esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 1 Introduction
### Scope and Contributions of the Article  
The dynamic task allocation system presented in this work differs from the approach in [43] by focusing on the dynamic reduction in cycle times, even in the case of unforeseen delays on the one hand and by integrating the approach into a block-based programming framework on the other. With the latter, we aim to address--in addition to the core subject matter of task allocation--the reported challenge of intuitive programming (cf. Table 1) and aim to enable easy transferability to different application scenarios. Thus far only very few approaches to task allocation utilize block-based or visual programming for task allocation, but hard-code the program for the specific application instead. We only identified one approach [30] that utilizes a BPMN-based drag-and-drop programming interface for defining tasks as either human or robot. However, these tasks are only assigned statically without a possibility for dynamic adaptation.  
The dynamic task allocation system proposed in this article is therefore the first that integrates intuitive block-based programming with dynamic task allocation. The block-based programming facilitates planning and enables transferability to different use cases as well as different robotic systems.  
The dynamic task allocation methodology further differs from the related work approaches that mostly require an initial static process planning prior to execution, which is then adapted during execution in the case of plan deviations by flipping the task assignment. As such deviations occur frequently in high-mix, low-volume assembly, resulting in a high variability of the time spent on each task [44], we present a system that minimizes the initial planning effort by only requiring the task classification and the assembly priority chart as inputs to create a process flow. The process control and the task allocation are then performed automatically during task execution.  
Within this article, we further intend to evaluate the effects of static and dynamic task allocation depending on planning quality, duration of delays, and the degree of process flexibility. For this, we propose an evaluation system and perform the evaluation in a comprehensive user study. By systematically investigating the dependencies on these aspects, we seek to derive a first recommendation regarding scenarios in which either static or dynamic task allocation is preferable.
## 5 Experiments
### Experimental Results
#### 5.3.1 Flame Image Generation
Significant regions are highlighted with red boxes. We also conducted ablation experiments on the improvement of the attention module in the U-Net network, with evaluation metrics including SSIM, LPIPS, and FID, to demonstrate that our improvements, indeed, enhance the generation effect. While adding more self-attention distributions may increase the computational cost of model training and slow down the training speed, it allows the network to extract more effective features, resulting in generated images with richer textures that better match the on-site images. The original U-Net's attention mechanism is distributed in 16 \(\times\) 16 layers. We improved it to 32 \(\times\) 32, 16 \(\times\) 16, and 8 \(\times\) 8 layers. The specific performance is shown in Table 2.  
Using the image generation method we proposed, we expanded the original collected dataset. The normal combustion data were reduced to 1000 images, while the over-sintered images were increased to 1000 images and the under-sintered images were also increased to 1000 images. We partitioned the dataset such that 80% was used as the training set, 10% as the test set, and 10% as the validation set.The sample distribution of the expanded dataset is shown in Table 3.  
In order to verify the effectiveness of the proposed image generation method in augmenting the dataset, experiments were conducted on the original data, DDPM-augmented data, and RE-DDPM-augmented data. The experimental results demonstrate the effectiveness of the proposed method.The experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Recognition Method** & **Accuracy** \\ \hline Original dataset & FasterNet & 87.2\% \\ DDPM dataset & FasterNet & 91.4\% \\ RE-DDPM dataset & FasterNet & 93.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recognition accuracy for different datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Resolution Layers** & **SSIM** & **LPIPS** & **FID** \\ \hline
1 & [] & 0.60825 & 0.062 & 44 \\
2 & [16] & 0.66372 & 0.056 & 39 \\
3 & [8,16] & 0.73625 & 0.059 & 36 \\
4 & [16,32] & 0.67491 & 0.051 & 42 \\
5 & [8,16,32] & 0.76335 & 0.049 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: U-Net network attention distribution experiment. Figure 15: **(a)** Input image; (b) the addition of a mask to the image; (c) image generated by traditional local redrawing; (d) image generated by our proposed local redrawing method. Significant regions are highlighted with red boxes.
