esa-sceva/satcom-chunk-collection huggingface dataset:
# Latency-Bounded Target Set Selection in Signed Networks
## 1 Introduction
In more detail, we consider a generalization of the Linear Threshold Model in which the set of neighbors of each node is partitioned into two subsets: one subset of trusty neighbors (or friends), containing the neighbors able to positively influence the node, and one subset of unreliable neighbors (or enemies), containing the neighbors able to negatively influence the node. Informally speaking, in our model an unaware node becomes informed at some time step only if at that time step the cumulative influence coming from its informed trusty neighbors is at least as large as the node threshold, and at that time step as well as at any previous time step the cumulative influence coming from its informed unreliable neighbors is less than the node threshold. Notice that, as in [30], positive and negative influences are slightly asymmetric in that negative feedback effects are more powerful than positive ones in influencing a node: indeed, in [30] a negatively informed node always tries to negatively inform its unaware neighbors (while there is a chance that a node gets negatively informed by a positively informed neighbor), and in our model an unaware node will never become informed if at some time step the cumulative influence from its enemies exceeds the threshold (also if in the same time step the cumulative influence from its friends exceeds the threshold too). In fact, several studies in social psychology (e.g., [39; 40; 41; 42]) point out that negative impact is usually stronger and much more dominant than positive impact in shaping people's decisions. Notice also that our rule according to which an unaware node becomes informed is quite similar to that in [34], discussed above (although [34] deals with the Influence Maximization problem). The main difference between the two rules is that, while the rule herein defined directly generalizes that in in the Linear Threshold model in its considering the cumulative amount of information reaching an unaware node (i.e., we sum up the weights of informed neighbors), the rule in [34] is somehow related to that in the Independent Cascade model: in fact, in that paper, the function associated to the set \(S^{+}\) of the informed friends (and similarly as to the informed enemies) is \(1-\Pi_{u\in S^{+}}(1-p(u,v))\) which denotes the probability of accepting, let's say, the suggestion coming from at least one informed friend in the hypothesis of independent probabilities of accepting the suggestion from any of them. Within this setting, we consider a problem that is closely related to that studied in [24], that is, computing a minimum size set able to spread a feature to all the network nodes within a given number \(k\) of time steps. We shall call such a set a \(k\)-target set.
# Forest Fire Detection Using New Routing Protocol
## 9 Conclusions  
In this paper, we present a new protocol called LAR-Based Reliable Routing (LARRR) in the detection of wildfire. The proposed LARRR protocol is performed using three criteria: route length, temperature sensing, and route busyness (i.e., the number of packets within nodes buffers). The proposed LARRR protocol is assessed and compared with the PCE-OLSR protocol with respect to E2E Delay, PDR, EC, and RO. The simulation outcomes show that the proposed LARRR protocol outperforms the PCE-OLSR in terms of a PDR of 46.6%, energy consumption of 418.5%, and RO of 87.22%. Meanwhile, the PCE-OLSR protocol outperforms the LARRR protocol with respect to E2E delay. This is because the PCE-OLSR is based on the proactive protocol, where the nodes establish the routes before they are required. Meanwhile, the LARRR protocol is based on the reactive protocol, where the nodes establish the routes only when they are needed. Therefore, the process of  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LARRR Protocol** & **PCE-OLSR Protocol** \\ \hline
1. & In the proposed LARRR protocol, the routing is performed based on three criteria: the route length between nodes, the temperature sensing, and the route busyness (i.e., node’s buffer). & In the PCE-OLSR protocol, the only criteria are the number of hops and the temperature. \\
2. & The category of LARRR is an on-demand routing protocol. & 2. \\
3. & The LARRR uses the location information of sensor nodes. & 3. \\
4. & The nature of the criteria in LARRR is continuous. & 4. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the methodology between the proposed LARRR and PCE-OLSR.  
Figure 14: RO results between LARRR and PCE-OLSR.  
route discovery in the PCE-OLSR protocol is implemented faster than the process existing in the LARRR protocol. Future work can include using the development process of the LARRR protocol in another routing protocol such as OLSR for the purpose of elevating its performance in forest fire detection.  
