esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 3 Routing Scheduling Algorithm
### Sub-Network Clustering
#### 3.1.1 Time-Varying Topology Sub-Network Set Sub-Network Clustering  
For any node to search with a physical distance \(D_{sub}\) as the radius, it is necessary to ensure that the current network topology remains unchanged for a certain period of time \(\Delta\). Therefore, it is necessary to search at the current time and two time points after time \(\Delta\). The target is considered to move at a uniform speed within time \(\Delta\), so position prediction can be performed based on simple kinematics. If some nodes have moved within the time frame, it is necessary to continue checking whether they belong to the sub-network at the new location. If the two searches do not belong to the same network, the sub-network home search continues for the location after time \(2\Delta\).  
The clustering process of time-varying topology sub-networks is as follows:  
Step 1: Sub-network tables \(G_{w}\), \(w\in Z^{+}\), with \(G_{w}\in U_{m}\) are constructed, representing a set of multiple communicable neighboring nodes. Taking node S(0) as an example (\(S(j)\in G_{w},j\in Z^{+}\)), if \(d(0,j)\leq D_{sub}\) exists, where \(j\) represents any one of the communicable nodes, then \(S(0)\in G_{0}\);  
Step 2: If the distance between nodes S(1) and S(0) is \(d(0,1)\leq D_{sub}\), then \(S(1)\in G_{0}\). If S(1) and nodes other than S(0) have \(d(1,j)\leq D_{sub}\) and \(j\
eq 0\), then \(S(1)\in G_{1}\);  
Step 3: The same operation is performed on node S(2) until all current nodes are traversed;  
Step 4: Based on the positions \(x+v_{x}\Delta\) and \(y+y_{x}\Delta\) of node S(0) after time \(\Delta\), the coordinate attributes of the new position can be calculated according to uniform motion. Steps 1 to 3 are repeated for the updated node;  
Step 5: The same operation as step 4 is performed based on the node location after time \(2\Delta\). The sub-network belonging to three time points operates in a two-out-of-three mode. Nodes belonging to different sub-networks three times operate according to the last node to which they belong.
## 3 Methodology
### Model Evaluation Metrics  
To assess the predictive capability of the model for displacement monitoring data, four evaluation metrics--Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R\({}^{2}\)), and Efficiency Index--were used to measure the prediction performance of the T-GCN model [48; 49]. Our evaluation uses standard metrics (RMSE, MAE, and R\({}^{2}\)) consistent with landslide prediction literature, enabling direct comparison with existing methods. The innovation lies in applying these metrics to assess spatio-temporal performance across interconnected monitoring points, extending their conventional single-point temporal usage.  
(1) Root Mean Squared Error (RMSE):  
\[RMSE=\sqrt{\frac{1}{M}\sum_{i=1}^{M}\Big{(}y_{i}-\hat{y}_{i}\Big{)}^{2}}, \tag{17}\]  
(2) Mean Absolute Error (MAE):  
\[MAE=\frac{1}{M}\sum_{i=1}^{M}\big{|}y_{i}-\hat{y}_{i}\big{|}, \tag{18}\]  
where \(M\) represents the number of time samples, and \(y_{i}\) and \(\hat{y}_{i}\) denote the actual and predicted values, respectively.  
(3) R-squared (R\({}^{2}\))  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{M}\Big{(}\text{d}_{i}-\hat{\text{d}}_{i} \Big{)}^{2}}{\sum_{i=1}^{M}\Big{(}\text{d}_{i}-\overline{\text{d}}_{i}\Big{)} ^{2}}, \tag{19}\]  
(4) Efficiency MetricsA standard metric for evaluating model efficiency is time complexity. In this study, the time taken for model training and prediction was measured to assess model efficiency. Specifically, RMSE, MAE, and R\({}^{2}\) were used to evaluate the prediction accuracy, with smaller values indicating better prediction performance. A shorter training and prediction time was considered more efficient.
