-hop path [6] and the high dynamics of wireless links [2, 9], single-path routing approach is unable to provide efficient high data rate transmission in wireless sensor networks. Nowadays, the multipath routing approach is broadly utilized as one of the possible solutions to cope with this limitation. This section presents the primary motivations behind using multipath routing approach in wireless sensor networks. We also discuss the main design issues in the development of the existing multipath routing protocols.
###### Abstract  
Etched polymeric sorbent sheets (solid-phase mesh-enhanced sorption from headspace (SPMESH) sheets) were recently described as an alternative to solid-phase microextraction (SPME) for rapid, parallel, multi-sample extraction and pre-concentration of headspace volatiles. In this report, a workflow was evaluated based on SPMESH sheet extraction followed by direct analysis in real time-mass spectrometry (DART-MS) using grape samples harvested from multiple commercial vineyards at different maturities. SPMESH sheet-DART-MS(-MS) was performed on two grape-derived odorants related to wine quality: 3-isobutyl-2-methoxypyrazine (IBMP) in Cabernet Sauvignon and Merlot grape homogenate (\(n\) = 86 samples) and linalool in Muscat-type grape juice samples (\(n\) = 18 samples). As part of the optimization process, an MS-MS method was developed for IBMP and an equilibration procedure prior to extraction was established for homogenate samples. Following optimization, we achieved good correlation between SPMESH sheet-DART-MS and SPME-GC-MS for both IBMP (range by GC-MS = \(<\) 2 ng/L to 28 ng/L, \(R^{2}\) = 0.70) and linalool (range by GC-MS = 135 to 415 ug/L, \(R^{2}\) = 0.66). The results indicate SPMESH sheet-DART-MS is suitable for rapid measurements of trace level volatiles in grapes.  
high-throughput analysis; volatile analysis; grape aroma; DART-MS; SPME
## 3 The Proposed Method
### Polarimetric Parameters Extraction
The scattering distributions of the PRDV and PV parameters of the sampled water and land regions are shown in Figure 5b. Comparing with Figure 5a, we can find that the contrast between the interference water and the calm water is enhanced. Figure 5: The scattering distributions of the polarimetric parameters of the sampled regions: (**a**) the scattering distributions of the \(P^{\prime}_{d}\) and \(P^{\prime}_{v}\) of the sampled regions; (**b**) the scattering distributions of the PRDV and PV parameters of the sampled regions.
# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
## 4 Experiments
### Results and Analysis  
Table 3 shows the performance of different models on the hold-out dataset. Among them, the first set of data is the result achieved in our experimental environment based on the previous paper. LSTM-baseline, ContextSum, and ContextWeight are all encoded using GloVe to generate vectors. The second set of results is the method we proposed, using GloVe and XLNet as encoders. Specifically, the NAS-CRE model encoded with GloVe achieved a Micro-F1 score of 88.40 and an accuracy of 90.08%, while the NAS-CRE model encoded with XLNet achieved even better results, with a Micro-F1 score of 90.87 and an accuracy of 91.25%. From this table, we obtain the following observations.  
1. The performance of our proposed method was consistently and significantly improved over all baselines. Specifically, when NAS-CRE was encoded with XLNet, the macro-F1 was approximately 7% better than that of ContextWeight. This demonstrates the robustness and adaptability of the proposed model, which uses NAS-RNN to automatically adjust the RNN structure.
2. The LSTM-baseline model had poor performance in the relation extraction task. Due to the lack of sentence content information, the impact of other entities in the sentence on the target entity was not considered. In contrast, the model that considers sentence content information was better, indicating that CRE had a significantly better effect when extracting sentence-level relations.
3. NAS-CRE performed better when encoded with XLNet than with GloVe, demonstrating the effectiveness of XLNet in fully understanding sentence semantics.  
To visualize the performance of various models, Figure 6 shows the results of the macro-precision-recall curve. It can be understood from the definition of precision and recall that the higher the precision and recall, the more efficient the model. In other words, the closer the drawn curve is to the upper right, the better the effect. As shown in Figure 6, we find that the models considering the content information of the sentence, such as our method, which is abbreviated as NAS-CRE and integrates NAS into the CRE task, are significantly better than LSTM baselines, indicating that the relationship between other entities in the sentence can be inferred from the relationship between entities. For the other two baseline methods, the effect of ContextWeight is better than that of ContextSum, and it can be noted that using the attention score to connect the relations between entities is better  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Encoder** & **Micro-F1** & **Accuracy** \\ \hline LSTM-baseline & GloVe & 62.78 & 62.07 \\ ContextSum & GloVe & 76.51 & 76.70 \\ ContextWeight & GloVe & 82.36 & 85.25 \\ \hline NAS-CRE & GloVe & 88.40 & 90.08 \\ NAS-CRE & XLNet & 90.87 & 91.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Micro-F1 of different models on the held dataset.  
than simply using concatenation. In addition, we can see that NAS-CRE consistently and significantly outperforms all baselines by a recall of at least between 0.07 and 0.2.
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 4 Structural Characteristics of TA Proteins from Pathogenic Bacteria
### CedAB  
The CcdB toxin from _V. fischeri_ shows a high sequence similarity (\(\sim\)80%) to those from pathogenic _Vibrio_ species, suggesting that they share highly conserved structures. The crystal structure of the CcdB-Gyrase complex from _V. fischeri_ was revealed as shown in Figure 3A. This protein can form a homodimer in which a relatively rigid dimeric interface is created by a hydrophobic core, and its individual monomers can rotate relative to each other due to their structural flexibility [170]. Binding stoichiometries of CcdA\({}_{2}\)-CcdB\({}_{4}\) and CcdA\({}_{2n}\)-CcdB\({}_{2n}\) are responsible for the neutralization of the toxicity and the down-regulation of its operon. In solution, the dimeric CcdB toxin can unfold into monomeric components, which have a partial secondary structure. There exist significant structural differences between the CcdB toxins from the _V. fischeri_ chromosome and the _E. Coli_ F plasmid, suggesting that they belong to different families. The chromosomal toxin has the CcdA- and gyrase-binding sites which have many non-conserved residues compared to the F plasmid toxin. In agreement with this, the two CcdB toxins show different affinities for their corresponding CcdA antitoxins and gyrases [171], suggesting the possibility that TA modules of plasmids and chromosomes play different cellular roles. The CcdB-gyrase complexes from _V. fischeri_ and _E. coli_ exhibit similar overall architectures [169; 171] (Figure 3). However, the region Asp27-Asn31 of _V. fischeri_ CcdB adopts different conformation from the corresponding region Asp23-Thr27 of _E. coli_ CcdB, which causes different interactions with gyrases. In addition, the gyrase binding mode involving the \(\alpha\)-helix region Glu91-Ile105 of _V. fischeri_ CcdB differs from the corresponding region Glu87-Ile101 of _E. coli_ CcdB. Among these regions, only three out of 10 residues that interact with gyrase are conserved between them. In the CcdB-gyrase complex from _V. fischeri_, Arg462 of gyrase forms two hydrogen bonds to Asp99 of CcdB (Figrue 3A). In the CcdB-gyrase complex from _E. coli_, there exist two different conformations for the side chains of Arg462, which form hydrogen bonds with Asn95 and Asn92, respectively (Figure 3B). The latter residue is replaced by Ala96 in _V. fischeri_ CcdB, which does not make a hydrogen bond to Arg462. Interestingly, in contrast to _V. fischeri_ CcdB, _E. coli_ CcdB undergoes a structural rearrangement of Trp99 upon gyrase binding such that its buried sidechain is exposed toward Arg462 of gyrase.