esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Rolling Optimization Algorithm
### Rolling Optimization  
The two sections above have introduced how collision-free trajectories are generated. In this section, we introduce the rolling optimization method to generate shorter trajectories with the condition of being collision-free.  
The key of the rolling optimization is the third factor (eigenvalue) influencing the weight for each candidate waypoint in the next move for UAVs. In the grid model, each crossing point has an eigenvalue. If a crossing point is selected as a waypoint in a trajectory, its eigenvalue should be updated in two ways. The local update represents that the eigenvalue of the point should be decreased after it is chosen to be one waypoint in the trajectory of the UAV. For the aim of global search, the local update indirectly increases the probability of other candidate positions. The mathematical expression of the \(n^{th}\) iteration is shown in Equation (10). Therein, \(P_{ijk}\) denotes the eigenvalue of the location \((i,j,k)\), while \(\lambda\) represents the decreasing coefficient.  
\[P^{\prime}_{ijk}(n)=P_{ijk}(n)*(1-\lambda) \tag{10}\]  
A global update would happen when several searched trajectories are determined in an iteration. The shortest path in the searched trajectories is selected, and all the waypoints are updated in the shortest trajectory. Equation (11) calculates the update process of the eigenvalue at location \((i,j,k)\) after the \(n^{th}\) iteration. Therein, \(\rho\) denotes the update coefficient. \(\triangle P_{ijk}\) describes the update value, and the mathematical expression is shown in Equation (12). In the equation, \(min\_length\) denotes the distance of the shortest trajectory, and \(K_{3}\) is a coefficient adjusting the update value.  
\[P_{ijk}(n+1)=(1-\rho)*P^{\prime}_{ijk}(n)+\rho*\triangle P_{ijk}(n) \tag{11}\]  
\[\triangle\ P_{ijk}(n)=\frac{K_{3}}{min\_length} \tag{12}\]  
The update of candidate waypoint eigenvalues makes the short distance waypoints have more probability to be selected. What is more, it helps the algorithm get rid of local optimal values.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter
### Modulation Waveforms  
#### 2.1.1 OFDM  
Compared with conventional OFDM, windowed orthogonal frequency division multiplexing (W-OFDM) has a similar transceiver structure but non-rectangular transmit windows are utilized to smooth the edges of rectangular pulse, accordingly provide better spectral localization and reduce ICI [86]. For a raised cosine shape based non-rectangular window used in W-OFDM, the CP needs to be extended to maintain the orthogonality and the spectral efficiency will decrease for W-OFDM compared to OFDM [87]. In order to utilize non-contiguous spectrum fragments, resource block filtered OFDM (RB-F-OFDM) aims to split the available spectrum fragments into several resource blocks which make a chunk of some contiguous subcarriers. It generates and filters the signal transmitted on each resource block individually [88]. Analogous to the subcarrier filtering based modulation, spectrum leakage among these resource blocks is unavoidable in the case that the spacing between these blocks is narrow [89]. In CP-OFDM with weighted overlap and add (WOLA), some data parts are copied and added to the right and left part of conventional OFDM, then a pulse with soft edges takes place in the rectangular prototype filter, in the meanwhile, the soft edges are added to the cyclic extension by a time domain windowing and this results in a sharper side-lobe decay in frequency domain [90]. Therefore, CP-OFDM with WOLA is a special case of OFDM, aiming to improve the prototype filter such that it has more reasonable pulse shape used in regular CP-OFDM. Furthermore, for the sake of addressing the sacrifice of time resource resulting from added parts and avoiding possible data collision before transmission due to windowing process, overlap process is employed [91].
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 4 Discussion
### The Effects of Afforestation on Sand Dunes  
4.5.1. Implications of Afforestation on Groundwater and Vegetation  
Our study shows both the positive and negative effects of aforestation on sand dunes. These impacts vary based on the geographic context, tree species, and management goals. This fact was also pointed out in a study realized by [PERSON] [89] where dune systems from arid areas were marked as important for groundwater recharge because of their high infiltration capacity. As such, afforestation can increase water uptake through tree transpiration and dune stabilizing. Our recommendation is that desert dune management plans should consider this trade-off between vegetation cover and water availability.  