# Improving the Lot Fabrication Stability and Performance of Silica Optical Films during PECVD
## 2 Lot Fabrication  
A 6 \(\mu\)M \(\pm\) 0.4 \(\mu\)M thick GeO\({}_{2}\)-SiO\({}_{2}\) optical film was deposited on 23 lots (with 15 pieces per lot) of 6\({}^{\prime\prime}\) fused quartz wafers, 1 mm thick and 1.4574 at 632.8 nm. The RI contrast between the core layer and the cladding layer was 0.36% (\(\Delta\) = 0.36% \(\pm\) 0.05%). The GeO\({}_{2}\)-SiO\({}_{2}\) optical film was produced by means of SiH\({}_{4}\)/GeH\({}_{4}\)/N\({}_{2}\)O high density plasma chemistry in an AMAT CENTRAU 5200 PECVD machine, where the plasma was generated at 13.56 MHz. The plasma parameters were fixed as follows: An RF (Radio frequency) power of 350 W; a processing pressure of 2.7 Torr; an electrode and wafer gap of 450 mils (11430 \(\mu\)M); a wafer temperature of 350 \({}^{\circ}\)C; and a chamber wall temperature of 60 \({}^{\circ}\)C. The other processing parameters were as follows: A SiH\({}_{4}\) flow of 40 sccm; a GeH\({}_{4}\) (combined with 95% H\({}_{2}\)) flow of 72 sccm; and a N\({}_{2}\)O flow of 2500 sccm. The deposition time was set to 1090 s. Each wafer was complete after the PECVD deposition. The deposition chamber was cleaned by running 20 cycles of a standard plasma cleaning process with the gas CF\({}_{4}\). After the PECVD deposition process, the wafers were annealed at 1100 \({}^{\circ}\)C for 2 h.  
One lot of the fused quartz wafers was subjected to deposition each day for 23 days, so that we ended up with a total of 345 wafers. The GeO\({}_{2}\)-SiO\({}_{2}\) optical film's RI was measured using a prism coupling instrument and its thickness was measured using a film thickness gauge. The measurement error was approximately \(\pm\)0.0001 for the RI and \(\pm\)0.1% for the thickness. For each wafer, five positions (T for top, C for center, B for bottom, R for right and L for left) were measured on the wafer and the RI and thickness values were analyzed. RI-T, RI-C, RI-B, RI-R and RI-L refer to the RI of the top, center, bottom, right and left points on each wafer, respectively. THK-T, THK-C, THK-B, THK-R and THK-L refer to the thickness of the layer at the top, center, bottom, right and left points on each wafer, respectively.
# Optical Boundaries for LED-Based Indoor Positioning System
## 5 Results and Discussions
### Estimation of Total Receiver Noise  
The total receiver noise is measured by the experimental setup shown in Figure 6 using LED\({}_{2}\) with the parameters given in Table 2. The transmitter uses ATMEG 32 micromottrollers to implement the processes illustrated in Figure 2 for the transmission of positional information. The receiver is a TSOP 38238 detector with an ATMEG 32 microcontroller. The experimental setup is used to measure the values of \(v_{n}\), \(v_{f}\), \(\sigma_{n}\), and \(\sigma_{f}\) using an (Agilent) oscilloscope. The measured parameters are used to compute the value of \(Q\) by (6). Without loss of generality, we assume unity of receiver responsivity coefficient, and using the values from the experimental measurements as presented in Table 4, the total receiver noise is computed as \(\sigma_{t}^{2}=1.04\times 10^{-12}\) V\({}^{2}\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Value** \\ \hline \(v_{n}-v_{f}\) & 4.575 V \\ \(\sigma_{n}\) & 281.28 mV \\ \(\sigma_{f}\) & 175 mV \\ \(h\) & 1 m \\ \(P_{r}\) & 10.23 μW \\ \(\phi\) & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental data for receiver noise estimation.  
