esa-sceva/satcom-chunk-collection huggingface dataset:
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology
### Post-Processing  
We combine the predictions from multiple image outputs by averaging them to obtain the final prediction. A full EEG recording typically lasts around 20 min (see Table 4), and we used a 5 min segment. Therefore, each EEG file produces approximately three to five images. The final prediction for each EEG file is made by calculating the average of the prediction probabilities across all images generated from that file. Figure 2 shows the flowchart of the proposed method.  
Let \(n_{i}\) represent the number of images generated from EEG file \(i\), where \(n_{i}\in[3,5]\).  
For each image \(j\) from EEG file \(i\), let \(p_{ij}\) denote the prediction probability.  
The final prediction \(P_{i}\) for EEG file \(i\) is calculated as the average of the prediction probabilities across all images:  
\[P_{i}=\frac{1}{n_{i}}\sum_{j=1}^{n_{i}}p_{ij} \tag{9}\]  
The performance of the model is evaluated based on these final predictions.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Normal** & **Abnormal** & **Total** \\ \hline Train & 1045 & 970 & 2015 \\ Duration/file (mins) & 20.50 & 20.37 & 20.44 \\ Duration (mins) & 21.425 & 19,760 & 41,185 \\ \hline Validation & 261 & 242 & 503 \\ Duration/file (mins) & 20.06 & 20.91 & 20.47 \\ Duration (mins) & 5235 & 5060 & 10,295 \\ \hline Test & 148 & 120 & 268 \\ Duration/file (mins) & 20.17 & 20.79 & 20.45 \\ Duration (mins) & 2985 & 2495 & 5480 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number and duration of the TUAB EEGs used in the training, validation, and test set.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Hyperparameter** & **Value** & **Justification** \\ \hline Base Model & DenseNet121 (pre-trained on ImageNet) & Provides strong feature extraction capability from image-based EEG representations \\ \hline Trainable Layers & Last 10 layers unfrozen & Allows task-specific fine-tuning while preserving general visual features from ImageNet \\ \hline Top Layers & Three dense layers (128, 64, 32 units) & Adds task-specific nonlinearity and representation capacity \\ \hline Activation Function & ReLU (for hidden layers), Sigmoid & ReLU prevents vanishing gradient; Sigmoid suitable for binary classification \\ \hline Regularization & L2 = 0.005 & Helps prevent overfitting by penalizing large weights \\ \hline Loss Function & Binary Cross-Entropy & Appropriate for binary classification (normal vs. abnormal) \\ \hline Optimizer & Adam & Adaptive learning rate, widely used and effective for deep networks \\ \hline Learning Rate & 0.01 & Empirically chosen to balance convergence speed and stability \\ \hline Early Stopping & \
\begin{tabular}{l} Patience = 20 (monitoring validation \\ loss) \\ \end{tabular} & Stops training when performance no longer improves to avoid overfitting \\ \hline Model Checkpoint &
\begin{tabular}{l} Best model saved by validation \\ accuracy \\ \end{tabular} & Ensures optimal model selection during training \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters used for model training and validation.
# Phosphine-Free-Synthesized ZnSe/ZnS Core/Shell Quantum Dots for White Light-Emitting Diodes
## 2 Materials and Methods
** To fabricate the nUV LED-pumped ZnSe/ZnS C/S QD-converted blue LEDs, the ZnSe/ZnS C/S QDs were redispersed in 3 mL CHCl\({}_{3}\) (optical density \(\sim\) 1.892 at 365 nm) and the solution was mixed with silicone resin mixture (0.2074 g, OE-6630B-OE-6630A = 4:1, Dow Corning Co., Midland, MI, USA). The silicone-QD blend was degassed under a vacuum to remove the CHCl\({}_{3}\) and air bubbles. The mixture of ZnSe/ZnS C/S QDs and silicone resin was coated on 395 nm nUV LEDs (non-molded, surface-mounted-device (SMD) type, 5.0 \(\times\) 5.0 mm\({}^{2}\)). The nUV LED packages, encapsulated with ZnSe/ZnS C/S QD-siloxane resin, were cured at 60 \({}^{\circ}\)C for 60 min, followed by curing at 110 \({}^{\circ}\)C for 60 min. **Fabrication of the blue and yellow QD-converted white LEDs. ** To fabricate the ZnSe/ZnS and ZCIS/ZnS QD-converted white LED, the ZnSe/ZnS and ZCIS/ZnS QDs were redispersed in CHCl\({}_{3}\) (optical density of ZnSe/ZnS - 2 at 365 nm and ZCIS/ZnS - 0.544 at 450 nm) and the 3 mL QD CHCl\({}_{3}\) solution was mixed with silicone resin (0.4019 g, OE-6630B-OE-6630A = 4:1). The QDs and silicone mixture was degassed under a vacuum to remove the CHCl\({}_{3}\) and