esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Random Forest Surrogate Model  
To speed up training the agent, we use the surrogate model to establish the mapping of hyperparameter settings to the corresponding rewards. We adopt the RF algorithm to predict rewards in this work. RF belongs to the bagging algorithm in ensemble learning (EL) and has many advantages, such as determining the importance of features, determining the interaction between different features, being relatively simple to implement, etc.  
Although RF can speed up the search for hyperparameters, using the surrogate model for a long time can make the agent unstable since the predicted reward is not the true accuracy after all and will produce a certain degree of bias. Therefore, we follow the works of [15] to control the adoption of the surrogate model dynamically. In the initial stage of searching, we use real rewards to update the agent. Then, we use all the obtained hyperparameter combinations and corresponding rewards as samples to train the RF algorithm and save the policy at this episode, defined as \(\pi_{\theta}\). The KL distance of the before-and-after policy is used to control the utilization of the surrogate model dynamically. When the KL distance is less than the threshold, the surrogate model is used to predict the evaluation results of validation data set to avoid training the model. When the distance exceeds the threshold, indicating that the policy changes too much, the agent is updated by real rewards. The threshold is set to 0.1 in this work. In this way, we can utilize surrogate models to make the search process more efficient.  
Figure 5: Calculating the intrinsic reward. First embed \(s_{t}\) and \(s_{t+1}\) to get \(\Phi(s_{t})\) and \(\Phi(s_{t+1})\), then send \(\Phi(s_{t})\) and \(a_{t}\) to the forward network. The forward network outputs \(\Phi^{{}^{\prime}}(s_{t+1})\) with the same dimension as \(\Phi(s_{t+1})\), and then calculate the MSE error of \(\Phi(s_{t+1})\) and \(\Phi^{{}^{\prime}}(s_{t+1})\). The MSE is used as an intrinsic reward and becomes part of the reward for the current time step \(t\).
###### Abstract
While iterative techniques require a shorter search time, they may present higher misdetection rates. When a multipath channel is considered in a dense, urban environment, the exhaustive approach is more preferable since it offers lower misdetection rates. This paper further investigates the initial cell search problem based on the iterative search method proposed in [21] to see whether we can exploit directional transmissions with a carefully designed scanning pattern, instead of random scanning, to enhance the cell search process. We show that for a given coverage area, it is possible to design a scanning procedure for the BS using a set of directional antenna patterns so that the misdetection rate is significantly reduced compared to omnidirectional transmissions or random scanning. It should be noted that the previous work in [22; 23; 24] is implemented only in angular spaces, which simplified the beamforming procedure. In this paper, we consider the 3D transmission scheme where beamforming of BS and UE are created based on both azimuth and elevation angles. Furthermore, to maximize the performance of the search process, we propose the use of an adaptive array antenna to reduce the total scanning time while keeping the coverage capability the same and misdetection rate low. We believe that our transmission scheme is comprehensive; therefore, it can be applied to practical problems. The mobile terminals are assumed to detect the signals from the base station based on a GLRT detector, which will be discussed in detail in Section 3. Furthermore, we consider the 28 GHz band, which is also referred to as the Local Multipoint Distribution Service (LMDS) band in the USA, and operates under FCC Part 101 rules for Fixed Microwave Services [25]. Compared with other bands, the signals at 28 GHz band suffer from lower rain attenuation (0.18-0.9 dB/200 m) and oxygen absorption (0.04 dB/200 m) [26]. The remainder of the paper is organized as follows. Section 2 introduces the fundamentals of the initial cell search problem in traditional cellular networks, and our proposed framework. Section 3 presents the formulations for GLRT detection. Section 4 applies the GLRT detector to analyze the performance of the cell search procedure with omnidirectional antenna at BS. Section 5 describes the proposed scanning scheme, and evaluates its performance under line-of-sight (LOS) channels assumption. Section 6 discusses the applicability of the proposed algorithm to non-line-of-sight (NLOS) channels assumption. Section 7 presents conclusions of the paper.