Using the values in Table 4, the SNR is estimated at 20 dB. However, as the receiver moves towards the half angle, the SNR drops to 8 dB, and as the distance between the transmitter and the receiver is increased from 1 m to 3 m, the SNR further drops to about 1 dB. This fluctuation in SNR is compensated by the automatic gain controller (AGC) in the receiver circuitry [86]. This ensures that the received signal is amplified based on the displacement of the receiver from the transmitter so that the positioning information is always received. Towards the optical boundaries as the strength of the optical signal is reduced, the receiver bit error increases. The effect of this increase in bit error on positioning time is subsequently quantified.
## 2 Materials and Methods  
### The ESP32 Microcontroller  
The ESP32 microcontroller [25], known for its affordability and versatility in IoT applications [26] is used in our experiment (Figure 1), which can accurately gather quantitative CSI data. It supports 64 subcarriers but is limited to the 2.4 GHz frequency and a single channel connection with a 22 MHz bandwidth per channel. CSI represents the characteristics of the Wi-Fi signal as it travels from the transmitter to the receiver, interacting with the surrounding environment [6]. This interaction affects the signal, and CSI captures these variations, providing detailed insight into how the signal is modified along its path. CSI is accessible at both the sending and receiving ends, allowing for a comprehensive understanding of the signal's journey. This is crucial because the receiver can also send data back, creating a two-way flow of information. CSI data is collectible from both access points (AP) and stations (STA), typically yielding similar results illustrated in Figure 2. An AP is a device that allows wireless devices to connect to a wired network using Wi-Fi, while an STA is a device that connects to the network, such as a laptop, smartphone, or IoT device. This study focuses primarily on STA-based CSI data for detailed collection and analysis of the signal variations directly from the connected devices.  
Figure 1: Two ESP32s with external antenna. One act as a sender and another act as a receiver.  
Figure 2: 2 CSIs from sender-side ESP32 (ESP32 AP) and receiver-side ESP32 (ESP32 STA).  
While comparing with other IoT alternatives, ESP32 microcontroller is a superior choice due to its cost-effectiveness and performance capability [27]. In terms of compatibility, the ESP32 integrates both Wi-Fi and dual-mode Bluetooth, providing extensive connectivity options in a single chip. This integration simplifies design and reduces the need for additional modules and external components [28]. Despite its advanced features, the ESP32 maintains a low complexity [16], making it accessible for both beginners and experienced developers.
# Comparative Analysis of Urban and Rural Ecosystems in the Context of Suitability for Honeybees
###### Abstract  
The present study set out to compare the performance of honeybees maintained in urban and agricultural environments across various regions in Poland. A range of measurements and observations were used to assess overwintering, development, strength, swarming behaviour, honey yield, and _Varroa destructor_ infestation in bee colonies in five towns and five surrounding villages. The rural apiaries were located 4.5-39.5 km away from the urban apiaries. The colonies were kept in wooden Dadant hives and were led by _Apis mellifera cartica_ queens. The brood count and colony strength were assessed based on the Liebefeld method, while _Varroa_ destructor infestation levels in the colonies were determined using icing sugar at the end of July. The urban apiaries exhibited significantly higher brood-rearing dynamics. Consequently, an increase in brood count in spring resulted in a significant increase in _Varroa_ infestation levels in summer. In certain regions, urban apiaries exhibited higher honey production, though this was influenced by geographic location and the availability of bee forage. The significant variation in most traits, including honey production, was observed depending on the region. Variations between years in the study were observed in bee colonies overwintering, brood numbers in the pre-spring period, and bee population in summer. This may suggest the presence of differing weather conditions.  