air bubbles. The final ZnSe/ZnS QDs and ZCIS/ZnS QD- silicone resin mixture was coated on a 395 nm-emitting nUV LED chip (non-molded 5.0 \(\times\) 5.0 mm\({}^{2}\) SMD type). The white LED packages encapsulated with ZnSe/ZnS and ZCIS/ZnS- silicone resin were cured at 60 \({}^{\circ}\)C for 60 min, then at 110 \({}^{\circ}\)C for 60 min. **Characterization. ** The PL and absorption spectra of the ZnSe-based QDs were collected with a Hitachi F-7000 spectrophotometer and a PerkinElmer LAMBDA 25 spectrophotometer, respectively. Transmission electron microscopy (TEM) was conducted with a Tecnai G2 F20 scanning transmission electron microscope operated at 200 kV to investigate the size and morphologies of the QDs. The particle sizes of the ZnSe C QDs and ZnSe/ZnS C/S QDs were determined by measuring one hundred of the QDs in the TEMimages. X-ray diffraction (XRD) was performed with a Bruker D8 ADVANCE operated at 40 kV and 40 mA (\(\lambda_{\text{CuK}\alpha}\) = 1.5406 A) to analyze the crystal structure of the QDs. The absolute PL quantum yields (QYs) of the ZnSe C QDs and ZnSe/ZnS C/S QDs were measured with a QE-1100 quantum efficiency measurement system (Otsuka Electronics Co., Ltd., Osaka, Japan) using an integrating hemisphere. Optical properties, including electroluminescence (EL) spectra, Commission Internationale de l'Eclairage (CIE) color coordinates, color rendering index (\(R_{\text{a}}\)), and the correlated color temperature (\(T_{\text{c}}\)) of the white LEDs, were evaluated using an OPI-100 total luminous flux measurement system (Withlight Co., Ltd., Jeonju, Korea).
## 4 Example
### Extracting Feature Data  
The experimental data selected includes telemetry data from a single frame Control Moment Gyroscope (CMG) subsystem of a spacecraft during its 42-month in-orbit operation. Approximately one day of data are extracted every 10 days, totaling 155 days and involving 2.4 million records and 17 telemetry parameters. First, we perform data cleaning.
## 3 State of the Art
### Graph-Based Design Languages Implemented with UML and DC43
#### 3.2.2 General Concept and Entities of Graph-Based Design Languages  
Graph-based design languages are an approach to formulating the entities of a technical system, the processes within and in the surrounding of this technical system, and the processes that lead to this technical system in a consistent manner, which provides multiple possibilities to process the formulated information. The term \"graph-based\" means that a graph is employed that consists of nodes and edges. A graph is usually described as a representation of a set of objects where some pairs of objects are connected by links (or associations). The so-called \"nodes\" of a graph are mathematical abstractions and serve as abstract placeholders for real objects (e.g., components of the example multicopter product). The connections between these interconnected objects are also represented by abstractions; these are usually called \"edges\". Figure 6 represents the structure of a design graph for the \"multicopter\" sample product (adapted from [47]).  
On the basis of prior work, [PERSON] [48] and co-workers have developed a consistent system to represent product geometry and other forms of product information using graphs, and have developed a GBDL that can be described by means of the underlying design language process (Figure 7  
Figure 6: Structure of a design graph.  
Figure 7: Graph-based design language process.  
The geometry (concrete geometrical model) can be abstracted to its objects (e.g., trigger, motor) and their abstract geometry. These objects can be represented in UML classes (with attributes and operations), which represent the vocabulary of the design language and constitute the nodes of the design graph (Figure 6). The objects are organized using an appropriate UML diagram--the class diagram. This diagram represents the structure both by expressing inheritance (e.g., from abstract geometrical classes such as cylinder or cuboid) and multiple associations (e.g., the fact that an object is a part of a module). The structure constitutes the edges in the design graph. This structured vocabulary can be used together with rules expressed in UML activity diagrams in order to create the geometrical model of the future product. It is important to note that all information concerning a certain technical system is stored within the engineering framework based on GBDLs, not just metadata.
