esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Optical communication using the 2 \(\upmu\)m wavelength band is attracting growing attention for the sake of mitigating the information 'capacity crunch' on the way, where on-chip nonlinear waveguides can play vital roles. Here, silicon-rich nitride (SRN) ridge waveguides with different widths and rib heights are fabricated and measured. Linear characterizations show a loss of \(\sim\)2 dB/cm of the SRN ridge waveguides and four-wave mixing (FWM) experiments with a continuous wave (CW) pump reveal a nonlinear refractive index of \(\sim\)\(1.13\times 10^{-18}\) m\({}^{2}\)/W of the SRN material around the wavelength 1950 nm. With the extracted parameters, dimensions of the SRN ridge waveguides are optimally designed for improved nonlinear performances for the 2 \(\upmu\)m band, i.e., a maximal nonlinear figure of merit (i.e., the ratio of nonlinearity to loss) of 0.0804 W\({}^{-1}\) or a super-broad FWM bandwidth of 518 nm. Our results and design method open up new possibilities for achieving high-performance on-chip nonlinear waveguides for long-wavelength optical communications.  
four-wave mixing; nonlinear figure of merit; silicon-rich nitride; ridge waveguide; conversion bandwidth +
Footnote †: journal: Applied Physics Letters  
1
## 3 Proposed Method
### MFFM  
In MFFM, if the number of MFFUs in MFFM is \(n\), it is denoted as MFFM of \(level=n\). The connection form of MFFU in MFFM is shown in Figure 3.  
The input of the n-th MFFU is denoted as \(U_{n-1}\), and the processing of the n-th MFFU is denoted \(V(.)\). At first, \(R3\), \(R4\), \(R5\) feature maps generated by the backbone network are taken as inputs, and it is specifically denoted as \(U_{0}\). For any n-th MFFU, its input \(U_{n-1}\) is processed by the n-th MFFU, then multiplied by the learnable adaptive weights \(W_{n}\), and will be added with \(U_{n-1}\) multiplied by the learnable adaptive weights \(Wn^{\prime}\). The result of that is used as the input of the \((n+1)\)-th MFFU. The formula is described as follows:  
\[U_{n}=W_{n}\cdot V(U_{n-1})+W^{\prime}_{n}\cdot U_{n-1},n\geq 1 \tag{7}\]  
Within each MFFU, where \(U_{n}=\{u_{n\_R3},u_{n\_R4},u_{n\_RS}\}\), \(W_{n}=\{w_{n\_R3},w_{n\_R4},w_{n\_RS}\}\), \(W^{\prime}_{n}=\{w^{\prime}_{n\_R3},w^{\prime}_{n\_R4},w^{\prime}_{n\_RS}\}\).  
In which \(u_{n\_R4}\) represents the weight in the weighted training branch corresponding to \(Rk\) feature map (\(k=3,4,5\)) in \(U_{n}\), and \(u_{n\_Rk}\) is obtained by the following process:  
\[u_{n\_Rk}=w_{n\_Rk}\cdot V(u_{n-1})+w^{\prime}_{n\_Rk}\cdot u_{n-1},n\geq 1,k =3,4,5 \tag{8}\]  
Figure 3: Connection form of MFFU in MFFM, \(w_{n}\) represents a weight that can be learned. The fusion ratio of each part depends on the shortcut and the learnable weight.  
Figure 2: Internal structure of MFFU. Through horizontal connection, vertical connection, shortcut and learnable weights, feature information is fused.  
The advantage of such processing is that, as the number of MFFUs in MFFM increases, the lower-level features become less and less, and many of them are advantageous for detection. The weight of features in each stage can be adaptively determined by parameter \(W\), which is equivalent to adding a special attention mechanism. It can make the training process smoother, and the effect is also better.  
