esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Research of Intelligent Assembly and Welding Equipment for Three-Dimensional Circuit
## 1 Introduction
### Overall Equipment Function Planning  
In order to overcome the difficulties of the processing three-dimensional circuit processing on the surface of a conformal antenna, such as high personnel dependence and poor batch consistency, the equipment functions are divided into three functional modules: solder paste spraying, resistor mounting, and laser welding. To achieve high-precision processing at the end, a multi-object recognition algorithm that integrates gray-scale features and image pyramid technology is introduced to achieve high-precision visual guidance and position compensation for all functional modules. Based on fully automatic high-precision spraying technology and using process database parameters, a solder paste spraying module is designed that can adaptively spray solder paste for processing points. To achieve high-quality welding of flexible circuits, a resistor mounting module is designed based on force/position coupled smooth motion control technology, which achieves high-quality mounting of resistor chips through smooth control. Based on the relational evaluation model of laser welding process parameters such as laser welding power, spot size, and heating curve, a laser welding module is designed to achieve high-quality welding of small-sized processing points. Considering the geometric deviation between actual processed workpieces and theoretical processed workpieces, a visual detection module is introduced. The tasks of the visual detection module include the following: (1) Before the start of the  
Figure 4: Variable curvature shape diagram.  
Figure 3: Partial schematic of flexible three-dimensional integrated circuits.  
machining task, the three-dimensional reconstruction of the actual workpiece is carried out to obtain the actual machining point coordinates. (2) Take pictures to identify the processing points after welding to achieve post-welding AOI detection.  
The four main functional modules introduced above constitute a set of three-dimensional circuit intelligent assembly and welding equipment, and the overall process solution is shown in Figure 5.
## 3 Proposed Method  
We propose DRST-Net to reduce the impact of strong arc light noise, metal spatter, and smoke on weld seam recognition in automated welding. ResNet50 was chosen because it effectively extracts local features, such as edges and textures, and its residual connections address the vanishing gradient problem, allowing for deeper networks with a better gradient flow. This is crucial for accurate weld seam recognition while balancing complexity and efficiency. Swin Transformer was selected for its ability to capture long-range dependencies and global semantic information. Unlike CNNs, it models images at multiple scales and captures global context using self-attention, which is essential for understanding complex weld seam structures. Swin Transformer complements ResNet50 by handling both local and global features efficiently, improving the model's robustness in challenging industrial environments.  
It combines ResNet50 [23] and Swin Transformer [28] to achieve the high-precision extraction of weld seam features. This design combines the advantages of both models, reduces interference during welding, and improves the accuracy and speed of weld seam recognition.By combining both, our model benefits from the strengths of each architecture, resulting in improved weld seam recognition accuracy and robustness, especially in noisy and variable conditions.  
As shown in Figure 1, the model structure of DRST-Net adopts an asymmetric encoder-decoder architecture. It includes an encoder, a decoder, and a cross-attention feature fusion module. Unlike traditional single-branch encoding methods, DRST-Net uses a dual-branch structure in the encoder. It integrates ResNet50 and Swin Transformer to extract local detail features and global semantic information, capturing multi-scale features of the weld seam image. Additionally, we design a CaFF to efficiently merge the features from the two branches. Through skip connections, it fuses the features extracted via the encoder with the decoder to promote the seamless transmission and reconstruction of multi-scale features. These improvements significantly enhance DRST-Net's ability to resist interference and improve weld seam recognition accuracy. The detailed implementation will be discussed in the following sections.
