esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The DRR Traffic Control Algorithm  
### General Description  
The DRR [10] algorithm appears as an evolution of the Chain Of Reservations (COR) strategy introduced in [13], and it relies on a conflict prevention approach.  
The basic features of the method are as follows: (i) AGVs have their own parking spot from where they start each assignment and to where they are directed after completing the drop-off; (ii) each resource point, or node of the corresponding graph model, can be reserved, and subsequently occupied, by at most one AGV; (iii) an AGV located at a certain node can reserve at most the next node in the current task.  
The allocation mechanism works as follows: Let AGV \(R_{i}\), located at node \(N_{x}=T_{ij}\), request permission to resume to \(N_{y}=T_{ij+1}\); permission is granted if any of the next two conditions is met:  
**Condition A.**\(N_{y}\
otin\Sigma_{i}\).  
**Condition B.**\(N_{y}\in\Sigma_{i}\) and \(SN_{z}=0\), \(\forall N_{z}\in\Sigma_{i}\), \(N_{z}\
eq N_{x}\).  
Essentially, Condition A guarantees that \(N_{y}\) is not in the shared route of \(R_{i}\), which means that \(N_{y}\) is free, i.e., \(SN_{y}=0\), and the move is completely conflict-safe. In turn, Condition B allows \(R_{i}\) to access the shared route just in case all its nodes are free. This suggests that, as the cardinals of \(\Sigma_{i}\), \(i=1,\ldots,n_{R}\) grow, AGVs will have to wait longer for the allocation of shared resource points, which may have a negative effect on the ending time of the task. A simplified flowchart of DRR is displayed in Figure 3, where \(N_{x}=T_{ij}\), \(N_{y}=T_{ij+1}\) are two adjacent nodes to be visited by AGV \(R_{i}\).  
Moreover, the role of discretization seems to be linked to the fact that it bounds the distance between the shared route and the adjacent zone where an AGV is waiting to access it. As pointed out in Section 1, lower distances mean that AGVs would wait nearer and may reach the shared route sooner, but it also scales up permission requests and acceleration/deceleration maneuvers, so the combined effect on the ending time of the task is to be assessed. Finally, if the control period which governs the traffic controller allocation mechanism works faster, the waiting times realized by the AGVs should be shorter than those associated with higher control periods. However, as advanced in the Introduction, the numerical results in Section 6 will show that only the control period plays a significant role.  
Figure 3: General DRR flowchart. \(N_{x}\) and \(N_{y}\) represent adjacent nodes to be visited by AGV \(R_{i}\).  
**Remark 2**: _Conditions A and B have been adapted to the fact that, differently from [10], the definition of shared route used in this paper considers the current AGV position as a part of it (see Definition 4)._
# Control-Flow Integrity: Attacks and Protections
## 4 Classification of CFI Techniques  
Control-Flow Integrity is a defense policy which restricts unintended control flow transfers to unauthorized locations [40]. It is able to defend against various types of attacks whose primary intention is to redirect the execution flow elsewhere. Many CFI techniques [41; 42; 43; 44; 45; 46; 47; 48] were proposed over the past few years. However, they were not fully adopted due to practical challenges and significant limitations.  
The CFI techniques are primarily classified as fine-grained and coarse-grained. Both classifications comprise advantages and drawbacks in-terms of control-flow security. The development and research of each particular CFI techniques were mainly focused on either fine or coarse-grained approach with justification why a particular approach was adopted. In this section, we discuss both approaches to understand the security level.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 6 Dataset Analysis
### General (Maint) Behaviour and Talk Type Distributions  
As Table 11 demonstrates, the most marked contrast between therapist behaviours in MI-adherent and non-adherent therapy is the proportions of Reflection and Input. The average MI-adherent therapist employs Reflection in 28% of their utterances, whereas it is only 7% in non-adherent therapy, echoing the MI requirement of trying to understand the client's perspective and communicating it. On the other hand, Input is given 33% of the time in low-quality MI but only 11% in high-quality MI, which, together with the statistics of Reflection, conforms to the observation [1] that high-quality MI emphasises understanding the client as opposed to speaking from their own point of view. The correlation between MI quality and the share of Question and Other is relatively weak.  