4.5.2. Biodiversity and Ecosystem Function  
Our findings point to the dual nature of afforestation, a fact also pointed out by many case studies. While regional biodiversity may benefit from afforestation [55], the local ecosystems and biodiversity can also decline. From the articles published in the database, we found that the afforestation with _Tamarix aphylla_ from northern Negev led to a considerable decrease in the richness of plant species, to an increased soil salinity, and to decreased plant cover and biomass [103]. Other authors have also pointed out these aspects. For example, the afforestation from Israel has led to a loss in the sandy ecosystem's biology and ecology. By analyzing these results, our recommendations are: the reexamination of afforestation policies; avoiding the afforestation of naturally or low tree cover ecosystems (ex. grasslands, shrubs, open savannas); to create a clear set of criteria for afforestation projects and separate them from environmental projects (habitat restoration, carbon-sink projects, commercial tree plantations); and to allocate appropriate resources [127]. All these aspects also point to our suggestion that it is important to assess both the direct and indirect impacts of afforestation on ecosystems. This is even more important in arid and semiarid environments where the interactions between soil and vegetation are very dynamic.  
As for the impact on soils, our findings show that fertility was improved by afforestation, including an increased organic carbon and nitrogen concentrations [55]. As other authors have mentioned, this can also lead to dune productivity improvements. However, this fact should always be analyzed in comparison with the shifts that take place in the plant composition and the possible declines of local species [81; 107].
## 1 Introduction  
Balance is an intrinsic part of any movement and is the cornerstone of postural control [1]. Several studies have shown that balance training (BT) interventions lead to improved postural control in children and adolescents older than 12 years [2; 3; 4; 5]. However, the evidence that BT can improve postural control in younger children is much weaker.  
It is well established that 4-6 years is a critical age in the process of acquiring balance. This is mainly because of the changing predominance between the main sensory information used for balance [6; 7]. Babies can only rely on their visual system for postural control. Adult balance system relies mainly on proprioceptive information. However, the visual system in children aged 4-6 years takes back its predominant role, due to changes in somatosensory information. Children experience a peak of growth at this age and their somatosensory database is no longer reliable [8]. Some authors found that the influence of peripheral vision in balance increased at 3-6 years of age, reaching a peak when the child was 6 years old [9]. This makes this age especially interesting for balance intervention programs.  
The criteria for drawing up a BT program depend on the concept of the program on the regulatory mechanisms of balance. It will either focus on the exclusive training of motor components or use a perspective interaction among different systems, including motor components, sensory components (visual, proprioceptive, and tactile), and cognitive components, such as attention [10]. Several studies have performed training programs in adolescents [11], young adults [12; 13], and older adults [14; 15] with favorable results being observed in these study populations; however, interventions in early stages, such as the age of children used in this study sample, have been largely unexplored despite the importance of the processes taking place at this time. Therefore, the development of BT programs is part of the daily practice of numerous healthcare professionals, including education providers, physical therapists, and physical activity. It is important to deepen our understanding of the factors that compete for balance resources during children's performance, so that teachers can design age-appropriate environments to improve balance, instead of general physical education programs that address all motor components without a specific rationale of intervention. At this age, 4-6 years, when the balance system is changing from proprioceptive to visual main control, the importance of specific training needs to be addressed.  
The aim of this study was to investigate whether a BT program with multitask BT was effective in improving balance and postural control in healthy preschool children. The design of the BT program was under the perspective of the multisystem models of balance control, which have shown strong evidence about their efficacy in other populations [11; 12; 13; 14; 15].
