esa-sceva/satcom-chunk-collection huggingface dataset:
## Minimum Spanning Tree  
### Definition  
Minimum spanning tree is often used to solve practical problems of applying graph theory knowledge. It is widely used in computer science, mathematics, and data structure fields. The Kruskal algorithm and the Prim algorithm are the two most used algorithms in the minimum spanning tree. The minimum spanning tree is defined as [29] follows:  
Figure 1: The main research content of this paper.  
Let G = (V, E) denote a connected graph, where V and E represents the vertex set V = {\(v_{1}\), \(v_{2}\),..., \(v_{n}\)} and the edge set E = {1, 2,..., m}, respectively. A spanning tree T = T (V, S) is defined as a connected acyclic subgraph of G that includes all vertices. Obviously, S is a subset of E. To simplify the model, we denote a spanning tree T by its edge set S in this paper. represents the edge connecting vertex \(u\) to vertex \(v\), and \(w\) represents the weight of this edge. A spanning tree \(T^{0}\) is said to be a minimum spanning tree if Equation (1):  
\[\sum_{(u,\ v)\in T^{0}}w(u,\ v)\leq\sum_{(u,\ v)\in T}w(u,\ v), \tag{1}\]  
holds for any spanning tree \(T\).  
A graph G with 4 vertices and 6 edges is shown in Figure 2. When E = (1, 3, 4, 2, 2, 3), the unique minimum spanning tree is \(T^{0}\) = {AB, AD, AC} with \(\sum_{(u,\ v)\in T^{0}}w(u,\ v)=1+2+2=5\).  
According to the principle of minimum spanning tree, if the studied area is simplified to a connected graph, where the distance relation is the weight of the connected graph, then the calculated result is the topological project with the shortest total pipeline length. It can be inferred that the minimum spanning tree algorithm is suitable for the design and research of pipe network in DES. Prim algorithm is employed for resolution of the model, and the calculation operated on MATLAB R2018a software. The calculation steps are briefly described as follows:  
1. Input: A weighted connected graph, G = (V, E). The set of vertices is V, the set of edges is E;
2. Initialization: V\({}_{\text{new}}\) = {x}, x is any node (starting point) in set V. E\({}_{\text{new}}\) = {} is an empty set;
3. Repeat the following until V\({}_{\text{new}}\) = V: 1. Select the edge (\(u\), \(v\)) with the smallest weight in set E, where u is an element in set V\({}_{\text{new}}\) and \(v\) is not in the set V\({}_{\text{new}}\), and \(v\in\) V; 2. Add v to set V\({}_{\text{new}}\), and add edge (\(u\), \(v\)) to set E\({}_{\text{new}}\);
4. Output: The collections V\({}_{\text{new}}\) and E\({}_{\text{new}}\) are used to describe the resulting minimum spanning tree.
## 2 Proposed Vehicle Detection and Tracking System
### Vehicle Tracking with Occlusion Handling
First, an existing light pair tracker \(TP_{j}^{t-1}\in\mathbf{TP^{t-1}}\) cannot be matched by any light pairs \(P_{i}^{t}\in\mathbf{P^{t}}\) in the current frame \(t\). Second, the predicted positions of the left and right lights in \(TP_{j}^{t-1}\) do not match any light trackers \(TL_{i}^{t}\in\mathbf{RTL^{t}}\). Thus, the light pair tracker \(TP_{j}^{t-1}\) is not added to the set of light pair trackers \(\mathbf{TP^{t}}\) in the current frame. 4. **Occlude:** a light pair tracker is determined as occluded if it satisfies the following two conditions. First, an existing light pair tracker \(TP_{j}^{t-1}\in\mathbf{TP^{t-1}}\) cannot be matched by any light pairs \(P_{i}^{t}\in\mathbf{P^{t}}\) in the current frame \(t\). Second, in the case of a pairing failure due to the disappearance of one lamp in a light pair as shown in Figure 11a, the predicted position of the left or right light in \(TP_{j}^{t-1}\) matches a light tracker \(TL_{i}^{t}\in\mathbf{RTL^{t}}\) (see Equation (8)). Otherwise, in the case of the distorted appearance of one lamp in a light pair as shown in Figure 11b, the predicted positions of both the left and right lights in \(TP_{j}^{t-1}\) match two light trackers \(TL_{i}^{t}\) and \(TL_{i^{\prime}}^{t}\in\mathbf{RTL^{t}}\). Then, depending on the whether the first or second case occurs, the light pair tracker \(TP_{j}^{t}\) is updated by associating \(TP_{j}^{t-1}\) with one or two light trackers in \(\mathbf{RTL^{t}}\) and it is added to the set of light pair trackers \(\mathbf{TP^{t}}\). At the end of the vehicle tracking step, the set of light pair trackers \(\mathbf{TP^{t}}\) in the current frame \(t\) is retrieved. Next, for any vehicles with two light pairs, we apply the rules for grouping two light pairs into one pair. Two light pairs \(TP_{i}^{t}\) and \(TP_{j}^{t}\) are grouped into one pair if they satisfy the following rules:  
