esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.7 Data Packet  
The data packet for routing was a 104-byte data packet containing status data (node position, velocity, acceleration, timestamp, and node addresses). However, for the SDVN paradigm, status data need to be broadcasted among neighbors first, and then the collected status data need to be unicast along with the additional metadata required for routing. The additional metadata required to be sent to the centralized controller for generating knowledge regarding the parameters required for computing the routes using the proposed algorithm from a given node (\(i\)) are the node address set of node \(i\)'s one-hop neighbors (\(S_{i}\)), the average queue size of node \(i\) (\(\overline{\mathcal{C}_{i}}\)), and the average pending transmission packet distribution factor of node \(i\) (\(\overline{\mathcal{Q}_{i}}\)). However, [PERSON]'s algorithm needs only a set of node addresses of all one-hop neighbors of each node (\(\mathcal{S}_{i}\)) and the positions of each node to generate sets of shortest paths to destination nodes. Therefore, the total broadcasting payload size of the proposed routing containing status information was 104 bytes, while that of Dijkstra was 56 bytes. Additionally, take notice that, depending on the routing protocol and the number of neighbors broadcasting to the agent or node, the payload sizes of the packets unicasted to the control server by the agents employing the optimization framework vary. The unicasting uplink payload size of an agent node for the proposed routing in SDVN was \(24\sum_{m\in\mathcal{S}_{i}}(\mathcal{N}_{m}Z_{m}+\frac{1}{3})+104(\mathcal{ N}_{i}+1)+8\) bytes. Here, \(Z_{m}\) is a variable that determines whether the \(m\)th node is a broadcasting node or not (if \(Z_{m}=1\), it is a broadcasting node). On the other hand, the unicasting uplink payload size of an agent for routing using Dijkstra was \(24\sum_{m\in\mathcal{S}_{i}}(\mathcal{N}_{m}Z_{m})+56(\mathcal{N}_{i}+1)\) bytes. The size of the packet_in message was 24 bytes (consisting of source and destination IP, MAC, and port addresses), and the Flow_Mod packet was 76 bytes (24 bytes for rules + 16 bytes to modify source fields + 8 bytes to forward to port + 8 bytes to forward to controller + 20 bytes for statistics) for the proposed routing and 68 bytes for Dijkstra (due to the additional 8-byte route valid timestamp in the proposed routing, which was not implemented in Dijkstra).
## 5 Discussion
### Convergent Validity  
Both throwing tests showed moderate to strong correlations with grip strength, supporting their validity as measures of upper extremity function. The correlation between throwing distance and grip strength (r = 0.78-0.81 in controls, r = 0.61-0.7 in RCRSP) is consistent with previous findings in healthy populations [38], suggesting that these relationships persist in the presence of shoulder pain. The mild to moderate correlations observed between the throwing tests and self-reported measures (QuickDASH, FABQ) indicate that these tests capture aspects of function that are relevant to patients' perceived disability and fear-avoidance behaviors. However, the relatively weak correlation with pain intensity (VAS) suggests that throwing performance may be influenced by factors beyond pain alone, such as strength, motor control, or psychological factors.  
Comparison with Previous Studies  
Our findings for both the SMBT and ULSPT are consistent with previous research, supporting the validity of our results (Table 5). For the SMBT, our results for the control group (3.39 \(\pm\) 1.24 m) align closely with those reported by [PERSON] and [PERSON] (3.21 \(\pm\) 0.77 m) in a sedentary population [39]. Similarly, our female results (2.53 \(\pm\) 0.49 m) are comparable to those reported by [PERSON] and [PERSON] (2.34 \(\pm\) 0.39 m) in overhead athletes [11].  
For the ULSPT, our normalized results for the RCRSP group (1.01 \(\pm\) 0.4 m) are similar to those reported by [PERSON] et al. (1.16 \(\pm\) 0.22 m) [13], which is the only other study  
we identified using ULSPT in a similar clinical population. Our control group results (1.3 \(\pm\) 0.46 m) are comparable to those reported by [PERSON] et al. (1.2 \(\pm\) 0.48 m) [9].  
Interestingly, despite assessing a general population compared to the athletic population in [PERSON] et al.'s study [13], our throwing distances are similar. This may be explained by the more persistent nature of symptoms reported in their athletic sample (40.96 \(\pm\) 36.32 months, compared to 6.33 \(\pm\) 5.7 months in our study).  