## 4 Numerical Simulations  
The 2 DTV-CES performance in 2D-DOA estimation is evaluated by simulations starting with spatially extended sources. We consider a planar array with \(M=16\) sensors and spacing \(d=\lambda/2\). The complete array manifold matrix \(\overline{A}\) in Equation (21) is defined on angular grids \(\theta\in[0^{\circ}:1^{\circ}:90^{\circ}]\) and \(\varphi\in[0^{\circ}:1^{\circ}:90^{\circ}]\). The spatially extended sources are divided into two categories with the same amplitudes. Case 1 is at DOAs \(\theta\in[5^{\circ}:1^{\circ}:15^{\circ}]\) and \(\varphi\in[5^{\circ}:1^{\circ}:15^{\circ}]\), Case 2 is at DOAs \(\theta\in[18^{\circ}:1^{\circ}:28^{\circ}]\) and \(\varphi\in[25^{\circ}:1^{\circ}:35^{\circ}]\), and two-point source DOAs are (\(3^{\circ}\), \(5^{\circ}\)) and (\(6^{\circ}\), \(8^{\circ}\)), respectively. The spatially extended sources are modeled as a series of monopoles with the equal constant amplitudes 1 dB with uniform distribution phase in Case 1, and the linear amplitude [1:0.1:0.6] dB with random uniformly distributed phase and two-point sources in Case 2. Monte Carlo experiments were introduced to compare the performance of 2 DTV-CES, LASSO [23], and the Improved ESPRIT [30] in the presence of the same spatially extended sources under different SNR and different sparsity \(K\).
## 3 Test of Bio-Inspired Algorithms on a Transportation Planning Problem
### Problem Description  
Suppose there are \(m\) distribution centers (sources) and \(n\) demand targets; the quantity of relief supplies that source \(i\) can provide is \(a_{i}\), and the quantity of demanded by target \(j\) is \(b_{j}\) (\(1\leq i\leq m,1\leq j\leq n\)). According to our experience, in most disaster relief operations, the total quantity of supplies is much larger than the total demand, and the bottlenecks of relief distribution mainly lie in the availability of vehicles/drivers and the capacity of the traffic network. Thus, for simplicity, here, we assume that a target does not need to be supplied by more than one source, and a source can have only one route, due to the limit of traffic flow. The problem is to assign the demands of the targets to the sources and for each source schedule, a sequence (route) in which the targets are supplied, in order to distribute the supplies to targets as efficiently as possible. Under the assumption, the transportation planning problem can be considered as a multi-depot version of the cumulative VRP [63], while one depot has only one route.  
Let \(\pi_{i}\) be the sequence of targets of source \(i\) and \(C(\pi_{i})\) be the set of targets of \(\pi_{i}\); a solution should satisfy the following constraints:  
\[\sum_{j\in C(\pi_{i})}b_{j} \leq a_{i} \tag{5}\] \[t_{j} \leq \widehat{t}_{j} \tag{6}\]  
where \(t_{j}\) is the arrival time and \(\widehat{t}_{j}\) is the last allowable arrival time of supplies at target \(j\). Let \(\widetilde{t}_{i,j}\) be the travel time from source \(i\) to target \(j\) (including the preparation time of source \(i\)), \(t_{j,j^{\prime}}\) be the travel time from target \(j\) to \(j^{\prime}\) and \(k_{j}(\pi_{i})\) be the index of target \(j\) in \(\pi_{i}\); \(t_{j}\) can be calculated based on the paths in the roadway network and the schedule \(\pi_{i}\) to which \(j\) belongs:  
\[t_{j}=\widetilde{t}_{i,j}+\sum_{k=1}^{k_{j}(\pi_{i})-1}t_{\pi_{i}(k),\pi_{i}(k +1)} \tag{7}\]  
In emergency conditions, the cost of transportation is not considered as a major factor, and the objective is defined to minimize the total weighted waiting time of the targets:  
\[\min f=\sum_{j=1}^{n}w_{j}t_{j} \tag{8}\]where \(w_{j}\) is a predefined importance weight of target \(j\).
