esa-sceva/satcom-chunk-collection huggingface dataset:
# Lossless WDM PON Photonic Integrated Receivers Including SOAs
## 1 Introduction
In [19], a numerical model was reported for the simulation of all nonlinear effects of the SOA. All these models proposed to optimize the operating conditions and improve SOA design, either stand-alone or on chip, for different applications. Depending on the application, SOAs are operated either in linear regime, as in-line amplifiers in optical communication networks [20,21] or in nonlinear regime, as all optical logic gates [22], all-optical switching [23] and wavelength conversion [24]. SOA performance has been investigated in terms of input signal with different modulation formats, different input pulse shapes [25,26] and nonlinear pulse propagation [27]. In this paper, we propose an architecture of a lossless polarization insensitive hybrid photonic integrated WDM PON ONT receiver that employs SOAs. The proposed SOAs not only compensate the link loss but also compensate the on-chip losses. Dual polarization grating couplers and the twin parallel SOA structure provides polarization insensitive design. SOI based tunable micro-ring filters are proposed to drop the relevant wavelength. We investigate the design of a SOA device for bidirectional 32 \(\times\) 25 Gb/s short reach optical access links through numerical simulation modelling. The optimum SOA device parameters and dimensions are evaluated under ODN link loss and integrated ONT receiver fabrication constraints on a reference architecture. Steady state and dynamic analysis are also performed to evaluate WDM transmission performance. We also validate our model experimentally, using commercial SOAs in realistic working conditions. The impact of cross gain modulation (XGM) and amplified spontaneous emission (ASE) on receiver performance is also evaluated experimentally in worst case power and polarization imbalance conditions.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 2 Theoretical Background
### Sensitivity to Inundation  
The CYGNSS constellation is composed of eight satellites, each with receivers measuring up to 4 reflections simultaneously. This architecture provides up to 32 simultaneous measurements per second globally. The CYGNSS constellation has an orbit inclination of 35\({}^{\circ}\), limiting global coverage to between \(\pm\) 38\({}^{\circ}\) latitude. This latitudinal coverage, combined with the high spatial resolution and temporal repeatability, make CYGNSS an ideal sensor for monitoring dynamic surface processes such as inundation in tropical wetlands.  
Although CYGNSS was designed for ocean applications, very distinctive signatures are also detected over land where coherent scattering is dominant, such as inundated areas. Figure 3b shows an example of a DDM peak SNR over the Amazon Basin, showing strong returns from rivers and inundated vegetation. The main channel of the Amazon River and Rio Negro (marked in Figure 3b)are clearly delineated, as are many of the large number of tributaries that make up the watershed of the Amazon Basin. Large regions of inundated wetlands (e.g., the Pantanal, marked in Figure 3b) are also apparent at this scale. The high SNR of the CYGNSS signal over inundated areas relates to the highly reflective nature of surface water and to the low surface roughness of these areas. The presence of open water or flooded vegetation (characterized by having very low roughness and high surface reflectance within the first Fresnel zone) causes the reflected signal to be dominated by coherent scattering and increases the received power by 15-20 dB in comparison to other surrounding surfaces with higher surface roughness and minimal or no inundation.  
We utilized a biomass map of the Amazon Basin [45] to estimate vegetation density (Figure 3a) and account for CYGNSS data sensitivity to inundation under various levels of vegetation density. Much of the Amazon basin is characterized by dense vegetation, with an average aboveground biomass density over 200 MgHa\({}^{-1}\), reaching up to 340 MgHa\({}^{-1}\) in some areas (darker green in Figure 3a). L-band signals can penetrate through the vegetation canopy to a certain extent, depending on vegetation density and water content, and provide information on whether or not such areas are inundated. Standing water beneath vegetation canopies gives rise to a coherent scattering component that generates a strong return in comparison to non-flooded vegetation or bare soil. Evidence of this is the high return obtained from a large number of tributaries (observed in Figure 3b) that are actually covered by dense vegetation (shown by Figure 3a), and the lower return observed from non-flooded areas surrounding those tributaries.
