esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodological and Operational Framework for CDTs
### Application AreasReference Scenarios
#### 3.3.3 Optimize Material Flows in the Supply Chain  
This scenario is more on the logistics aspects where we can optimize the flows from the factory (product deliveries) and the incoming materials thus improving demand forecasting and synchronized planning [40].  
We can model all involved actors (LSP, origin supplier, factory and other external LSPs) as CDTs where they can propagate information about their delivery status (delivery from the factory, load factor during transportation, delivery destinations, SLA terms, etc.). through a peer-to-peer network all CDTs can assess potential alternatives (merging deliveries, exception management as a response to an event) and negotiate with other CDTs optimal solutions to minimize deliveries times and cost. To do this, the CDTs need to consider other stakeholders' plans (delivery plans, MRPs for the factories, etc.) so a more holistic view and approach is needed that extend beyond each CDT status.  
3.3.4. Localization: Ad-Hoc or Constant Collaborations with Local Manufacturers  
One of the key factors for resilience in supply chain networks, is the ability to make the best use of local manufacturers. The deployment of flexible shop floors (3D printing, micro-scale manufacturers, etc.) enables more agile and local manufacturing sites. Furthermore, smart specialization platforms (S3P) [41] are local catalogues assisting companies to find local partners. To cope with the needs of personalization, customization and production flexibility, responsiveness (time to market, time to fixing, time to replacing parts, etc.) and cost effectiveness (inventory, production and other costs) are the key factors for sustainability and resilience within the whole supply network. In such a model, we can have the local manufacturers as CDTs where we assess various strategies for SLA management (production or spare parts in response to customer claims).  
In the long term, we can model different production networks, distribution and local support centers as CDTs and perform various scenarios for optimal response taking into consideration aspects such as: capacity, delivery times, historical customer claims data, etc. Each actor as a CDT will have its own behaviour and in such simulation scenarios we can assess the behaviour of the network.  
In the short term, we can have also ad-hoc decision making for a particular part that is not in the production pipeline (for e.g., a part requested by a specific customer). Through the utilization of CDTs we can assess various alternatives depending on the real-time information about availability, capacity, delivery times and other parameters.
## 3 System Model and Basic Assumptions
### A&F MIMO Signal Model
In the expression (5), the first and second terms represent the signal desired and noise in the Backhaul and Access Links, respectively.
# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 6 Case Studies  
As illustrated in Figure 8 (WikiEvents case study), the event type detection model correctly identifies two events: \"conflict.attack.n/a\" and \"contact.commitment promiseex-pressintent\". However, the TabEAE model fails to extract the argument \"Turkey's air force\" for the first event and misassigns the \"Communicator\" role of the second event to \"Ankara\" (ground truth: \"Damascus\"). This error likely stems from TabEAE's over-reliance on local sentence tokens and trigger proximity, whereas SPARE successfully captures cross-sentence  
Figure 6: Comparison of the performance of SPARE and TabEAE on events that vary with the number of arguments. (**a**) illustrates the F1-score gap between SPARE and TabEAE models on the RAMS dataset. (**b**) presents the F1-score gap between the two models on the WikiEvents dataset.  
Figure 7: Analysis following the distance between arguments and triggers in events. (**a**) shows the F1 values of SPARE and TabEAE models on the RAMS dataset across different distance ranges. (**b**) shows the F1 values of the two models on the WikiEvents dataset across different distance ranges.  
arguments through its position-aware semantic role attention mechanism, which explicitly models document-level argument distribution.  
As shown in Figure 9 (Campus Public Opinion case study), the SPARE model demonstrates strong real-world applicability in detecting emerging public opinion events. In this case, it accurately identifies two interrelated events--\"conflict.demonstrate.n/a\" and \"contact.requestadvise.n/a\"--from a short news-style report about a student protest. While the TabEAE model correctly extracts some arguments (e.g., \"Students\" as demonstrators and communicators), it fails to recognize the university administration as the recipient of the students' demands and overlooks the location \"Greenfield University\" for the advice request event. This shortcoming likely results from TabEAE's limited ability to associate roles beyond the sentence scope.  
In contrast, SPARE's position-aware role attention mechanism allows it to trace arguments across sentence boundaries, enabling precise extraction of multi-role arguments. This showcases SPARE's advantage in public opinion discovery, particularly in campus settings where protests and administrative responses are distributed across multiple utterances or documents.
