esa-sceva/satcom-chunk-collection huggingface dataset:
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 6 Experiment and Evaluation
### Evaluation Metrics  
We measured the results of two stages. In the barcode location stage, we propose a novel method to predict the rotation angle. We prepared several images where the 1D barcode rotated with different angles and labeled their rotation angles manually as ground-truth. It was to make sure that the images could be decoded correctly when rotated in a labeled angle. Then, we tested those images in the barcode location stage by measuring the deviation of the predicted angle compared to the ground-truth. The angle deviation is defined as  
\[P(A)=\frac{\left|A-A_{g}\right|}{\pi}, \tag{2}\]  
where \(A\) is the predicted angle, \(A_{g}\) is the ground-truth, and \(P(A)\) is the deviation value. In this work, we measured it with 86 images at different angles, and the results can be seen in Figure 5. For visual effect, we propose the average angle deviation every 20 degrees.  
Figure 4: Data processing for the region estimation network: 1D barcode color image and the mask image rotated at the same angle \(\theta\). We used the rotated 1D barcode color image as training data. The rotated mask image generates a minimum bounding box where the position and range information (four values in the blue box) are the ground-truth.  
In the region extraction stage, since it gives the final result, we measured it using the same metric for result comparison as in [5]. The main measurement aims at evaluating the Jaccard index between the ground-truth and the detection. Both the detection result \(R\) and the ground-truth \(G\) are given as binary masks over the whole image [2]. The Jaccard index is defined as  
\[J(R,G)=\frac{|R\cap G|}{|R\cup G|}. \tag{3}\]  
\(J_{arg}\) is the average Jaccard accuracy over the dataset. The overall detection rate \(D_{0.5}\) corresponds to the proportion of the files in the dataset achieving at least 0.5 Jaccard accuracy (also called \(OA^{bb}\) in [5]):  
\[D_{0.5}=\frac{\#(i\in S\mid J(R_{i},G_{i})\geq 0.5)}{|S|}, \tag{4}\]  
where \(S\) is the set of files in the dataset. For completeness, we provide the detection for varying accuracy thresholds in steps of 0.1. The results for the two datasets compared with other methods can be seen in Figure 6.  
Figure 5: Average predicted angle deviation for different angles. The deviation value \(P(A)\) at extremely low values indicates that our approach to the predicted rotation angle is a valid method.  
Figure 6: (**Left**): Test results on the ArteLab Rotated Barcode Dataset. (**Right**): Test results on the WWU Muenster Barcode Database.
## 2 Theoretical Model of Loss Calculation and Thermal Analysis  
### Motor Structure and Key Parameters  
The high-speed magnetic field-modulated motor analyzed in this study features a novel topology specifically optimized for flywheel energy storage applications. Figure 1 illustrates the motor's cross-sectional structure, highlighting the key components: a 12-slot stator with concentrated windings, a permanent magnet rotor with four pole pairs using N42 SH magnets, and five ferromagnetic modulators positioned between the stator and rotor. This configuration enables effective torque transmission while maintaining mechanical robustness at high rotational speeds. The key parameters of the high-speed magnetic field-modulated motor are listed in Table 1.  
The selected geometry and materials represent a carefully optimized balance between electromagnetic performance, thermal management capabilities, and mechanical integrity at high speeds. The modulator configuration creates a magnetic gearing effect that enables effective energy conversion across a wide speed range while maintaining high efficiency--a critical requirement for flywheel energy storage applications.
