esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this research regarding the measurement of integrated road-embankments in the Nanjing Riverside Avenue Project, many measures including field tests and numerical simulations were used, and the law of depositional deformation of the embankment under different drainage patterns and construction conditions was analyzed and obtained. The main conclusions are as follows:  
(1) Using the drainage consolidation method to treat the soft soil foundation near water achieves a good effect, effectively reducing the settlement of soft soil foundation near water. During construction, the rate of construction and the preloading time of settlement are adjusted according to the feedback of the monitoring data, and as a result the embankment should be kept safe and stable, the consolidation time should be adequate, and the settlement after construction should be reduced. Because the silt clay is very deep, the settlement of the embankment will persist until a certain time after the finish of the filling construction. According to field monitoring data and numerical calculations, the settlement after construction and the total settlement are predicted on a test embankment, and every stage late is calculated, combined with specifications, so that the unloading time of the preloading soil is predicted. After this, advice is provided for the relevant engineering practices.  
(2) The applicability of the numerical simulation is verified in that the model of the drainage line and equivalent sand-wall can be used to simulate the plastic drainage plate in treating the soft soil foundation, as the settlement deformations of both are highly similar. By combining this with field test data, it is shown that the settlement deformation law calculated using the numerical simulation is in agreement with the actual situation in the field. This means the model of the drainage line and equivalent sand-wall can be used to simulate the actual situation in the field. The model of the drainage line and equivalent sand-wall can be further used in subsequent simulations of the soft soil foundation, and will provide reference information for the design and construction of other projects.  
Data curation, J.W., J.L. and S.S.; Investigation, Z.S. and Y.B.; Methodology, J.W. and J.L.; Writing--original draft, J.W., Z.S. and J.L.; Writing--review & editing, D.P.K. This research is financially supported by the National Natural Science Foundation of China (Grant No. 41672258 & 41472241). We acknowledge the help of [PERSON], [PERSON] and [PERSON] of School of Earth Sciences and Engineering, Hohai University.  
The authors declare no conflict of interest.  
Figure 16: Displacement diagram of the vertical increment according to the model of the equivalent sand-wall in the first year of operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drainage Line Model (mm)** & \multicolumn{2}{c}{**Equivalent Sand-Wall Model (mm)**} \\ \hline
**C Point** & **F Point** & **C Point** & **F Point** \\ \hline \(-146.18\) & \(-142.08\) & \(-146.64\) & \(-141.31\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: The creep settlement according to the numerical calculation.
## 5 Optimization Design of Degree Distributions for Proposed Enhanced Distributed Fountain Codes  
The performance of the proposed EDFC is determined by its degree distributions. By using well-designed degree distributions at the sources and relays, the symbols received by the destination can be made to follow a Soliton-like distribution while maintaining a low coding complexity. In this section, a joint optimization method based on Lemma 2 will be presented to optimize the degree distributions \(\Phi_{i}(x_{i})\) and \(\Gamma_{i}(x)\).  
According to Lemma 2, for source \(S_{i}\), the decoding process must be convergent to ensure successful decoding. That is, the decoding failure probability \(y_{i,l}\to 0\) when the iteration number \(l\to\infty\), where \(y_{i,l}\in(0,1]\). This requires that the decoding failure probability must decrease as the number of decoding iterations increases, i.e., \(y_{i,l}\leq y_{i,l-1}\). This is an important basis for the optimization of degree distributions and can be used as an inequality constraint for the optimization equations.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 6 Ablation Study
### Ablation Results  
#### 6.2.1 Patch Encoder Integration  
Implementation Details: For Variant B, we integrated a patch encoder into the baseline FUNIT model to enhance the local style encoding capabilities. This encoder processes the input images in a segmented fashion, dividing them into patches and encoding each patch's style information separately. By doing so, we aim to capture more granular style features that are essential for high-fidelity font generation.  
Results: The introduction of the patch encoder led to significant improvements in style accuracy (Acc (S)), which increased from 8.14% to 30.15%. Content accuracy (Acc (C)) also saw a slight enhancement, rising to 76.35%. LPIPS improved to 0.253, indicating a better perceptual match with the target style, while SSIM increased to 0.512. FID decreased to 134, reflecting a reduction in the discrepancy between the generated and real font distributions.