url
## 3 Aeroelasticity and Aerodynamics of Cable-Supported Bridges
### Flutter-Aeroelasticity of the Deck  
3.1.1. Mechanism of Flutter  
[PERSON] and [PERSON] [50] used the term flutter to describe the wake oscillation that occurs on bridge decks and compared this to similar vibration patterns found on propellers and airfoils. It is an instability that starts to grow when the structural damping cannot resist dynamic effects of aeroelastic forces induced by the relative angle of attack [9]. [PERSON] [10] extends this explanation from an energy perspective:  
\"If the energy input by the aerodynamic forces due to strong winds in a cycle is larger than that dissipated by the damping in the bridge structure system, the amplitude of vibration of the bridge deck will increase. This increasing vibration will then amplify the aerodynamic forces, resulting in self-excited forces and self-exciting oscillations. The vibration amplitude of the bridge deck can build up until it results in the collapse of the bridge.\"  
Different from the IVVs that only occur at certain frequencies, flutter will grow in time after the ambient flow has passed the critical velocity [51], which will lead to divergent oscillations that can be catastrophic. In the case of the Tacoma Narrows Bridge (Figure 4), severe vibrations were observed before the collapse. Since then, flutter instability has drawn significant attention.  
Figure 4: Collapse of Tacoma Narrows Bridge due to flutter [52].  
[PERSON] [4] developed the fundamental flutter theory, in which flutter is described as a self-excited aeroelastic instability with divergent vibration amplitude. The divergent characteristic of vibration is due to the coupling between structural motions and unsteady aerodynamic forces--namely, the lift force, drag forces and the pitching moment [1]. To quantitatively describe the relationship between the aeroelastic forces and the unsteady motions, [PERSON] [4] introduces flutter derivatives which are functions of the dimensionless velocity and frequency. In bridge design, flutter derivatives are employed to determine the critical flutter velocity of a bridge deck and thus determine if a bridge is susceptible to flutter. Therefore, it is essential to accurately identify these parameters. The bridge deck is a three-degree-of-freedom (3-DOF) system that has lateral, vertical and torsional displacements. Since it was challenging to design a 3-DOF suspension system for the experiment, early research normally simplified the bridge deck into a two-degree-of-freedom (2-DOF) system that would ignore the lateral displacement. However, ignoring the lateral displacement is not suitable for long-span bridges as they normally have relatively low lateral stiffness. Decades of development in the apparatus has enabled the state-of-the-art research to study all 18 flutter derivatives of a 3-DOF system in three-dimensional (3D) coordination.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 5 Experiment and Analysis
### Cross-Domain Dialogue Modeling
#### 5.2.3 Few-Shot Cross-Domain Results  
We conduct further domain adaptation experiments in the few-shot learning in Table 7. In few-shot setting, we utilize all source domain data combined with 100 randomly sampled dialogues from the target domain's training data for training. Testing is conducted on the target domain's test dataset. To ensure the stability of the results, we conduct three random samplings for training and testing, and we will report both the mean and variance of the results. We report the results of the state-of-the-art baseline UBARv2 [15] and our CSTOD. Moreover, we also add two model variants of CSTOD(zero) trained on only data of the source domain (see Table 6) as a lower bound and CSTOD(full) trained on all the labeled data of the target domain as an upper bound. We find CSTOD outperforms UBARv2 on all the domains by a large margin and closes the gap between few-shot(low resource) and full data settings. Additionally, using a few annotated dialogues of the target domain significantly improves the performance of complex target domains like Hotel and Res, demonstrating that our framework can effectively alleviate the discrepancy between different domains in the domain adaptation using a few labeled data.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Att** & **Hotel** & **Res** & **Taxi** & **Train** & **Avg** \\ \hline DAMD † & 28.7 & 26.9 & 24.4 & 52.3 & 51.4 & 36.7 \\ UBAR † & 28.3 & 29.5 & 23.5 & 59.5 & 53.9 & 38.9 \\ MinTL † & 33.4 & 37.3 & 31.5 & 60.4 & 77.1 & 47.9 \\ BORT † & 33.6 & 38.7 & 32.0 & 62.7 & **85.6** & 50.5 \\ \hline CSTOD & **60.8** & **48.6** & **44.8** & **95.3** & 67.4 & **63.4** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Zero-shot cross-domain end-to-end evaluation. †: we cite these results from the original paper [14]. Att means we use the other four domains as training data and test on the attraction domain. We only report the combined scores for brevity.