## 3 Results
### Laser-Induced Damage Threshold  
LIDT measurements were performed on 480 nm HfO\({}_{2}\) layers with low stress (\(\approx\)0.5 GPa), medium stress (\(\approx\)1.5 GPa), as well as high-stress level (\(\approx\)3 GPa) deposited on fused silica.  
Figure 4 shows the estimated damage probability of these samples as a function of the laser fluence. A single slope characterizes the high-stress case, while the moderate and lower stress cases are composed of two parts--a less steep initial part followed by a second part with a similar slope as the high stress.  
Figure 3: (a) Calculated film stress for Si-substrates coated with 480 nm HfO\({}_{2}\) at 100 \({}^{\circ}\)C as function of the beam voltage of the assisting ion beam source. The dashed line is a spline fit of the data. (b) Fitted stress curves as a function of beam voltage of the assisting ion beam source for 100 nm as well as 480 nm HfO\({}_{2}\) films.  
In other words, the zero damage probability is independent of the stress state; however, the 0.5 damage probability is achieved at much lower fluence for the moderately and highly stressed sample. The same is true for the damage probability of 1.  
Figure 5 shows typical pictures of damaged sites for low-stress and high-stress samples, respectively. The low-stress samples (Figure 5a) show a clear, finite plasma scald-type damage morphology with dimensions similar to the laser beam size. In contrast to this, the high-stress samples (Figure 5b) show the irradiated spot but surrounded by a large cracked area.
## 3 Method
### Waterdrop Trajectory Detection
As described in Algorithm 1, the \(i\)-th column aggregated value \(g_{i}\) that has higher than \(g_{\text{thresh}}\), which is two standard deviations of the mean, is detected as the water drop trajectory. This threshold value can be adjusted based on these scenes. Moreover, non-maximum suppression with window size \(g_{\text{win}}\) is applied to the detection result to prevent a waterdrop trajectory from being detected as multiple lines in the images. Finally, the detection result is obtained as \(h_{i}^{L},h_{i}^{R}\in\{\text{true},\text{false}\}\), where the waterdrop trajectory is detected at the \(i\)-th column of the left image if \(h_{i}^{L}\) is true. Figure 3: Waterdrop trajectory detection. (**a**) Images obtained after background subtraction. Waterdrops and radiation noise are extracted from the images. (**b**) Vertically aggregated values of \(\mathcal{F}\). (**c**) Waterdrop trajectory detection followed by non-maximum suppression. The red rectangles represent the final detection results. ```
Data: Aggregated values \(\mathcal{G}=\{S_{1},\ldots,S_{W}\}\) Result: Detection result \(\{h_{1},\ldots,h_{W}\}\) // Initialize detection result with false \(\{h_{1},\ldots,h_{W}\}\leftarrow\) false; // Calculate mean and standard deviation of \(\mathcal{G}\) \(\mathcal{G}_{\text{mean}},\mathcal{G}_{\text{std}}\leftarrow\texttt{mean}( \mathcal{G}),\text{std}(\mathcal{G})\); // Detect waterdrop trajectory for\(i\gets 1\) to \(W\)do \(g_{\text{thresh}}=g_{\text{mean}}+2g_{\text{std}}\); \(h_{i}\leftarrow(g_{i}>g_{\text{thresh}})\);  end for // Non-maximum suppression for\(i\gets 1\) to \(W\)do if\(h_{i}==\) true then // Local maximum inside window \(g_{\text{max}}\leftarrow\texttt{max}(\mathcal{G}(i-g_{\text{win}}:i+g_{\text{ win}}))\); \(h_{i}\leftarrow(g_{i}==g_{\text{max}})\);  end for  end for
```  
**Algorithm 1**Waterdrop trajectory detection.
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 5 Discussion
### Limitations of the Study  
While the study provides a robust framework for assessing the impact of cyclic blasting on bridge structures, there are several limitations to consider. First, the study focuses on a specific type of bridge structure and material composition. The results may not be directly applicable to different bridge designs or materials without further validation. Second, the environmental conditions and geological settings of the study area are unique, and variations in these factors could influence the vibration response of nearby structures.  