The feature maps with scales of \(R3\), \(R4\) and \(R5\) after \(n\) MFFUs have fused features of different scales at multiple levels. These three feature maps are all deconvolved into \(R2\) scale, and then concatenated into one feature map. Then, the dimensions of the concatenated feature map will be compressed through a 3 \(\times\) 3 convolution layer to further fuse features, consequently the final feature map \(\Phi_{det}\) is generated.
# Review of the Main Mechanical Testing Methods for Interlayer Characterization in Laminated Glass
## 3 Evaluation of Interlayer Properties According to EN 16613:2019
### F.E.M. Modeling
#### 3.2.1. Materials  
Glass is an elastic-brittle material for which density (\(\rho_{G}\)), elastic modulus (\(E_{G}\)), strength (\(f_{y}\)), and Poisson's ratio of glass (\(v\)) were assumed as 2.48 g/cm\({}^{3}\), 70 GPa, 45 MPa, and 0.23, respectively.  
For the interlayer, a viscoelastic constitutive law with formerly derived material parameters was adopted to characterize the behavior of PVB. The domain of influence (DOI) method [49] was employed to approximate the Prony Series coefficients and exponents of PVB, because this method allows fine-tuning a fitting algorithm with a fixed number of coefficients. And the shear relaxation modulus (\(g_{i}\)) and relaxation time (\(\tau_{i}\)) were obtained through the relationship between the shear modulus (\(G\)) and applied time (\(t\)), and shown in Table 3.
## 2 Materials
### Data Acquisition and Preprocessing
#### 2.2.3 Field Survey Data  
The ground survey data for this study were collected from representative GGP stands in Inner Mongolia, with sample plots established across six county-level administrative regions: Horqin Right Wing Middle Banner, Duolun County, Linxi County, Songshan District, Qingshuihe County, and Jungar Banner. A total of 79 sample plots were surveyed during 2023 and 2024, and the major tree species of GGP stands were poplar, Mongolian Scots pine, and Chinese red pine. In this study, we selected a homogeneous forest plotwith an area larger than 120 m \(\times\) 120 m located in the fallow forest plot, and we laid a 30 m sample line in a due north direction and two other 30 m sample lines at 60\({}^{\circ}\) intervals, with the midpoints of the three sample lines intersecting at the center of the sample plot. A series of parameters were recorded during the field survey, including the RTK coordinates of the sample centers; geographic information such as elevation; and the diameter at breast height (DBH) of individual trees, tree height, crown width, and the number of trees in the sample plots.  
To estimate the aboveground biomass of the sample plots, this study employed the biomass models for poplar, Mongolian Scots pine, and Chinese red pine outlined in the national standard \"Tree biomass models and related parameters to carbon accounting for major tree species\" (GB/T 43648-2024) [26], developed under the guidance of the National Forestry and Grassland Administration. The equations used for the aboveground biomass estimation are presented in Table 1.  
Here, Ma represents the AGB of individual trees, D denotes the diameter at breast height, and H refers to tree height. To calculate the AGB, different models were applied based on the distribution of tree species within each sample plot. Using the above equations, the biomass of individual trees for each species was calculated and then multiplied by the number of trees in the corresponding sample plot. This approach allowed for the determination of the biomass distribution within each plot. Finally, the total AGB of each sample plot was standardized and expressed in grams per square meter (g/m\({}^{2}\)).