Conceptualization, F.T.A.-D.; methodology, F.T.A.-D.; software, F.T.A.-D.; validation, N.S., M.A.A.A. and M.H. ([PERSON]); formal analysis, F.T.A.-D.; investigation, M.H.H.K. and R.C.M.; resources, S.K. and M.A.A.A.; data curation, N.M.A.L.; writing--original draft preparation, F.T.A.-D.; writing--review and editing, M.H.H.K., M.H. ([PERSON]), N.S.K. and M.H. ([PERSON]); visualization, F.T.A.-D. and R.C.M.; supervision, R.C.M., N.S. and N.M.A.L.; project administration, F.T.A.-D. and R.C.M.; funding acquisition, M.H. ([PERSON]), S.K. and M.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research is supported by Universiti Malaysia Sabah, Jalan UMS, 88400, Kota 599 Kinabalu, Malaysia, under the grant name (UMS No. DFK2005) \"Smart Vertical farming Technology for Temperate vegetable cultivation in Sabah: practicing smart automation system using IR and AI technology in agriculture 4.0\".  
Not applicable.  
The authors declare no conflict of interest.
## 2 Design Schematic and Principles
Thus, the transportation energy-saving potential is feasible because the following occurs:  
* Water flow is controlled as required so there is no extra water flow. * The pump operating point remains stable to ensure pump efficiency. * The removal of valves prevents extra pressure loss. * The PPs are removed, and not all of the water flow needs to overcome main pipe resistance. On the source side, the cooling towers and cooling tower pumps are quantity controlled to change the flow rate as required. In this case, the cooling tower pumps only provide the pressure loss on the source side; this drop is quite small. Additionally, the storage tank affords increased pump flexibility and cooling tower control flexibility. As based on the time-of-use electricity tariff, the cooling source would generate an increased amount of cold water and store it in the water tank during the time of minimum electricity cost, and subsequently reduce the usage during the peak cost time. In order to make full use of the water tank, it is stratified into several horizontal layers [25]. Small holes exist throughout the partition to allow water to freely flow between layers. This study takes a five-layer water tank as an example. The relatively hot water returning from each chiller condenser flows into the top layer, where it is extracted and pumped into the cooling tower. The outlet water from the cooling tower flows into the bottom layer and is then pumped to the transportation side. Middle layers 2-4 are storage and transition layers between the relatively hot upper layer and cold bottom layer. This design minimizes the intermixing of the water, which is partitioned according to temperature level, to ensure that the water transported into the end use is always the coldest. It is considered as energy-saving because colder water decreases the condensing temperature and increases the COP of the chiller. Although the energy-saving potential of current related systems has been briefly discussed, the new system proposed in this paper includes additional components, such as cooling tower pumps; thus, the total energy consumption and actual benefits of this system need to be studied in detail; and, these results are discussed below.
# A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment
## 2 The Motion Constraints
### The Constraint of Collision Avoidance with Obstacle
#### 2.3.3 The Case of Lane-Change Maneuver
By Substituting the motion model in Equation (10) into Equation (11), the minimum safe distances during the lane change process considering prediction uncertainty are obtained. If the host vehicle is preparing for lane-change or is under the lane-change process, \(s_{min}\) is the minimum safe longitudinal position, \(s_{max}\) is the maximum safe longitudinal position, shown as the shadowed red vehicles in Figure 7. The safe longitudinal position \(s\) should satisfy the constraint \(s_{min}\leq s\leq s_{max}\). If the host vehicle starts lateral motion at \(s_{max}\), it will arrive at the critical position with the front vehicle \(L_{0}\) or the front vehicle \(L_{d}\), as shown in Figure 6b,d. If the host vehicle starts lateral motion at \(s_{min}\), it will arrive at the critical position with the rear vehicle \(F_{0}\) or the rear vehicle \(F_{d}\), as shown in Figure 6a,b. The safe longitudinal position of the host vehicle should be in \([x_{min,safe}\,x_{max,safe}]\). Figure 6.0 Figure 6.1  
where \(s_{Fd}\), \(s_{F0}\), \(s_{Ld}\), \(s_{L0}\) are the longitudinal positions of the vehicles \(F_{d}\), \(F_{0}\), \(L_{d}\), \(L_{0}\); \(D_{Ld,s}\), \(D_{L0,s}\), \(D_{Fd,s}\), \(D_{F0,s}\) are the required minimum safe longitudinal distances for the surrounding vehicles for a lane-change maneuver.