As for Talk Type, Change Talk is more frequent in high-quality MI--25% vs. 17%--whereas Sustain Talk has a stronger presence in low-quality MI--11% vs. 15% (Table 12). These contrasts are, nevertheless, less obvious than those found in Reflection and Input. Possible explanations include (a) some clients in low-quality MI could adopt tepid change-talk-like speech such as \"Yeah, maybe\" only to end the counselling quickly, and (b) some clients in high-quality MI are simply more reluctant to change but the therapist still respects this, as is recommended in MI. On the other hand, most (64-68%) client utterances belong to the neutral talk category regardless of MI quality, for which the prevalence of short utterances such as \"Mhmm\" and \"Uh huh\" can be a major contributing factor.
## 3 Fabrication and Measurement of the Proposed Feed Array Antenna  
Figure 9 shows the photographs of the proposed feed array antenna. As can be seen in Figure 9a, the fabricated feed array antenna has 16 elements, and the optimum array  
Figure 8: Cost variation according to operating frequency.  
Figure 7: Normalized electric field distribution of the feed array antenna. (**a**) \(h=0.4\lambda\). (**b**) \(h=0.6\lambda\).  
design parameters obtained using the GA are applied to the proposed array antenna. To adjust the input phase of each element, the different transmission line lengths are used for the central and outer elements of the array, as shown in Figure 9b,c. The near-field distribution of the proposed feed array antenna is measured in a full anechoic chamber. The dimensions of this chamber are 10 \(\times\) 10 \(\times\) 10 m\({}^{3}\). A simple waveguide antenna is used as a probe to scan the aperture of the antenna being measured after antenna calibration based on the three-antenna method [25]. Herein, a resolution of the measured near-field data is 0.01 m. SMA connectors are mounted on the bottom face of the ground plane of the array, and all SMA connectors are connected to the network analyzer using five four-way power dividers, as shown in Figure 9d. The array parameters of the proposed feed array antenna are listed in Table 4.  
Figure 9: Photographs of the proposed feed array antenna. (**a**) The 4 \(\times\) 4 feed array antenna. (**b**) Vivaldi radiator of the central elements. (**c**) Vivaldi radiator of the outer elements. (**d**) Measurement setup.  
Figure 10 illustrates the normalized electric field distribution of the proposed feed array by measurement and simulation at 3.05 GHz. The electric field distributions are observed to be 30.2 mm (0.3\(\lambda\)) above the aperture of the feed array antenna. The average and deviation of the measured electric field are 1.7 dB and 6.8 dB (cost = 3.2), respectively. This is in good agreement with the simulated results, which are an average of 1.3 dB and a deviation of 6.9 dB (cost = 2.9). The results demonstrate that the feed array with the optimum array configuration and the parasitic rings can obtain uniform electric field distribution in the near-field region.  
Table 5 provides the comparisons with other feeders. As can be seen in this table, the feeders in [13,14] are used the single element, which has an advantage of low structural complexity. However, these feed antennas are often mounted in the far-field region, increasing the overall volume of the antenna system. The feeder in [16] is used the array configuration to enhance the uniformity of the near-field distribution. Then, the proposed antenna can achieve the required near-field distribution on the virtual target surface by optimizing the radii of the parasitic rings, transmission line length of each Vivaldi radiator (phase distribution), and array spacing.
## 4 Design
### Problem Description and Data Processing  
In this subsection, the designs for data preprocessing, except feature selection, are demonstrated. In this work, this task is defined as a binary classification problem, as described in Section 2. The input is a series of disk SMART data, and we used 10 days' data as a sample, as in [8; 22; 23]. For example, if there is only one attribute \(a\) for SMART data, _ai_  
Figure 3: Overview of the proposed scheme.  
Figure 2: Observation 2: **(a)** The results of model quality with the increase in the amount of attributes amount; **(b)** The modeling latency.  
is the value for \(a\) on the ith day; thus, the input would be \([a1,a2,a3,...,a10]\). Moreover, the label set is \(\{0,1\}\), a sample is labeled as 1 when the disk will fail within the next 10 days, otherwise, it is labeled as 0. Thus, the output would be whether the disk would fail or not in the next 10 days. As there are many attributes with null values in the original SMART data, we delete these attributes for constructing better prediction models. Furthermore, since the range of values for different attributes across different disks and vendors varies widely, and it is hard to perform meaningful comparisons. Thus, min-max normalization was used, as shown in Equation (1), where \(x_{i}\) is the original value of the ith variable in \(X\), \(x_{min}\) and \(x_{max}\) are the minimum and the maximum values in \(X\), respectively.  