Figure 11: Pairing failure: (**a**) disappearance of one lamp in a light pair; (**b**) distorted appearance of one lamp in a light pair. 1. They are vertically close to each other: \[0<D_{p}(TP_{i}^{t},TP_{j}^{t})<2.0\times min\{H(TP_{i}^{t}),H(TP_{j}^{t})\}\] (15)
2. Two pairs have highly overlapped horizontal projections: \[O_{h}(TP_{i}^{t},TP_{j}^{t})>\tau_{hp}\] (16)
3. They have similar widths: \[\frac{min\{W(TP_{i}^{t}),W(TP_{j}^{t})\}}{max\{W(TP_{i}^{t}),W(TP_{j}^{t})\}}> \tau_{w}\] (17)  
In the post-processing step, \(\tau_{hp}\) and \(\tau_{w}\) are the thresholds for grouping multiple pairs that form one vehicle. They are chosen statistically as 0.9 and 0.7, respectively. The results of the post-processing are illustrated in Figure 12.
# MTRSRP: Joint Design of Multi-Triangular Ring and Self-Routing Protocol for BLE Networks
## 3 Motivation
For example, the nodes can play different roles in different application scenarios. Similarly, a device may impose more restrictions based on its role. To meet traffic demands, standards must also be designed to form participating nodes with scatternet formations that are largely dependent on specific applications. To develop a load-balancing protocol with a balanced connectivity configuration suitable for both piconets and scatternets, this study proposes the following constraints to construct multiple networks, resulting in a topology structure that is easier to control:  
1. According to the Bluetooth Core Specification, each master node can support up to seven active slave nodes due to the 3-bit address space (AM_ADDR). This constraint ensures stable scheduling and connection management under time-division duplexing (TDD). 2. Each piconet is connected to others through slave/slave (S/S) bridge nodes. This supports stable scatternet construction without routing overhead, especially during topological changes, and improves resilience through decentralized ring-based connectivity. 3. Each bridge node is limited to connecting two piconets to reduce time-division switching complexity, avoid communication overload, and minimize energy consumption on resource-constrained BLE devices. 4. Limiting the connections between two piconets to one or two bridges helps define the minimum number of required piconets and ensures deterministic termination of the formation protocol. It also avoids routing loops and supports modular ring construction.
# Spin Dependent Transport through Driven Magnetic System with Aubry-Andre-Harper Modulation
## 2 Magnetic Junction, TB Hamiltonian and Theoretical Formulation
### Theoretical Formulation  
The spin polarization coefficient of the above Hamiltonian (Equation (1)) is obtained by determining the spin dependent transmission probabilities which we compute by using [PERSON]'s function formalism where the effects of S and D are incorporated through self-energy corrections. For comprehensive analysis of this formalism, we recommend the general readers to see the Refs. [78; 79]. The effective [PERSON]'s function of the magnetic chain reads as [78; 79]  
\[G^{\prime}=\left(G^{a}\right)^{\dagger}=\left(EI-H_{mag}-\Sigma_{S}-\Sigma_{D }\right)^{-1} \tag{8}\]  
where \(E\) is the energy of the incoming electron from the source end and \(I\) is the identity matrix having dimension (\(2N\times 2N\)). \(\Sigma_{S}\) and \(\Sigma_{D}\) are the self-energy matrices of the S and D, respectively. From this [PERSON]'s function, we determine the transmission probabilities using the Fisher-Lee expression [78; 79; 80; 81; 82]  
\[T_{\sigma\sigma^{\prime}}=\mathrm{Tr}\left[\Gamma_{S}^{\sigma}G^{\tau}\Gamma_ {D}^{\sigma^{\prime}}G^{a}\right] \tag{9}\]  
where \(\Gamma_{S}^{\sigma}\) and \(\Gamma_{D}^{\sigma^{\prime}}\) are the coupling matrices. These coupling matrices are found from the self-energy matrices via the relations  
\[\Gamma_{S(D)}^{\sigma(\sigma^{\prime})}=i\left[\Sigma_{S(D)}^{\sigma(\sigma^{ \prime})}-\left(\Sigma_{S(D)}^{\sigma(\sigma^{\prime})}\right)^{\dagger} \right]. \tag{10}\]  
Using Equation (9) we get both pure (\(\sigma=\sigma^{\prime}\)) and spin flip (\(\sigma\
eq\sigma^{\prime}\)) transmissions through the magnetic junction. With these co-efficients we get the net up and down spin transmission probabilities as  
\[T_{\uparrow} = T_{\uparrow\uparrow}+T_{\downarrow\uparrow}, \tag{11}\] \[T_{\downarrow} = T_{\downarrow\downarrow}+T_{\uparrow\downarrow}. \tag{12}\]Determining \(T_{\uparrow}\) and \(T_{\downarrow}\), we evaluate the spin polarization coefficient \(P\) with the relation [83; 84; 85; 86; 87]  
\[P=\frac{T_{\uparrow}-T_{\downarrow}}{T_{\uparrow}+T_{\downarrow}}. \tag{13}\]  
\(P=\pm 1\) represents complete up (down) spin polarization, while \(P=0\) denotes vanishing polarization.