## 2 Simultaneous Segmentation and Detection Network (SSADNet)
### Detection Branch
#### 2.2.2 Object Function  
The overall loss function for optimizing the performance of backpropagation in the network proposed in this study is defined as shown in Equations (3)-(5). The overall loss function was calculated by separately multiplying the respective loss weight with a segmentation loss function and with a detection loss function and summing the resulting values. The loss function for segmentation, shown in Equation (4), is the cross-entropy loss between the ground truth and predicted results of the pixelwise classification performance [30]. The loss function for detection, shown in Equation (5), is the mean squared error of the bounding box label and predicted results regarding the x and y coordinates of a center point as well as the width and height of the bounding box. When training SSADNet, the detection loss was 10 times larger than the size of segmentation loss, which was set as a base. Hence, 0.1 times the loss weight of the detection loss was applied. In Table 2, the overall objective  
Figure 6: Components of the detection branch: (**a**) feature extractor block, (**b**) boundary box regressor block.  
Figure 7: Detailed structure of the detection branch.  
function consisted of a segmentation loss function and a detection loss function, and the notations of each item are shown. See Appendix A for more details.  
\[L_{tot}=lw_{seg}\times L_{seg}+lw_{det}\times L_{det} \tag{3}\]  
\[L_{seg}=-\frac{1}{N\times W\times H}\sum_{n=1}^{N}\sum_{w=1}^{W}\sum_{h=1}^{H}( y_{n,w,h}{}^{true}\times log\big{(}y_{n,w,h}{}^{pred}\big{)}) \tag{4}\]  
\[L_{det}=L_{center\_x}+L_{center\_y}+L_{width}+L_{height} \tag{5}\]  
\[L_{center\_x},\ L_{center\_y},\ L_{width},\ L_{height}=\frac{1}{N}\times \sum_{n=0}^{N-1}\left(y_{n}{}^{true}-y_{n}{}^{pred}\right)^{2} \tag{6}\]
## 3 LDCP Problem
### Notation
We assume that the flows may have different latency and throughput requirements. Let \(L^{max}(f)\) denote the maximum one-way latency allowable for flow \(f\in\mathcal{F}\). Let \(H(d,f)\) denote the bit-rate of flow \(f\) of demand \(d\). Let \(\mathcal{V}^{src}(d,f)\) and \(\mathcal{V}^{dest}(d,f)\) be the sets of allowable source and destination nodes, respectively, of flow \(f\in\mathcal{F}\) of demand \(d\in\mathcal{D}\). Assuming that \(v^{R}(d)\) denotes the RU node belonging to demand \(d\), sets \(\mathcal{V}^{src}(d,f)\) and \(\mathcal{V}^{dest}(d,f)\) are defined as following:  
1. if \(d\) is an uplink demand, then: \(\mathcal{V}^{src}(d,f)=\{v^{R}(d)\}\) and \(\mathcal{V}^{dest}(d,f)=\mathcal{V}^{P}\) if \(f=\{\mathrm{FH}\}\), \(\mathcal{V}^{src}(d,f)=\mathcal{V}^{P}\) and \(\mathcal{V}^{dest}(d,f)=\mathcal{V}^{P}\) if \(f=\{\mathrm{MH}\}\), and \(\mathcal{V}^{src}(d,f)=\mathcal{V}^{P}\) and \(\mathcal{V}^{dest}(d,f)=\mathcal{V}^{DC}\) if \(f=\{\mathrm{BH}\}\);
2. if \(d\) is a downlink demand, then: \(\mathcal{V}^{src}(d,f)=\mathcal{V}^{P}\) and \(\mathcal{V}^{dest}(d,f)=\{v^{R}(d)\}\) if \(f=\{\mathrm{FH}\}\), \(\mathcal{V}^{src}(d,f)=\mathcal{V}^{P}\) and \(\mathcal{V}^{dest}(d,f)=\mathcal{V}^{P}\) if \(f=\{\mathrm{MH}\}\), and \(\mathcal{V}^{src}(d,f)=\mathcal{V}^{DC}\) and \(\mathcal{V}^{dest}(d,f)=\mathcal{V}^{P}\) if \(f=\{\mathrm{BH}\}\). For each source-destination pair of nodes in the network, a single path is given. The paths are defined by means of parameter \(\alpha(d,f,i,j,e)\), which equals to \(1\) if flow \(f\) of demand \(d\) originated innode \(i\in\mathcal{V}^{src}(d,f)\) and terminated in node \(j\in\mathcal{V}^{dest}(d,f)\) is routed through link \(e\), and 0 otherwise. Let \(L(d,f,e)\) denote the delay produced by transmission of the burst of frames of flow \(f\) of demand \(d\) at link \(e\).