It is important to note some methodological differences between studies, including variability in ball weight, number of throws, and lack of ball diameter records in some studies (Table 5) [9; 13; 18; 39]. These differences highlight the need for standardized protocols to enable more direct comparisons between studies and populations.
# An Integrated Metabolomics Study of Glucosinolate Metabolism in Different Brassicaceae Genera
## 2 Results
### Glucosinolate Metabolites are Potentially Useful as Chemotaxonomic Markers  
The traditional classification of plants relies more on the morphological taxonomy. Recently, secondary metabolites-based species classification has emerged as an effective chemotaxonomic tool, providing detailed biochemical information on the similarities and differences among plant species [29]. Extensive information of metabolites among the different species of Brassicaaceae has been obtained in the discovery set, and the linkage between the morphological taxonomy and the chemotaxonomy was explored. Figure 2 illustrates principal component analysis (PCA) score plots of the 12 vegetables studied based on the non-volatile compounds (Figure 2a) and the volatile compounds (Figure 2b). Although these data are not directly comparable due to different analysis methods used, both datasets  
Figure 1: Correlations between metabolites in 5 vegetables confirmed by targeted quantitative analysis of amino acids, glucosinolates. SIN, sinigrin; GIB, glucoiberin; GRH, glucoraphasatin; GST, Glucosativin; GER, glucoerucin; GRE, glucoarphenin; GRA, glucoarphanin; GNP, glucoarphenin; PRO, progoitrin; GAL, glucoalysissin; GBN, gluco Brassicaanpin; GNL, glucoapoleiferin; GBS, glucobrassicin; NGBS, neoglucobrassicin; 4-MGBS, 4-methoxyglucobrassicin; 4-OHGBS, 4-hydroxyglucobrassicin; GTP, glucotropaeolin; GNS, gluconasturitin; GBB, glucobbararin.  
show separation of samples from different vegetables into 12 clusters and closer grouping of samples from vegetables of the same genera, with watercress and radiishes clearly separate from the other vegetables, and these 12 clusters could further fall into 4 groups according to their morphological classification, as shown in Figure 2a,b. The loading plots of PCA as shown in Figure 2c,d facilitated the visualization of the crucial metabolites for differentiating these Brassicaceae genera. These findings suggest that glucosinolates and their volatile breakdown products could be potential biomarkers for chemotaxonomy, which aligns with classical morphological taxonomy [30].  
Figure 2: Cont.  
Figure 2: Principal component analysis (PCA) showing separation of 12 vegetables for data from two different experiments and two different datasets. (**a**) Score plot of PCA analysis based on non-volatile compounds, *Cherry Radish, *Radish, *Rocket Salad, *Watercress, *Chinese Cabbage, *Cabbage, *Cauliflower, *Broccoli, *Choysum, *Pakchoi, *Kai Lan, *Brussel Sprouts; (**b**) score plot of PCA analysis based on volatile compounds; (**c**) loading plot of PCA analysis based on non-volatile compounds; (**d**) loading plot of PCA analysis based on volatile compounds. The specific names of metabolites are listed in Supplementary Materials Figure S1.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell Edge
#### 3.4.2 Interference  
The interference \(I_{\text{small}}\) offered by one interfering ring at a distance of 4\(R\) from the central small cell eNodeB \(x\) can be given using Equation (9) as:  
\[\begin{array}{c}I_{\text{small}}=2\pi\int_{3R}^{4R}\rho_{ue}p_{\text{o\_small }}\text{A}^{-\alpha_{\text{small}}}(4R-r)^{\alpha_{\text{small}}\eta}\text{A}r ^{-\eta}rdr\\ +2\pi\int_{4R}^{5R}\rho_{ue}p_{\text{o\_small}}\text{A}^{-\alpha_{\text{small} }}(-4R+r)^{\alpha_{\text{small}}\eta}\text{A}r^{-\eta}rdr\end{array} \tag{24}\]  
Using Equations (18) and (24), the total interference in the HetNet environment can then be given as:  
\[I_{\text{HetNet}}=I_{\text{macro}}+I_{\text{small}} \tag{25}\]
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 3 Methodology  
Vehicle re-identification is a retrieval task, and its key to problem-solving is constructing a judgment basis for identifying vehicles. Considering the visual and spatial-temporal cues of vehicles, we extract their visual features and STPro for vehicle re-identification. As illustrated in Figure 2, our approach comprises two streams: visual features and STPro. Within the visual feature stream, we exploit an SaGT that contained a transformer encoder for embedding basic features, an SFM for constructing stripe-based features, and a standard transformer layer for extracting a global feature. All features are first passed through the batch normalization layer (BN) and then used to calculate the ID loss. Meanwhile, the SaGL is directly calculated by the original global and stripe-based features. In the STPro stream, the pattern of spatial-temporal transfer for vehicles is based on the kernel density estimation model. Finally, during inference, the visual (global) feature distance and STPro corresponding to the pair of query and gallery images are fused to obtain the final similarity of the pair.