## 5 Experiments
### Experimental Evaluation
#### 5.3.3 Custom Environments
The SMP method achieves significantly lowest scores with configurations 9a, 10a (low vehicle utilisation). LSAP scores best with configuration 9b (low P/T ratio, high vehicle utilisation). Regarding the total travelled distance, STTF scores worst for all configurations. Here, STTF performs best only for configuration 9a. The efficiency-related metrics do not show a clear results here. For configurations 9a and 10a we find LSAP significantly outperforms the other two methods, and STTF being outperformed by the other two. Configuration 10b (high P/T ratio, high vehicle utilisation), on the other hand, shows best performance on the side of STTF for both scores in contrast, here SMP being lowest. Additionally, for configuration 9b (low P/T ratio, high vehicle utilisation), we observe best performance by STTF in terms of travel efficiency (worst here: SMP), but lowest performance in terms of throughput effort (best here: LSAP). Regarding Custom 3 layout, STTF performs significantly worst throughput-wise at configurations 11a and 12b, but best at configuration 12a. For the configurations 11b and 12a, SMP achieves the lowest throughput performance, whereas it scores best at configuration 11a. The total travelled distance demonstrates the same result as before, with STTF scoring lowest for all configurations. Here, best performance is achieved by LSAP for configuration 11b and SMP for configuration 12a. With the efficiency scores, we reproduce the pattern of worst performance achieved by STTF only for configurations 11a, 12a, and 12b. Configuration 11b does not show a clearly worst performance regarding the travel efficiency, here SMP scores worst with the throughput effort. The best efficiency scores are achieved by SMP for configurations 11a, 12b (both only significant for travel efficiency), and 12a (both scores). Best efficiency with configuration 11b is achieved by the LSAP method. The reassignment-based methods SMP and LSAP tend to outperform STTF especially on the efficiency scores, but for some configurations we observe the opposite effect. Compared against each other, we find that LSAP scores better than SMP, but this again does not hold for all configurations. LSAP and STTF tend to outperform SMP in terms of throughput rate for most, but not for all configurations.
# Improving Named Entity Recognition for Social Media with Data Augmentation
## 3 Method
### Bi-Lstm  
Contextual information is crucial when processing sequential data to accurately understand and predict each token in the sequence. To extract this information, Bi-LSTM can be used as an encoder and decoder. By processing both the forward and backward information of the sequence, bidirectional LSTM can better capture the long-range dependencies between elements and extract context vectors. The vector representation of the hidden layer output of LSTM model is defined as follows:  
\[f_{t}=\sigma\Big{(}W_{fx}\cdot x_{t}+W_{fh}\cdot h_{t-1}+b_{f}\Big{)} \tag{4}\]  
\[i_{t}=\sigma(W_{ix}\cdot x_{t}+W_{ih}\cdot h_{t-1}+b_{i}) \tag{5}\]  
\[\widetilde{c}_{t}=\tanh(w_{cx}\cdot x_{t}+w_{ch}\cdot h_{t-1}+b_{c}) \tag{6}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\widetilde{c}_{t} \tag{7}\]  
\[o_{t}=\sigma(W_{ox}\cdot x_{t}+W_{oh}\cdot h_{t-1}+b_{o}) \tag{8}\]  
\[h_{t}=o_{t}*\tanh(c_{t}) \tag{9}\]where \(W_{fx}\), \(W_{fh}\), and \(b\) represent weight matrices and bias vectors connecting the input \(x_{t}\) and the previous output \(h_{t-1}\) to the LSTM unit. The sigma symbol \(\sigma\) represents the sigmoid activation function. The input gate, forgetting gate, and output gate are represented by \(i_{t}\), \(f_{t}\), and \(o_{t}\). The point multiplication operation is denoted by \(*\). The variable \(\widetilde{c}_{t}\) represents the input modulation gate, and \(c_{t}\) and \(h_{t}\) represent the cell state and output of the LSTM unit at time \(t\), respectively.  