## 3 Distance-Aware Replica Adaptive Data Delivery Scheme
### Message Replica Number Calculation  
To solve the performance problem that results from the fixed replica number in quota-based protocols, DRADG dynamically decides the replica number of each data message based on the distance between the sensor which generates the message and the sink node. We assume that each message head includes a field of integer type that keeps its replica ticket. The value of ticket denotes the upper bound of a message replica number.  
Take node \(i\), denoted as \(n_{i}\), for an example. When \(n_{i}\) generates a new message \(M\) and just has an appropriate neighbor node for the next hop, it sets the ticket of M according to the following Equation 1:  
\[ticket_{M}=\left\lceil k\times T_{\max}\times\sqrt{\frac{d_{i}}{D_{\max}}}\right\rceil \tag{1}\]  
where \(k\in\) (0,l) is scaling constant; \(d_{i}\) is the current distance between \(n_{i}\) and the sink node; \(D_{max}\) is the longest distance between sensor nodes and the sink node in the whole network. Clearly, \(D_{max}\) can be computed by the size of network and the location of the sink node. \(T_{max}\) is the maximum value of ticket, that is, for any one message its ticket is an integer between 1 and \(T_{max}\). From Equation 1, it can be found that the ticket value increases with the distance of between \(n_{i}\) and the sink node. By this way, we effectively avoid both the data message redundancy when sensors and the sink node are close to each other and the poor performance when they are far from each other.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 4 Experimental Implementation
### Multi-Hop Integration with and Without REM Placement  
* Without a REM: The performance of the multi-hop IoT network was evaluated in terms of packet loss under two different node placements. Figure 8 illustrates the results of a  
Figure 7: Node-RED flow configuration for real-time monitoring.  
random node placement strategy, where relay nodes were positioned without prior signal analysis. This setup demonstrates high packet loss (up to 39.6%), primarily due to inadequate signal strength in certain areas and overlapping interference zones. These inefficiencies highlight the importance of strategic node placement in ensuring reliable communication.
* With a REM: The primary goal of this study is to establish an efficient multi-hop connection and ensure reliable data transmission between workspaces, minimizing packet loss through optimal relay node placement. Figure 9 demonstrates a detailed visualization of signal strength distribution across the network using the REM. The REM analysis reveals optimal relay node locations, with signal strengths consistently above the \(-30\) dBm threshold in critical regions. This visualization underscores the role of REM in mitigating interference and identifying coverage gaps.  
The color scale indicates that signal strength values range from \(-10\) dBm to \(-80\) dBm, with \(-30\) dBm identified as the ideal threshold for strong signal performance and optimal connectivity. This visualization aids in determining the best node placement to maximize network efficiency and coverage, as shown in Figure 10. Compared to the random node placement in Figure 8, this setup eliminates weak signal areas and achieves uniform network coverage. The sub-figures highlight how strategic node adjustments based on REM analysis enhanced packet delivery rates and reduced transmission delays. Comparing Figures 8 and 10, it is evident that REM-guided node placement significantly improves network performance by addressing the shortcomings of random placement. The REM's ability to provide actionable insights for node optimization is validated by the experimental results, with zero packet loss recorded in the REM-based configuration.  
Figure 8: Node placement in the multi-hop network, detailing the source, relay, and gateway nodes’ interactions without a REM.  
Figure 9: REM-Based Signal Distribution.
# Computing RF Tree Distance over Succinct Representations
## 4 Implementation
### Distance Calculation
#### 4.2.3 Weighted/Extended Robinson-Foulds  
We also implemented eRF, wRF and weRF distances using PostOrderSelect, and using NextSibling and FirstChild. There are just a few differences with respect to Algorithms 2 and 3. For instance, when phylogenetic trees are fully labelled, it is also necessary to take into account the labels of internal nodes for LCA computation. In the weighted variations, it is also necessary to have two vectors of size \(n\), where \(n\) is the number of nodes of each tree, and variable weightsSum, which is updated during the tree transversal. More details can be found in Appendix A, with the implementation of eRF and wRF using PostOrderSelect.  
```
1:Input:\(bv_{1},bv_{2},CodeMap\)
2:\(\triangleright\) To guarantee a post-order transversal, the initial call to this function initiates current to tree root index
3:\(\triangleright\) Let \(numInternalNodes1\) and \(numInternalNodes2\) be the number of internal nodes of both trees.
