esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The next-generation 6G wireless network is expected to support essential user applications and increase the efficiency of communication with low latency and high throughput. Advanced SS schemes should be formulated to acquire the full potential of the spectrum for the current 5G network. Undoubtedly, spectrum management plays a critical role in achieving this goal; however, many issues arise while designing such future networks. Therefore, this study provides a comprehensive review to describe the technologies involved in the current 5G network design and the issues that occur while performing resource sharing. The five major topics covered in this study are CA, CR, small cell networks, high-spectrum access, and M-MIMO. Several main concepts of each of the approaches with its recent related researches, including the methodologies, advantages, and limitations, are discussed. This study concludes that from the perspective of design and measurement of future 6G networks, the requirement to use different techniques concurrently in the current 5G network could enhance the overall spectral efficiency. We also believe that the future research challenges presented in this study can provide a new perspective for researchers to mitigate spectrum management issues in the design of 6G wireless networks.  
**Author Contributions:** Conceptualization, F.Q. and M.N.H.; Visualization, M.U.A.S. and M.N.H.; Writing--original draft preparation, F.Q. and M.U.A.S.; Writing--review and editing, F.Q., M.N.H., R.H., and Q.N.N.; Funding acquisition, R.H.; Supervision, R.H. and Q.N.N.; Project administration, Q.N.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is supported under the Dana Impak Perdana UKM DIP-2018-040 and Fundamental Research Grant Scheme FRGS/1/2018/TK04/UKM/02/07.  
**Acknowledgments:** The authors would like to acknowledge the support provided by the Network and Communication Technology (NCT) Research Groups, FTSM, UKM in providing facilities throughout this paper. The authors would also like to thank the Editor and the anonymous reviewers for their valuable comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Network Model and Network Initialization
### Layering Algorithm  
To avoid the void area routing problem and improve transmission reliability, a layer update algorithm is proposed, which is shown in Algorithm 1.  
In the TAFLRLR protocol, each node maintains a layer that represents the hop count from the node to the sink. The layer (\(L_{\textit{sink}}\)) of the sink node is set to 0, and the layer (\(L_{\textit{cur}}\)) of other nodes is initialized to \(0 xFF\)[17; 18; 19]. The layer is the only basis for determining whether a node is a candidate forwarding node. When a node receives a HELLO packet, it updates its layer according to the layer (\(L_{\textit{send}}\)) of the sending node in the header of the packet. As shown in Algorithm 1, a layer aging timer (i.e., layer_aging_timer) is set for each node. The layer aging timer is initialized to the flooding period \(T_{\textit{flood}}\). Received_HELLO_seq_num represents the sequence number of the HELLO packet received by the node.  
After the layers of all the nodes are configured, the HELLO packets are transmitted only along the paths from nodes with lower layers to nodes with higher layers. On the contrary, DATA packets are transmitted along the paths from nodes with higher layers to nodes with lower layers until the sink node.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**ID** & \(L_{n}\) & \(E_{r}\) & \(N_{n\_c}\) & \(N_{a\_c}\) & \(N_{n\_c}\) & \(N_{n\_d}\) & \(N_{a\_d}\) & \(\cdots\) \\ \hline
3 & 0 & \(E_{r3}\) & \(N_{n\_c,3}\) & \(N_{a\_c,3}\) & \(N_{n\_d}\) & \(N_{a\_d}\) & \(\cdots\) \\
17 & 1 & \(E_{r17}\) & \(N_{n\_c17}\) & \(N_{a\_c17}\) & \(N_{n\_d17}\) & \(N_{a\_d17}\) & \(\cdots\) \\
41 & 2 & \(E_{r41}\) & \(N_{n\_c41}\) & \(N_{a\_c41}\) & \(N_{n\_d41}\) & \(N_{a\_d41}\) & \(\cdots\) \\
105 & 3 & \(E_{r105}\) & \(N_{n\_c105}\) & \(N_{a\_c105}\) & \(N_{n\_d105}\) & \(N_{a\_d105}\) & \(\cdots\) \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \\ \hline \end{tabular}
\end{table}
Table 2: The structure of the neighbor information table.