## 4 Forecasting Methods
### Pros and Cons  
This section explores the advantages and disadvantages of prediction-based time series anomaly detection methods. CNNs excel as feature extractors for capturing local features in time series data; however, their kernel size and operational mechanisms limit their ability to capture global dependencies, which restricts their performance as a backbone for time series data. RNN-based models perform well in capturing long-term dependencies, but face scalability challenges when processing long sequences due to their sequential nature and memory limitations. GNN-based models provide deep insights into spatio-temporal dynamics, but their increased computational complexity poses additional challenges when handling large-scale or high-dimensional data, and they may not be suitable for non-graph or fuzzy graph structures. In contrast, Transformer-based Models typically outperform others in anomaly detection tasks, as they are better at handling long-range dependencies and achieving parallel processing. However, these models may require further adjustments to ensure that important contextual information is not overlooked.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 2 Background
### RSSI Algorithm  
In this paper, we used RSSI (Received Signal Strength Indication) [28] to measure the distance between nodes that the platform captures. As a wireless positioning algorithm based on measurement distance, RSSI calculates the distance between unknown node and beacon node according to the signal intensity decline in its spreading process, and then calculates the coordinates of unknown node with trilateration. When indicating RSSI positioning based on the intensity of the signals received, the intensity of transmitting signal of the transmitting node is known, the receiving node will calculate the transmission loss according to the intensity of the signal received, and converts it into distance using the theoretical and empirical model, and then calculates the position of node with the existing algorithm. The general computational equation is:  
\[P(d)=P\left(d_{0}\right)-10n\log\left(\frac{d}{d_{0}}\right) \tag{1}\]  
where, \(d_{0}\) represents the reference distance, \(P\left(d_{0}\right)\) is the signal intensity corresponding to \(d_{0}\), and both of them are known values. The distance \(d\) can be roughly calculated with the signal intensity \(P\left(d\right)\) detected.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 3 Design Challenges  
The design of data transmission protocols in UWSNs is different from terrestrial wireless sensor networks (TWSNs) and presents many challenges [3, 26] due to the unique characteristics such as involuntary mobility of underwater nodes and acoustic communication. These characteristics would lead to high latency, high energy consumption and high error probability [27]. Thus, data transmission protocols previously designed for TWSNs need to be redesigned for using in complicated underwater environments. For the most part, protocols in underwater environments require low-latency, energy-efficient and reliable data transmission.
## 4 Results and Discussion
### Semi-Empirical Models
#### 4.4.3 Poisson's Ratio  
Similar to the shear modulus, there are very few models in the literature to predict the bulk Poisson's ratio of cellular microstructures. By removing the purely geometric component of the response (Poisson's ratio resulting from a Syroid of zero-Poisson material), it was found that this also follows a similar curve to the longitudinal modulus. Figure 15 shows the FEA and semi-emperical predictions.  
\[\
u(D,\
u_{m})=\
u_{g}(D)+\
u_{m}\cdot\left(E_{n}(D)+C_{4}\cdot(1-D^{2})\right) \tag{14}\]  
Figure 14: FEA predictions vs. semi-empirical equation for shear modulus.  
Figure 13: FEA predictions vs. semi-empirical equation for longitudinal modulus.  
where the geometric component is represented by the polynomial:  
\[\
u_{g}(D)=C_{5}\cdot(1-D^{3})+C_{6}\cdot(1-D) \tag{15}\]  
The best value of fitting parameters \(C_{4}\), \(C_{5}\), and \(C_{6}\) were determined to be 0.28, 0.088, and 0.16, respectively. This produces an average error of 1.59% (maximum 6.54%).
# Dimensioning of Reactive Power Compensation in an Autonomous Island System
## 2 The Autonomous Power System of Rhodes  
Figure 1 shows the 150 kV transmission network of the island of Rhodes, located in the south-eastern Aegean Sea. The non-interconnected island systems in Greece are operated by the Hellenic Electricity Distribution Network Operator, under its capacity as the non-interconnected island operator (NII-O). The principles governing the generation management in the Greek non-interconnected islands are described in [17; 23].  
The peak load of Rhodes exceeds 200 MW, occurring during the summer period. The lowest load periods are presented during March and November, when the total system load drops down to 35 MW. Until recently, the power generation system consisted of a single thermal power plant, the Soroni TPP, with 11 units of 207 MW total installed capacity (see Appendix A). Five wind power plants (WPPs) are also installed on the island, with a total installed capacity of 49.15 MW, while another 18.16 MW of PV units are distributed at the MV and LV levels.  
As is typically the case in small autonomous power systems, frequency stability is the main challenge due to low inertia. In Rhodes, frequency stability relies on an under-frequency load shedding (UFLS) protection scheme. However, in recent years, another difficulty emerged as it became necessary for the generators of the Soroni TPP to absorb significant amounts of reactive power during low load periods, systematically requiring the underexcited operation of synchronous generators. The main reasons that led to such operating conditions are identified as follows:  
* The upgrade of the transmission system from 66 kV to 150 kV, resulting in a higher reactive power generation from the HV lines.
* High MAX to MIN load ratio, which leads to a practically unloaded operation of the transmission system in low load conditions occurring in Autumn and Spring.  
* Increasing number of MV cables with significantly higher capacitance compared to overhead lines. The analysis presented in this paper demonstrates that excess reactive power generation conditions in the local system are further exacerbated by two additional developments that took place recently:
* A new TPP located in the southern part of the island entered operation, with seven units of 119 MW cumulative capacity, increasing the total length of HV overhead lines.
* A new urban HV/MV substation was installed to supply the main load center of the city of Rhodes at the north, interconnected via underground HV cable lines.