# Time Shared Optical Network (TSON): A Programmable Network Edge Solution for Multi-Access Support
## 5 Conclusions  
We proposed the TSON in order to provide a dynamic optical transport solution in support of 5G technology and beyond. The TSON solution has been adapted and evolved over the course of the TOUCAN project. This work presents the evaluation stages. We started with the TSON baseline stage, focusing on the TSON solution idea, its architecture, and functionality. TSON baseline only supports one client with Ethernet protocol; therefore, we have extended it to support multi-protocol with multi clients. xHaul, as the multi-protocol scenario, was chosen for this extension, and synchronisation capabilities were added to the TSON. Then, the TSON was extended to support NC for resilient C-RANs for the sake of 33% reduction in utilisation. The goal of the next evolutionary step is creating a disaggregated edge node to support both metro and long-haul networks. Finally, the current TSON architecture was presented and evaluated using the 5 GUK test network. The evaluation results denote the reliability of the TSON solution over dark fibres.  
The following are available online at [[https://www.youtube.com/watch?v=F6](https://www.youtube.com/watch?v=F6) xYhiNO_g]([https://www.youtube.com/watch?v=F6](https://www.youtube.com/watch?v=F6) xYhiNO_g): Video S1: Programmable multi-technology 5G node.  
Conceptualisation, A.F.B., A.T., R.N. and D.S.; methodology, A.F.B., A.T. and R.N.; software, A.F.B.; validation, A.F.B.; formal analysis, A.F.B.; investigation, A.F.B.; resources, A.F.B., A.T. and R.N.; data curation, A.F.B., A.T. and R.N.; writing--original draft preparation, A.F.B.; writing--review and editing, A.F.B., A.T. and R.N.; visualization, A.F.B.; supervision, D.S.; project administration, D.S.; funding acquisition, D.S.  
This research was funded by the U.K. Engineering and Physical Sciences Research Council (EPSRC), grant number EP/L020009/1, TOUCAN.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 5 Energy-Efficient Transmission Algorithm
### Problem Formulation
In applications that require relatively low latency, the buffer threshold should be reduced. The impact of buffer threshold on transmission performance will be presented in quantitative analysis and simulation, which can be used to select buffer threshold.
## 3 Results and Discussion
### Field-Size Dependence  
For circular beams with an homogenous lateral distribution, as those used in the current work, a proportionality of the detector signal with the square of the beam radius is expected. In order to verify such a dependence, the collected charge was measured using irradiation fields of different diameters (5 mm, 10 mm, 15 mm, 20 mm, 25 mm, and 36 mm). A second-order polynomial fit was applied and an \(r^{2}\) value equal to 0.9978 was obtained, confirming the expected behavior of the detector response.
## 4 Experiments  
### Experimental Data  
To perform a comprehensive performance evaluation of the proposed method, six actual scan datasets were used in this study. Four of these datasets were point cloud data obtained from the parking lot, basement, and apartments 1 and 2 of an apartment complex construction site. The remaining two datasets were point cloud data obtained from lecture rooms 1 and 2 at Inha University. Figure 10 shows the six experimental datasets after voxel-grid downsampling and removal of floors and ceilings. Detailed descriptions of these datasets can be found in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Initial Number of** \\ **Points (Size)** \\ \end{tabular} & \
\begin{tabular}{c} **Downsampled Points** \\ **(Size)** \\ \end{tabular} &
\begin{tabular}{c} **Volume Size of Point** \\ **Cloud (m)** \\ \end{tabular} & **Indoor Complexity** \\ \hline Parking lot & 158,575,772 (6.3 GB) & 877,184 (10.0 MB) & 100.5 \(\times\) 52.6 \(\times\) 2.2 & high \\ \hline Basement & 16,518,293 (0.7 GB) & 333,587 (3.8 MB) & 26.3 \(\times\) 21.6 \(\times\) 2.5 & low \\ \hline Apartment 1 & 121,652,038 (3.9 GB) & 149,896 (1.7 MB) & 10.2 \(\times\) 16.7 \(\times\) 2.3 & low \\ \hline Apartment 2 & 52,150,675 (1.4 GB) & 106,391 (1.2 MB) & 15.4 \(\times\) 9.9 \(\times\) 2.3 & low \\ \hline Lecture room 1 & 22,082,415 (0.7 GB) & 65,027 (0.8 MB) & 12.6 \(\times\) 7.7 \(\times\) 2.2 & high \\ \hline Lecture room 2 & 43,856,412 (1.4 GB) & 116,533 (1.9 MB) & 12.6 \(\times\) 15.5 \(\times\) 3.3 & high \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of datasets used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Structuring Method** & \
\begin{tabular}{c} **Original Point Cloud** \\ **(52,150,674 Points)** \\ \end{tabular} &
\begin{tabular}{c} **Downsampled Point Cloud** \\ **(106,391 Points)** \\ \end{tabular} \\ \hline KD-tree & 2 m 11 s & 0.4 s \\ Ball-tree & 1 m 48 s & 0.2 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of KD-tree and ball-tree methods for the unsupervised radius-based nearest neighbor search algorithm (radius = 0.1 m).  