## 3 Results and Discussion
### Distribution of MC in the Thickness Direction  
Figure 7 presents the MCs of T1, T2, and T3 in different thickness layers at 30%, 20%, and 12%. The figure shows that in the three experimental groups, a deviation in MC was observed between the core layer and the surface layer during the drying process. The curves of T1 and T3 fluctuated gradually, and with a decrease in moisture, the deviation gradually decreased from 4.43% and 3.12% at 30% to 1.18% and 2.27% at 10%, respectively (Figure 7d). The reason could be that during drying, the broken-down invaders and the volatiles in the conduit were gradually pushed from the surface layer to the core layer, increasing the permeability of the wood samples and promoting the outward movement of moisture in the core layer, thus reducing the deviation in MC between the core layer and the surface layer.  
The curve of T2 fluctuated sharply; however, as the MC decreased, the curve fluctuated moderately, from 11.75% at 30% to 2.97% at 10% (Figure 7d). When the MC reached 30%, the MC deviated between 7.32% and 8.63% (deviation larger than those of T1 and T3). In regards to drying quality, the larger the deviation in the MC, the higher the probability of stress, which led to the  
Figure 6: Final shrinkage rates (**a**) and anti-swelling efficiencies (ASEs) (**b**) of T1, T2, and T3.  
generation of drying defects. The large MC deviation was caused by the modifier filling in the wood cell cavity, which was not conducive to the mass and heat transfer of the wood during the drying process. After the surface moisture evaporated, the moisture in the wood failed to spread to the surface of the wood samples, resulting in a large moisture gradient. In the T3 group, the result of impregnation was affected by the higher initial MC, and the modifier did not solidify inside the wood conduit, which did not affect the water discharge.
## 4 Introduction of FAD Key Parameters
### Calculation of \(L_{r}\) and \(K_{r}\)  
According to BS 7910, the fracture ratio (\(K_{r}\)) and load ratio (\(L_{r}\)) can be calculated as follows:  
\[K_{r}=\frac{K_{r}}{K_{mat}}\] \[(Y\sigma)_{P}=Mf_{w}\{k_{tm}M_{km}M_{k}P_{m}+k_{tb}M_{kb}M_{b}[P_ {b}+(k_{m}-1)P_{m}]\}\sqrt{\pi a} \tag{8}\] \[(Y\sigma)_{S}=M_{m}Q_{m}+M_{b}Q_{b}\] \[L_{r}=\frac{\sigma_{ref}}{\sigma_{Y}} \tag{9}\]  
where \((Y\sigma)_{P}\) is the contribution from principle stresses; \((Y\sigma)_{S}\) is the contribution from secondary stresses; \(K_{t}\) is the tensile stress intensity factor; \(a\) is the crack depth; \(M\) is the Bulging correction factor, which is \(M\) = 1 for the outer surface circumferential crack; \(f_{w}\) is the finite width correction factor, which in this case is \(f_{w}\) = 1.000; \(M_{m}\) and \(M_{b}\) are the stress intensity magnification factors of membrane and bending stress components, respectively; \(M_{km}\) and \(M_{kb}\) are respectively the membrane and bending stress intensity magnification factors at the weld toe; \(k_{m}\) is the stress concentration factor due to misalignment; \(\sigma_{ref}\) is the reference stress corresponding to the applied load; \(\sigma_{Y}\) is the base material yield stress, and \(k_{tm}\) and \(k_{tb}\) are the membrane and bending stress concentration factors, respectively. Since the specific equation is not provided in Annex B, the membrane and bending stress concentration factors are not considered for the time being, and are taken as 1.  
Figure 5: Option 1 and 2 evaluation curves of the \(\mathcal{O}813\times 17.5\) mm X65 pipe base metal.
## 5 Results and Discussion
### Discussion and Result
#### 5.2.1 Domain  
The current systematic review surveyed the previously proposed IDMS solutions that adopted a decentralized approach. Previous literature has illustrated that the use of blockchain technology improved the security and privacy of IDMSs in many domains, such as IOT [12; 13; 14; 15; 16]; supply chain [17; 18; 19; 20]; AC and Identity Management in [21; 22; 23; 24; 25; 26], cloud IDM in [27], ad-hoc network (VANET) in [28; 29; 30], healthcare in [31; 32; 33], internet of connected vehicles in [34; 35], and even for the undirected graph authentication, as discussed in [36].