\[x_{norm}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
In addition, an extreme imbalance between healthy (negative) and failure (positive) samples is common in this task, where the failure samples are much fewer than healthy samples. If this is not handled well, the prediction results tend to predict healthy disks and be insensitive to failures, which is not our goal. Thus, we used sub-sampling to reduce this impact, which was commonly used in other works [13; 24] In this work, we keep all failure disks and randomly select healthy disks. To choose a reasonable ratio of healthy and failure disks, we experimented with different ratios from 1:1 to 20:1 and found that the middle range of this ratio had the best balance between the number of training data and prediction accuracy. Thus, we chose 10:1 in this paper by default.
# The Effects of Fertilization on the Growth and Physiological Characteristics of _Ginkgo biloba_ L.
## 1 Introduction
One important role of _Ginkgo_ is planting in rows in corridors, squares, or on both sides of a street as shade trees, as well as planting in gardens and vestibule entrances as landscape trees. _Ginkgo_ wood has value for many purposes from the help of science and technology. In recent years, with the development of the _Ginkgo_ industry, _Ginkgo_ cultivation area continues to expand; many researchers have developed a variety of optimum fertilization schemes for _Ginkgo_ plantations (especially leaf-producing plantations, nuts plantations, and seedlings) in China. However, studies concentrated on timber plantations are comparatively insufficient. This study was conducted to examine the relationship between fertilization and growth, photosynthesis characteristics, and foliage nutrient content of pure _Ginkgo_ timber plantations. The main objectives were to assess the effect of fertilization on growth, nutrient content in _Ginkgo_ foliage, and photosynthesis characteristics of new _Ginkgo_ shoots in a 10-year-old _Ginkgo_ plantation over two years. Information generated from this study is expected to be of great value for providing optimal fertilization measures for improving the yield of _Ginkgo_ timber forests and realizing balanced nutrient management for its fast growth. In addition, the application of a suitable fertilizer ratio after foliage nutrient content analysis, photosynthesis, and tree growth characteristics are some of the most effective ways to reduce cost and fertilizer waste.
## 4 The ISFLA for Solving the Simultaneous DCBAP
### The Main Flow Chart of the Two-Stage Approach  
Figure 10 shows the main flow of the two-stage approach for dealing with the DCBAP. The first stage (steps 1 and 2) focuses on generating a ship placement sequence by using different approaches.  
Figure 8: The movement of a target ship towards the +Y or -Y direction.  
Figure 9: The movement of a target ship toward the +X direction.  
The second stage (steps 3 to 10) is a heuristic used to place ships into a berth plan and resolve overlaps of ships. Each step is detailed as follows.  
* Generate data (including \(a_{j}\), \(d_{j}\), \(l_{j}\) and \(h_{j}\)) for all calling ships (\(j=1,\ldots,n\); where \(n\) is the total number of calling ships).
* Develop a placement sequence using the ISFLA (or FCFS, GA that are to be compared); set \(s=1\), where \(s\) indicates the placement order of a ship.
* Place the ship \(j\) (at the placement order \(s\)) into the berth plan, with the lower-left and upper-right corners being located at the coordinates \((x_{0}^{j}\), \(y_{0}^{j})=(a_{j}\), \(c_{j})\) and \((x_{1}^{j}\), \(y_{1}^{j})=(a_{j}+h_{j}\), \(c_{j}\) + \(l_{j})\), respectively; Set \(k=1\).
* Check whether the ship \(j\) with the placement order \(s\) has overlapped with the ship with the placement order \(s\)-\(k\) by using Equation (29). If \"No\", then go to Step 5; otherwise, go to Step 8.
* Check whether \(s=k\). If \"Yes\", go to Step 6; otherwise \(k=k+1\) and go to Step 4 to check the next ship.
* Check whether the ship \(j\) remains with overlap(s) after repositioning. If \"Yes\", go to Step 10; otherwise, go to Step 7.
* Check whether \(s=N\). If \"Yes\", go to Step 11; otherwise \(s=s+1\) and go to Step 3.