Additionally, the numerical model, while highly accurate, is based on certain assumptions and simplifications that may not capture all of the complexities of real-world blasting scenarios. For instance, the model assumes uniform material properties and does not account for potential variations in the blasting process or unforeseen geological anomalies.
## 5 Implementation and Simulation Setup
### Description of Simulation Setup

Table 3: Overview of common simulation parameters across different Objective Function (OF) and/or interface-type configurations. Figure 8: Flow of events related to pseudo-periodic data packet transmission (towards the root node) by non-root RPL nodes. Note that \(T_{p}\) stands for “periodic timer”, \(T_{s}\) stands for “send timer”, and \(SEND\_INTERVAL\) is the amount of clock ticks in a time-span of ten seconds.
## 2 Design Methodology  
### Design Concept  
Piezoelectric (PZT) actuators are essential for achieving precise and accurate motion control in various applications. However, the maximum displacement of these actuatorsis typically limited to the micrometer range. By using CMs to transmit and amplify their driving motion, it is possible to achieve much larger displacements than with the PZT actuator alone, while maintaining high precision and accuracy levels. For designing high-precision positioning CMs, the most common configuration is of rigid links combined with flexure pivots that facilitate bending and twisting around central points. Popular shapes for these elastic hinges include circular, elliptic, parabolic, or hyperbolic. Of these, semicircular hinges are favored for their advantages of minor concentrated stress, immovable rotation center, high accuracy movement transmission, and simplicity and ease of fabrication. Therefore, in this design, semicircular elastic hinges are used to facilitate the relative movement among the links. Figure 2 illustrates the schematic design concept of a CM for a turning machining application. This design combines the lever and parallelogram mechanisms to address the design problem. In this design, the lever mechanism is used twice for displacement amplification. The parallelogram mechanism aims to increase rigidity and partially amplify the input displacement. The coupling effect among these mechanisms, that would induce unwanted motion in the other direction, is usually the critical concern. The current design intentionally uses a symmetrical arrangement to mitigate this effect. Additionally, the evaluation of the parasitic motion is also imposed as a constraint function in the optimization design process to carefully consider the coupling effect, which can impact the performance of the mechanism.
## 3 Results and Discussion
Thus, the synthesized CaS could be used as the auxiliary electrode in company with the residual CaAl\({}_{2}\)O\({}_{4}\) and CaAl\({}_{4}\)O\({}_{7}\) phases in the laser cladding layer. Therefore, when the assembled sulfur sensor was used for measuring the sulfur content, the actual sulfur sensor was as follows:  
\[\mathrm{Mo}\left|\mathrm{Cr}+\mathrm{Cr}_{2}\mathrm{O}_{3}\right|\mathrm{ZrO}_{2} \left(\mathrm{MgO}\right)\left|\mathrm{CaAl}_{2}\mathrm{O}_{4}+\mathrm{CaAl}_{4 }\mathrm{O}_{7}+\mathrm{CaS}\right|\left[\mathrm{S}\right]_{\mathrm{Fe}}\left| \mathrm{Mo}\right. \tag{5}\]  
Figure 5 shows a typical result of the sulfur sensor for a laboratory experiment at 1773 K and 1823 K, where the sensor EMF was plotted against sulfur content in the range of 0.02-0.1 wt pct analyzed using chemical analysis method in the iron. As can be seen, the sensor EMF was sensitive to the variation of the sulfur content and the accuracy was satisfactory. Meanwhile, the relationship between EMF and [wt% S] is logarithmic. Therefore, the sulfur content can be calculated through the following empirical equation based on the EMF measured with the sulfur sensors:  
\[E =52.53\ln\left[\mathrm{wt\%S}\right]+42.04\;\;R\;=\;0.96\;\;T\;=\;1773\; \mathrm{K} \tag{6}\] \[E =54.74\ln\left[\mathrm{wt\%S}\right]+14.71\;\;R\;=\;0.99\;\;T\;=\; 1823\;\mathrm{K} \tag{7}\]  
The above sulfur sensor overcame the defect that sulfide was easy to oxidize as auxiliary electrode, and obviously simplified the manufacturing process. The processing cost was greatly reduced and the sensor could be kept for a long time without any special protection, which created a necessary condition for industrial applications. Figure 4: Arrhenius plots of ionic conductivity of the fabricated sample.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work
### Multi-Path Routing  
Multi-path TCP (MPTCP) is the de-facto standard multi-path transport protocol [11]. MPTCT can only make use of a single path per local network interface and, thus, does not permit path choise as in MMP. The partial-reliability extension of the stream control transmission protocol (PR-SCTP) offers a primitive the possibility to define a lifetime parameter [19; 20]. [PERSON] et al. [21] used linear programming to evaluate multi-path routing from a traffic engineering perspective. Although PR-SCTP offers multihoming capabilities, additional IP addresses are used as a backup in the case of a failure; thus, PR-SCTP is not a fully multi-path protocol and does not address the problem that we describe in this paper [22].  
