esa-sceva/satcom-chunk-collection huggingface dataset:
# Converting MST to TSP Path by Branch Elimination
## 5 Conclusions  
We have introduced heuristic algorithm that takes minimum spanning tree as input and converts it to open-loop TSP solution by eliminating all branches from the tree one by one. The randomized version of the proposed MST-to-TSP algorithm clearly outperforms Christofides and other MST-based heuristics. With the O-Mopsi and Dots datasets it reaches near-optimal results not far from more sophisticated local search algorithm. The proposed algorithm has educational value by showing the connection between two problems, MST and TSP, of which one can be solved optimally using greedy heuristics while the other is known to be NP hard problem. The result of the algorithm can also be used as initial solution for local search heuristic or optimal algorithms like branch-and-cut to improve the efficiency of the search [36; 37].  
Figure 16: Examples of Christofides, Kruskal-TSP, and MST-to-TSP (greedy variant) with the three more challenging O-Mopsi games. _Barcelona Grand Tour_ and _Turku City 2.2.2015_ have four branches while _Odense_ has five. None of the algorithms finds the optimal solution.  
**Author Contributions:** Conceptualization, P.F. and T.N.; implementation, T.N. and M.Y.; experiments, T.N. and M.Y.; writing, P.F. All authors have read and agreed to the published version of the manuscript.  
No funding to report.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All datasets are publicly available: [[http://cs.uef.fi/o-mopsi/datasets/](http://cs.uef.fi/o-mopsi/datasets/)]([http://cs.uef.fi/o-mopsi/datasets/](http://cs.uef.fi/o-mopsi/datasets/)).  
**Acknowledgments:** The authors want to thank [PERSON] and [PERSON] for helping in the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements
## 4 Simulation Calculation of Gravity Gradient
The side length of the three test points at 50 m below the mean sea level needs to reach 108 m, which is 2.2 times the observation point depth. Furthermore, the gravity gradient forward modeling result is related to the integration range, and the selection of the integration range is related to the calculation height (depth). Therefore, to obtain relatively accurate forward modeling results, different integration ranges need to be considered when calculating at different heights (depths). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Side Length of**} & \multicolumn{2}{c}{**Vertical Gravity Gradient at 10 m below Mean Sea Level (E)**} \\ \cline{2-4}  & **Position A** & **Position B** & **Position C** \\ \hline
4 & 1.821 & \(-\)0.874 & 0.819 \\
8 & 6.008 & \(-\)2.813 & 2.675 \\
12 & 10.305 & \(-\)4.687 & 4.537 \\
16 & 13.446 & \(-\)5.952 & 5.858 \\
20 & 15.258 & \(-\)6.603 & 6.588 \\
**24** & **16.038** & \(-\)**6.818** & **6.874** \\
28 & 16.140 & \(-\)6.765 & 6.878 \\
32 & 15.838 & \(-\)6.566 & 6.719 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variation of gradient with integration range at 10 m below mean sea level. Figure 7: Relation between gradient and integration range.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Bed Sensor Event Data Pre-Processing  
For visualization purpose, we transform the second-based binary signal into and hourly based bedtime diary, by computing the time-percentage the sleep sensor was 'on' within each hour. Figure 3 illustrates the hourly bedtime diary of a user (from February 2019 to the end of June 2020) as a heat-map. We can clearly identify night sleep, which starts and ends with a repeating pattern, while day sleep is visible for only some periods of the time. Furthermore, two periods of close to no sleep are visible, resultingfrom a hospitalization from May 28 to July 15 in 2019, and a holiday from 28 November to 24 December 2019.  
Periods of continuous sleep sensor 'on'-state are considered to identify individual sleep events (e.g., night sleep, day naps). However, short 'off'-state periods can occur due to toss and turns, awakenings and other movements in bed. In such cases, it is necessary to merge the over-segmented events. We use medically recommended rules to identify those interruptions, based on which the over-segmented events are merged. Those interruptions will also be taken into consideration as toss and turns, awakenings, or breaks accordingly for sleep quality analysis.  