4:Output: Robinson-Foulds distance
5:\(equalClusters\longleftarrow 0\)
6:rf_nextsibling_aux(0)
7:\(distance\longleftarrow(numInternalNodes1+numInternalNodes2-2\times equalClusters)/2\)
8:returndistance
10:
11:procedurerf_nextsibling_aux(current): int
12:ifIsLeaf(\(bv_{1},current\)) then
13:\(lcas\leftarrow\)PreOrderSelect(\(bv_{2},CodeMap[\)PreOrderMap(\(bv_{1},current)-1]+1\)
14:else
15:\(lcas\leftarrow\)rf_nextsibling(FirstChild(\(bv_{1},current\)))
16:ifNumLeaves(\(bv_{1},current\)) =NumLeaves(\(bv_{2},lcas\)) then
17:\(equalClusters\longleftarrow equalClusters+1\)
18:endif
19:endif
20:whilecurrent\(\leftarrow\)NextSibling(\(bv_{1},current\))!=\(-1\)do
21:ifIsLeaf(\(bv_{1},current\)) then
22:\(lcas\leftarrow\) LCA(\(bv_{2},lcas,\)PreOrderSelect(\(bv_{2},CodeMap[\)PreOrderMap(\(bv_{1},current\))\(-1]+1\))
23:else
24:lcas_aux\(\leftarrow\)rf_nextsibling(FirstChild(\(bv_{1},current\)))
25:\(lcas\leftarrow\) LCA(\(bv_{2},lcas,lcas\_aux\))
26:ifNumLeaves(\(bv_{1},current\)) =NumLeaves(\(bv_{2},lcas\_aux\)) then
27:\(equalClusters\longleftarrow equalClusters+1\)
28:endif
29:endif
30:endwhile
31:return\(lcas\)
32:endprocedure
```  
**Algorithm 3**rf_nextsibling Implementation
## 2 Materials and Methods
### Magnetoinductive Directional Filters
#### 2.3.2 Bandwidth  
An analytic expression for the 3-dB bandwidth of \(|S_{41}|^{2}\) may be obtained in the lossless case. The half-power frequencies can be found as follows:  
\[|S_{41}|_{k^{\prime\prime}=0}^{2}=\frac{\mu^{4}e^{2k^{\prime}_{\rm f}a}\Big{(}e ^{2k^{\prime}_{\rm f}a}-1\Big{)}^{2}}{1-e^{j4k^{\prime}_{\rm f}a}\Big{(}4\mu^{ 4}+2-e^{j4k^{\prime}_{\rm f}a}\Big{)}}=\frac{1}{2}. \tag{15}\]  
Within the passband, there are two solutions for \(k^{\prime}_{f}\). If these are expressed in terms of \(\kappa,\mu\), and \(\omega_{0}\), the half-power bandwidth \(B\) is given by:  
\[B=\omega_{0}\Bigg{(}\sqrt{\frac{1}{1-\kappa p}}-\sqrt{\frac{1}{1+\kappa p}} \Bigg{)},\quad p=\frac{\sqrt{\mu^{4}-\sqrt{\mu^{8}+4}+2}}{2}. \tag{16}\]  
Thus, the width of the bandpass channel increases with \(\mu\) and \(\omega_{0}\). It is simple to calculate the Q-factor of the bandpass response, as \(Q_{bp}=\frac{\omega_{0}}{B}\). Figure 3b shows a contour map of the variation of \(Q_{bp}\) with \(\kappa\) and \(\kappa_{c}\) thus obtained. High-filter Q-factors require high values of \(\kappa\) and low values of \(\kappa_{c}\); however, the achievable Q-factor is limited since the use of real resonators with finite Q-factors will result in high losses if \(\kappa_{c}\) is too low.