## 5 Experiments and Results
### Methodology
#### 5.2.2 Influence of Different Input Features  
In order to evaluate the contribution of any particular feature out of those described in Section 4.2, we evaluate Model A with ELMo in combination with emotion markers, PoS and MESHRUS, MESHRUS-2 and Vidal dictionaries. In these experiments, texts are passed to the language model split into independent sentences.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Word Vector Representation** & **Vector Dimension** & **ADR** & **Medication** & **Disease** \\ \hline FastText & 300 & 22.4 \(\pm\) 1.6 & 70.4 \(\pm\) 1.1 & 44.1 \(\pm\) 1.7 \\ \hline ELMo & 1024 & 24.3 \(\pm\) 1.7 & 73.4 \(\pm\) 1.5 & 46.4 \(\pm\) 0.6 \\ \hline BERT & 768 & 22.1 \(\pm\) 2.4 & 71.4 \(\pm\) 3.3 & 45.5 \(\pm\) 3.2 \\ \hline ELMO\(|\)BERT & 1024\(|\)768 & 18.7 \(\pm\) 9.8 & 74.1 \(\pm\) 1.1 & 47.9 \(\pm\) 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy (%) of recognizing ADR, medication and disease entities in the first version of our corpus (1660 reviews) by Model A with different language models.  
The results presented in Table 11 (compare to the results of ELMo in Table 10) show that adding these features improves the accuracy for the least-represented class ADR.  
Addition of any of the individual features separately leads to an increase in ADR recognition accuracy by 2% to 3%. In particular, part of speech and tonality tags give a 2% increase. These features are of a generic nature, which is the reason why these features give less increase in the accuracy compared to the features based on the MESHRUS vocabulary. The latter contains a lot of medical terminology, so words marked with features of MESHRUS are more important for the NER model. This is why MESHRUS and MESHRUS-2 give a 3% accuracy increase. Increasing the depth of the network with additional LSTM layers helps the model to extract more high-level features and gives a 4% increase compared to base Model A with ELMo, but it makes the process of convergence of the neural network harder. The CRF layer helps to predict more probable sequences of tags. It gives us 4% more accuracy without other additions. Combining all the features gives a significant increase in accuracy for ADR mentions (+8%).
# CNN-Based Pill Image Recognition for Retrieval Systems
## 3 Methodology
### Proposed CNN Architecture  
The first step constructs a Convolutional Neural Network (CNN) to extract the query image features, namely, shape, color, and imprint. The second step uses a classifier to match the extracted query image features with those of an existing pill image. The overall proposed architecture is illustrated in Figure 6.  
The first layer of the proposed CNN network is responsible for accepting the input pill image. In our case, the input layer accepts RGB images of size 227 \(\times\) 227 pixels. After that, they are fed into the CNN model, which processes them as follows:  
* Pill images go through one convolutional layer (Conv1) with 56 \(\times\) 56 \(\times\) 96, which means that the input to the layer is a pill image with a height and width of 56 pixels and that has 96 color channels.
* The resulting tensors (images) go through four additional convolution layers with a smaller height and width (13 pixels) than the previous layer, and the number of input channels is increased to 256 color channels.
* The resulting feature maps are converted to a fully connected (FC) layer of 4096 neurons, which is connected to a second fully connected layer of 4096 neurons.
* Then, the extracted features (color, shape, and imprint) are fed into the classification layers; we then employ a k-NN classifier to handle the prediction more accurately and with less runtime.
* Finally, the classification layers output a predicted class, i.e., a matched set of images from a stored database. For more details on the CNN layers and processing, we refer the reader to [30,47].  
We also note that we use the terms \"prediction\" and \"identification\" interchangeably throughout the paper.
## 4 Calibration Comparison of Landsat 8 and Sentinel 2
### Calibration Comparison Using Coincident Acquisitions  
Due to the orbital properties of Sentinel-2 and Landsat-8, the instruments acquire coincident images of specific locations on Earth every 80 days. For Sentinel-2A, two of these locations happen to be two of the CEOS-defined PICS regions [22], long used for instrument calibrations: Libya-4 and Algeria-3. Since the launch of Sentinel-2A, the instruments have acquired 5 cloud-free images of Libya-4 and 7 of Algeria-3. For these image pairs, the top-of-atmosphere reflectance can be directly compared once accounting for the spectral band differences and the solar zenith angles. Figure 8 shows the ratios between the lifetime average top-of-atmosphere (TOA) reflectance between OLI and MSI for the common spectral bands. In general, the instruments agree to within 1%. There is less agreement in CA and Blue bands; though the instruments agree to within 1.5%, the differences between the sites are larger. In the CA and Blue, the reflectance differences are consistent to within 0.7% (1-sigma) for a given site. This suggests that there are still some spectral differences that are not accounted for in the SBAF correction.  