According to the National Sleep Foundation's (NSF) sleep quality recommendations [16], sleep interruptions of more than 5 min can be considered to be 'awakenings'; a break of less than 30 min indicates good sleep; breaks longer than an hour are considered as not 'appropriate'. Based on these recommendations we defined gaps of less than 5 min as 'toss and turn', gaps from 5 to 30 min as 'awakening', and gaps from 30 to 60 min as 'break', and merge the corresponding segments. Events of less than 10 min duration, which could be caused by sitting on the bed rather than sleeping, are considered to be not relevant and are therefore eliminated. Sleep events of less than 30% efficiency (percentage of 'on bed' time of a sleep event), which could result from merging short 'on bed' fractions (often sensor noise) with comparably large gaps in between, are also removed in the experiments.
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 3 Research Design
### Stage 2: Named Entity Extraction Model  
#### 3.3.1 Embedding Layer  
With the continuous development of deep learning technology, BERT has gradually become an important method for solving named entity recognition, especially in character-level embedding. Compared with traditional pre-training models, BERT adopts a bidirectional Transformer architecture based on the attention mechanism, which can realize global interaction between units [PERSON] and [PERSON] (2000). The graphical architecture of BERT is shown in Figure 2.  
BERT's input representation (E1, E2,..., En) consists of token embedding, sentence embedding and position embedding, while the output (T1, T2,..., Tn) can capture rich information at the word, sentence and position levels. During the pre-training process, BERT introduced two key tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP) [23]. MLM helps the model learn richer language representations by randomly masking 15% of the words in a sentence and requiring the model to predict these masked words, while NSP is used to determine whether a pair of input sequences are connected, helping the model understand the correlation between sentences.  
When processing Chinese text, the performance of the model is easily affected by the existence of confusing categories and typos in the text. Traditional pre-training models have difficulty in handling the problem of polysemy, which limits their ability to understand complex texts. BERT overcomes these shortcomings and can dynamically obtain the semantic information of each word from the context [24]. With this architecture, BERT has achieved remarkable results in many natural language processing (NLP) downstream tasks, demonstrating its powerful capabilities in information extraction and feature encoding. Therefore, this paper selects BERT as the embedding layer.
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 3 Network Model  
### Network Architecture  
The basic framework of this paper is shown in Figure 1, which consists of the following three parts: parked vehicles, parking clusters and moving vehicles on the road.  
(1) Parked vehicles: Parked vehicles parked on both sides of the roads. Parked vehicles own computation and storage capacities. They are member nodes of the parking clusters.  
(2) Parking cluster: It consists of parked vehicles on both sides of the road. A parking cluster includes many cluster members and a cluster head. The cluster head collects cluster member information for maintaining the structure. The cluster head is also responsible for maintaining the historical parking records of parked vehicle members. Moreover, to predict relatively stable parked vehicles in the cluster, a parking prediction model is executed by the cluster head.  
(3) Moving vehicle: Moving vehicles are intelligent vehicles driving on the road. Most smart vehicles are equipped with electronic maps, positioning systems, wireless communication modules and various intelligent sensing devices. Moving vehicles can communicate with each other by using the IEEE802.11p protocol [18].  
In this paper, some moving vehicles may be rogue nodes, which need to be detected in time through the formed fog network which is composed of stable parked vehicles in the parking cluster.
## 4 Experimentation and Loss Separation
### Winding Losses Results  
Under the same current level, the winding losses are then measured for each coil case at different frequency levels. The measured winding losses are compared with those obtained using the FEM, as shown in Figure 19. A very good agreement is obtained between all these approaches. Further, the measured winding losses of all five samples are compared in Figure 20. It is found that the flat copper coil (Coil I) has the lowest loss up to 400 Hz. Above this frequency, the 10-strand winding (Coil II) has the lowest losses up to 1.5 kHz. At frequencies above 1.5 kHz, the flat litz wire with 45 horizontal strands (Coil IV) is the best choice.  
Figure 19: Comparison between measured losses and the analytical calculation as well as the FEM simulation.  
Figure 20: Comparison between the measured power loss for the test samples at different frequency levels.  