## 4 Discussion
### Effect of the Midsole Properties on Running Performance and Influence of the Stack Height  
The peak midsole deformation, in conjunction with the material stiffness, determines the energy returned [27], which seems to have an effect on the athletes' running economy [17; 23; 24]. [PERSON] et al. [24] reported that the different properties of the EVA (8 mm peak deformation, 176 N/mm, 69% energy returned) and TPU (10 mm peak deformation, 130 N/mm, 79% energy returned) materials used in the same footwear prototype resulted in a 1% difference on running economy. Using an alternative experimental design that enabled to control the potential influence of the shoe mass, [PERSON] et al. [23] tested the energetic cost of running barefoot on a standard treadmill surface compared to a cushioned surface made with foam slats of 10- and 20 mm thickness of the Nike Free 3.0 footwear midsole material. Running on the 10 mm surface resulted in a 1.6% lower energetic cost compared with the standard surface, but no significant improvements were observed on the 20 mm one [23]. In this regard, [PERSON] et al. [17] observed that reducing the stack height of the four identical footwear prototypes (i.e., 50, 40, 35, and 30 mm) resulted in a decrease in the peak vertical deformation (i.e., from 5 to 3 mm) in conjunction with an increase in the midsole stiffness (i.e., from 100 to 176 N/mm) which resulted in a similar energy return (i.e., from 86.5 to 83.5%) and running economy.  
It was initially proposed that reducing the stack height could bottom out the midsole and, therefore, deteriorate the energy returned and its benefits on running performance [12]. With the example of the force-deformation profile (i.e., 200 N/mm) of the PEBA material used in the Nike footwear models, a midsole of 10 mm thickness would stiffen up before its maximal deformation is reached (i.e., 12 mm) when the mechanical load of an athlete of 68 kg running at 18 km/h is applied (i.e., 2000 N in 185 milliseconds) [12]. Thus, [PERSON] [12] stated that considering the stack heights and midsole deformation profiles of current shoe models, the midsole properties would not be affected by the current regulation unless the stack height is reduced further than the maximum deformation capacity of the midsole. In this regard, it is worth noting that, according to the aforementioned results reported by [PERSON] et al. [17], reducing the stack height does not seem to alter the energy returned, given that the midsole deformation and stiffness decreased and increased, respectively. However, it should be noted that the midsole deformation and restitution should be synchronized with the mid and propulsive stance phases in order to facilitate the cushioning in conjunction with the vertical and forward propulsion [14]. Therefore, this fact is specific to the running speed to which the midsole properties should be matched.
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
## 3 The Proposed Fault Direction Detection Principle
### Principle for Detecting Fault Direction Under Asymmetric Phase-to-Phase Faults
#### 3.1.2 Negative Sequence Impedance Angle Direction Criterion  
The negative sequence impedance angle direction criterion is shown in Equation (5):  
\[\varphi_{\text{set.max}}>\arg(\frac{\mathbf{U}_{p4}^{-}}{\mathbf{I}_{p4}^{-}})>\varphi _{\text{set.min}} \tag{5}\]  
Among them, \(\varphi_{\text{set.max}}\) and \(\varphi_{\text{set.min}}\) are the upper and lower boundaries of the operating region during forward faults, and they can be calculated using Equations (6) and (7):  
\[\varphi_{\text{set.max}}=-\frac{1}{2}\big{[}\arg(Z_{\text{up.min}})-\arg(Z_{ \text{down.min}})+180^{\circ}\big{]}+\arg(Z_{\text{up.min}}) \tag{6}\]  
\[\varphi_{\text{set.min}}=\arg(Z_{\text{up.max}})+\frac{1}{2}(\arg(Z_{\text{ down.max}})+180^{\circ}-\arg(Z_{\text{up.max}})) \tag{7}\]  
where \(Z_{\text{up.max}}\) and \(Z_{\text{up.min}}\) are the maximum and minimum negative sequence impedance upstream of PR4, and \(Z_{\text{down.max}}\) and \(Z_{\text{down.min}}\) are the maximum and minimum negative sequence impedance downstream of PR4. Substituting the system parameters into Equations (6) and (7) yields values of \(-15.19^{\circ}\) for \(\varphi_{\text{set.max}}\) and \(-178.78^{\circ}\) for \(\varphi_{\text{set.min}}\).  