## 3 Results
### Fatty Acid Nutritional Indices  
#### 3.2.1 \(\omega\)-6/\(\omega\)-3  
The fatty acid \(\omega\)-6/\(\omega\)-3 ratio for SGP fat was found to be 2.1 in the present study (Table 1) and can be considered to be within the recommended range, as the WHO has recommended the ratio of \(\omega\)-6/\(\omega\)-3 should be \(<\)10, as reported in [PERSON], et al. [28]. The \(\omega\)-6/\(\omega\)-3 ratio for _T. molitor_ was reported to be 21.5 [18], large Huhu grub was 16.2 [14], _B. craniffer_ was 6.1 [17], _R. ferrugineus_ was 5.4 [19], and _R. phoneicis_ was 3.7 [7], which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fatty Acid** & & **\% Total Fatty Acid** &
\begin{tabular}{c} **g FA/100 g Dried** \\ **Insect Material** \\ \end{tabular} \\ \hline Lauric acid & 12:0 & 0.90 \(\pm\) 0.72 & 0.54 \(\pm\) 0.43 \\ Myristic acid & 14:0 & 2.39 \(\pm\) 0.06 & 1.41 \(\pm\) 0.04 \\ Palmitic acid & 16:0 & 42.6 \(\pm\) 0.65 & 25.2 \(\pm\) 0.38 \\ Stearic acid & 18:0 & 1.30 \(\pm\) 0.04 & 0.77 \(\pm\) 0.32 \\ Arachidic acid & 20:0 & 0.18 \(\pm\) 0.01 & 0.11 \(\pm\) 0.01 \\ Saturated fatty acid & SFA & 47.3 \(\pm\) 0.23 & 28.1 \(\pm\) 0.15 \\ Cis-palmitoleic acid & 16:1 cuo7 & 12.2 \(\pm\) 0.21 & 7.22 \(\pm\) 0.13 \\ Cis-oleic acid & 18:1 cuo9 & 39.0 \(\pm\) 0.55 & 23.1 \(\pm\) 0.08 \\ Monounsaturated fatty acid & MUFA & 51.2 \(\pm\) 0.76 & 30.3 \(\pm\) 0.45 \\ Cis-linoleic acid & 18:2 cuo6 & 1.02 \(\pm\) 0.13 & 0.61 \(\pm\) 0.08 \\ \(\alpha\)-linolenic acid & 18:3 \(\omega\)3 & 0.50 \(\pm\) 0.05 & 0.28 \(\pm\) 0.03 \\ Polyunsaturated fatty acid & PUFA & 1.50 \(\pm\) 0.18 & 0.89 \(\pm\) 0.11 \\ Nutritional indices & & & \\ Polyunsaturated fatty acid & PUFA: SFA & 0.03 \(\pm\) 0.00 & - \\ Omega 6: Omega 3 ratio & \(\omega\)-6/\(\omega\)-3 & 2.17 \(\pm\) 0.43 & - \\ Index of atherogenicity & IA & 1.01 \(\pm\) 0.01 & - \\ Index of thrombogenicity & IT & 1.65 \(\pm\) 0.01 & - \\ Hypocholesterolem/ & \(\mathrm{H}/\mathrm{H}\) & 0.88 \(\pm\) 0.01 & - \\ hypercholesterol ratio & HELL-promoting index & HPI & 0.99 \(\pm\) 0.01 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean fatty acid content (% of total FA and g FA/100 g dried insect material of SGP).  
were higher than that observed in the present study. However, the \(\omega\)-6/\(\omega\)-3 reported for _R. bilineatus_ of 1.9 [8] is lower than that reported for SGP (Table 1). It has been reported that the difference in the \(\omega\)-6/\(\omega\)-3 ratio depends on the insect diet [18]. A lower \(\omega\)-6/\(\omega\)-3 ratio is considered to be more beneficial for preventing chronic diseases, such as diabetes, autoimmune, and cardiovascular disease [23].
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 2 Methods and Data Preparation
### FLECHT SEASET Reflood Tests  
The FLECHT SEASET unblocked test data are designed to observe the reflood phenomena for the unblocked flow in a \(17\times 17\) fuel rod [30]. Via these reflood tests, many effects have been examined, such as the pressure, initial clad temperature, flooding rate, rod peak power, and subcooling transient effect. Figure 1 illustrates the bundle cross-section of the FLECHT SEASET experiment. This configuration will be modeled using SPACE to calculate the cladding temperatures and quenching times.  