* Estimate the moving cost of each moving direction for repositioning the target ship with the placement order \(s\) using Equations (31) and (33). In addition, let \(i=1\) index the least-cost movement direction as the first priority; \(i=2\) index the second least-cost movement direction as the second priority; \(i=3\) index the most-cost movement direction as the 3 rd priority.
* Repositioning the target ship towards the direction with the priority index \(i\). Update the coordinates of the lower-left and upper-right corners of the target ship using Equation (30) or (32). Go to Step 5.
* Check whether the ship \(j\) has been ever overlapped with this ship or had an overlap resolved. If \"Yes\", set \(i=i+1\) (choose the next repositioning direction) and then go to Step 9; otherwise, go to Step 8.
* Check whether there is another iteration to perform. If \"Yes\", go to Step 2; otherwise go to Step 12.
* Stop.
# Vortex: Network-Driven Opportunistic Routing for Ad Hoc Networks
## 5 Conclusions  
In this paper, we proposed a novel hierarchical OR protocol, VORTEX, to address the disadvantages and drawbacks of conventional routing protocols that put restrictions on network adaptability. In the proposed VORTEX, end terminals initiate their forwarding by simply broadcasting packets into a network and the terminal composing the network autonomously forwards them toward the desired destination terminal once the network has been set up. This allows endpoints to use the network as if it were a black box that can somehow deliver packets to desired destinations. Moreover, VORTEX performs the periodic tier update to adapt to dynamic topology changes in ad hoc networks. The simulation results show that VORTEX can achieve higher reliability with shorter delay as well as higher energetic efficiency. Therefore, we conclude that the proposed VORTEX realizes network-driven packet forwarding without using pre-established forwarding guides with reasonable network performance and also shows that the well-maintained hierarchical structure of VORTEX contributes to reusing or sharing routing information among the network for reducing routing cost.  
Although VORTEX could achieve better performance than the conventional protocols from the above aspects, drawbacks such as an increase in the number of forwarders compared to conventional unicast routing protocols, which is essentially inevitable in ORs, still remain one of the issues to be addressed. However, suppressing unnecessary forwarding by using forwarding of other terminals as an implicit acknowledgment, which has been realized in another work [39], can deal with the drawbacks. Moreover, excluding infrequently used routed by measuring route usage can help suppress the number of forwarders. Furthermore, improving tier assignment or update procedure of VORTEX by dividing the hierarchy into smaller domains could be one of the solutions to the aforementioned limitation of VORTEX.  
Conceptualization, R.Y., T.Y. and S.O.; methodology, R.Y.; software, R.Y.; validation, R.Y. and T.Y.; formal analysis, R.Y.; investigation, R.Y. and T.Y.; resources, R.Y. and S.O.; data curation, R.Y.; writing--original draft preparation, R.Y.; writing--review and editing, R.Y. and T.Y.; visualization, R.Y.; supervision, S.O.; project administration, R.Y.; funding acquisition, R.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by JSPS KAKENHI Grant Number 20K11765.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to NDA and licensing issue.  
The authors declare no conflict of interest.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 5 Stability Analysis of the GF-VSC with the Proposed Observer
Figure 5 shows the eigenvalues of the closed loop system for the four observers of Observer#1-to-Observer#4 in the six operating points under the full range of active power and reactive power production, as given in (36):  
\[ \begin{array}{l}\left(P_{dc},Q_{ref}\right)=\left\{\{\text{Point 1},\text{Point 2}, \text{Point 3},\text{Point 4},\text{Point 5},\text{Point 6}\}\right.\\ \left.=\left\{\left(P_{nom},-0.4P_{nom}\right),\left(P_{nom},0\right),\left(P_{ nom},0.4P_{nom}\right),\left(0,-0.4P_{nom}\right),\left(0,0\right),\left(0,0.4P_{nom} \right)\right\}\right\}\end{array}  \tag{36}\]  
From Figure 5, it can be seen that the overall system is stable for all four observers in all six operating points. In addition, increasing the observer bandwidth moves the poles to the far away points in the s-plane, but as expected, decreases the poles damping coefficient \(\zeta\). This is due to the difference between the model of (24), used for the design  
Figure 5: The change of the overall system poles for 4 different observers in 6 different operating points, (**a**) Observer 1; (**b**) Observer 2; (**c**) Observer 3; (**d**) Observer 4.  
of matrix \(L\), and the exact model of system in (3). Moreover, it decreases the delay margin of the system, which, considering the time delay imposed in the control loop by VSI, may destabilize the system. On the other hand, reducing the observer bandwidth may increase the estimation error in the presence of the uncertainty. Accordingly, a compromise is needed in the selection of the observer bandwidth and the system stability. In this regard, the third observer, whose corresponding matrix \(L\) is presented in Table 1, is chosen as the final observer.