In Reference [23], the authors consider the problem of routing data over multiple disjoint paths and propose a framework for multi-path routing in mobile ad hoc networking (MANET). In Reference [24], they develop an analytical framework for evaluating multi-path routing, and Reference [25] shows the Secure Message Transmission (SMT) protocol, which safeguards the data transmission against arbitrary malicious behavior of other nodes in MANET. Recently, flying ad hoc networks (FANETs) are rapidly proliferating and leading the emergence of the Internet of Drones and its applications. Reference [26] proposes a jamming-resilient multi-path routing protocol, also called JarmRout so that intentional jamming and disruption or isolated and localized failures do not interrupt the overall network performance of FANETs. The JarmRout is designed based on a combination of three major schemes, which are link quality scheme, traffic load scheme, and spatial distance scheme, to select maximally spatial node-disjoint multiple paths with high link quality and light traffic load to deliver the data packets from an source to destination nodes. In our paper, we focus on multi-path for the global network, not the local network.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### GNSS and World Coordinate System Conversion  
According to the Horn absolute coordinate transformation, the rotation and translation transformation parameters between two Cartesian coordinate systems can be solved if there are three non-collinear coordinate points.  
According to Equations (6) and (7), the real-scale translation vector for the movement of \(C_{1}\) to \(C_{3}\) could be obtained in the same way as (10), as follows:  
\[\widetilde{t}\,_{C_{3}}^{C_{1}}=t_{C_{3}}^{C_{1}}/\lambda_{3,1} \tag{10}\]  
The GNSS coordinate system and the world coordinate system were both rigid-body coordinate systems, and so the transformation matrix consisted of the following two parts: the rotation vector \(\mathbf{R}_{W}^{n}\) and the translation vector \(t_{W}^{n}\). Here, the position \(X_{C_{1}}^{n}\) of \(C_{1}\) in the GNSS coordinate system was known to be the translation vector \(t_{W}^{n}\), and the positions of \(C_{2}\) and \(C_{3}\) in the GNSS coordinate system were known to be \(X_{C_{2}}^{n}\) and \(X_{C_{3}}^{n}\). The relation could then be jointly established using Equation (11), as follows:  
\[X_{C_{2}}^{n}=\mathbf{R}_{W}^{n}\,\widetilde{t}\,_{C_{2}}^{W}+t_{W}^{n}\; \text{and}\;X_{C_{3}}^{n}=\mathbf{R}_{W}^{n}\,\widetilde{t}\,_{C_{3}}^{-W}+t_ {W}^{n}. \tag{11}\]  
In addition, the rotation matrix \(\mathbf{R}_{W}^{n}\) was a 3 * 3 orthogonal matrix that satisfied the following constraints:  
\[\left\|R_{W}^{n\,1T}\right\|=1,\;\left\|R_{W}^{n\,2T}\right\|=1,\;\text{and} \;\left\|R_{W}^{n\,3T}\right\|=1, \tag{12}\]  
where \(R_{W}^{n\,1T}\), \(R_{W}^{n\,2T}\), and \(R_{W}^{n\,3T}\) denote the first, second, and third column vectors of \(\mathbf{R}_{W}^{n}\), respectively.  