## 3 Results and Discussion
### Myofibrillar Proteins Gels
#### 3.2.2 Low-Field Nuclear Magnetic Resonance  
The T\({}_{2}\) relaxation time of the MP sample was monitored by LF-NMR, which identified the distribution of each water component in the sample matrix system [39; 40]. The alteration in water migration and state was influenced by the structural changes of MPs gel. The T\({}_{2}\) relaxation time of MP gel was distributed between 0.1 and 1000 ms with 3 peaks, namely combined water (T\({}_{21}\): 0.02-0.30 ms), and non-flowable water (T\({}_{22\text{a}}\): 5.88-12.31 ms, T\({}_{22\text{b}}\): 94.03-1088.42 ms). The non-flowable water T\({}_{22\text{b}}\) was the main existing state of water in the MP gel, so the migration and state of this part directly reflects the change to the gel network structure during frozen storage. The shorter the relaxation time of T\({}_{2}\), the tighter the water molecules. A longer T\({}_{2}\) relaxation time indicates that the interaction between protein gel macromolecules and water molecules is weak [41]. It can be seen from Figure 7 that the relaxation time of MPs gels gradually prolonged with the decrease in OVA/\(\beta\)CD concentration. In the early stage of frozen storage, the T\({}_{22\text{b}}\) relaxation time of the MP gel samples was kept at about 220, and after 90 days, the relaxation time of the control (0%) increased to 625, but the relaxation time of the 6% OVA/\(\beta\)CD treatment sample only increased to 452. These results proved that the addition of 6% OVA/\(\beta\)CD could effectively enhance the binding of the protein macromolecules and water molecules, which led to lower T\({}_{22\text{b}}\) relaxation time and formed a well-structured MP gel. The results of the LF-NMR were in accordance with the above-mentioned changes in structure and viscoelasticity.  
Figure 5: Dynamic rheological properties of myofibrillar proteins treated with different concentrations of OVA/\(\beta\)CD during 90 days of frozen storage at \(-18\)\({}^{\circ}\)C. (**A**) Storage modulus at 0 days. (**B**) Loss modulus at 0 days. (**C**) Storage modulus at 90 days. (**D**) Loss modulus at 90 days. (**E**) The tan\(\delta\) at 0 days. (**F**) The tan\(\delta\) at 90 days. G’ indicates the storage modulus and G” indicates the loss modulus
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 5 Numerical Results
### Results in External Eavesdropping Scenario  
In an external eavesdropping scenario, eavesdroppers are assumed to be external nodes and their receiving operations are not restricted to the receive random beamforming technique. Thus, we assume that eavesdroppers could achieve their channel capacity for intercepting the information from desired users.  
Figure 5 shows the average achievable secrecy sum-rate for varying the number of users, where system parameters are set as \(M=2\), \(N=200\), and \(\rho=10\) dB. Differently from the results of internal eavesdropping scenario, compared to _OS-MRC_, the secrecy performance of the proposed schemes is not good when the number of users is small. In addition, a significant performance gap is shown between _IHP_ and _TOS_. These results come from the fact that information leakage is significant in case of external eavesdropping scenario since we assume that eavesdroppers achieve the channel capacity. However, by comparing the slopes of proposed schemes and _OS-MRC_, we observe that proposed schemes still achieve full degrees-of-freedom gain.  
Figure 6 shows the average achievable secrecy sum-rate for varying SNR, where system parameters are set as \(M=2\), \(N=200\), and \(K=2\). Interestingly, all schemes except for _OS-MRC_, which selects single users (\(S=1\)), show performance degradation as SNR increases and secrecy sum-rate finally converges to zero whereas the performance of _OS-MRC_ is saturated. Since we assume that eavesdroppers achieve the channel capacity, the capacity between eavesdroppers and users scales as \(\log(\rho)\) whereas the sum-rate between users and desired receiver is limited by inter-beam interference. Although both proposed schemes and _OS-MRC_ are effectively utilizing multiuser diversity to improve secrecy performance, the proposed schemes require a large number of users than _OS-MRC_ since they have to select users having a small amount of information leakage and inter-beam interference at the same time. Specifically, for the proposed schemes, the required number of users to fully exploit the multiuser diversity scales as \(\Theta\left(\rho^{\frac{M(K-1)+1}{1-\rho_{0}}}\right)\approx\rho^{3}\) whereas it scales as \(\rho^{2}\) in the case of _OS-MRC_. Thus, the proposed schemes show better secrecy performance than _OS-MRC_ only in a low SNR regime since we consider a finite number of users (i.e., \(N=100\)).  
Figure 5: Average achievable secrecy sum-rate for varying the number of users when \(M=2\), \(K=2\), and \(\rho=10\) dB [21].  