## 3 Results and Discussion
### Limitations  
While our study demonstrates promising results for automated job-candidate matching in the IT recruitment domain, several limitations must be acknowledged:  
* Dataset Size and Representation Bias: Although our dataset is drawn from real-world IT recruitment processes, it may not cover all role types or industry segments comprehensively. Additionally, the proportion of resumes in English vs. Polish could bias the model's multilingual performance. Models trained under these conditions might not generalize well to other languages or drastically different domains without further fine-tuning.
* Domain Specificity: We focused on IT-related positions, where job descriptions often include specific technical terms and resumes list programming languages or frameworks. Hence, models trained on this domain may need additional data and adaptation for non-technical roles (e.g., marketing or finance) or for specialized subfields in IT (e.g., data science vs. cybersecurity).
* Model Complexity: Transformer-based heads, in particular, can have high memory and computational demands during training and inference. Organizations with limited GPU or CPU resources might find GRU or LSTM architectures more practical, albeit with slightly reduced performance. Hyperparameter tuning can further inflate the total cost of model deployment.
* No Explicit Handling of Structured Fields: Our current approach encodes job descriptions and resumes as free-text strings, largely ignoring tabular or structured information such as discrete skill lists, candidate location, or salary ranges. While the neural representations implicitly capture some structure, explicit incorporation of tabular or metadata features could further improve matching accuracy.
* Evaluation Based on Historical Outcomes: Our ground truth labels reflect the final result of each recruitment process, which may include factors beyond direct skill or qualification matches (e.g., salary negotiation, candidate relocation constraints, etc.). Relying on these labels assumes that historical decisions perfectly correlate with \"best match\" outcomes, which might not always hold. Future studies could refine labeling schemes to isolate skill-based matching from external hiring factors.  
Despite these constraints, our findings highlight the effectiveness of combining multilingual embeddings with specialized sequential heads for the matching of job descriptions and candidate resumes. Future directions include expanding the dataset to encompass additional industries, incorporating structured features and exploring more advanced attention-based architectures to refine the semantic matching process.
## 3 Results
### Effect of JGTW on the iNOS Expression and NO Production  
To investigate the effect of JGTW on LPS-induced inflammatory responses, RAW264.7 cells were pretreated with various amounts of JGTW (6.25 to 100 \(\upmu\)g/mL) for 1 h and stimulated with 1 \(\upmu\)g/mL LPS for 24 h. Expression of iNOS was visualized using Western blot analysis. As shown in Figure 6A, iNOS expression was elevated with LPS stimulation, and pretreatment with JGTW suppressed iNOS overexpression in a dose-dependent manner. We also measured NO production from LPS-stimulated RAW264.7 cells with or without JGTW pretreatment. Furthermore, JGTW pretreatment had a suppressive effect on NO production induced by LPS showing statistically significant decrease at doses over 50 \(\upmu\)g/mL (Figure 6B).  
Figure 4: Effect of JGTW on the activity of MMP-9 in BEAS-2B cells. The representative photographs of MMP-9 activity after 48 h IT (50 ng/mL) treatment with or without JGTW pretreatment in gelatin zymography (**A**) and band intensities of MMP-9/MMP-2 (**B**). The values are presented as mean \(\pm\) SEM. ##\(p<0.01\) versus vehicle-treated cells; and **\(p<0.01\) versus IT-treated cells.
## 2 Materials and Methods
### ECG and PPG Recording and Processing  
The ECG and PPG signals were recorded using a custom-made acquisition system developed by the Federal University of Santa Catarina's Institute of Biomedical Engineering with a sampling frequency of 500 Hz. The ECG data were obtained following a bipolar three-lead configuration using disposable adhesive electrodes (3M, Red Dot, 2560). The PPG was measured with a pulse oximeter sensor on the index finger.  