In the encoder, the input sequence is encoded into a context vector that is passed to the decoder for the next prediction step. In the decoder, this vector serves as contextual information that is combined with the current state of the decoder and previous output to generate the next output. Specifically, the hidden state of Bi-LSTM can be expressed as follows:  
\[h_{i}=\overset{\rightarrow}{h}_{i}\bigoplus\overset{\leftarrow}{h}_{i} \tag{10}\]  
where \(\overset{\rightarrow}{h}_{i}\) and \(\overset{\leftarrow}{h}_{i}\), respectively, represent the hidden states of the forward and backward LSTM at position \(i\); \(\bigoplus\) represents the concatenation operation.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 3 Traveling Wave Head Detection Algorithm Based on CEEMDAN-TEO
### Algorithm Adaptation Analysis
#### 3.4.2 Different Fault Types  
Simulation tests of different types of short circuits with a fault resistance of 50 \(\Omega\) were conducted on a transmission line located 80 km from the M terminal. The fault distance was estimated using the algorithm presented in this paper, and the errors are shown in Table 2.  
As demonstrated in Table 2, the method proposed in this paper exhibits higher positioning accuracy. It can conduct fault location for any fault type, meeting the requirements for accurate line positioning. This is in contrast to [PERSON] et al. [19].  
\begin{table}
\begin{tabular}{c} **Relative** \\ **Error/\%** \\ \end{tabular} \\ \hline
0 & 49.921 & 79 & 0.158 & 49.837 & 163 & 0.326 \\
10 & 49.912 & 88 & 0.176 & 50.198 & 198 & 0.396 \\
50 & 50.096 & 96 & 0.192 & 50.241 & 241 & 0.482 \\
200 & 50.111 & 111 & 0.222 & 49.764 & 236 & 0.472 \\
500 & 49.846 & 154 & 0.308 & 49.743 & 257 & 0.514 \\
1000 & 49.824 & 176 & 0.352 & 50.346 & 346 & 0.692 \\
2000 & 50.251 & 251 & 0.502 & 50.428 & 428 & 0.856 \\ \hline \end{tabular}
\end{table}
Table 1: Positioning results under different transition resistors.  
Figure 3: The comparative experimental results of this paper and [PERSON] et al. [19].
## 5 IoT Device Identification Based on Time Series Representations
### Experimental Conditions  
In the experiments, two comparison targets were prepared to evaluate the effectiveness of the proposed method. First, we used the matrices based on the time series representations with one- and two-stage clustering as the datasets. Second, the \(n\times 60\) (\(n=\{1,2,\cdots,6\}\)) matrices obtained by extracting n rows from the top of the representation shown in Figure 14 were used as the input data. The second comparison evaluated whether analyzing IoT traffic with multiple values of units of time can reveal time variability in more detail.  
For the parameters of the identification model, the number of LSTM layers and their hidden layers were set at 2 and 64, respectively. As the input traffic data, the same dataset [23] consisting of 21 types of IoT devices listed in Table 1 used for the IoT traffic analysis in Section 3 was used. For the training phase of the LSTM network, the traffic data captured on 28 September 2016 were used: 80% of them were randomly selected as the training dataset, and the remaining 20% were used as the validation dataset. For the evaluation phase of the trained model, traffic data captured on 4 October 2016 were used as the test dataset.  
As indices for performance evaluation, we introduced four typical machine learning metrics: accuracy, recall, precision, and F-measure. As the recall, precision, and F-measure were obtained for each IoT device classification, we used their averages to evaluate the overall classification performance.
## 2 Materials and Methods
### Dosimetry for Therapeutic X-ray Beams
#### 2.2.4 Angular Dependence  
The angular dependence of the edgeless array was assessed as a function of the angle between the beam and the array plane over a range spanning from 0\({}^{\circ}\) to 180\({}^{\circ}\), achieved by rotating the LINAC gantry while keeping the detector in a fixed position. The setup of the experiment is illustrated in Figure 3. When the gantry was rotated 90\({}^{\circ}\) (aligned along the X-axis), the edgeless array was facing the beam. This setup allowed measurements for angles between the beam and the array plane to vary between 0\({}^{\circ}\) and 180\({}^{\circ}\), avoiding the patient couch from generating any distortion of the beam.  