# Low-Cost Active Anomaly Detection with Switching Latency
## 3 The DMSC Policy
### Performance Analysis
#### 3.3.2 Performance Analysis for Scenario 2  
Here, we focus on the scenario where the parameter \(s\) satisfies \(s=\Omega(B)\) as \(\alpha\to 0\). In this subsection, we define the order-optimal and present Theorem 2, which shows that the DMSC policy is order-optimal in this scenario.  
**Definition 2**.: _We say that policy \(\Gamma\) is order-optimal in \(\alpha\) if_  
\[\lim_{\alpha\to 0}\frac{E(C|\Gamma)}{C^{*}}=O(1). \tag{26}\]  
**Theorem 2** (Order-Optimality of DMSC Policy).: _Let \(E(C|DMSC)\) denote the expected cumulative cost incurred by the DMSC policy. If \(s=\Omega(B)\), then the expected cumulative cost of the DMSC policy satisfies_  
\[\lim_{\alpha\to 0}\frac{E(C|DMSC)}{C^{*}}=O(1). \tag{27}\]  
_In other words, the expected cumulative cost of the DMSC policy is order-optimal in \(\alpha\)._  
Proof.: The proof for this scenario is shown in detail in Appendix A.4 based on the similar approach outlined in the proof of Theorem 1.  
Since \(s/B>0\) as \(\alpha\to 0\), the offset \(\triangle(M,L)\) can not be ignored in this scenario and the analysis is more complicated than that of Scenario 1. We show that the ratio of the expected cumulative cost of DMSC policy to the asymptotic lower bound is larger than 1 but bounded by a positive constant as \(\alpha\to 0\), which completes the proof for Scenario 2.
# Stochastic Model Predictive Control for Urban Traffic Networks
## 3 Stochastic MPC-Based Model
### Constraints  
In general, to guarantee the safety of pedestrians and cyclists, there usually exists a green constraint for each phase \(p\) of intersection \(i\). In other words, the control variable \(u_{i,p}(k)\) should satisfy the following constraints:  
\[g_{i,p}^{min}\leq u_{i,p}(k)\leq g_{i,p}^{max},\;\;k=1,2,3,..., \tag{10}\]  
where \(g_{i,p}^{min}\) and \(g_{i,p}^{max}\) are the predefined minimum and maximum green time, respectively. In addition, the control variables \(u_{i,p}(k)\)\((k=1,2,\cdots)\) should satisfy the cycle time constraint \(C_{i}=L_{i}+\sum_{p\in\mathbb{N}_{i}}u_{i,p}\) as defined in Section 2.1.  
There can be no doubt that the maximum number (\(N_{i,r}\)) of vehicles in a link \(r\) is determined by the length of link \(r\) between intersection \(i\) and \(j\). Accordingly, to avoid causing serious congestion, the state variable \(x_{i,r}\) usually should satisfy the following constraints:  
\[0\leq x_{i,r}(k)\leq N_{i,r},\;\;k=1,2,3,... \tag{11}\]  
Assume that the uncertain demand \(d_{i,r}(k)\) and the disturbance \(e_{i,r}(k)\) are independent random variables. Then, according to Equations (7)-(9), we derive easily that the state variable \(x_{i,r}\) is a random variable that also follows normal distribution. As a result, Equation (11) can be rewritten as  
\[\textbf{P}\big{\{}0\leq x_{i,r}(k)\leq N_{i,r}\big{\}}\geq\beta_{i,r},k=1,2,3,..., \tag{12}\]  
where \(\textbf{P}(\cdot)\) is the probability distribution function, and \(\beta_{i,r}\) is the confidence level that is usually set close to 1. It means that the number of vehicles on the link \(r\) of intersection \(i\) is set below the maximum allowed \(N_{i,r}\) with the probability, which is less than \(\beta_{i,r}\). As noted in [27], to avoid the system becoming overly conservative, one does not impose \(\beta_{i,r}=1\).