## 4 Experiments and Analyses
### Ablation Study
#### 4.5.4 Influence of Training Set Size  
To verify the performance of CGR-NER under the condition of insufficient training data, we randomly select 10\(\sim\)90% samples from the training sets of the Weibo and OntoNotes 4 datasets as the training set, respectively, while keeping the development set and test set unchanged, and we use BERT-BiLSTM-CRF as the baseline to compare with CGR-NER. As can be seen from Figure 5, the performance of CGR-NER surpasses that of BERT-BiLSTM-CRF under different training set sizes, indicating that CGR-NER has a stronger learning ability. In addition, we can find that CGR-NER has a more obvious improvement effect on the Weibo dataset. When the training data amount is 10%, the F1 score of CGR-NER on the Weibo dataset is improved by around 10% compared with BERT-BiLSTM-CRF. This is because the corpus of the Weibo dataset is collected from social media, the total amount of data is small, and the text format is chaotic, making entity recognition more challenging. At the same time, the glyphs of Chinese characters can be used as a data supplement to provide additional semantic knowledge for CGR-NER, so that CGR-NER can still have an excellent fitting ability in the case of insufficient training data.
# Strategies and Methods for the Fault-Tolerant Function Development of Multi-Domain Systems +
## 1 Introduction  
In industrial practice, components of complex systems such as sensors and actuators are vulnerable to faults and may deteriorate the system's performance [1]. Reliability plays a decisive role in the operation of current industrial engineering systems and faults can unexpectedly affect the entire system [2]. Fault-tolerant control, i.e., passive or active control algorithms with the intention to accommodate faults, has attracted significant attention in the research community, because systems with fault-tolerant control have the potential to minimise the effect of faults. However, in these studies, the design of the system itself (not of the control systems) is not the main point of interest; consequently, current research seeks to address this [3, 4]. In this area, several fault accommodation possibilities have already been proposed, but they mainly concern design stages with concrete product models. In recent years, the enormous importance of the functional level has been emphasised [5] and the special challenges of functional development investigated [6, 7]. This article is a revised and expanded version of a paper entitled \"Fault-Tolerant Function Development for Mechatronic Systems\", which was presented at the 17 th International Design Conference Design 2022, Cavtat Croatia (Online), 23-26 May 2022 [6]. This paper explores the fault-tolerant function development of multi-domain systems; the exploration is based on an intensive literature review, on the experience of the authors as design engineers, and numerous talks with engineers in design, assembly planning, and production. This research is at an exploratory stage and is intended to foster discussion and further investigations inthe worldwide engineering design research community. It is important to note that the basis for the presented analysis is a rather small number of case studies; these were analysed in detail and, because of an active role of the authors (see Section 2), the underlying logic was transparent. Obviously, for generalisation of the findings, the empirical basis has to be widened (see also Section 8). The main research question can be formulated as:  
_How can system design on the functional level support the accommodation of faults?_  
For this exploration, Section 2 explains the underlying research approach and research background; Section 3 presents the state of the art concerning fault-tolerant design and function development. Section 4 elucidates the emergence of faults in industrial technical systems, and Section 5 investigates possible countermeasures against these faults. Examples of such countermeasures are explained in detail on the basis of product development problem solving cycles in industry and academia in Section 6. The application of the research is the focus of Section 7. Section 8 concludes the paper, provides an outlook, and describes promising directions for future research.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 7 Alleviating Abiotic Stress in Crops
### Enhancing Drought Tolerance in Sophora vicifolia  
Drought stress is a major constraint on plant growth and productivity, particularly in arid and semiarid regions [26]. The study on _Sophora vicifolia_ seedlings investigates the role of exogenous ABA in improving drought tolerance (Contribution 8). Key findings include the following:  
Improved germination rates: ABA enhanced the germination rate, potential, and index under drought conditions simulated by polyethylene glycol (PEG).  
Enhanced antioxidant defense: ABA increased the activities of antioxidant enzymes (POD, SOD, and CAT), reducing oxidative damage caused by drought-induced ROS.  
Proline accumulation: ABA promoted the accumulation of proline, an osmoprotectant that helps maintain cellular osmotic balance under water-deficit conditions.  
Optimal concentration: the study identified 50 mg L\({}^{-1}\) ABA as the most effective concentration for enhancing drought resistance, balancing germination performance and stress tolerance.