## 3 Energy-Saving Principle of HPCA
### Pressure Characteristics of HPCA
#### 3.2.3 Working Principle of Accumulator Used on HPCA  
In traditional energy-regeneration hydraulic systems [30; 31], the gravitational potential energy or kinetic energy is converted to pressure energy by the hydraulic pump/motor, and it is stored in the accumulator. However, a quadruped robot keeps moving forward through the rhythmic swing of the limbs according to the planned trajectory, and the motion and load characteristics cause the robot to have no potential gravitational potential or kinetic energy that can be utilized. Therefore, as can be seen from Figure 6, unlike the traditional use of the accumulator to save energy, the working principle of the accumulator used on the HPCA combines the pressure characteristics of the HPCA with the trajectory of the piston under the planned gait, which can be detailed and compared as follows.  
1. In the swing phase, all actuators conform to the law that varies first retraction and then extension, and the pressure in the piston chamber tends to be less. For the actuator with original structure, the flows required for the further extension of the piston and compensation for the pressure drop are all provided by the servo valve. But, for the HPCA, part of the required flows mentioned above can be supplied by the accumulator. Thus, the supply flow from the power source needed by the system with HPCA is less than that with the original actuator in this process.
2. In the supporting phase, a rather large difference in the piston trajectory exists between the actuators that drive different joints. For the same actuator, the different initial point will also cause differences. If the piston is in the process of retraction (Figure 6A), the change in direction from elongation to shortening causes the pressure in the piston chamber to be greater. For the actuator with original structure, the raised backward pressure can result in increased pressure loss in the throttle of the servo valve, so the inessential energy-loss will also increase. For the HPCA, the pressure increase due to the piston reversal is slowed down by the accumulator (as explained in Equations (2) and (6)) on the one hand, on the other hand, the oil that flows out from the piston chamber can be partially stored in the accumulator and is supplied to the next gait cycle. When the piston is in the process of extension (Figure 6B), the flow required by this process is only provided by the servo valve. The abrupt point of the chamber pressure occurs when the trajectory transitions from the support phase to the swing phase of the next cycle, and then the law described above is carried out.  
From the above analysis, it can be seen that the oil storage and offloading process of the accumulator due to the piston reversal always occur in each gait cycle, no matter which rotating joint the HPCA drives nor where the initial point is located. Therefore, the HPCA has lower energy consumption than the original actuator.
## 1 Introduction  
Microfluidic systems for lab-on-chip applications, implantable or wearable medical devices often necessitate active flow path control that is achieved by using microvalves [1, 2]. While passive microvalves show diode-like fluid path opening at forward pressure and closing at backwards pressure [3] or serve as constant flow regulators [4], active microvalves based on piezoelectric [5, 6], shape memory alloy [7, 8], phase change [9, 10], or other actuation mechanisms [3] allow for opening and closing of fluid paths in an arbitrary, hence flexible and dedicated manner. External control of active microvalves enables functionalities like controlled dosing of drugs [11, 12], mixture of reagents [10, 13], confinement of a high pressure fluid to other volumes [14, 15], as well as handling of small fluid volumes in medical devices and implants [16, 17, 18, 19, 20]. Such active flow path control can be achieved by piezoelectrically actuated microvalves, which are either of normally open (NO) or normally closed type, defined by their function in a non-actuated state [3], and typically consist of a piezoactuator, a valve diaphragm, and a valve seat [2]. Key figures of a high-performance microvalve are low leakage in closed state, a high flow rate in an open state, low power consumption, high particle contamination tolerance, and high stability of performance over lifetime. Additionally, medical applications like wearable drug dosing devices or implants require hermetic sealing of the device, low risk of component failure, as well as biocompatibility of all wetted surfaces [21].  
Most recently, we reported on the development of an NO microvalve for microfluidic implants [14], showing low leakage rates ((10.9 \(\pm\) 28.1) \(\mu\)L/min) as well as high flow rates in open state ((27.5 \(\pm\) 5.3) mL/min) at a water pressure of 20 kPa. Insufficiently low manufacturing precision of the integrated O-ring sealing led to large sample-to-sample variations and induced various failure mechanisms of the microfluidic device. Constant closing due to flow-induced lift of the O-ring resulted in obstruction of the flow path and, hence, in partial or full blockage of the valve.  