Despite the flat coil (I) having low losses at low frequencies, the slope of its loss curve is the highest among all the samples. This means that AC losses in this coil increase much higher with frequency compared to the other stranded cases. On the counterpart, the flat lift coil (IV) has the lowest slope. As a result, it has remarkably lower AC losses, especially at high frequencies. Interestingly, this is not the case for Coil V, which has two vertical conductors per layer despite using the exact same wire. So, it can be concluded that using one conductor per layer is the optimal arrangement.  
Also, by comparing Coil III and Coil IV, it is found that both coils have almost the same profile up to 800 Hz. At higher frequencies, the advantage of the twisted strands of the flat litz wire (Coil IV) results in a significant decrease in the AC losses compared to the coil with random parallel strands (Coil III).
# Application of the Parabola Method in Nonconvex Optimization
## 2 Univariate Optimization: Parabola Method
### The Parabola Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Function and Interval** & **Solution** & **g/l** & **Record** & \(\mathbf{k}_{f}\) \\ \hline
1. & \(f(x)=\frac{x^{\varepsilon}}{6}-\frac{52}{82}x^{\varepsilon}+\frac{99}{80}x^{4}+\) & \(f^{*}=-29763.23\) & g & \(f^{\sharp}=-29763.23\) & 22 \\  & \(\frac{71}{10}x^{3}-\frac{79}{20}x^{2}-x+\frac{1}{10}\) & \(x^{*}=10\) & & \(x^{\sharp}=9.9996\) & \\  & \(x\in[-1.5,11]\) & & & & \\ \hline
2. & \(f(x)=\sin(x)+\sin\Bigl{(}\frac{10x}{3}\Bigr{)}\), & \(f^{*}=-1.899599\), & g & \(f^{\sharp}=-1.899599\) & 20 \\  & \(x\in[2.7,7.5]\) & \(x^{*}=5.145735\) & & \(x^{\sharp}=5.145733\) & \\ \hline
3. & \(f(x)=-\sum\limits_{k=1}^{5}k\sin((k+1)x+k)\), & \(f^{*}=-12.031249\), & g & \(f^{\sharp}=-12.031234\) & 22 \\  & \(x\in[-10,10]\) & \(x^{*,1}=-6.774576\) & & \(x^{\sharp}=-0.491707\) & \\  & \(x^{*,2}=-0.491390\) & & & \\  & \(x^{*,3}=\phantom{-}5.791794\) & & & \\ \hline
4. & \(f(x)=(3x-1.4)\sin(18x)\), & \(f^{*}=-1.489072\), & 1 & \(f^{\sharp}=-0.4612863\) & 17 \\  & \(x\in[0,1.2]\) & \(x^{*}=0.966086\) & & \(x^{\sharp}=0.6291664\) & \\ \hline
5. & \(f(x)=\sin(x)+\sin\Bigl{(}\frac{10x}{3}\Bigr{)}+\) & \(f^{*}=-1.601308\), & g & \(f^{\sharp}=-1.601308\) & 20 \\  & \(\ln(x)-0.84x+3\) & \(x^{*}=5.199778\) & & \(x^{\sharp}=5.199901\) & \\  & \(x\in[2.7,7.5]\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test problems 1-5. The sign g in the g/1 column corresponds to the case when a global minimum point was determined, and the sign l corresponds to the case when only a local minimum point was determined. Symbol \(k_{f}\) corresponds to the number of function evaluations. Surprisingly enough, only in four problems was a local minimum determined; in all other problems, the Golden Section Method found global solutions. The geometrical interpretation of failure of the Golden Section Method in finding a global minimum can be given on the basis of problems 7 and 11 from Table 2. In these cases, only local minimum points were determined, and, as can be seen from Figures 1 and 2, the global minimum points are located closer to the endpoints of the feasible intervals. Hence, we can make the following assumption: univariate multimodal problems with global minimum points located more or less in the middle of the intervals are more efficiently tractable by the Golden Section Method from the global optimization point of view. This topic needs further theoretical investigation and is not the subject of our paper.