The edgeless array is placed at a 15 cm depth in a cylindrical phantom having a 30 cm diameter and 40 cm length, and made of PMMA, which is described in previous studies [30; 31; 41]. Precise positioning of the central diode was achieved at the isocenter of the LINAC using a computed tomography (CT) scan.  
To facilitate accurate dose calculations, a computed tomography (CT) scan of the cylindrical phantom, which included a detector probe without mounting the diodes, was conducted. This CT scan served the dual purpose of providing relative electron density information for dose computations and aligning the central diode precisely with the isocenter of the LINAC. The CT scan was executed at a voltage of 120 kVp with an exposure setting of 450 (mAs). The field of view spanned 600 mm, and the CT slices were configured at a thickness of 1 mm.  
The doses detected by each edgeless diode were then normalized by dividing them by the corresponding dose values provided by the Varian Eclipse Treatment Planning System (TPS), employing the Acuros XB algorithm (version 15.6) as the reference.
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## 3 Link Efficiency for 300-mm and 600-mm Aperture Ground Stations
Table 2 summarizes the baseline parameters of the setups required for further modeling. The 600 mm ground station is a fixed telescope equipped with a quantum states decoder, as detailed in Ref. ([PERSON], 1996). The 300 mm ground station is a portable receiving telescope equipped with a quantum communications optical system ([PERSON], 1996), which has a better optical efficiency than a 600 mm telescope. The detailed description of the experimental setups are provided in Appendix C.  
Figure 3: The experimental data and the trend lines of the relationship between atmospheric extinction increase and air mass as estimated by stars’ fluxes observations on clear and foggy nights. The raw data are listed in Table 1 and were acquired with the 600-mm ground station.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 2 State of the Art
### Architecture  
The multiplicity of solutions to address each of the challenges posed provides an indication of the current landscape of standardisation. Currently, the main IoT architectures are:  
* The European Telecommunications Standards Institute M2M Technical Committee [29].
* International Telecommunication Union--Telecommunication sector view [30].
* Internet Engineering Task Force architecture fragments [31].
* Open Geospatial Consortium architecture [31].
* Internet of Things-Architecture (IoT-A) [32].  
The IoT-A differs from the rest in that it creates a reference architecture consisting of a horizontal ARM (_Architectural Reference Model_) enabling the design of any IoT application. IoT-A and FIWARE are both European initiatives. Though they are in many aspects independent to each other, FIWARE usually follows the model defined by the IoT-A. Despite these efforts, there is still no architecture for IoT systems, nor is there a widely recognised universal set of standards.
## 3 Findings and Discussion
### Effects of the HRT on the Treatment Efficiency of CW  
In general, the levels of UV-254, DOC, sCOD, and NH\({}_{3}\)-N in the effluent in the last two runs of the first phase experiment were less than those in the influent. It can be also realized that there was a relatively stable removal during the operational period, which could be seen from the low standard deviation values. As can be seen from Figure 6, most of monitored water quality parameters in the case of a HRT of 2 days had higher removal efficiencies than those of HRT of 1 day; however, there was only a small discrepancy among all values in these two treatment processes. For example, the percentages of removal of UV-254 and DOC were 80.8 and 71.9 in the 1 day HRT test, compared to 85.0 and 73.0 in the HRT 2 day test, respectively.  
In terms of OMs removal, the results showed that there was a slight decline in concentrations of UV-254, DOC and sCOD during both operation time of 1 day-HRT and 2 day-HRT. However, the difference in the removal rate of OMs between two runs was small. This can be explained by the fact that most of the labile biological organic compounds were degraded during the first day of the treatment process, although there was still a process of degradation of OM by microorganisms in CW which kept occurring on the second day, shown by a slight decrease of UV-254 from 0.178 \(\pm\) 0.012 cm\({}^{-1}\) for 1 day HRT to 0.135 \(\pm\) 0.017 cm\({}^{-1}\) for 2 day HRT. This process just helps to degrade the large molecule OM into smaller species without complete mineralization because the level of DOC in the effluent in the two cases recorded little change, at 3.119 \(\pm\) 0.149 mg/L for 1 day-HRT, compared to 3.017 \(\pm\) 0.292 mg/L for 2 day-HRT.  
On the other hand, a considerable increase from 61.7 \(\pm\) 7.0% to 73.0 \(\pm\) 4.4% in the removal efficiency of NH\({}_{3}\)-N can be seen from Figure 6 during two studied runs (Run 3 and Run 4). This leads to the conclusion that the more the contact time with the CW is, the higher ammonia removal efficiency is obtained, thanks to the more complete ammonification process. In fact, HRT plays a more significant role in the removal of ammonia [39].