The flowchart of the proposed principle in this section is shown in Figure 3.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Evaluation Metrics  
For the testing dataset with reference images, full-reference evaluation was conducted using peak signal-to-noise ratio (PSNR) [50] and structural similarity (SSIM) [51]. Additionally, nonreference evaluation metrics were applied to those datasets lacking ground truth images. We used the underwater image quality measure (UIQM) [52], which is the most widely used no-reference evaluation metric, as the metric. The details about the three metrics are as follows:  
1. PSNR [50] reflects the proximity to the reference, where a higher PSNR value represents similar image content.
2. SSIM [51] represents the degree of similarity of structure and texture for an image pair.
3. UIQM [52] consists of three image attribute measurements: underwater image color measurement (UICM), underwater image sharpness measurement (UISM), and underwater image contrast measurement (UIConM). UIQM is a linear combination of these methods, and each property is inspired by the human visual system to assess an aspect of underwater image degradation. A higher value indicates better human visual perception of the color, sharpness, and contrast of the image. The UIQM is calculated as follows: \[UIQM=c_{1}\times UICM+c_{2}\times UISM+c_{3}\times UIConM,\] (15) where \(c_{1}\), \(c_{2}\), and \(c_{3}\) are weighting coefficients. In this paper, we follow the literature [52] and set these as 0.0282, 0.2953, and 3.5753, respectively.
## 1 Introduction
For the boost converter, the predictive control method has been deeply studied. Reference [6] [PERSON], [PERSON] and [PERSON], [PERSON] adopted predictive control of BTL using a dynamic model of inductance current and DC bus voltage. Adopted regression methods in [7] to predict the next cycle current of digital controller delay compensation by [PERSON], [PERSON], etc. In [8], the FCS-MPC method is adopted to reduce the CMV and balance the NP voltage with fast dynamics of the three-level T-type inverter by [PERSON], [PERSON], etc. In [9], the modified model predictive control algorithm for the high efficiency with reduced switch stress t-type three-level inverter with the neutral point clamped is presented by [PERSON], [PERSON], etc. In [10], a radial basis function neural network optimization model predictive control (MPC) was proposed by [PERSON], [PERSON], etc. for large wind turbines, which meets the requirements of the specified operation region. Reference [11] [PERSON], X. and [PERSON], D proposes a three-vector-based low complexity model predictive direct power control strategy for doubly-fed induction generators (DFIGs) in wind energy applications, which improve the steady-state performance and achieves error-free control. In [12], the proposed sensorless method by [PERSON], [PERSON], etc. offers as a model-free solution, the MPC strategy has been used as a current controller to overcome the weaknesses of the inner control loop and considers the discrete-time operation of the VSC. In this paper, a new predictive current control method without any kind of linear controller or modulation technology is proposed for the BTL and three-level T-type inverter cascaded topology, with stable performance of THD improved significantly. The structure of this paper is organized as follows: the principle of boost-TL converter and the principle of the T-type three-level inverter are introduced in Sections 2 and 3, respectively; the predictive current control strategy is presented in Section 4; the simulation and experimental implementation and validation are shown in Section 5; and the conclusion of the paper can be found in Section 6.
## 2 Materials and Methods
### SMD Algorithm  
The SMD algorithm can be described by the following steps:  
1. Using a geometric mean filter, choose a specific point (row number and column number) in the data matrix \(\mathbf{M}\) at which a displacement (or pressure) from a coherent wave was most likely recorded.
2. Using cross-correlation, find this wave in as many surrounding columns as possible. For each column, the relative row positions of the said wave are recorded in the shift vector.
3. Shift the columns of \(\mathbf{M}\) using the shift vector \(\mathbf{s}\) and record the row vector and the column vector. Subtract the shifted outer product of row vector and column vector from matrix \(\mathbf{M}\) and shift the columns of \(\mathbf{M}\) back to their original positions.
4. Repeat steps 1-3 until a certain performance criterion is satisfied. A visual representation of the algorithm can also be found in Figure 5. To provide a more in-depth understanding of the algorithm, in the following paragraphs we will give a detailed description of each of the four steps.  
Figure 4: (**a**) A matrix showing a wave arriving to the receivers at different times. (**b**) The matrix being expressed as a pair of singular vectors and a shift vector. (**c**) The singular vectors and the shift vector being used to reconstruct a matrix that matches the original.