## 4 Applications of ML-Based Routing Algorithms in WSNs  
WSNs integrates microelectronics, embedded computing, mobile networks, wireless communication, and distributed information processing technologies, which can accomplish real-time monitoring, sensing, and collecting information under any environmental conditions and location through various kinds of integrated miniature sensors in cooperation. Meanwhile, ML-based algorithms enable WSNs that are more powerful and intelligent. Therefore, ML-based WSNs have become one of the frontier hotspots involving a high degree of interdisciplinary and knowledge integration that is currently attracting international attention. Currently, ML-based WSNs are widely used in many fields such as intelligent healthcare [74; 75], industrial applications [69], underwater sensing [76; 77], intelligent transportation systems [78; 79; 80], and smart home [81] in recent years, because of the low cost, easy deployment, and high adaptability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Routing** & **ML Algorithm** & **Overhead** & **Scalability** & **Delay** & **QoS** & **Energy Efficiency** \\ \hline ESDFM [64] & Multi-hop & Kernel linear regression & Low & Limited & High & No & Reducing redundant data \\ EBC-S [65] & Geographic & SOM + K-means & High & Medium & Medium & No & Optimizing paths \\ ANNR [66] & Multi-hop & SOM & High & Limited & Medium & Yes & Optimizing paths \\ WL-DCNN [67] & Multi-hop & CNN & High & Good & Low & Yes & Optimizing paths \\ SeeDL [68] & Geographic & DNN & High & Good & Low & Yes & Reducing redundant data + \\ QLRR-WA [69] & Multi-hop & Q-learning & Low & Good & Low & No & Optimizing paths \\ Q-PR [70] & Geographic & Q-learning & Low & Limited & High & Yes & Optimizing paths \\ RLPropth [71] & Multi-hop & RL & Low & Good & Medium & Yes & Optimizing paths \\ DRLSOR [72] & Multi-hop & RL & Low & Good & Low & Yes & Optimizing paths \\ EL [73] & Multi-hop & Hybrid & High & Good & Medium & No & Optimizing protocols \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparisons of ML-based routing algorithms.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)  
A UAV, sometimes known as a drone, is a type of flying aircraft that can be controlled from the ground without the use of a human pilot. The primary application of UAVs is as temporary flying BS in B5G communication. UAVs often fly via low-reliability point-to-point connectivity, which leads to lost signal at any moment during the flight. High
## 2 Materials and Methods
### Samples Preparation and the Measurement of UV-Vis Absorbance Spectra  
According to the standards for surface water environmental quality in China (GB3838-2002), the water quality of the surface water source of centralized drinking water is divided into 5 categories, and the limit values of COD are 15, 15, 20, 30, and 40 mg/L, respectively. In this paper, COD and turbidity chemical standard solutions were used to prepare water samples. The COD solution (100 mg/L) was prepared by dissolving 0.02125 g potassium hydrogen phthalate in deionized water and diluting to 250 mL. COD standard solutions whose values were 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, and 32 mg/L, and were prepared by appropriate dilution. In addition, the NTU (nephometric turbidity unit) standard (7027-1984 ISO) was used to determine the water turbidity. Turbidity solutions of 1, 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 80, and 100 NTU were prepared by diluting a 400 NTU turbidity solution with deionized water. All samples were scanned on the SHIMADZU (Model No: UV-3600) UV-vis spectrophotometer interfaced to a microcomputer, with deionized water used as a blank in the spectrophotometer reference cell. The resolution of this spectrophotometer was 0.1 nm. The experimental setup is shown in Figure 1, and the optical path length in quartz cells was 10 mm. Each sample was measured 15 times, and the average was calculated as the spectral measurement of the sample. The absorption spectra of the COD samples and the turbidity samples is shown in Figures 2 and 3, respectively.