Figure 7: Comparison of the spectral response functions for Landsat-8 and Sentinel-2. Even though there is a high degree of similarity among the three instruments, differences require calculation of a spectral band adjustment factor (SBAF) to make data from the three sensors interoperable.
_Article_  
Direct-Current Forced Interruption and Breaking Performance of Spiral-Type Contacts in  
Aero Applications  
[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2  
1 School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (B.J.); [PERSON]S] (M.C.); [EMAIL_ADDRESS] (S.M) Institute of Spacecraft System Engineering CAST, Beijing 100191, China; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-10-8233-8384 Academic Editor: [PERSON]  
Received: 21 March 2017; Accepted: 10 May 2017; Published: 26 May 2017  
This paper analyses the transient characteristics and breaking performance of direct-current (DC) forced-interruption vacuum interrupters in 270 V power-supply systems. Three stages are identified in forced interruption: the DC-arcing stage, current-commutation stage, and voltage-recovery stage. During the current-commutation stage, the reverse peak-current coefficient \(k\), which is a key design factor, is used to calculate the rate of current at zero-crossing (\(di/dt\)). MATLAB/Simulink simulation models are established to obtain the transient characteristics influenced by the forced-commutation branch parameters and the coefficient \(k\). To study the breaking performance of spiral-type contacts, experiments are conducted for different contact materials and arcing times for currents less than 3.5 kA. During the DC-arcing stage, a locally intensive burning arc is observed in the CuW80 contact; however, it is not observed in the CuCr50 contact. On examining the re-ignition interruption results of the CuW80 contact, the intensive burning arc is found to be positioned within a possible re-ignition region. When the arcing time is longer than 1 ms, the intensive burning arc occurs and affects the breaking performance of the spiral-type contacts. If the DC-arcing stage is prolonged, the total arcing energy increases, which leads to a lower breaking capacity.  
transient characteristics; breaking performance; forced interruption; spiral-type contact; intensive burning arc; contact materials; arcing time; breaking capacity
# A Hybrid Vibration Isolator Based on Elastomeric and Electromagnetic Restoring Force
###### Abstract  
The protection of structures and machines from ground vibrations is a deeply felt and widely studied problem. These vibrations are typically generated by the operation of machines, vehicular traffic, seismic events, shocks, explosions, etc., and are generally characterized by frequencies that are not known a priori, making the use of passive isolation systems problematic. Just away from the source, ground vibrations have a predominantly horizontal component. To meet the conflicting requirements of passive isolation systems, which should have higher stiffness when the structure is at rest and lower stiffness when the ground forces it to vibrate, active or semi-active isolation systems can be adopted. In the following article, the possibility of adopting a hybrid isolator is evaluated; it consists of shear-stressed elastomeric pads coupled with an electromagnetic actuator; the latter consists of three coils, two of which are connected to the ground and the other one to the body to be isolated. This kind of isolator has the same flexibility and adaptability as the active ones, with the advantage of ensuring its functioning even in the absence of an external energy source. Its flexibility is due to the presence of a smart element that allows one to tune the characteristics of the isolator to consider the instantaneous isolation requirements. The proposed solution allows one to modify the isolation system's characteristics in a sufficiently wide range of displacements equal to that defined by the maximum allowed deformation of the elastomeric pads. This paper reports the description of the isolation system and an analytical model to describe its restoring force; then, an experimental setup is adopted to identify the parameters of the analytic model; finally, several simulations are reported to compare the analytical and the experimental trends of the restoring force and to characterize the isolator.  
ground vibrations; hybrid isolators; hybrid vibration isolators; electromagnetic actuators +
Footnote †: journal: Applied  
Article
# Entropy-Regularized Federated Optimization for Non-IID Data
## 5 Results
### PneumoniaMNIST Image Classification Task  
Experimental results on the PneumoniaMNIST medical imaging dataset are summarized in Table 3. Consistent with the intrusion detection findings, ERFO again achieves top performance, with a final accuracy of about 88% and a macro-\(F_{1}\) score around 0.86. This represents a significant improvement over the baseline federated approaches, which struggle on this highly skewed binary classification problem. For instance, one baseline method (SCAFFOLD) reaches only \(\sim\)65.6% accuracy with macro-\(F_{1}\approx 0.46\), which is barely above random guessing due to a severe class imbalance in the distributed data. Other baseline optimizations (e.g., FedAvg variants) improve upon this but still fall short of ERFO--typically achieving percent accuracy scores in the mid-70s to low 80s and correspondingly lower macro-\(F_{1}\) scores (indicating many misclassified minority-class examples). In contrast, ERFO's macro-\(F_{1}\approx 0.85\) on PneumoniaMNIST is nearly as high as its accuracy, implying that the model performs equally well on the minority class (e.g., normal cases, which are fewer in number) as on the majority class (pneumonia cases). This balanced performance is crucial in medical diagnosis tasks: ERFO not only improves the overall accuracy but also ensures that rare but critical normal/pneumonia instances are recognized with high precision and recall.  