## 4 Observational Plan and Intended Measurements in Orbit  
### Observational Strategy and Operation in Orbit  
Following the commissioning phase, the proposed observation plan in orbit with the developed payload includes the following steps:  
* payload tests, instrumentation performance in the orbit and space environment conditions
* verifying the use of the planner and testing cyclic commands such as slides
* exposure prediction depending on the flight over a specific area from 10 ms to 20 s (for 2-day data collection)
* measurement of the earth's radiation map (two days--one map)
* long-term monitoring of the solar spectrum with the need for single-photon data filtering
* In the case of the function of orientation and stabilization, the search for bright triggers--including the long-term monitoring of the sky in Gamma-rays. In connection with the VIS camera--star-tracker--position determination of the source could be feasible.
## 5 Data Analysis and Results
### Path Analysis and Hypothesis Testing  
According to the path analysis results presented in Figure 5, all proposed paths are statistically significant. Among the factors influencing IU, EE exhibits a highly significant impact (\(\beta\) = 0.525, \(p\) \(<\) 0.001), confirming hypothesis H4. Additionally, TTF (\(\beta\) = 0.417, \(p\) \(<\) 0.001), PE (\(\beta\) = 0.408, \(p\) \(<\) 0.001), SI (\(\beta\) = 0.043, \(p\) \(<\) 0.05), and FC (\(\beta\) = 0.128, \(p\) \(<\) 0.05)  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|} \hline
**Variables** & \begin{tabular}{c} **Subject** \\ **Matter** \\ \end{tabular} & \begin{tabular}{c} **Standardized** \\ **Loadings** \\ \end{tabular} & **CR** & **AVE** & **Variables** & \
\begin{tabular}{c} **Subject** \\ **Matter** \\ \end{tabular} &
\begin{tabular}{c} **Standardized** \\ **Loadings** \\ \end{tabular} & **CR** & **AVE** \\ \hline \multirow{3}{*}{PE} & PE1 & 0.874 & \multirow{3}{*}{0.842} & \multirow{3}{*}{0.772} & \multirow{3}{*}{Presence} & PR1 & 0.865 & \multirow{3}{*}{0.871} & \multirow{3}{*}{0.758} \\ \cline{2-2} \cline{6-11}  & PE2 & 0.897 & & & & PR2 & 0.874 & & \\ \cline{2-2} \cline{6-11}  & PE3 & 0.743 & & & & PR3 & 0.911 & &  
also positively influence IU, supporting hypotheses H3, H6, H7, and H9, respectively. IN (\(\beta\) = 0.275, \(p\) < 0.05), PR (\(\beta\) = 0.311, \(p\) < 0.001), and NA (\(\beta\) = 0.255, \(p\) < 0.05) significantly affect FL, validating hypotheses H10, H11, and H12. Moreover, FL strongly affects IU (\(\beta\) = 0.401, \(p\) < 0.001), supporting hypothesis H13. Furthermore, TEC (\(\beta\) = 0.260, \(p\) < 0.001) and TAS (\(\beta\) = 0.336, \(p\) < 0.001) significantly influence TTF, confirming hypotheses H1 and H2. EE also significantly affects PE (\(\beta\) = 0.325, \(p\) < 0.01), and SI has a positive effect on FC (\(\beta\) = 0.025, \(p\) < 0.01), supporting hypotheses H5 and H8. The hypothesis testing results are summarized in Table 8.
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 5 Experiments
### Experimental Results
#### 5.3.2 The Effect of Training Proportions  
This section evaluates the model performances concerning the proportion of training data. We set the training proportion to vary from 10% to 50% with a step size of 10%. Figure 3(a) illustrates the change of Hit-Precision of JARUA on social networks. The presented results are improved with the increase in proportion, following our expectations. JARUA outperforms all comparison methods when the training proportion is less than 50%, while Grad-Align and Grad-Align+ achieve the best scores when training data are 50%. So, the proposed method enjoys higher performances in semi-supervised scenarios.  