## 3 Two-Stream UDN for Pedestrian Detection
### Improved UDN
Convolution layer 2: The second convolution layer has 20 part-based filters \(F_{n}\in R^{p_{n}\times q_{n}\times 64},\ n=1,\ldots,20\) with different sizes. The filters are the same as with UDN. The part detection map \(C_{n}\) is computed as follows: \[C_{n}\ =\ \textit{Swish}\ (u_{n}\ +\ \sum_{m=1}^{64}\left[F_{n}\right]_{:,m}\ *\ B_{m})\] (3) where \(n\) represents the number of the filtered data maps, \(\left[F_{n}\right]_{:,m}\) represents the \(m\)th slice of the three-order tensor \(F_{n}\), \(\ *\) represents the convolution operator, and \(u_{n}\) represents the bias parameters obtained by random initialization. Before training, \(F_{n}\) uses a Gabor filter for initialization. 4. Deformation layer: The deformation layer is same with the UDN [21] and returns score \(s_{P}\) for the \(pth\) part, \(\left\{s_{P}|p=1,\ldots,20\right\}\). 5. Classification layer: The classification layer is the same with the UDN [21] and estimates label \(y\) of the input image, indicating it is pedestrian or non-pedestrian. The improved UDN adopts Swish [34] as activation function, which is different from Sigmoid used in UDN [21]. Otherwise, ReLU is the de facto standard activation function in deep learning in recent years, since it can solve the vanishing gradient problem, but its derivative is discontinuous. Swish is a smooth function. That means that it does not abruptly change direction as ReLU does near x = 0. Rather, it smoothly bends from 0 toward values <0 and then upwards again. Figure 4 shows the visualization of Sigmoid, ReLU, Softplus, Tanh, Swish, and FTSwish [35]. Figure 4: Common baseline activation functions. Best viewed in color. Swish amends the drawback of Relu which zeros out negative values. When the training gradient decreases, Swish has a faster convergence rate than the traditional Sigmoid and other saturated nonlinear functions, which improves the training speed and guarantees the predictive performance of the network meanwhile. For an input \(x\), its corresponding output transformed with Swish is \(x(1+e^{-x})^{-1}\).
## 2 Assessment Approach
### Topology Optimization  
The reference finite element model to be considered for ultimate analyses and validations is composed of four load cases: Top Dead Centre during Combustion (TDCC); Top Dead Centre at the beginning of the Induction stroke (TDCI); Left and Right Piston Thrust (LPT, RPT).  
To compare studies to be performed among different TSs, each of them has been performed using the same parameterization. A simplified model of the piston has been defined so that each software used will fit with the optimization settings. This simplified model is based on the most basic FEM tools, such as:  
* Forces and pressures as structural loads;
* Fixed displacements as structural constraints;
* Half-geometry of the piston model.  
Moreover, as every optimization software cannot afford to perform multiple case analyses, a single one will be considered, resuming the key elements of the previously defined load cases.  
* TO1--Geometry preparation.  
Geometry preparation requires the initial CAD model design to achieve the correct Design Space definition and the AlSi10 Mg alloy for PBF material definition. In particular, piston crown, pin housing, rings housings, and skirts are set to keep a minimum thickness. Figure 4 depicts the simplified model Geometry Preparation phase performed on the different TSs and it shows the different approaches for the Design Space and Non-Design space definition.  
* TO2--Optimization settings.  
As explained before, the simplified model is based on a single load case, resuming the key elements of the four reference load cases. Figure 5 provides the loads and constraints definitions by analytical calculation and the FEM scheme setup on the different TSs. The load case is modelled in the worst case considering the maximum possible load related to engine working conditions (e.g., 12 MPa comb. pressure, 14,000 rpm).  
As the topology optimization will be performed only with half of the piston, symmetry boundary conditions must be added to the model. Thus, fixed translation displacement constraints have been added to the normal symmetry plane and fixed rotational constraints have been added to the axes lying on the symmetry plane. The optimization algorithms are set up to minimize parts compliance (target) for a given mass (constraint) that is calculated to achieve a 15% weight reduction.  
* TO3--Results and Post-processing.  
Figure 6 provides preliminary FEA results of the geometries generated by the optimization software. Where possible, automation is exploited to generate a solid model, therefore limiting or potentially avoiding manual geometry redesign.  
In some cases, this feature is not allowed by the software, and design interpretation is required. Moreover, this step becomes necessary to achieve even a preliminary validation with respect to model stress. Figure 7 depicts the TO workflow output, to be further improved according to additional simulations and industrialization tasks.