In this work, we suggest and investigate different alternative sealing concepts in order to overcome such problems. To this end, we design a novel, metal-based microvalve for liquid applications using biocompatible materials to meet requirements for medical applications. We omit movable sealing components, therefore reducing the risk of device failure, and investigate features like sophisticated geometric design, coating of the valve seat, or a high force actuator to further reduce leakage rates. A comprehensive analysis of parameters governing the performance provides insight into the microvalve's characteristics and allows for optimization and future design adaptation.
## 6 Conclusions and Perspectives  
In the previous sections, we have provided an overview of the current achievements in the field of continuous-variable quantum key distribution, focusing in particular on the status of the security proofs for the various CVQKD protocols and the performance and limitations of practical fiber optic implementations using coherent states. These developments have undoubtedly established CVQKD as a major technology for performing secure quantum communications.  
Some challenges for improving the performance of current systems, with respect in particular to the communication rate, the range of the implementations and the perspective of achieving composable security against arbitrary attacks in practice, have been discussed previously. Another major challenge for the widespread use of this technology for high-security applications involves the reduction of the size and cost of the corresponding implementations by means of photonic integration. Continuous-variable QKD is particularly well suited for integration using, for instance, silicon photonic chips, because of the standard components that it requires. Indeed, the first steps in this direction are currently being pursued [96].  
Furthermore, an important practical issue concerns the ability of QKD systems to be integrated into classical network infrastructures by means of wavelength division multiplexing techniques; here, again, CVQKD is a good candidate to achieve this goal, as has been shown recently [97,98].  
An impairment towards further development of CVQKD systems is linked to the local oscillator that needs to be sent over the quantum channel together with the signal in current standard implementations (see Figure 1). Its presence leads to security breaches, as discussed in Section 5, but also is at the source of several practical problems in long-distance implementations, where, for instance, it prevents reaching a very low signal-to-noise ratio [64]. This will be even more the case in future on-chip CVQKD experiments or systems adapted for free-space or satellite communications [99,100]. Recent preliminary theoretical and experimental studies of a scheme that does not require the transfer of the local oscillator are promising [79,80], and further advances in this direction are likely to lead to important simplifications of practical CVQKD implementations.  
These research directions, together with the possibility of using encoding on continuous variables for quantum cryptographic protocols beyond key distribution, such as bit commitment [101, 102, 103], secret sharing [104] or position-based cryptography [105], will bring this technology a step closer to a wide range of applications within future quantum information networks.
## 3 Conceptual Framework and Methods
### Preliminary Study and Prototype Development  
To establish a foundation for the live-prototyping study, developing a modifiable prototype design was necessary while gaining a clear understanding of user needs for live modifications. Thus, a preliminary study was conducted, focusing on the initial design of an AI-powered SLA tool. At this stage, the prototype primarily served as an interface linked to generative AI features, including a conversational agent powered by GPT-4 and a scenario-based image generator utilizing Stable Diffusion. However, live modification capabilities have not yet been implemented. The prototype was developed using Pygame for the user interface, with a supporting background pipeline that leveraged generative AI to create a personalized language learning experience. Users interacted with the system through two input modalities: text and voice.  
Five non-native German speakers, aged 22 to 27, were recruited to participate to assess the usability and effectiveness of the prototype in its early form. These individuals came from diverse academic and cultural backgrounds and had been learning German for the past six months, with proficiency levels ranging from basic to intermediate (A1 to B1). Over three weeks, three iterative feedback sessions were conducted, each lasting approximately one hour. The primary objective of these sessions was to gain deeper insights into language learners' needs and refine the participant-facing interface accordingly (see conceptual framework model, Figure 1).  
Each session combined semi-structured interviews with observational methods, ensuring a comprehensive collection of user feedback on desired features and design requirements. The findings from this preliminary study informed the development of a more contextually grounded and realistic environment for the subsequent live-prototyping study. At this stage, the focus remained solely on refining the participant-facing interface for language learners. In contrast, the control interface for designers--an essential component of the conceptual framework--was developed and integrated later for the live-prototyping phase with HCI practitioners.  