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 1 Introduction
It appears that the congestion control scheme proposed in this paper is quite different from load balancing and traffic scheduling, and therefore, the experiments in this paper are not compared with them. While different applications solve different networking problems, the effective deployment of these applications greatly depends on the utilization of programmable switches. Programmable switches bring unlimited possibilities for offloading complex packet processing pipelines directly in the high-speed data plane [10]. Programming protocol-independent packet processors (P4) can be used to define the forwarding behavior of packets and to identify packets queued in the data plane. All operations on packets are performed in the data plane, allowing the switch to target operations and enhancing the flexibility of packet processing [11; 12]. The main contributions are as follows:  
* We summarize and reveal the shortcomings of the existing end-to-end congestion control and traffic management in a high-bandwidth DCN. * This paper defines a new method for congestion detection and congested flow identification. This method uses the queue length in the buffer and the time that packets reside in the queue to classify congestion into three different cases. Switches can effectively detect congestion and identify the real-congested flow based on the queue occupancy. * This paper designs a switch-driven rate congestion control. In this approach, upstream switches can respond to congestion based on the downstream congestion feedback in a one-hop RTT. This adjustment process helps accelerate the convergence of speeds. * BFCC is implemented in the PDP, and extensive simulation experiments confirm that this congestion control mechanism maintains high throughput and low latency with a low buffer.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results
### Indoor Measurements
#### 4.2.4 Basement  
The basement walls are constructed of 30 cm thick concrete blocks. Furthermore, the slabs between the basement and the ground floor consist of reinforced concrete with a thickness of 34 cm. Remarkably, the Orbcomm satellite signals propagated into the basement. Nevertheless, it should be noted that the signals arrived with very little power and were only briefly visible. Furthermore, only 80% of the satellite passes were received. When analyzing Figure 4, the mean value was the lowest of all rooms.
# Simulation Research on the Effects of Air Gaps and Ambient Temperature on a 27.5 kV Power Cable
## 2 Cable Model Construction  
In this study, an EN50264 standard 27.5 kV semi-rigid high-voltage cable and terminal are used for modeling [32]. The EN 50264 standard provides important input parameters for cable simulation such as material, geometric structure, thermal characteristics and electrical properties, which can greatly improve the accuracy and practical applicability of the simulation. Especially in complex, high-safety scenarios such as railway environments, the guidance provided by the standard can be used to develop safer and more efficient cable designs. The cable model and its terminal structure are mainly composed of a cable core and seven layers of an insulation structure [32], as shown in Figure 1. The outermost layer is an umbrella skirt to provide creepage distance; the second, third, fourth and sixth layers are insulated tubes, which play the main role of withstanding voltage; and the fifth and seventh layers are stress control tubes, which are used to improve the electric field distribution at the end of the cable and suppress electric field distortion.
## 3 Development of RF WPT-Enabled IoT Application
### Feasibility Analysis and Design Dimensions Definition
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sensor Type** & **Consumption min** & **Consumption max** \\ \hline
**Movement** & & \\ \hline Accelerometers & dozens \(\upmu\)W & hundreds \(\upmu\)W \\ Gyroscopes & units mW & dozens mW \\ \hline
**Environmental Conditions** & & \\ \hline Ambient temperature & units \(\upmu\)W & hundreds \(\upmu\)W \\ Ambient pressure & dozens \(\upmu\)W & dozens \(\upmu\)W \\ Ambient humidity & units \(\upmu\)W & hundreds \(\upmu\)W \\ Ambient light (incl. UV) & dozens \(\upmu\)W & units mW \\ Air quality & dozens \(\upmu\)W & hundreds mW \\ Gas presence & hundreds \(\upmu\)W & hundreds mW \\ Dust (optical) & dozens mW & dozens mW \\ Smoke (optical) & hundreds \(\upmu\)W & units mW \\ \hline
**Presence** & & \\ \hline Magnetic/compass & hundreds \(\upmu\)W & units mW \\ Touch & dozens \(\upmu\)W & hundreds \(\upmu\)W \\ \hline
**Physical Conditions** & & \\ \hline Heart rate & units mW & dozens mW \\ Gestures (optical) & units mW & dozens mW \\ \hline
**Other** & & \\ \hline Audio & hundreds \(\upmu\)W & dozens mW \\ Video & dozens mW & hundreds mW \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Consumption of the state-of-art commercial sensors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subband [ERC 70-03]** & **Frequency (MHz)** & **Effective Radiated Power (mW/dBm)** & **Duty Cycle (\%)** \\ \hline g1 & 433.050–434.790 & \(\leq\)25/14 & \(\leq 10\) \\ g2 & 433.050–434.790 & \(\leq\)1/0(–13 dBm/10 kHz) & No restriction \\ g3 & 433.040–434.790 & \(\leq\)10/10 & No restriction \\ h1.1–1.3 & 863.000–870.000 & \(\leq\)25/14 & \(\leq\)0.1 \\ h1.4 & 868.000–868.600 & \(\leq\)25/14 & \(\leq\)1 \\ h1.5 & 868.700–869.200 & \(\leq\)25/14 & \(\leq\)0.1 \\ h1.6 & 869.400–869.650 & \(\leq\)500/27 & \(\leq\)10 \\ h1.7 & 869.700–870.000 & \(\leq\)5/7 & No restriction \\ h1.7 & 869.700–870.000 & \(\leq\)25/14 & \(\leq\)1 \\ h2 & 870.700–873.000 & \(\leq\)25/14 & \(\leq\)1 \\ i & 2400.000–2483.500 & \(\leq\)10/10 (EIRP) & No restriction \\ j & 5725.000–5875.000 & \(\leq\)25/14 (EIRP) & No restriction \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sub-bands for non-specific short range devices (based on Refs. [60; 61]). At the same time, in the case, if antennas are not circularly polarized, the rotation may result in energy losses due to polarization mismatch. These two effects also need to be investigated experimentally in the process of design.
## 3 Results and Discussion
### Weber-Morris Model  
The Weber-Morris intraparticle diffusion model was applied to the kinetic data of biomass-based carbon against the Zn\({}^{2+}\). If a plot of \(q_{t}\) versus t\({}^{1/2}\) presents a straight line from the origin, the adsorption mechanism only follows the intraparticle diffusion process. As can be seen in Figure 8, the data of Zn\({}^{2+}\) sorption on biomass-based carbon shows four plots that do not pass through the origin. The first stages can be attributed to the instantaneous or external surface adsorption. The majority of Zn\({}^{2+}\) ions  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline
**Experiments** & \(q_{e(\mathrm{exp})}\) [mg/g] & 14.36 \\ \hline
**Pseudo-1-order** & \(q_{e(\mathrm{all})}\) [mg/g] & 12.15 \\  & \(k_{1}\) [min\({}^{-1}\)] & 0.22 \\  & R\({}^{2}\) & 0.83 \\  & RMSE & 1.66 \\ \hline
**Pseudo-2-order** & \(q_{e(\mathrm{all})}\) [mg/g] & 13.01 \\  & \(k_{2}\) [g/ mg min] & 0.023 \\  & R\({}^{2}\) & 0.94 \\  & RMSE & 1.04 \\ \hline
**Elovich** & \(\beta\) [g/mg] & 0.69 \\  & \(v_{0}\) [mg/g min] & 60.28 \\  & R\({}^{2}\) & 0.97 \\  & RMSE & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kinetic parameters of pseudo-first-order, pseudo-second-order, and Elovich models.  
Figure 7: Pseudo-first-order, pseudo-second-order, and Elovich models applied to the experimental results.  
are diffused through the solution to the external surface of the adsorbent in which the instantaneous sorption takes place. In the second stage, the adsorption capacity increases only slightly. This stage can be attributed to the slow diffusion of Zn\({}^{2+}\) ions from the surface sites into inner pores. In the third stage, the adsorption rate stays almost constant due to the low Zn\({}^{2+}\) ion concentration left in the solution [45; 46].
## 1 Introduction
Urban areas, with their dense high-rise buildings and obstructions, further complicate the task of maintaining stable communication links. Therefore, there is increasing interest in developing routing protocols that can sense network conditions and dynamically adjust routing paths in real time. In order to ensure the real-time and accurate data transmission of unmanned cluster networks in complex urban environments, the AODV-EM protocol proposed in this paper comprehensively considers two routing metrics, network node energy consumption based on node degree balance and network node relative mobility, as the criteria for routing selection. According to the network link status and data transmission requirements, this protocol can plan appropriate transmission links for the data exchange among nodes, so as to reduce the amount of data forwarding among nodes and minimize the network routing cost. The significance of this study lies in its potential to fundamentally transform the application of unmanned clusters in complex environments, significantly improving data transmission efficiency and task success rate. The rest of this paper is organized as follows. Section 2 describes the application scenarios and current issues. Section 3 proposes two routing metrics. In Section 4, the frame structure and workflow of the AODV-EM protocol are designed. The experimental setupsand results that validate our approach are presented in Section 5. Finally, conclusions are drawn in Section 6.