Figure 3(b) depicts the performances of JARUA on coauthor networks. With the increasing training proportion, all methods (except REGAL) present similar upward trend curves. ABNE and IONE increase faster than JARUA-s, TADW, and PALE. However, JARUA achieves the best final results, e.g., JARUA beats ABNE and IONE by 21.8% and 28.9%. We can also recognize that JARUA performed encouragingly when using a small number of pre-aligned users as training data. For example, when the training proportion is 10%, JARUA scores 42.0% and 81.8% in terms of Hit-Precision on WD and DBLPs, respectively. Therefore, JARUA is expected to be well adapted to annotate constrained scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Weibo-Douban**} & \multicolumn{3}{c}{**DBLP17-DBLP19**} \\ \cline{2-7}  & **k = 1** & **k = 3** & **k = 5** & **k = 1** & **k = 3** & **k = 5** \\ \hline IONE & 0.022 & 0.033 & 0.042 & 0.282 & 0.367 & 0.418 \\ PALE & 0.012 & 0.021 & 0.029 & 0.062 & 0.104 & 0.136 \\ ABNE & 0.089 & 0.113 & 0.128 & 0.362 & 0.451 & 0.502 \\ REGAL & 0.016 & 0.030 & 0.041 & 0.411 & 0.436 & 0.452 \\ TADW & 0.004 & 0.006 & 0.008 & 0.241 & 0.337 & 0.398 \\ CENALP & 0.076 & 0.076 & 0.076 & 0.175 & 0.175 & 0.176 \\ Grad-Align & 0.300 & 0.316 & 0.329 & 0.306 & 0.335 & 0.353 \\ Grad-Align+ & 0.299 & 0.302 & 0.305 & 0.288 & 0.304 & 0.316 \\ \hline JARUA-s & 0.156 & 0.197 & 0.220 & 0.467 & 0.563 & 0.615 \\ JARUA-a & 0.368 & 0.401 & 0.419 & 0.696 & 0.723 & 0.738 \\ JARUA-v & 0.383 & 0.418 & 0.436 & 0.740 & 0.767 & 0.782 \\ JARUA-t & 0.159 & 0.196 & 0.221 & 0.785 & 0.812 & 0.829 \\ JARUA & **0.416** & **0.448** & **0.464** & **0.821** & **0.845** & **0.858** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hit-Precision scores of all comparison methods.
# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 4 Experiments
### AdaFER for Unsupervised CD-FER  
To evaluate the effectiveness of AdaFER, we compare several baseline methods with our proposed AdaFER, using RAF-DB as the source dataset and testing on the CK+, JAFFE, ExpW, FER2013, and FERPlus datasets We implement five baseline methods in total, as follows:  
* #1: We train the ResNet-18 (also pre-trained on MS-Celeb-1M) model on source data and directly test on target data;
* #2: We first extract AUs for both the source and the target data, and then use the AUs of each image in the target data to query the source data. Finally, we assign the most-frequent category of retrieval images to the target image;
* #3: We first use the trained model on the source data to predict hard pseudo-labels of the target data, then fine-tune the model on target data;
* #4: This method is identical to method #3, except that the predicted pseudo-labels are kept as vectors (i.e., soft labels);
* #5: We use both the images and the detected AUs as inputs to train a classification on the source set, and then fine-tune the model on pseudo-soft labelled target data.  
The results are shown in Table 1. Several observations can be concluded, as follows: first, our AdaFER almost outperforms all other baseline methods by a large margin, especially when testing on the lab-controlled CK+ and JAFFE datasets. Second, using pseudo-labels (#3 and #4) to fine-tune model on target data also achieves large improvements over #1. Third, the naive AU-based method (#2) performs better than method #1 on the FERPlus and ExpW datasets, which indicates that AUs are useful among similar data. Last, but not least, the naive AU-based method degrades in the lab-controlled FER datasets, which suggests that there exists an AU domain gap between in-the-wild datasets and the lab-controlled datasets. Nevertheless, our AdaFER mitigates the domain gap and achieves large improvements on both the in-the-wild and the lab-controlled datasets.  