During prototype use, learners initiated a conversation by selecting a provided scenario and were presented with a corresponding image generated using a latent diffusion model. The conversation was displayed in a text field, with responses also delivered through audio output. The participant interface of the prototype is illustrated in Figure 2. Although the user needs identified during this preliminary study were not directly integrated into the prototype at this stage, they were translated into end-user requests. These requests were later voiced by a researcher simulating an SLA learner in the main study involving HCI practitioners, ensuring that the live modification process could be evaluated in a controlled yet dynamic manner.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 2 Materials and Methods
### Scientometric Analysis  
CiteSpace is a powerful scientometric analysis software that can analyze document co-citations, keyword co-occurrences, and cooperative maps [63]. In this study, we conducted a co-citation analysis, keyword co-occurrence analysis, and collaborative mapping analysis using CiteSpace [63] and used these to explore the knowledge base, subject structure, and research focus of articles related to research based on EO satellite data and to analyze the cooperative relationships between authors. The results of this analysis provided a basis for evaluating the current impacts of the application of EO satellite data as well as future trends. The results will also promote the exploration of the potential of EO data.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 5 Results and Discussion
### Image Processing and Quantitative Evaluation  
The results for the fourth row in Figure 5 show that some subsurface defects cannot generate visible contrasts. This suggests the need for image processing to improve the resolution. Different algorithms, including MF, and a combination of PPT as a transform-based technique were employed to increase the SNR and the visibility of the defects. We will use the term \"Step Phase and Amplitude Thermography\" (SPAT) for the analysis of thermograms after heating and successfully used for passive thermal data processing.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 3 Experimental Evaluation
### Experimental Setup
#### 3.1.2 The Server  
We show three _Aggregate_ procedures: arithmetic mean, as used by the original _FedAvg_, and two additional procedures that replace the arithmetic mean with robust mean estimators. The first of the latter uses the coordinatewise median [11; 12]. That is, each server model coordinate is taken as the median of the clients' corresponding coordinates. The second robust aggregation method uses the coordinatewise trimmed mean [12], which, for a given hyperparameter \(\beta\), first removes the \(\beta\)-proportion lowest and \(\beta\)-proportion highest values in each coordinate and only then calculates the arithmetic mean of the remaining values.  
When preprocessing the client-declared sample size, we compare three options: we either ignore client sample size, truncate according to \(\alpha=10\%\) and \(\alpha^{*}=50\%\), or just pass through the client sample size as reported. Note that the \(\alpha^{*}\) value is derived from the fact that the robust median and trimmed mean defenses require at least 50% honest clients [12].
# Application of Dynamic Time Warping (DTW) in Comparing MRT Signals of Steel Ropes
## 4 Discussion  
This study demonstrates the applicability of Dynamic Time Warping (DTW) for improving the interpretation of magnetic rope testing (MRT) signals, particularly in steel wire ropes subjected to non-uniform loading, variable resolution, and elongation-induced inconsistencies. The proposed approach enhances signal synchronization across repeated measurements and allows for more accurate detection of rope defects, including subtle changes often missed by threshold-based analysis.  
The principal contribution lies in applying DTW to low-frequency (LF) magnetic signals recorded in varying operational modes (TIMER/ENCODER), showing that alignment of degraded or offset signals can still yield diagnostically relevant differences. This is especially valuable for polymer-impregnated ropes, in which internal damage develops before any visible anomalies.  
Nevertheless, several limitations remain. The experimental validation was performed on a limited set of rope types and operating scenarios. The method has not yet been deployed in real-time systems or under conditions of high external noise. In addition,  
Figure 22: Analysis of the DTW algorithm for steel rope impregnated with polymer STRATOPLAST MF 40.0 8 \(\times\) 26 WS+EPIWRC 1960 B/N(Zn/Al) z/Z (s/S) [6] **(a)** spectra of 1/24 octave rows of LF1 and LF2 signals after the first month of rope operation; **(b)** spectra of 1/24 octave rows of LF1 and LF2 signals after seven months of rope operation; **(c)** the LF1 signal components induced by rope vibrations separated by bandstop filtration; **(d)** DTW differential signal after positive synchronization of LF1 signal fragments recorded with a resolution of 2.5 mm (400 encoder pulses/rope meter).  
integration with other diagnostic modalities (e.g., LMA, vibration, tension) was beyond the scope of this work, though such fusion would likely improve robustness.  
These constraints highlight the need for continued development and broader validation before the technique can be adopted in industrial diagnostics.