Figure 1: Binned (10-segment) validation accuracy (solid) and macro-\(F_{1}\) (dashed) on the _UNSW-NB15_ dataset. Each marker represents the mean of ten communication rounds; therefore the smoothed endpoints differ slightly from the final means reported in Table 2.  
The training dynamics for PneumoniaMNIST are shown in Figure 2. We observe that ERFO learns rapidly on this task: within the first \(\sim\)five rounds, the accuracy shoots up from near 60% to over 85%, and the macro-\(F_{1}\) rises from an initial 0.4 to above 0.8. After this swift start, the curves enter a plateau phase with slight oscillations; both accuracy and macro-\(F_{1}\) stabilize at high values (oscillating around the mid-80s) through round 100. The convergence behavior on PneumoniaMNIST thus mirrors that on UNSW-NB15: ERFO quickly converges to a high-performance regime and remains stable, evidencing no significant performance drop despite the non-i.i.d. client data (different hospitals or sources of X-ray images). The baseline methods, by contrast, show either slow progress or early saturation at much lower performance levels--for example, the SCAFFOLD baseline's learning curve (Figure 2) is essentially flat, reflecting an inability to learn under the strong data imbalance. These results highlight ERFO's effectiveness in handling heterogeneous data distributions, as it consistently attains higher macro-\(F_{1}\) and accuracy while converging robustly on both tasks.
# InP-Based Foundry PICs for Optical Interconnects
## 4 Conclusions  
We have developed a generic foundry process for the realization of a wide variety of complex InP PICs. The generic foundry process provides PIC designers access to almost all devices available in InP technology, such as lasers, SOAs, high-speed photodiodes, balanced detectors, uniform-, sampled-, and superstructure gratings, spot-size converters, EAMs, phase modulators, and three types of passive waveguides serving as on-chip optical interconnects, as well as the basis for a wide range of passive-optical elements. We have highlighted two InP PICs that have been realized with this process. The first PIC was a fully-integrated dual-polarization electro-absorption-modulated laser, where we demonstrated operation at 20 Gb/s. The second PIC was a balanced detector composed of avalanche photodiodes for the detection of multi-level-format signals with a higher sensitivity than achievable with balanced detectors based on conventional pin photodiodes. Those APD structures can be seamlessly incorporated into HHI's PIC platform just by modifying the epitaxial PD layer structure. The balanced APD showed a bandwidth of more than 17 GHz, and can be used for detecting multi-level optical signals at 28 Gb/s.  
We have devised three concepts to facilitate the hybrid integration of InP PICs from our generic foundry platform to PICs from other technology platforms. These concepts require only minor modifications to the fabrication process, and all the other devices can still be realized with these modifications. The modifications that we are transferring are the following. First, introduction of  
Figure 10: Schematic showing the implementation of vertical mirrors on the generic-foundry-service process for coupling light upward (**a**), and downward (**b**).  
waveguide terminations through etched facets instead of cleaved facets, which allows PIC designers to reduce the back reflections into the PIC and simultaneously control the angle of the output light. Second, addition of grating couplers to the PDK of the generic-foundry platform, which allows PIC designers to design grating coupler structures for coupling light out of the PIC through the surface. Third, introduction of vertical mirrors, which can alternatively be used to couple light out of the PIC through the top- or bottom surface of the PIC. We believe that these additional input/output interfacing options will prove useful in this era, where hybridization of PICs is becoming very important for datacenter- and optical-interconnect applications.  
Conceptualization, F.M.S., M.B., M.M., T.B., and P.R.; funding acquisition, N.G.; investigation, T.B.; methodology, T.G. and T.B.; project administration, F.M.S., M.M., and P.R.; resources, M.S.; supervision, M.M., P.R., and M.S.; writing--original draft, F.M.S.; writing--review and editing, M.B.  
This research originated from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement ICT 257210 PARADIGM, and grant agreement NMP 228839 EuroPIC.  
The authors declare no conflict of interest.