Figure 9: Structural elements and indoor clutter objects classification example based on the use of the unsupervised radius-based nearest neighbor search algorithm (red points: structural elements; blue points: indoor clutter objects).
## 2 Materials and Methods
### Experimental Set-Up
In the measurements of air temperature (\(T_{e}\)) and pear surface temperature (\(T_{s}\)), J type iron-constantan thermocouples were used with a manually controlled 20-channel automatic digital thermometer (ELIMKO, 6400), with reading accuracy of \(\pm 0.1\)\({}^{\circ}\)C. Drying cabinet air velocity was measured by a 0-15 m/s range anemometer (LUTRON, AM-4201), with reading accuracy of \(\pm 0.1\) m/s. Mass loss of pears were measured during drying by digital balance (BEL, Mark 3100, Monza, Italy) in the measurement range of 0-3100 g and an accuracy of \(\pm 0.01\) g. The solar radiation during the operation period of drying system was measured with a Kipp and Zonen pyrometer in \(\pm 0.1\) W/m\({}^{2}\) accuracy and its CC12 model digital solar integrator. The initial and final moisture content of mushrooms was determined at 80 \({}^{\circ}\)C by Unibloc moisture analyzer (Shimadzu MOC63u) in \(\pm 0.001\) g accuracy.
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 3 Materials and Methods
### Spatial Similarity-Aware Module  
The SSA consists of two main components: similarity feature extraction and similarity feature fusion. The former primarily focuses on extracting object features in the spatial dimension, while the latter fuses the extracted features with the query features. The structure is illustrated in detail in Figure 1.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 3 Deep-Reinforcement-Learning-Based Cooperative Downloading Scheme
### MDP Formulation
#### 3.2.1 State  
The current state of satellite networks should represent the amount of retaining data and connection information of satellites. First of all, to effectively capture the topological relationship, a satellite network is represented as an undirected graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}=\mathrm{L}\cup\mathrm{K}\) denotes a set of entire nodes in the satellite network and \(\mathcal{E}=\left\{(v_{i},v_{j})|v_{i},v_{j}\in\mathcal{V}\right\}\) denotes a set of entire links. The time-varying feature of nodes and the shape of the graph are determined by a feature matrix \(F(t)\in\mathbb{R}^{|\mathcal{V}|\times(1+2|\mathcal{V}|)}\) and an adjacency matrix \(A(t)\in\mathbb{R}^{|\mathcal{V}|\times|\mathcal{V}|}\), respectively. Specifically, each row of the feature matrix represents a feature vector of each node. The feature vector \(F_{\mathrm{v}}(t)\) of node \(v\in\mathcal{V}\) is defined as  
\[F_{\mathrm{v}}(t)=\left[m_{\mathrm{v}}(t)||c_{\mathrm{v}}^{0}(t)||c_{\mathrm{ v}}^{1}(t)\right]\in\mathbb{R}^{(1+2|\mathcal{V}|)}, \tag{1}\]  
where \(\cdot||\cdot\) denotes the concatenation symbol and \(m_{\mathrm{v}}(t)\in\mathbb{R}\) represents the amount of retaining data. \(c_{\mathrm{v}}^{0}(t)\in\mathbb{R}^{|\mathcal{V}|}\) denotes the remaining time until the contact with all other nodes begins, and \(c_{\mathrm{v}}^{1}(t)\in\mathbb{R}^{|\mathcal{V}|}\) denotes the remaining time until the contact with all other nodes is terminated. Note that, as satellites follow their assigned orbits, it is easy to obtain such contact information of satellites [22]. The adjacency matrix \(A(t)\) is symmetric, and its element \(A_{ij}(t)\in\{0,1\}\) represents whether there is a link between node \(v_{i}\) and node \(v_{j}\), which can be defined as  
\[A_{ij}(t)=\begin{cases}1,&\text{if }v_{i},v_{j}\in\mathrm{L}\text{ and }d_{ij}(t)\leq d_{\mathrm{th}}^{\mathrm{SL}}\\ 1,&\text{if }v_{i}\in\mathrm{L},v_{j}\in\mathrm{K},\text{ and }d_{ij}(t)\leq d_{\mathrm{th}}^{ \mathrm{SCL}}\\ 0,&\text{otherwise}.\end{cases} \tag{2}\]  