**Visualization of AU-guided annotating.** To further investigate AdaFER, we visualize the pseudo-labels of the target images annotated by the AGA module and baseline method #3. We use our T-soft assignment and list the top three categories. As shown in Figure 4, AGA achieves better results than baseline method #3. In the first six images, AGA assigns the highest weights on the ground-truth category. The last two bad cases are extremely ambiguous, even for humans, and our AGA seems to assign reasonable categories for them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **CK+** & **JAFFE** & **ExpW** & **FER2013** & **FERPlus** \\ \hline \#1 & 70.54 & 46.51 & 61.53 & 52.91 & 62.40 \\ \#2 & 65.11 & 27.50 & 66.52 & 46.31 & 68.35 \\ \#3 & 74.42 & 55.81 & 68.13 & 55.89 & 63.81 \\ \#4 & 73.64 & 58.14 & 69.41 & 54.81 & 70.02 \\ \#5 & 71.32 & 53.49 & **73.58** & 57.15 & 77.99 \\
**AdaFER** & **81.40** & **61.37** & 70.86 & **57.29** & **78.22** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance (%) comparison between the proposed AdaFER and baseline methods. RAF-DB is used as source dataset.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 3 Mathematical Model
### MINLP Model without Fairness
* Keep track of whether a flow divides into subflows at the source node (Equation (A14)). * A multipath-enabled flow may not have a subflow yet because none might have been computed yet during the solution process. After the computation, the flow may be assigned one or more than one subflow (Equation (A15)). * A data flow cannot be split into subflows if both end nodes only have one interface, respectively (Equation (A16)). The previously mentioned equations concerning subflows ensure that the creation of subflows is logically correct, but do not limit the number of subflows. This is ensured by a second equation set:Keep track of how many subflows a flow is split into (Equation (A17)). * The number of subflows between a sender and receiver node cannot be larger than the product of sender and receiver interfaces (Equation (A18)). * The number of subflows is equivalent to the number of paths between the sender and the receiver node (Equations (A19) to (A21)). * No subflow can be created if both the sender and the receiver only have one interface. Between a pair of sender and receiver interfaces, at a maximum, one subflow can be created (Equations (A22) to (A24)). _Congested links' constraints for flows_: The aim of transport protocols is to maximize link utilization in order to make efficient usage of the network. This means that each flow should experience at least one congested link; if all links which are occupied by a flow had capacity left, it would mean that there is space left for additional throughput. The corresponding equations express the following constraints:  
* There is at least one congested link for each flow (Equation (A25)). * A congested link is fully utilized by flows, there is no capacity left (Equation (A26)). * A link can be on a path for a flow even though it might not be fully utilized and the bottleneck link for a subflow must be on the path allocated to that subflow (Equation (A27)). _Congested link constraints for subflows_: If a flow splits into multiple subflows, each of them should be a part of at least one congested link, as it is the case for single-path flows. It can however happen that on a given link two or more subflows belong to the same flow. The following constraints describe links congested by subflows:  
* A necessary condition that a link is identified as congested by a particular subflow is that the subflow exists on that link (Equation (A28)). * Each subflow has at least one congested link (Equation (A29)). * A subflow cannot be congested on a link if the parent flow is not congested on that link (Equation (A30)). * If a flow is bottleneck on a link, then all its subflows are bottleneck on that link (Equation (A31)).
## 4 Computation and Communication Performance Modeling Approaches
### Simulation Model  
In our simulation, the execution of an SDF application for a pre-configured mapping and scheduling on a pre-defined hardware platform is simulated. The amount of consecutive iterations of the SDF application can be configured as well. To represent the timing behavior of the simulated application, the computation delay is defined by the compute model (Section 4.1). The communication delay is defined by the communication model (Section 4.2). For each communication attempt and each execution of an actor, the delays get calculated. The overall concept is visualized in Figure 5. The simulation has been implemented in SystemC [11].  
Figure 5 shows an abstract architecture of our simulation. On the top, the SystemC Module structure is shown. There exists a SystemC Thread (SC_THREAD) for each tile that shall be simulated. These tiles are communicating over the interconnect module with the shared memory module for data exchange as well as maintaining contention information required for the communication models.  
The delay models themselves (including hardware delay) are part of the SDF execution on a tile within the simulation. The model of executing code on a tile is split into two parts. The Actor Execution corresponds to the actual execution of an SDF actor on a tile. The Communication Driver represents the communication via an interconnect. In this communication driver representation, resource contention is included.  
When simulating an actor, the three phases (read, compute, write) get simulated individually. For the read and write phase, the communication delay model gets executed. Depending on the configuration of the simulation, the Cycle Accurate model [5] (Section 4.2.1), the Message Level model [6] (Section 4.2.3) or the Transaction Level model (Section 4.2.2) is used. For the computation phase, the computation model is executed. The computation phase is represented by a single delay drawn from a set of possible delays, following a specific distribution that has been defined before the simulation. The distribution of delays can be configured and will be generated before the simulation starts. In our experiments, we compare three different distributions:  
* A single average delay. (First presented in [2])
* A Gaussian distribution fitted to the observed executions times of an actor. (First presented in [5])
* A new, more sophisticated fitting approach using kernel density estimation.