The ECG signals were bandpass-filtered (fourth-order Butterworth FIR filter, 0.5-40 Hz) to remove high-frequency noise, including power line interference and baseline wander. Subsequently, the RR intervals between successive R peaks of QRS complexes were detected and calculated based on the Pan-Tompkins algorithm [57]. Lastly, the RR sequence was inspected to remove spurious RR peaks where the RR intervals changed more than 20% within a median value window of the following five and the previous five RR intervals [58]. The corrected HRV signals proceeded for analysis.
## 3 Results and Discussion
### Comparison between Buffer Layers Grown on \(n\)-Type and SI 4H-SiC
We now turn our attention to the buffer layers grown with different \(T_{\rm{Ar}}\) on SI 4H-SiC substrates. The same trend of suppressed reconstruction with increasing \(T_{\rm{Ar}}\) is found. In fact, for the case of \(T_{\rm{Ar}}\) = 1300 \({}^{\circ}\)C heating up to 1600 \({}^{\circ}\)C did not result into a buffer layer formation and heating up to 1800 \({}^{\circ}\)C was needed for a clear buffer layer Raman spectrum to be obtained. Interestingly, the buffer layers grown with \(T_{\rm{Ar}}\) = 800 \({}^{\circ}\)C and \(T_{\rm{Ar}}\) = 1300 \({}^{\circ}\)C exhibit very similar G\({}_{\rm{BL}}\) positions (Figure 6c,g) and \(A_{\rm{D_{BL}}}/A_{\rm{G_{BL}}}\) ratios (Figure 6d,h), indicating similar \(sp^{3}\) hybridization contents and densities of defects. A slightly broader distribution is observed for the case of \(T_{\rm{Ar}}\) = 1300 \({}^{\circ}\)C for both n-type and SI 4H-SiC substrates, reflecting a slightly larger variation of the crystallite size. Figure 5: A comparison between the average \(\mu\)-Raman scattering spectra for buffer layer samples with \(T_{\rm{Ar}}\) = 800 \({}^{\circ}\)C: (**a**) on n-type and SI 4H–SiC, and (**b**) the buffer layer features in fully-formed MLG at \(T_{\rm{Gr}}\) = 2000 \({}^{\circ}\)C for 0 s on \(n\)-type and SI 4H–SiC. Figure 6: Histograms of the G\({}_{\rm{BL}}\) band position and the ratio of the D\({}_{\rm{BL}}\) and G\({}_{\rm{BL}}\) band areas, \(A_{\rm{D_{BL}}}/A_{\rm{G_{BL}}}\), for the buffer layers grown with \(T_{\rm{Ar}}\) = 800 \({}^{\circ}\)C (**a–d**) on \(n\)-type (**a,b**) and SI (c,d) 4H–SiC; and for the buffer layers grown with \(T_{\rm{Ar}}\) = 1300 \({}^{\circ}\)C (**e–h**) on \(n\)-type (**e,f**) and SI (**g,h**) 4H–SiC. The histograms are obtained over Raman maps of 3 \(\mu\)m \(\times\) 3 \(\mu\)m. Three Lorentzian lineshapes centered around G\({}_{\rm{BL}}\) of 1585–1600 cm\({}^{-1}\), D\({}_{\rm{BL}}\) of 1330–1530 cm\({}^{-1}\) and a band centered at 1340 cm\({}^{-1}\) were used for the fitting. Based on these results we can conclude that the temperature at which Ar is introduced has a determining role in the formation of the buffer layer in high-temperature sublimation in closed crucible independently of the 4H-SiC substrate conductivity. As a result of an interplay between oxidation and restructuring in Ar atmosphere, the formation of the buffer layer is shifted to higher temperatures for increased \(T_{\rm Ar}\) of 1300 \({}^{\circ}\)C. Increasing \(T_{\rm Ar}\) also leads to reduction of \(sp^{3}\) hybridization contents and densities of defects on \(n\)-type 4H-SiC. However, \(T_{\rm Ar}\) has a less pronounced effect for SI substrates, where ordered buffer layers form with similar structural properties.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 5 Conclusions  
This paper presents a new approach to tackle the problem of incremental learning-based SAR target recognition. Through research, it is found that the bias caused by the imbalance between old and new classes is the primary factor that influences the performance of incremental learning. Our incremental approach deals with these problems at different levels and achieves a good balance between the retention of old knowledge and the learning of new knowledge. First, this paper combines the knowledge distillation method and old sample preservation method that preserve representative samples to re  
Figure 8: Comparison of confusion matrices under three methods while the experiment is carried out under the condition of 5-phase incremental learning. (**a**) iCaRL-NME. (**b**) United-NME. (**c**) Our-method.  