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 1 Introduction  
A tunnel boring machine (TBM) is a mechanized tunnelling tool that excavates a wide range of geological formations. Unlike conventional tunnelling, mechanized tunnelling is widely preferred in the industry due to the safety, speed, and versatility of tunnel boring machines. However, TBMs encounter different adverse effects such as rock burst, clogging, groundwater, and cutter tool wear [1; 2; 3; 4]. These events lead to time-consuming tunnel construction and high operational and maintenance costs for the tools. Furthermore, the failure of the cutting tool dominates the downtime and high maintenance cost in mechanized tunnelling [5; 6; 7]. [PERSON], [PERSON], et al. [8] stated that unproper tool wear inspection leads to cutter head damage while the machine excavates a tunnel. According to [PERSON] et al. [9], if overworn cutters are not changed in time, higher torque occurs, and other parts of the TBM are damaged, resulting in excessive delay in the excavation process. Therefore, cutter wear prediction is crucial in the mechanized tunnelling industry. Several models have been provided by many researchers. They are classified as empirical, probabilistic, and artificial intelligence models.
# Evaluation of Autonomous Mowers Weed Control Effect in Globe Artichoke Field
## 1 Introduction
However, machine learning algorithms such as convolutional neural networks (CNN) and classic Bayesian classifiers still present insufficient computational performance to classify plants in complex scenes, such as high weed density [22]. Moreover, these techniques require large data sets to train the model, and in many cases the multi-year seasonal stability of the data has not been demonstrated [19]. Crop signaling could be a promising novel technique consisting of marking vegetables crops with a fluorescent machine readable compound, not present on weeds, to facilitate an accurate weed-crop discrimination [19; 23]. Autonomous mowers moving with random trajectories, despite being developed mainly for lawn mowing, have shown an interesting potential when applied for agricultural purposes, such as weed and cover crop management, both in vineyard [24; 25] and horticultural contexts [26; 27]. The adoption of these autonomous mowers in agriculture presents several advantages. Indeed, small autonomous machines such as these exert a reduced pressure on the soil, and usually have a low energy consumption and low cost [28]. Their high cutting frequency hinders the weed growth, thus limiting their competition with the crop [24]. In addition, random path planning means there is no need for expensive computational resources or complex sensors for localization [29]. Furthermore, weed control with mowing allows to maintain a permanent sod that helps to prevent soil erosion [30]; therefore, the adoption of a conservative approach is favored, which turns out to be more sustainable for the environment. The globe artichoke (_Cynara cardunculus_ var. _scolymus_ [PERSON]) is a typical vegetable of the Mediterranean basin currently cultivated on all continents [31; 32]. Italy is the largest producer with 39,420 ha\(\cdot\)year\({}^{-1}\) and 378,820 t\(\cdot\)year\({}^{-1}\)[33]. This horticultural crop presents a plant density of about 7000-10,000 plants per hectare; the distance between the rows generally ranges from 1 m to 1.8 m, while the distance ranges from 0.6 to 1.3 m across the row [34]. Due to the large planting layout, the globe artichoke could be particularly suitable for the use of autonomous mowers with random trajectories as means of weed control. Hence, the aim of the present study was to evaluate if these autonomous mowers could provide for an acceptable weed control on globe artichoke. The specific objectives of this study were: (1) to evaluate the performances of three different autonomous mowers in a globe artichoke field; (2) to compare the weed management of an autonomous mower with a conventional weed management system.
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 4 Simulation Results  
The performance of the NMPPT algorithm is compared with the P&O [20] and CNNMPPT algorithm [38], as well as with the recently proposed MPPT algorithm that is based on the PV equivalent circuit (EMPPT) [29]. The EMPPT algorithm uses the irradiance estimator which is referred to as the EEST [29], while the irradiance estimator in the CNNMPPT is refered to as the CNNEST. In the first set of examples, we used the \(I\)-\(V\) curves generated by the PV equivalent circuit. The PV panel consists of two serially connected PV modules, whose parameters are: \(I_{L}=5.447\,\mathrm{A},\mathrm{I}_{0}\;=\;4.839\;\times\;10^{-12}\,\mathrm{A}\), \(R_{p}\;=\;309.047\;\Omega\), \(R_{s}=0.566\,\Omega,V_{t}=1.807\,\mathrm{V}\). On the other hand, the second subsection is related to the use of the large \(I\)-\(V\) curve repository provided by the National Renewable Energy Laboratory (NREL) [45]. We selected the \(I\)-\(V\) curve dataset of the PV module defined by the NREL identifier: HIT05662. The HIT05662 is modeled by the same equivalent circuit that is applied in the simulated examples. In all examples, the NN model consists of 4 neurons. The parameters in the P&O and EMPTT are chosen so that they provide the best performance. To ensure a fair comparison, the number of neurons in the CNNMPPT is set to 4. The parameters of the proposed method are \(\mu\;=\;0.8,\gamma\;=\;50,\delta\;=\;1500\) in the first set of examples, and \(\mu=1.5,\gamma=50\) in the second set of examples.