## 3 Experimental
### Equipment  
The magnetic and morphological characteristics of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) were carried out by using a Lake Shore 7404 vibrating sample magnetometer (VSM) (Westerville, OH, USA) and Zeiss Supra 55 Field emission scanning electron micrographs (SEM) (Zeiss, Oberkochen, Germany). X-ray photoelectron spectroscopy (XPS) data were obtained by Thermo Escalab 250 XI (Thermo Escalab, Waltham, MA, USA). The specific surface area and pore size of the adsorbent were measured and calculated by Brunner-Emmet-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods (Micromeritics, Norcross, GA, USA). Furthermore, C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was also analyzed by Fourier transform infrared spectroscopy (FTIR).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Sample** & **Sorbent** &
\begin{tabular}{l} **Detection** \\ **Method** \\ \end{tabular} & **Recovery (\%)** & **RSD (\%)** & **LOD (ug/L)** & **Ref.** \\ \hline The aqueous food & Silica C18 cartridge & LC-DAD & 78–104\% & 2.0–7.7 & 34,000–71,000 & [41] \\ Food packages & Silica C18 cartridge & HPLC-UV & 67.48–108.5\% & 2.7–9.8 & 900–1720 & [42] \\ Plastic packaging & Fe\({}_{3}\)O\({}_{4}\)CTAB & HPLC-UV & 84.5–98.9\% & 0.4–2.4 & 90–170 & [43] \\ Soft drink samples & Al\({}_{2}\)O\({}_{5}\) & HPLC-DAD & 90.4–109.2 & 0.7–5.1 & 9.5–9.9 & [44] \\ Drink samples & Fe\({}_{3}\)O\({}_{4}\)@rGO & HPLC & 88.9–95.9 & \textless{}2.7 & 10.0–15.9 & [45] \\ Liquor samples & Decanoic acid & HPLC & 88.9–105.4 & 2.9–3.8 & 0.9–2.4 & [46] \\ River water & PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) & HPLC-DAD & 43.6–67.3 & 2.5–9.8 & 15.1–26.7 & In this work \\ River water & C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) & HPLC-DAD & 86.5–106.3 & 0.4–8.8 & 0.6–3.1 & In this work \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed method with other previously reported methods for the determination of food additives in different samples.
# Gesture-Based User Interface for Vehicle On-Board System: A Questionnaire and Research Approach
## 2 Related Works
On premium vehicles, solutions based on the recognition of gestures can be found [27]. In the case of BMW, the Gesture Control system allows to accept or reject a phone call, increase or decrease the volume, and define a favorite function with an assignment to the victory gesture. In recent years, wireless approaches of human interaction with devices are also considered, both in the context of gesture control [28] and human activities recognition [29; 30]. The idea of wireless gesture control is to utilize the RFID signal phase changes to recognize different gestures. Gestures are performed in front of tags deployed in an environment. And then, the multipath of each tag's signal propagation is changed along with hand movements, which can be captured by the RFID phase information. The first examination brings promising results [28]. It seems that smart watches and wristbands, equipped with many sensors, will become another way of gesture control [31]. Solutions of this kind are supported by the deep-learning methods [32; 33]. However, it should be noted that it is customary to wear smart watches and smart wristbands on the left hand, which makes the use of such equipment for drivers reasonable for vehicles with the steering wheel placed on the right side. This is an interesting research area, but the authors of this article are not currently concerned with it. Gesture recognition technology is widely expected to be the next generation in-car user interface. A market study conducted in 2013 by IHS Automotive examined gesture-recognition technology and proximity sensing. According to IHS, the global market for automotive proximity and gesture recognition systems that allow motorists to control their infotainment systems with a simple wave of their hand will grow to more than 38 million units in 2023, up from about 700,000 in 2013. Automakers including Audi, BMW, Cadillac, Ford, GM, Hyundai, Kia, Lexus, Mercedes-Benz, Nissan, Toyota and Volkswagen are all in the process of implementing some form of gesture technology into their automobiles [34].
## 3 Results
### ATP Levels in C2C12 Myotubes under Low, Medium or High Glucose Conditions  
To evaluate the metabolic impact of the glucose concentration in the culture media on energy metabolism, ATP levels were determined in C2C12 myotubes cultured in Low (1.0 g/L), Medium (2.0 g/L), and High (4.5 g/L) glucose media (Figure 2). The results showed that ATP levels were significantly increased in the Low and Medium groups compared to the High group. This result clearly indicated that increased expression of proteins involving glycolysis, oxidative phosphorylation, and fatty acid oxidation positively contributes to cellular ATP metabolism.  
mRNA Expression of Myosin Heavy Chain (Myth) in C2C12 Myotubes under Low, Medium or High Glucose Conditions  
Next, we examined the effects of glucose concentration in culture media on myotubes formation. First, we analyzed the morphology of C2C12 cells from 3 to 5 days post-differentiation using a phase-contrast microscope (Figure 3a). There was no significant change in myotubes formation in either group. To analyze the effects of glucose concentration on differentiation induction, transcriptomic changes of _Myod1_, _Myog_ and _Myf5_, which are regulators of myogenic differentiation, were determined in myotubes at 3 days post-differentiation, and no significant changes were observed (Figure 3b).  