In this investigation, nine reflood tests were selected. According to this selection, the features of the unblocked reflood test, such as reflooding rates, powers, initial cladding temperatures, and pressures, are appropriately selected (see Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & **Flooding Rate** & \multirow{2}{*}{**Power (W/m)**} & **Initial Clad** & **Pressure** \\  & **(m/s)** & & **Temperature at 1.83 m** & **(MPa)** \\ \hline
31021 & 0.0386 & 1300 & 1153 & 0.28 \\
31302 & 0.0765 & 2300 & 1142 & 0.28 \\
31504 & 0.0240 & 2300 & 1136 & 0.28 \\
33849 & 0.0259 & 1900 & 1018 & 0.28 \\
34103 & 0.0381 & 2400 & 1158 & 0.28 \\
34316 & 0.0250 & 2400 & 1162 & 0.28 \\
34420 & 0.0389 & 2400 & 1392 & 0.27 \\
34711 & 0.0170 & 1400 & 1161 & 0.13 \\
35050 & 0.0259 & 1600 & 1031 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The selected FLECHT SEASET reflood tests.  
Figure 1: The FLECHT SEASET bundle cross-section.
## 2 Background
### Robot Position Control
#### 2.2.2 Position Control Problem  
The experiment involves moving the robot from its current position (\(C\)) to a target point (\(Tp\)) by adjusting its angular and linear velocities. Traditionally, these velocities are generated by the controller and then transformed into speeds for the left and right motors. The proposed DRL directly outputs the wheels' velocities. Figure 2 shows the variables involved in this experiment.  
The kinematic behavior of these robots appears simple, but non-holonomic constraints pose a challenge in control law design. Previous works detail this issue, [24; 29]. In typical motion, the differential robot follows a circular trajectory with radius \(R\) and center \(ICC\). The position control algorithm aims to minimize orientation error (\(e=\alpha-\theta\)), where \(\alpha\) is the angle to the target, and \(\theta\) is robot orientation. Simultaneously, the robot decreases the distance to the target point (\(d\to 0\)).  
Figure 3 illustrates the position control algorithm, with the inner square as the controller and the outer square as the robot. The Position Sensor is an IPS (Indoor Positioning System), providing the absolute position and orientation of the robot [18].  
Equation (2) gives the distance \(d\), and Equation (3) computes the angle to the target point \(\alpha\), using the coordinates of \(Tp(x_{p},y_{p})\) and \(C(x_{c},y_{c})\). Both equations are part of the Compute block [24].  
Figure 3: Block diagram of the position control problem.  
Figure 2: Position control variables for the differential robot.  
\[d=\sqrt{\left(y_{p}-y_{c}\right)^{2}+\left(x_{p}-x_{c}\right)^{2}} \tag{2}\]  
\[\alpha=\tan^{-1}\left(\frac{y_{p}-y_{c}}{x_{p}-x_{c}}\right) \tag{3}\]  
The algorithm calculates the wheel speeds required to reach the destination point based on distance and angle. This is performed using the block labeled Control Law (in light red color), which can be designed in different ways. One approach, known as _Villela_[21], generates control signals \(V\) and \(\omega\) using the following equations:  
\[V(t)=\begin{cases}V_{max}&if\quad d>k_{r}\\ d\frac{V_{max}}{k_{r}}&if\quad d\leq k_{r}\end{cases} \tag{4}\]  
\[\omega=\omega_{max}sin(\alpha-\theta) \tag{5}\]  
with \(V_{max}\) and \(\omega_{max}\) as defined before, and \(K_{r}\), a docking area radius. This docking area allows a fast approach to the target when situated further away, but then slowing down nearby. From the robot's velocities, the wheels' (left and right) velocities are obtained by the relations \(v_{L}=(2V+\omega L)/2\) and \(v_{R}=(2V-\omega L)/2\).
# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 3 Methods
### Multi-Scale Graph Attention Feature Aggregation  
After acquiring the graph information, we feed the graph structural details, comprising the features of nodes and the adjacency matrix, into our proposed multi-scale graph attention feature fusion module. Details of this module's architecture are depicted in Figure 2. Each branch employs a dedicated graph attention module to extract features, with each branch configured to operate at a different feature scale. The feature calculation formula for the MSGA module is as follows:  
\[e_{i,k,s}^{(l)}=W_{k,s}^{(l)}h_{i,k,s}^{(l)}, \tag{6}\]  
\[\alpha_{i,j,k,s}^{(l)}=softmax_{j}\bigg{(}LeakyReLU\bigg{(}a_{k}^{(l)^{T}} \Big{[}e_{i,k,s}^{(l)}\Big{]}\Big{[}e_{i,k,s}^{(l)}\Big{]}\bigg{)}\bigg{)}, \tag{7}\]  
\[f_{i,k,s}^{(l)}=MLP\bigg{(}ReLU\bigg{(}\sum_{j\in N}\alpha_{i,j,k,s}^{(l)}e_{j, k,s}^{(l)}\bigg{)}\bigg{)}+h_{i,k,s}^{(l)},\;k=1,2,\ldots,\;K \tag{8}\]  
\[F^{(l+1)}=F_{s1}^{l}+F_{s2}^{l}+F_{s3}^{l}. \tag{9}\]  
In the formula, \(h\) represents the input features, \(\alpha\) represents the multi head attention score in the graph attention module, \(f\) represents the features of a single node aggregated by the module, and \(F\) represents the overall feature map fused from multiple scales. For each attention head \(k\), the graph attention computation is initiated by applying a weighting matrix to the feature vectors of each node, effectively transforming them into a new feature space. We control the scales of features with varying dimensionality by setting \(W_{k,s}^{(l)}\), where \(k\) denotes the attention head and \(s\) is the scale of each branch. Subsequently, attention coefficients are computed for each pair of nodes within each head, following which, the features of neighboring nodes are aggregated through a weighted sum according to their respective attention coefficients. Then, the outputs from all heads are concatenated to obtain the feature representation after the nodes have undergone graph attention extraction. Subsequently, a Multi-layer Perceptron (MLP) layer is employed to map features from different scales into a common dimensionality. Finally, the features extracted from the three branches at different scales are aggregated through summation to achieve feature fusion. To mitigate the potential loss of original feature information in deeper layers, residual connections are introduced to reinforce the representation of the original features.
## 3 Live Data Collection and Result Analysis  
A dual-frequency front-end from NSL is used to capture the real GNSS data. Among the two front-ends, the maxim 2769B front-end [24] is configured to receive BeiDou B1I signal at 1561.098 MHz, and the maxim 2112 front-end is configured to receive GPS L1 C/A signal at 1575.42 MHz, as mentioned in Table 2. The BeiDou B1I signal spectrum, time-domain plot and bin distribution of the digitized IF samples were already shown in Figure 1 of Section 2.  
The GNSS data was collected on 31 th January at around 9:30 AM UTC time at a static position with a roof antenna in Finnish Geodetic Institute, Kirkkonummi, Finland. The data was collected for about 60 s. The sky-plot for BeiDou constellation at the time of data collection is shown in Figure 8. There are one GEO satellite (PRN 05), three IGSO satellites (PRNs 7, 9, 10), and three MEO (PRNs 11, 13 and 14) satellites available at the moment of data collection. The FGI-GSRx receiver can acquire, track and compute a navigation solution with all the visible BeiDou satellites. The stand-alone positioning results with BDS are presented first followed by GPS-only and multi-GNSS positioning results with BeiDou and GPS.  
The horizontal error scatter plot and the position error variations in ENU frame are shown in Figures 9 and 10, respectively for BeiDou-only position fix. The 95% Circular Error Probable (CEP) for BeiDou-only horizontal position fix is within 3.76 m. The BeiDou-only position fix is also shown in Google Earth in Figure 11.  
The position error statistics for BeiDou, GPS, and multi-GNSS solutions are finally presented in Table 3. The error statistics were computed for a stand-alone code-phase based position solution. The broadcasted Klobuchar ionospheric model parameters are utilized to calculate the ionospheric corrections. The position error statistics were computed with respect to the true known position. The 3D RMS errors for BeiDou and GPS are 8.24 m and 2.80 m, respectively. The error contribution for BeiDou  
Figure 10: Position error variations with respect to true position in ENU frame.  
Figure 9: Horizontal error scatter plot.  
is coming from the vertical component, which is partly due to the fact that four out of seven satellites are low elevated, and hence, the broadcast ionospheric corrections are less accurate with those noisy measurements. Both BeiDou and GPS have comparable horizontal accuracy, whereas GPS outperforms BeiDou in terms of 3D RMS. In case of Multi-GNSS position fix, only the best two BeiDou satellites with the highest elevation angles were considered along with the eight GPS satellites for position computation. The improvement in 3D RMS is really minor with two extra measurements, but the improvement in PDOP is really significant. This eventually means that the multi-GNSS benefits will be more noticeable in harsh GNSS environment than in normal outdoor scenarios with good satellite visibility. In addition to that, the multi-GNSS can certainly offer higher reliability with the addition of new satellites from different constellations.