## 5 Experiment and Result Analysis
### Model Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MODEL** & **MeanRE** & **MaxRE** & **RMSE** & **R2** \\ \hline GA-ELM & 1.1554 & 5.2558 & 0.7978 & 0.99925 \\ LSSVM & 1.2999 & 5.285 & 1.0547 & 0.99868 \\ Xgboost & 1.8448 & 3.9963 & 1.474 & 0.99743 \\ RAB-CNN & 1.189 & 4.9172 & 0.7323 & 0.99936 \\ MRAB-CNN & **0.7089** & **2.6222** & **0.4441** & **0.99977** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison table of each model
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 3 Collision-Free Path Planning  
### Artificial Potential Field  
Artificial potential field (APF) is a path planning method that uses a virtual potential field, inducing attraction towards the destination and repulsion near obstacles. Compared to other path planning methods, APF is widely used due to its simplicity and low computational requirements. The repulsive potential field for obstacle \(i\), \(\text{U}_{rep,i}\), was constructed based on a quadratic form [32]:  
\[\text{U}_{rep,i}(\mathbf{P})=\left\{\begin{array}{cc}K_{r}\bigg{(}\frac{1}{D^{ \prime}\mathbf{P},\mathbf{P}_{obs,i}}-\frac{1}{D_{min,i}}\bigg{)}^{2}&\text{if }D(\mathbf{P},\mathbf{P}_{obs,i})\leq D_{min,i}\\ 0&\text{otherwise.}\end{array}\right. \tag{2}\]  
where \(\mathbf{P}\) represents the position of joint, \(\mathbf{P}_{obs,i}\) represents the position of obstacle \(i\), \(D(\mathbf{P},\mathbf{P}_{obs,i})\) is the euclidean distance between \(\mathbf{P}\) and \(\mathbf{P}_{obs,i}\), \(D_{min,i}\) is the safety distance for obstacle \(i\), and \(K_{r}\) is the repulsive gain.
## 2 System Model and Performance Metric
### Channel Model  
Now, let us turn to channel modeling. The received signal for downlink transmission at MS \(i\) and the received signal vector for uplink transmission at the BS, denoted by \(y_{i}^{(d)}\in\mathbb{C}\) and \(\mathbf{y}^{(u)}\in\mathbb{C}^{M\times 1}\), can be written as  
\[y_{i}^{(d)} = \beta_{i}^{(d)}{\mathbf{h}_{i}^{(d)}}^{\dagger}\mathbf{s}^{(d)}+ \sum_{j\in\mathcal{S}^{(u)}}\beta_{ij}^{(M)}h_{ij}s_{j}^{(u)}+n_{i}^{(d)}, \tag{1}\] \[\mathbf{y}^{(u)} = \sum_{i\in\mathcal{S}^{(u)}}\beta_{i}^{(u)}{\mathbf{h}_{i}^{(u)} }s_{i}^{(u)}+\mathbf{n}^{(u)}, \tag{2}\]  
respectively, where \(\beta_{i}^{(d)}{\mathbf{h}_{i}^{(d)}}\in\mathbb{C}^{M\times 1}\), \(\beta_{i}^{(u)}{\mathbf{h}_{i}^{(u)}}\in\mathbb{C}^{M\times 1}\), and \(\beta_{ij}^{(M)}h_{ij}\in\mathbb{C}\) denote the channel vectors from the BS to MS \(i\), from MS \(i\) to the BS, and channels from MS \(j\) to MS \(i\), respectively. Here, the channel coefficients \(\beta_{i}^{(d)}{\mathbf{h}_{i}^{(d)}}\), \(\beta_{i}^{(u)}\beta{\mathbf{h}_{i}^{(u)}}\), and \(\beta_{ij}^{(M)}h_{ij}\) consist of the large-scale path-loss component, which is independent of SNR, and the small-scale complex fading component. More specifically, \(\beta_{i}^{(d)}\), \(\beta_{i}^{(u)}\), and \(\beta_{ij}^{(M)}\) represent the nonnegative path-loss attenuation factor between the BS and MS \(i\) for downlink, between MS \(i\) and the BS for uplink, and between two MSs \(i\) and \(j\), respectively. We