Myofibers consist of slow and fast muscle fibers, which are classified by fiber type specific Myh proteins, and muscle fiber types are also characterized by different metabolic properties: _Myh7_/MyHCII, slow-oxidative; _Myh2_/MyHCIIA, fast-oxidative; _Myh1_/MyHCIIX, fast-glycolytic; _Myh4_/MyHCIIB fast-glycolytic [1]. Our proteome analysis revealed that _Myh1_/MyHCIIX and _Myh8_/MyHC perinatal were identified as differentially expressed proteins, which are increased in Low and Medium conditions compared with High condition (Figure 1c). To analyze the myosin composition under induced metabolic changes, the expression levels of each myosin isoform at 5 days post-differentiation were confirmed by qRT-PCR (Figure 3c). _Myh2_ and _Myh4_ were significantly upregulated in Low and Medium conditions compared with High condition. _Myh1_ isoform in the Low and Medium groups also showed an increased trend compared to the High group (\(p\) = 0.09). These results indicate that growth of C2C12 myocytes at Low and Medium glucose concentrations in culture media result in tendency to increase in myosin isoforms at transcriptional level.  
Figure 2: ATP levels in C2C12 myotubes under Low, Medium, or High glucose conditions. ATP levels were determined in C2C12 myotubes under Low, Medium, or High glucose conditions (\(n\) = 3, each group). **_p_ < 0.01.
Based on the requirements set by applications and the constraints posed by the wireless channel, the communication protocols and techniques are selected. The main features in WSNs design are described in Section 4. Specifically, the design of energy efficient communication protocols is a very peculiar issue of WSNs, without significant precedent in wireless network history. Generally, when a node is in transmit mode, the transceiver drains much more current from the battery than the microprocessor in active state or the sensors and the memory chip. The ratio between the energy needed for transmitting and for processing a bit of information is usually assumed to be much larger than one (more than one hundred or one thousand in most commercial platforms). For this reason, the communication protocols need to be designed according to paradigms of energy efficiency, while this constraint is less restrictive for processing tasks. Then, the design of energy efficient communication protocols is a very peculiar issue of WSNs, without significant precedent in wireless network history. Most of the literature on WSNs deals with the design of energy efficient protocols, neglecting the role of the energy consumed when processing data inside the node, and conclude that the transceiver is the part responsible for the consumption of most energy. On the other hand, data processing in WSNs may require consuming tasks to be performed at the microprocessor, much longer than the actual length of time a transceiver spends in transmit mode. This can cause a significant energy consumption by the microprocessor, even comparable to the energy consumed during transmission, or reception, by the transceiver. Thus, the general rule that the design of communication protocol design is much more important than that of the processing task scheduling is not always true. Some examples of network design are given in Sections 4.1 and 4.2, where a case study developed for environmental monitoring is reported. The process of standardization in the field of WSNs is very active in the last years and an important outcome is represented by IEEE 802.15.4 which is a short-range communication system intended to provide applications with relaxed throughput and latency requirements in Wireless Personal Area Networks (WPAN) [12]. The main features of the 802.15.4 standard are resumed in Section 5, where examples of performance indexes are illustrated in terms of area throughput and energy efficiency. Others technologies such as UltraWideBand (UWB), Bluetooth and other custom-defined technologies are reported in Section 6. We finally conclude the paper by giving our vision on future research directions in Section 7.