Figure 7: The effect of different preserved sample sizes on incremental learning performance. (**a**) 5 phases, 2 classes per phase. (**b**) 10 phases, 1 class per phase.  
duce the forgetting of old classes. Then an effective loss function for class separation is used to reduce the confusion between old and new classes. Finally, we find that the fully connected layer of the deep learning model has a stronger tendency to favor new classes in classification. Therefore, a linear model is used to correct the bias problem of the fully connected layer. Experiments on the MSTAR dataset show that the proposed approach in this paper has superior performance to existing methods.  
Conceptualization, Y.Z. and F.Z.; methodology, S.Z.; software, S.Z.; validation, F.M. and X.S.; formal analysis, F.M.; investigation, X.S.; resources, F.M.; data curation, X.S.; writing--original draft preparation, S.Z.; writing--review and editing, Y.Z., X.S., F.M. and F.Z.; visualization, S.Z. and Y.Z.; supervision, Y.Z. and F.Z.; project administration, Y.Z.; funding acquisition, Y.Z. and F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 62171016 and 61871413, and in part by the Fundamental Research Funds for the Central Universities under Grant buctrc202001.  
Not applicable.  
The authors declare no conflict of interest.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 2 Materials and Methods
### Gait Simulator
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Maximum Values** \\ \hline Static load, \(z\) axis & 700 N \\ Static load, \(x\) axis & 200 N \\ Static load, \(y\) axis & 100 N \\ Linear displacement, \(z\) axis & \(\pm\)150 mm \\ Linear displacement, \(x\) axis & \(\pm\)400 mm \\ Linear displacement, \(y\) axis & \(\pm\)50 mm \\ Rotation, \(z\) axis & \(\pm\)5\({}^{\circ}\) \\ Rotation, \(x\) axis & \(\pm\)5\({}^{\circ}\) \\ Rotation, \(y\) axis & \(\pm\)30\({}^{\circ}\) \\ Linear velocity, \(x\) axis & \(\pm\)1834 mm/s \\ Angular velocity, \(y\) axis & \(\pm\)3.5 rad/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Gait simulator workspace and output forces [20]. Figure 2: Schematic diagram illustrating the gait simulator composing units, including a representation of main coordinate systems \(G_{b}\) (fixed base) and \(G_{p}\) (mobile platform). kinematic control perspective, high-level platform motion commands computed by each of the control strategies proposed in this work must enable computing corresponding piston displacements that will provide an appropriate \(G_{p}\) trajectory. However, solving the inverse kinematic equations for SGP is complex, due to closed kinematic chain and constrained configuration. In this work, a numerical solution based on unique geometric features of SGP is used. Further details are provided in [14]. For all control strategies, hard-coded measures to avoid inter-platform collision are implemented by reducing the available operational space.