assume that each element of small-scale fading channels is independent and identically distributed (i.i.d.) according to \(\mathcal{CN}\left(0,1\right)\), where the notation \(\mathcal{CN}\left(\mu,\Sigma\right)\) indicates the complex Gaussian distribution with a mean vector \(\mu\) and a covariance matrix \(\Sigma\). The downlink transmit signal vector at the BS and the uplink signal at MS \(j\), denoted by \(\mathbf{s}^{(d)}\in\mathbb{C}^{M\times 1}\) and \(s_{j}^{(u)}\in\mathbb{C}\), respectively, satisfy the average power constraints \(\mathbb{E}\left[\left\|\mathbf{s}^{(d)}\right\|^{2}\right]=1\) and \(\mathbb{E}\left[\left|s_{j}^{(u)}\right|^{2}\right]=1\). The additive noise at MS \(i\), denoted by \(n_{i}^{(d)}\), and each element of  
Figure 1: The multi-antenna full-duplex system when \(M=2\) and \(N=15\).  
the additive noise vector at the BS, denoted by \(\mathbf{n}^{(u)}\), are i.i.d. complex Gaussian with zero mean and variance of \(N_{0}\), respectively.  
We assume the block fading channel model, i.e., channel coefficients are constant during one coding or communication block and changes to a new independent value for every transmission block. We further assume that full CSI is available at the receiver side, but only partial CSI (effective channel _gain_ information) is available at the transmitter side, which will be specified later on.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The raw UPLC/Q-TOF MS data were processed using the MarkerLynx v4.1 software (Waters, Milford, MA, USA) for alignment, deconvolution, data reduction, and normalization, among other processes. Next, the dataset was imported into the SIMCA-P\({}^{+}\) 13.0 software (Umetrics, Umea, Sweden) for multivariate pattern recognition analysis. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed to discriminate the groups. The variables with their importance in the projection (VIP) values > 1.0 and \(p\) values < 0.05 from the \(t\)-test were selected as candidate markers and metabolomics pathway analysis. The selected potential biomarkers were searched and matched in online databases, such as the METLIN ([[http://metlin.scripps.edu/](http://metlin.scripps.edu/)]([http://metlin.scripps.edu/](http://metlin.scripps.edu/)) (accessed on 2 February 2021)), HMDB ([[http://www.hmdb.ca/](http://www.hmdb.ca/)]([http://www.hmdb.ca/](http://www.hmdb.ca/)) (accessed on 2 February 2021)), MassBank ([[http://www.massbank.jp](http://www.massbank.jp)]([http://www.massbank.jp](http://www.massbank.jp)) (accessed on 2 February 2021)), and LIPID MAPS ([[http://www.Lipidmaps.org/tools/index.html](http://www.Lipidmaps.org/tools/index.html)]([http://www.Lipidmaps.org/tools/index.html](http://www.Lipidmaps.org/tools/index.html)) (accessed on 2 February 2021)). Potential biomarkers were imported into MetaboAnalyst (MetPA; [[http://www.metaboanalyst.ca/](http://www.metaboanalyst.ca/)]([http://www.metaboanalyst.ca/](http://www.metaboanalyst.ca/)) (accessed on 2 February 2021)), which is a comprehensive tool dedicated to identifying potential associated core metabolic pathways.