## 3 Optomechanical Design
### Emitting Subsystem
A beam expander with a power of 20\(\times\) is used to ensure eye safety levels. At the expander output, the laser beam has a diameter of 16 mm, a divergence of around 210 \(\upmu\)rad and is fully eye-safe (class 1M, IEC/EN 60825). Figure 6 shows the relative position of the laser transmitter and the beam expander. The laser unit is held in place by means of an XZ miniature translation stage whose function is to adjust the position of the emitted beam with the optical input of the beam expander. This translation stage is in turn held in place by means of a pitch and yaw accessory platform that allows angular alignment of the laser beam with respect to the optical axis of the expander. The whole emission subsystem (laser transmitter and beam expander) is held in place by a high-load pitch & yaw platform that allows precise adjustment of the tilt angle between the optical emission and reception axes. The overlap factor (OVF) and the range at which full OVF is achieved are critically dependent on the tilt angle. More information about the parameters which condition the OVF can be found in [20].
## 2 Materials and Methods  
### Plant Material and Extract Preparation  
Plant materials of _L. minuta_ were collected from the town of Banya located in the Plovdiv region (42\({}^{\circ}\)32\({}^{\prime}\)226\({}^{\prime\prime}\) N 24\({}^{\circ}\)50\({}^{\prime}\)213\({}^{\prime\prime}\) E). The plant had been developing in a small (about 30 m\({}^{2}\)) warm swamp formed by a hot mineral water flowing out from a \"Bancheto\"bath. The voucher specimens of _L. minuta_ were kept in the herbarium of Agricultural University in Plovdiv, Bulgaria (SOA 061400). _C. vulgaris_ (SKU: 100-CVC00-50), and S. _dimorphus_ (SKU: AC-1002) were supplied from the Algae depot--USA (www.algaedepot.com accessed on 21 May 2021) in 2013. Algae cultivation was maintained in a laboratory bioreactor of the Department of Biology and Aquaculture of Faculty of Agriculture, Trakia University, in Bold Basal Medium (BBM) [21]. Dried rhizomes of _A. calamus_ were purchased from an herbal and medicinal pharmacy, Sofia (Bilki EOOD), in a paper package of 50 g. The climate of Bulgaria (where _L. minuta_ and _A. calamus_ were harvested from) is transitional continental, with influence from the Mediterranean Sea.  
Plant material was airdried in shade at room temperature and ground in a mechanical grinder (with a final powder size of less than 400 \(\upmu\)m). The samples were stored in dark and cool rooms at 16-18 \({}^{\circ}\)C prior to analysis.  
The extracts were prepared via ultrasonication of the powdered plant material in methanol (in a ratio of 1:10) for 30 min at 40 \({}^{\circ}\)C in triplicate. Ultrasonic extraction is convenient and straightforward, and it was selected because of the high rate extraction of flavonoids and polar bioactive compounds [22].
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 5 Conclusions  
In this study, we examined the effects of a non-invasive, gait-synchronized, vibrotactile feedback system on the walking ability of eleven lower limb amputees. We assessed various aspects of walking, including risk of falling (TUG), coordination (FSST), walking speed (10 MWT), and endurance (2 MWT). Overall, we found a reduced risk of falling when walking with the VTFS (TUG, \(p\) = 0.0420 *), with a trend of significant change in coordination (FSST).  
Conceptualization, J.E., S.S. and A.G.; methodology, J.E. and A.G.; validation, M.W., J.E. and M.N.K.; formal analysis, M.W., M.N.K. and J.E.; investigation, M.N.K., V.H., L.J., J.E. and V.W.; resources, S.S. and J.E.; writing--original draft preparation, M.N.K. and J.E.; writing--review and editing, M.N.K., V.H., L.J., V.W., M.W., A.G., K.R.E., S.S. and J.E.; visualization, M.N.K. and J.E.; supervision, J.E.; project administration, J.E. and S.S. All authors have read and agreed to the published version of the manuscript.  
The study was realized and funded within the framework of the Advanced Clinical Scientist Research Group \"MOBILISE\" (AMputatiOns- und RehaBILItationS ChirurgiE) at Hannover Medical School and University Medicine Goettingen. The VTFS was provided by the manufacturer, no further funding from third parties was received.  
The study was conducted in accordance with the WMA Declaration of Helsinki and approved by the Ethics Committee of Hannover Medical School (10940_BO_K_2023) on 13 June 2023.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank [PERSON] for his support with this work.  
[PERSON] is the CMO of Saphenus Medical Technology. The company had no influence whatsoever on study design, data interpretation, or writing the manuscript. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.