## Materials and Methods
### Degradation Experiment  
In the case of humic andosol sample taken at Ome, pH of the soil samples was adjusted at 5, 7, and 9 by adding hydrochloric acid or sodium hydroxide, whereas original pH (5.8-6.7) was not controlled for other soils. In order to evaluate clearly the potential degradability of pharmaceuticals, the target pharmaceuticals were added to the samples before incubation at a known concentration. Each flask contained 4 g (wet weight) soil and 25 mL of pharmaceutical solution containing the seven pharmaceuticals at initial individual concentrations of 200 ug/L, glucose (100 mg/L), and yeast extract (25 mg/L) as carbon sources and nutrients. Oxygen in the solution was purged in advance with nitrogen gas not to inactivate the anaerobic bacteria in the soil samples. The prepared slurry was then incubated at 20 \({}^{\circ}\)C for 14 days on a shaker (60 rpm) to keep the slurry at a completely mixed state. Half of the flasks were capped with breathable sponge to keep the slurry aerobic, while others were completely sealed to provide anaerobic environments. The samples were kept in dark to avoid the effect of photo-degradation. Dissolved oxygen (DO) concentration after incubation was measured by a DO sensor (SG9, Mettler-Toledo AG, Switzerland). Concentration of the pharmaceuticals were measured at day 1, day 7, and day 14 by using whole volume of the slurry in a flask among three flasks prepared at day 0 for one test condition. The sterile samples with soil (121 \({}^{\circ}\)C, 20 min in an autoclave) were prepared as references to evaluate the contribution of biological degradation separately from adsorption and abiotic reactions. The pharmaceutical residual ratios were calculated by measured pharmaceutical dose in the flask divided by 5 ug, which was equivalent to the initial pharmaceutical dose (200 ug/L \(\times\) 25 mL \(\times\) 0.001 L/mL).
## 4 Eigenvalue Analysis
### Damaged Bridge  
If different crack locations and severities are introduced in the coupled VBI finite element model, the values of the eigenfrequencies in Figures 3-5 will change. Figure 6 illustrates the result of adjusting a bivariate normal distribution to \(F_{3}\) and \(F_{9}\) for the same bridge and truck fleet, but with different damage scenarios (step (5) in Section 2). This is the database mentioned in step (9) of the methodology that will be used for comparison to the frequencies extracted from the acceleration signal. Only damage locations in the first  
Figure 4: No. vehicles versus bridge eigenfrequencies associated with the 1 st axle located at: (**a**) 3 m and (**b**) 9 m, from 1 st support.  
half of the bridge (up to 7.5 m from the left support) are considered here. The ellipses in the figure represent the frequency distributions with 86.6% probability. Ellipses drawn with thick solid lines, dotted lines, and dashed lines represent cracks with \(\lambda\) = 5%, \(\lambda\) = 10%, and \(\lambda\) = 15% respectively. It can be seen that the distributions align along a straight line approximately. For the same crack location, deeper cracks lead to lower frequencies. As expected, the distribution for the healthy beam, in solid thick black, is located in the upper right corner of the figure. For the same crack severity (\(\lambda\)), the 1 st bridge frequency tends to decrease as the crack location becomes closer to the mid-span of the bridge. For the smaller values of damage severity, ellipses are more closely spaced.  
In addition to the local stiffness losses defined by [PERSON] et al. (2019) (Section 3.1), five global stiffness losses are also considered. Global stiffness loss is defined as a percentage (1%, 2%, 3%, 4% and 5%) of the healthy bridge stiffness (i.e., 18.4555 \(\times\) 10\({}^{9}\) N/m\({}^{2}\)). The five bivariate distributions representative of a potential global damage are highlighted in Figure 6 as dash-dotted ellipses. It must be emphasized that global stiffness losses are not necessarily related to damage, but to environmental causes such as changes in temperature  
Figure 5: Bivariate distribution of bridge eigenfrequencies associated with the 1 st axle located at 3 and at 9 m.  