The rotation vectors could be computed from the system by simultaneously using Equations (11) and (12). We noted that the three frames of the image constrained here were required to be sufficiently spatial, otherwise, a pathological transformation matrix would result.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 6 Battery Life Estimation
### System Regulations and Duty Cycle  
Each frequency band used by the LPWAN technologies imposes limit to the number of transmissions. These limits are defined as duty cycle and polite spectrum access restrictions [39]. Duty cycle is measured as ratio of total transmissions per observation period (default period is one hour). The range of duty cycle is between 0.1% to 10% which is the transmission time in the utilized frequency band. It helps in avoiding the congestion of the network. The battery life depends upon number of transmissions, while following the region specific duty cycle. There are no distinctions on the even spacing between messages but only maximum duty cycle needs to be respected. The number of transmissions depends on the application requirement such as alarm system, transportation or smart home. So, the total battery life estimation is proportional to the maximum limit of the duty cycle and the message transmissions required by the application. Few applications use polite spectrum access encompassing listen before talk and adaptive frequency agility. In this case, duty cycle restrictions are loosened and device uses channel assessment check to identify if medium is already in use and accordingly wait or change the frequency. This results into additional complexity and impacts the overall power consumption by the end-device.
## 2 Materials and Methods  
A citizen science study was conducted in the province of Reggio Calabria, Italy, during 2022. Six beekeepers from the local beekeepers' association were involved and trained on uniform protocols for hive inspections and data collection to assess the presence of SHB. Bekeepers were selected based on their extensive experience in managing SHB infestations. They inspected their colonies over three consecutive weekends in late winter 2022 (21-23 January, 11-13 February, and 4-6 March) and autumn 2022 (30 September-2 October, 21-23 October, and 4-6 November), with a three-week interval between each inspection. The MD device was placed on the left side of each hive between the last frame and the left wall at the beginning of the study. The device remained in the same position throughout the study and was only removed during inspections to count the number of SHB present behind it. During each inspection, beekeepers evaluated the number of SHBs present inside the hive using the MD method [20]. They began by inspecting the outer and inner covers of the hive then removed each comb from the side opposite the MD and transferred them to an empty hive, leaving the last three combs and the MD inside the original hive. Each removed frame was carefully inspected on both sides before being moved to the empty hive to count the number of SHBs present. Next, the beekeepers slowly moved the last three combs to the empty side of the hive and inspected each frame on both sides to count any SHBs present. After completing these evaluations, they extracted the MD device from the hive, examined it along with the space behind it, and recorded the number of SHBs found. Lastly, the beekeepers observed the content of the bottom board if present and completed a document separately recording the number of SHBs found behind the Mobile Divider and those found in the remaining volume of the hive. The number of SHBs in the remaining volume of the hive was calculated by adding the SHBs detected on both sides of the inspected frames before moving the MD device and inspecting the area behind it. Figure 2 provides a descriptive diagram of the method used in this study. We evaluated the efficacy of the MD method in concentrating SHBs by comparing the number of SHBs  
Figure 1: The Mobile Divider device placed inside the hive between the last frame and the hive wall.  
detected behind the MD device with those found in the remaining volume of the hive. Data were analyzed using IBM SPSS Statistics version 27.0.1.0 and Microsoft Excel 2007. The Kolmogorov-Smirnov and Shapiro-Wilk tests were used to assess the normality of data distributions. Non-parametric tests, such as the \(\chi^{2}\) analysis, have been applied on the number of SHBs detected by the two methods. We also calculated the percentage of SHBs found behind the MD relative to the total number of SHBs captured in the hive. In addition, we assessed the mean number of SHBs captured per hive per inspection to analyze the seasonal trend of SHB captures in late winter and autumn.