**Remark 5**.: _In an external eavesdropping scenario, differently from an internal eavesdropping scenario, the information leakage from the desired user to the eavesdroppers is significant since we assume that eavesdroppers achieve the channel capacity. It results in the severe performance degradation of the proposed schemes compared to OS-MRC. However, the proposed schemes still can achieve the secrecy sum-rate scaling as \(\Theta(M\log(\rho\log N))\) as the number of users increases._
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 2 System and Signal Model  
Consider a typical half-duplex two-way relay network that consists of three nodes: two source nodes \(S_{1}\) and \(S_{2}\) with continuously their power sources, and an energy harvesting relay node \(R\), which harvests the energy it needs from the RF signals transmitted by the two source nodes. It is assumed that there are no direct links between the two source nodes due to deep shadowing or blockage; thus information exchange between them only relies on the relay. A PS receiver architecture is employed at the relay as an energy harvester, and hardware impairments that result in distortion noises suffered by all nodes are modeled as shown in Figure 1a.
## 3 Results
### Measured and Modeled SIF  
Based on the retrieved plant traits and weather parameters, the SIF at 760 nm was modeled through the SCOPE and compared with the measured SIF at 760 nm (Figure 6). The measured and modeled SIF showed a clear seasonal pattern, where the SIF value started to increase in February, reaching the peak in July, and again showed a decrease in October. The differences in the SIF value of the CR site were more significant for manipulation than in the CL site, whereas the value of SIF in the CL site was higher during the growing season. The modeled SIF value kept a similar pattern as the measured SIF throughout the year. The modeled SIF was observed to be underestimated, but it showed a significant correlation with measured SIF with an R\({}^{2}\) of 0.86, where the correlation was performed for all the measurements on two sites for the year (Figure 7).  
Figure 5: Correlations of modeled leaf area index (LAI_mo), Normalize Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-Infrared Reflectance of vegetation (NIRv), modeled Sun-Induced Fluorescence (SIF_mo), measured Sun-Induced Fluorescence (SIF_meas), and measured leaf area index (LAI_meas) for both types of vegetation and all plots combined. Numbers represent \(r^{2}\) for every couple.  
Figure 6: The changes in measured (**A**) and modeled (**B**) Sun-Induced Fluorescence (SIF) at 780 nm for CL and CR climate manipulation sites at a different time in the year 2021. C is for control plots, W, is for warming plots, and WP represents warming and reduced-precipitation plots.
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 7 Conclusions  
This paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles in obstacle avoidance conditions. A robust invariant set was calculated with an efficient linear programming (LP) method, and this was used to guarantee that the constraints were satisfied when under common disturbance conditions. The results show that the robust positively invariant sets obtained by the LP method were an approximate of that obtained by the traditional MPT method. The computational cost of the robust positively invariant sets obtained by the LP method were much less than that obtained by the traditional MPT method. In addition, the traditional MPC and proposed tube MPC framework were employed in obstacle avoidance conditions when subjected to common disturbances. The tracking performances were compared for the traditional MPC and tube linear MPC methods. The results show that the range of the trajectories of the tube MPC system was much tighter and that all the trajectories successfully avoided the obstacle on the road. But the trajectories of the traditional MPC were scattered over a large area and some of the trials failed to avoid the obstacle. The proposed framework can effectively improve the path tracking performance of autonomous vehicles in obstacle avoidance conditions when subjected to common disturbances.  
J.C.: conceptualization, methodology, validation, and writing--original draft preparation. G.T.: supervision and writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 14: Trajectories of the tube MPC system when under both uncertain wind disturbance and friction coefficient conditions.  
Figure 13: Trajectories of the traditional MPC system when under both uncertain wind disturbance and friction coefficient conditions.
## 3 Testbed Implementation,Prototype, and Software Development
### Android Application  
Considering the mobility of the vehicle in an industrial environment, it will be difficult for the vehicle's operator to access the server and obtain the details of the battery. Therefore, we have designed a simple yet effective TCP socket-based android application that can be easily installed on the operator's android device and can be executed at any time by the operator on the run. Our android application in a running condition is shown in Figure 17. The operator should know the battery (device) IP address and port number so that it can be used to receive the serial data frame of a particular battery on a click of the connect button. The data of the data frame from single client is divided into its respective 4 bits and displayed in the columns separately, as shown in Figure 17.