Figure 6: Bivariate distributions of bridge eigenfrequencies at 3 m and 9 m associated with a healthy bridge, 18 local stiffness losses (six different damage locations and three damage severities) and 5 global stiffness losses.  
and humidity [50]. Hence, environmental conditions have to be monitored together with the frequencies. Figure 6 shows that the forced-frequencies due to local and global changes follow a similar pattern. A zoom onto two distributions next to each other, one local (green solid) and another global (orange dash-dotted), distinguishes small differences in their shapes, i.e., the global ellipse is larger than the local ellipse due to a higher value of the standard deviation for \(F_{9}\).
## Appendix B HDBSCAN Configurations  
For the hyperparameter tuning of HDBSCAN, we focus on two key hyperparameters: _min_cluster_size_ and _min_samples_. These control the minimum size of clusters and the density required for a point to be classified as part of a cluster, respectively. To streamline the search for optimal values, we follow a systematic approach by setting both parameters to the same value and testing a range of values from 5 to 50.  
We observe a trade-off between the Silhouette score and the min cluster size. Although a large min cluster size is favored for interpretation, it may introduce unsatisfactory Silhouette scores. We plot the average Silhouette scores and average unlabeled counts as a function of the minimum cluster size to determine the optimal cluster sizes, as shown in Figure A1.
## 4 Application Examples
### Blood Volume Pulse in fNIRS Signals  
Signals measured using functional near-infrared spectroscopy (fNIRS) contain a strong oscillation that is caused by the blood volume pulse (BVP) related to the arterial blood pressure changes (systole, diastole). fNIRS is a technique allowing non-invasive measurement of changes in the blood concentrations of oxyhemoglobin ([O\({}_{2}\)Hb]) and deoxyhemoglobin ([HHb]) by irradiating light in the near-infrared spectrum (600-950 nm) into the brain and analyzing the reflected light intensity changes [44; 45; 46]. The information of the BVP in fNIRS signals is relevant for various reasons, _i.e._, for a proper removal of the BVP from fNIRS signals to improve the detection of fast neuronal optical signals [47; 48], or for the usage of a surrogate to the heart rate variability measured with electrocardiography [49].  
In order to demonstrate the applicability of the AMPD algorithm to detect the BVP-peaks in a fNIRS time series, we used a self-recorded [O\({}_{2}\)Hb] signal measured on the prefrontal cortex (sampling frequency: 10 Hz, source-detector distance: 2.5 mm, device: MCP-II, multichannel continuous-wave NIR-spectroscope, for technical details see [50]). The time series analyzed had a duration of 200 s (\(\sim\)3.3 min). The results of the analysis are given in Figure 6. All 225 peaks were correctly detected (no false negative errors) and no false detection occurred (no false positive errors).  
Figure 5: Results of applying the AMPD algorithm to a time series comprising the monthly total number of sunspots.
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 1 Introduction
However, it is still not very clear how bumblebee pollination enhances tomato flavor. Many studies only compared the fruit set, size, weight, or sugar and acids of tomato among different pollination methods [37], and few studies focus on the tomato fruit volatiles of different pollination methods. The Gobi Desert spans 6.7 million hectares of Northwest China's Gansu province, which limits crop production opportunities and food security within this region. Agriculture in the region has increasingly taken advantage of solar energy to produce vegetables and fruits year-round in greenhouses, which now accounts for a large proportion of vegetables and fruits consumed in this region [38]. However, most tomato growers of this region, especially smallholders, still use PGR or vibrator to produce tomatoes, resulting in high labor costs, poor fruit quality and low profit. In this study, we examined the effects of bumblebee pollination (using reared colonies of the local species _Bombus lantschousesis_), vibrator and PGR treatment on tomato yield and flavor in Gobi Desert greenhouses of Northwest China. We determined how pollination by native bumblebee species affects tomato yield and tomato fruit flavor.