Finally, the state \(s_{t}\) is defined with the above-mentioned feature matrix and adjacency matrix as follows  
\[s_{t}=\{F(t),A(t)\}. \tag{3}\]
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 4 Simulation Results and Analysis  
### Simulation Parameter Settings  
In this paper, an extensive Monte Carlo simulation is carried out to verify the accuracy of the theoretical formula, and the performance of the system using NOMA, OMA, wireless single-hop, and other transmission schemes under different parameters is compared and analyzed. Without loss of generality, the following default Settings are adopted for simulation parameters in this section unless otherwise specified [18,24]: for the physical layer, the link distance of each hop is normalized--let \(d_{\text{SR}}=d_{\text{RD}}=1\)--and the fading factors of wireless and power line channel path are \(\
u=2\), \(\alpha=1.5\); let the average SNR of the wireless and power line channels be: Bernoulli-Gaussian pulse noise parameters are \(p=0.1\), \(k=0.02\), and wireless channel noise power is \(N_{w}=0.1\) W; the channel interrupt threshold is \(\gamma_{\text{th}}=0.1\); for the MAC layer, \(m=4\), \(CW=[16,32,64,128]\), \(CW_{\text{min}}=16\); the frame duration is \(T_{D}=5000\)\(\upmu\)s. In addition, it is assumed that all packets use the same physical rate and adopt the standard prescribed time slot duration and timing parameters. The fixed general parameter settings [14,15,25,26] of the various overhead introduced by the MAC layer due to priority recognition, leading codes, acknowledgements, and interframe space are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Numerical Value** \\ \hline Slot Time (\(\sigma_{w}\)) & 50 \(\upmu\)s \\ Packet Header (\(T_{\text{H}}\)) & 400 \(\upmu\)s \\ Short interframe space (\(T_{\text{SIFS}}\)) & 28 \(\upmu\)s \\ Distributed Interframe Space (\(T_{\text{DIFS}}\)) & 128 \(\upmu\)s \\ Acknowledgement frame (\(T_{\text{ACK}_{w}}\)) & 240 \(\upmu\)s \\ Channel propagation delay (\(\delta\)) & 1 \(\upmu\)s \\ \(T_{\text{AT}_{w}}\) & \(T_{\text{ACK}_{w}}+60\)\(\upmu\)s \\ \hline \end{tabular}
\end{table}
Table 2: Main parameters and values of MAC layer protocol of wireless communication.
## 3 Methodology
### Proposed LSTM-Attention-BiTCN Model  
#### 3.3.1 Long Short-Term Memory (LSTM)  
In this research, we incorporated Long Short-Term Memory (LSTM) networks, a specialized form of Recurrent Neural Networks (RNNNs) renowned for their ability to capture and preserve long-range dependencies within sequential data. Unlike traditional RNNs, LSTMs exhibit a heightened capacity to remember information over extended periods, thus mitigating the commonly encountered vanishing gradient issue often associated with standard RNNs [15]. The core functionality of LSTM networks resides in their unique architecture, characterized by three principal gates within each neuron: the forget gate, input gate, and output gate. These gates collectively govern the flow and modulation of information through the network. They execute this control by leveraging the hyperbolic tangent and sigmoid activation functions, which play a crucial role in updating and maintaining the cell state.  
In the proposed model, we employed an LSTM layer comprising 100 units. This layer was tasked with receiving the vector representations processed by a preceding spatial dropout layer. The LSTM layer then outputs a sequence of values, which effectively encapsulate the encoded information from the input vectors, tailored for subsequent stages of the neural network model. This design choice was guided by the necessity to capture both the spatial and temporal dynamics inherent in the dataset under analysis.