# Survey of Blockchain-Based Applications for IoT
## 11 Digital Assets and NFTs in the IoT
### Blockchain-Based Digital Twin  
A digital twin is a virtual replica of a physical object, system, or process that is continuously updated with real-time data, enabling monitoring, simulation, and predictive analysis. They are used in manufacturing, healthcare, smart cities, and aerospace sectors, enhancing decision-making, efficiency, and automation.  
Creating a digital twin in many applications requires collaboration among multiple domains and stakeholders, including designers, manufacturers, maintenance personnel, and users. This cross-stage, multi-enterprise cooperation involves integrating various data sources, such as design drawings, geometric models, process plans, and operational data, into a synchronized digital twin model. However, this process faces security challenges because of the involvement of multiple parties who may not fully trust one another. Ensuring data integrity, traceability, and uniqueness is essential to maintain consensus among stakeholders [82]. Blockchains secure and manage data from IoT devices and digital twins by enabling a decentralized, transparent system for secure, real-time data sharing and verification. This is crucial for handling large-scale IoT data efficiently [83].  
One example is the implementation of digital twins for construction projects [84]. Blockchain technology provides a framework for tracking file signatures, ensuring authenticity, and preventing tampering. Smart contracts automate construction processes, from project approvals to payments, reducing delays and disputes. By self-executing agreements, blockchain technology streamlines workflows, fosters trust, and improves collaboration.
# The Predictive Model of Surface Texture Generated by Abrasive Water Jet for Austenitic Steels
## Materials and Methods
### Statistical Modelling by Regression Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Term** & **Estimate** & **Std Error** & **t Ratio** & **Prob \(>\) |I** & **Lower 95\%** & **Upper 95\%** \\ \hline Intercept & 9.551 & 4.309 & 2.22 & 0.0305 \({}^{\star}\) & 0.929 & 18.174 \\ \(x_{1}\) & \(-\)1.841 & 1.034 & \(-\)2.08 & 0.0401 \({}^{\star}\) & \(-\)3.910 & 0.228 \\ \(x_{5}\) & 0.052 & 0.008 & 6.43 & \(<\).0001 \({}^{\star}\) & 0.036 & 0.068 \\ \(x_{6}\) & \(-\)0.074 & 0.029 & \(-\)2.56 & 0.013 \({}^{\star}\) & \(-\)0.133 & \(-\)0.016 \\ \(x_{1}\)\({}^{\star}\)\(x_{5}\) & \(-\)0.092 & 0.019 & \(-\)4.83 & \(<\).0001 \({}^{\star}\) & \(-\)0.130 & \(-\)0.054 \\ \(x_{1}\)\({}^{\star}\)\(x_{5}\)\({}^{\star}\)\(x_{5}\) & \(-\)0.002 & 0.001 & \(-\)2.09 & 0.041 \({}^{\star}\) & \(-\)0.003 & \(-\)7.76\(\cdot 10^{-5}\) \\ \(x_{3}\) & 0.703 & 0.697 & 2.01 & 0.047 \({}^{\star}\) & \(-\)0.692 & 2.097 \\ \(x_{4}\) & 0.004 & 0.006 & 2.00 & 0.049 \({}^{\star}\) & \(-\)0.007 & 0.016 \\ \(x_{3}\)\({}^{\star}\)\(x_{4}\) & 0.003 & 0.014 & 1.98 & 0.043 \({}^{\star}\) & \(-\)0.024 & 0.030 \\ \hline \hline \end{tabular} \({}^{\star}\) significant at a significance level of 5%.  
\end{table} 
Table 10: Estimation of model parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **Sum of Squares** & **Mean Square** & **F Ratio** & **Prob \(>\) F** \\ \hline Lack of Fit & 35 & 118.874 & 3.396 & 0.960 & 0.552 & 0.864 \\ Pure Error & 24 & 84.904 & 3.538 & & \\ Total Error & 59 & 203.778 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Lack of Fit test for the model.