## 2 Methodology
### Departmental and Interdepartmental Inference Modules: Architecture, Operation, and Justification
#### 2.3.1. Departmental Inference Module  
Each departmental module begins by processing pretreated sensor data using Long Short-Term Memory (LSTM) networks. The data, organized into fixed-length windows (for example, 50-time steps), are input to the LSTM network, which forecasts future sensor readings and process characteristics.  
LSTM networks were chosen for their proven ability to capture long-term dependencies and temporal patterns in sequential data, which is widely proven in the industry [19; 20]. This property is critical in our context, since sensor values remain nearly constant for each billet or roll; however, there exist fluctuations between products, and if this fluctuation follows the same tendence for a while, the deviation will be evident, and we have to take in  
Figure 8: Flow chart for the expert knowledge extraction module operation.  
account that even small deviations sustained over time may indicate an impending quality issue. The attention mechanisms help the network focus on the most relevant segments of the time window [21], ensuring that subtle yet critical deviations are not overlooked.  
The predictions from the LSTM component along with Boolean flags generated by the event filtering module in these predictions serve as input to a Multilayer Perceptron (MLP); this structure is illustrated in Figure 9. The MLP is trained using, as targets, historical expert decisions and the ontological rules formulated by those experts through the outputs obtained in the expert knowledge extraction module. This network processes the multi-dimensional input through several dense layers with nonlinear activation functions (e.g., ReLU) and ultimately outputs a categorical quality label (such as OK, BLOCK, ALARM, SCRAP, etc.).  
The choice of an MLP for decision emulation stems from its capacity to model complex, non-linear relationships between sensor forecasts and expert-derived quality labels. By integrating the outputs from the LSTM network and the filtered anomaly events, the MLP captures the interactions between multiple sensor variables and decision criteria. This layered approach not only mirrors the human expert's process but also provides the anticipatory power required to trigger early corrective actions.  
To achieve the desired results, we chose the following architecture for this departmental inference module. We start the module with an LSTM layer with 64 units, followed by a 10% dropout layer. After that, we define a custom attention layer, which consists of a dense type layer with the \"softmax\" activation function to calculate the attention weights, which are then used to create a context vector (defined as a weighted representation of the original input) at the output of the layer itself. This custom attention layer is followed by a dense type layer with 32 units, using \"relu\" as the activation function. Finally, we end with an output dense type layer with linear activation.
# A General Solution for the Errors in Variables (EIV) Model with Equality and Inequality Constraints
## 4 Experiment Analysis  
To verify the effectiveness of this method, three examples are gathered to validate the proposed general solution. First, an example with equality constraints is conducted to verify. Then, an example with inequality constraints is adopted for solving. Finally, an example with both equality and inequality constraints is applied for further analysis.
# Implicit Subspace Iteration to Improve the Stability Analysis in Grinding Processes
## 4 Discussion  
The subspace iteration method has been applied successfully to the generation of stability maps for cylindrical and centerless infeed grinding processes. The method has also been validated to calculate the instability reduction ratio maps with the application of continuous workpiece speed variation in cylindrical and centerless infeed grinding processes.  
Figure 9: Comparison of time for calculation of stability maps between time-domain and subspace iteration methods for centerless grinding with the application of continuous workpiece speed variation.  
Accuracy of this method has been validated by comparing it with previously published methods such as semi-discretization, frequency and time-domain simulations, obtaining almost the same results and validating the subspace iteration method in grinding processes.  
Reduction of calculation time related to the semi-discretization method is achieved in analyzed cases because the transition matrix is not explicitly calculated and, instead of calculating all the eigenvectors of the transition matrix, an iterative process calculating only the dominant eigenvalues is used. In the comparison with the semi-discretization method for cylindrical infeed grinding, subspace iteration is up to 138 times faster for stability maps and 47 times faster for instability reduction ratio maps. In the comparison with time-domain for centerless infeed grinding, subspace iteration is 27 times faster for instability reduction ratio maps.  
The influence of the number of segments related to the maximum natural frequency of the grinding system has been analyzed, concluding that subspace iteration has a better performance for a higher number of segments than semi-discretization.  