# An Intrusion Detection System for RPL-Based IoT Networks
## 2 Background
### Attacks in RPL-Based Networks
Version number attackThe version number in a DODAG is a variable used to maintain the DODAG version, which acts as a counter and can only be incremented by the root node. This variable keeps an order inside the DODAG so that if a node receives a higher version of it from the root node, it must renew its DODAG information. On the other hand, if the root receives a higher version number than the expected one, this will indicate that something is wrong with the DODAG, which will then prompt it to launch a global repair in order to rebuild the entire topology. The version number attack exploits the global repair mechanism in the RPL to disturb the network operation [24]. In particular, an attacker may send a higher version number of the DODAG to the neighbours so that a global repair is forced. By doing this, all the nodes in the network will consume more resources in order to handle the DODAG rebuild. In addition, depending on the topology and the distance between the attacker and the root node, loops can be created, thus exhausting the rest of the nodes even more [24]. Blackhole attackThe main purpose of a blackhole attack is to cause an internal DoS to the child nodes by silently discarding all the messages received from the rest of the nodes [27]. Furthermore, a node performing a blackhole attack does not send any messages, as it is a hole that absorbs everything. This behaviour, if executed at the right moment and position (i.e., when the attacker node is the parent of many children), may cause nodes in the downward route from the attacker node to be isolated from the network. Despite the fact that this attack may be effective in some scenarios, it has been shown that RPL's self-healing mechanisms will modify the topology during an attack so that the victims will become unaffected by the attacker, thus reducing the impact of the attack [38]. Greyhole attackGreyhole or selective forwarding attack acts similarly to a blackhole attack, but instead of blocking every packet received, a greyhole attack blocks all traffic, except for a selected type of message (e.g., RPL control messages). This attack avoids being detected by simple detection mechanisms and also prevents the self-healing mechanisms from fixing the problem.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Comparative Analysis of Simulation and Flight Validation  
(1) Trajectory and formation stability  
The trajectory tracking accuracy and formation stability observed during real-world tests closely matched the simulated performance. Although some deviations were inevitable due to environmental factors, the average trajectory error and formation deviations remained within an acceptable range, underscoring the robustness of the control strategies implemented by the MADDPG-VSC algorithm.  
(2) Hardware performance consistency  
The hardware platforms, particularly the ZCU104 task controller, demonstrated stable performance across both simulation and real-world tests. The low latency, efficient data processing, and real-time control capabilities highlighted in the simulations were reflected in the flight tests, further validating the hardware selection.  
(3) Response to innovation points  
The adaptive sweep angle control and UAV cooperation mechanisms, which form the core innovations of the MADDPG-VSC algorithm, were shown to enhance the flexibility and stability of UAV formation in dynamic environments. While these features were rigorously tested in the simulations, the flight validation confirmed their effectiveness in real-world scenarios, particularly in maintaining precise control during sudden maneuvers or obstacle avoidance.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Data Transmission Delay (s)** & **Data Transfer Rate (Mbps)** \\ \hline MADDPG–VSC & 0.2 & 1.4 \\ MADDPG & 0.3 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Data transmission delay and speed comparison between MADDPG–VSC and MADDPG.  
(4) Insights and implications for future work  
The validation of the MADDPG-VSC algorithm through both simulation and flight testing provides confidence in its applicability for complex, real-world multi-UAV missions. Its ability to maintain formation stability, trajectory accuracy, and efficient obstacle avoidance in dynamic and unpredictable environments highlights its potential for broader implementation in UAV operations.
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 2 Data
### Auxiliary Data  
Multiple auxiliary datasets were integrated to support subsequent analyses. Meteorological data were obtained from the China Meteorological Administration Monitoring Data Network, providing baseline and daily observations from surface weather stations. These datasets, characterized by a spatial resolution of 1 km \(\times\) 1 km, include key atmospheric parameters such as WS, air temperature (AT), relative humidity (RH), and atmospheric pressure (BP). High-resolution meteorological inputs are critical to accurately capture the spatiotemporal variability within the study area. Complementing these observations, normalized difference vegetation index (NDVI) data were sourced from the MOD13A3 vegetation product, available via NASA's data repository. The NDVI dataset, with a 1 km \(\times\) 1 km spatial resolution and monthly temporal resolution, provides valuable insights into vegetation dynamics, serving as a proxy for assessing land surface conditions and ecological processes. Additionally, a digital elevation model (DEM) was extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 1. This dataset, featuring a 30 m spatial resolution, offers detailed topographic information that is indispensable for understanding the terrain effects on meteorological conditions and vegetation distribution. By synthesizing these diverse  
Figure 1: Locations of the study area and ground PM\({}_{2.5}\) monitoring stations.  
datasets, the study constructs a robust framework for analyzing environmental interactions, ultimately enhancing the precision of modeling and interpretation of observed phenomena.