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
###### Abstract  
Based on the 0.5 \(\upmu\)m GaAs enhancement/depletion (E/D) Pseudomorphic High Electron Mobility Transistor (pHEMT) process, a 7.5-9 GHz two-channel amplitude phase control multi-function chip (MFC) was developed successfully. The chip was integrated with a 6-bit digital phase shifter, a 6-bit digital attenuator, and a single pole single throw (SPST) switch in each channel. A design for the absorptive SPST switch is deployed to optimize the return loss and control channel array calibration. In the 8 dB and 16 dB attenuation bit, a switched-path-type topology is employed in order to obtain a good flatness of attenuation characteristic and achieve low additive phase shift. A 27-bit serial-to-parallel converter (SPC) was introduced to decrease the control lines and pads of the chip, and the power consumption was less than 70 mW. The measurement result shows that the insertion loss is less than \(-13\) dB and the return loss is better than \(-19\) dB. In both channels, the 64-state root mean square (RMS) errors of the phase shifter is less than \(2^{\circ}\) and the RMS parasitic amplitude error is less than 0.2 dB. The RMS attenuation error is less than 0.45 dB and the RMS parasitic phase error is less than \(2.4^{\circ}\). The size of the chip is \(3.5\) mm \(\times\) \(4.5\) mm.  
Keywords:Multi-function; two-channel; phase shifter; attenuator; absorptive SPST switch; serial-to-parallel converter +
Footnote †: journal: Electronics Electronics
###### Abstract  
Ecosystem water use efficiency (eWUE) is a useful metric to examine the interactions between water and carbon cycles in ecosystems. To reveal the response and adaptation characteristics of different vegetation types within the context of global warming on a regional scale, the spatiotemporal characteristics and influencing factors of the seasonal eWUE of various vegetation types in Inner Mongolia from 2001 to 2020 were explored. Based on MODIS gross primary productivity (GPP), evapotranspiration (ET) data and meteorological data, in this study, we estimated eWUE in different seasons in Inner Mongolia and used trend analysis and correlation analysis methods to analyze the relationship between eWUE in spring, summer and autumn and the temperature-precipitation. From 2001 to 2020, in this region, the GPP and ET in spring, summer and autumn showed increasing trends. In addition, the growth rates of GPP and ET in spring and summer were higher than those in autumn. Under the combined effect of GPP and ET, eWUE in different seasons showed a significant decreasing trend (\(p<0.05\))--this is ascribed to the extent of ET increasing more than GPP, especially in summer, with the most obvious decreasing rate. In terms of spatial trend, in spring and summer, there is a decreasing trend from northeast to southwest. The effects of precipitation and temperature on the eWUE in Inner Mongolia were mainly negatively correlated in the northeastern part of Inner Mongolia with higher altitudes during the spring and autumn seasons. In total, 95.096% of the total area had positive correlations between eWUE and temperature in spring. In summer, the region in which the WUE of the vegetation had an inverse relationship with both the temperature and the amount of precipitation was the largest compared to these regions in spring and autumn.  
water use efficiency; hydro-thermal factors; temperature; precipitation; season +
Footnote †: journal: Atmosphere