Conceptualization, J.A. and M.Z.; Data curation, J.A. and B.I.; Formal analysis, M.Z. and J.I.M.; Investigation, J.A. and M.Z.; Methodology, D.B. and J.I.M.; Project administration, D.B.; Resources, J.A., D.B. and J.I.M.; Software, J.A.; Supervision, M.Z.; Validation, J.A., D.B. and B.I.; Visualization, J.A. and B.I.; Writing--original draft, J.A. and D.B.; Writing--review & editing, J.A. and M.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The authors declare no conflict of interest. References
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Advantages of Multi-Connectivity
#### 3.4.2 Improved Reliability  
Wireless medium is often termed to be a lossy medium, and re-transmissions usually make up for the reliability of wireless communications. This is time consuming and utilizes the rare radio resources, which not only affects the latency requirements but also negatively impact the data transmission on the radio links. Using MC, the re-transmissions can be reduced as packets can be sent over two channels simultaneously meeting the low-latency requirements. Spatial diversity also adds up to the reliability by reducing packet loss and error correction.
## 3 Methodology
### Joint Optimization Framework
#### 3.3.2 The Stroke-Aware Classification Loss Computation Strategy  
In the text recognition task, the model outputs a category distribution for each pixel. However, in full-page images, text regions typically occupy only a small proportion of the total pixels, while the majority of pixels belong to the background. Directly computing the cross-entropy loss over all pixels introduces a severe class imbalance, as the background dominates the loss. This imbalance leads to misleading gradients during training and weakens the model's ability to discriminate fine-grained features within text regions. To mitigate this issue, we propose a classification loss computation strategy based on a stroke region mask. During training, the loss is computed only within character stroke regions, thereby reducing the influence of background pixels and improving the model's capability to distinguish character categories.  
Specifically, let the model's pixel-wise classification prediction be denoted as \(P_{\text{cls}}\in\mathbb{R}^{B\times C\times H\times W}\). The corresponding ground-truth label map is denoted as \(G_{\text{cls}}\in\mathbb{Z}^{B\times H\times W}\), and the stroke region mask is denoted as \(M_{\text{stroke}}\in\{0,1\}^{B\times H\times W}\), where a value of 1 indicates that a pixel belongs to a character stroke. We define a binary mask \(M^{\prime}(x,y)\) as follows:  
\[M^{\prime}(x,y)=\begin{cases}1,&\text{if }M_{\text{stroke}}(x,y)=1\\ 0,&\text{otherwise}\end{cases} \tag{5}\]  
Using this mask, we select only the pixels within stroke regions and compute the classification loss as follows:  
\[\mathcal{L}_{\text{cls}}=\frac{1}{|M^{\prime}|}\sum_{(x,y)\in M^{\prime}} \text{CrossEntropy}(P_{\text{cls}}(x,y),G_{\text{cls}}(x,y)) \tag{6}\]  
where \(|M^{\prime}|\) denotes the total number of foreground pixels with \(M^{\prime}(x,y)=1\). This formulation ensures that the loss computation is constrained to semantically relevant regions, thereby reducing the risk of overfitting to background noise and enabling the model to better capture the visual patterns associated with individual character classes. By applying this targeted supervision mechanism, the model is encouraged to focus its learning capacity on the spatial locations that carry meaningful semantic information.
## 4 Related Work
### Multipath Transmission
The scheme utilizes multiple paths for end-to-end redundant transmission and improves the algorithm to adapt to limited satellite bandwidth resources. These studies demonstrate that multipath transmission has significant potential to improve network performance and reliability, especially in complex heterogeneous network environments. In conclusion, multi-objective optimization and multipath transmission techniques play a pivotal role in enhancing the security and reliability of network transmission. The multi-objective optimization strategy effectively addresses the actual needs in complex network environments by balancing multiple performance objectives. Furthermore, multipath transmission technology achieves higher data transmission efficiency and network reliability by utilizing multiple available paths. Consequently, we propose a multi-objective optimization multipath transmission algorithm for heterogeneous networks. The objective is to enhance transmission performance and security in heterogeneous network environments, better aligning with the needs of practical applications.