esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 5 Conclusions  
The results of this study show that the OLSR protocol has a shorter delay than the AODV protocol. The OLSR protocol has a delay of 0.02 s compared with that of the AODV protocol of 0.156 s, while, for other performance metrics, the AODV protocol is better than OLSR at every speed variation. OLSR has 11% lower PDR, 8% lower throughput, 46% higher packet loss, 49% higher energy consumption, and 94% higher routing overhead. This result shows that the AODV protocol can adapt better to topology change than the OLSR protocol.  
In the AODV routing algorithm, by reducing the combination value of RREQ_RETRIES, MAX_RREQ_TIMEOUT to (2, 10 s) and (3, 5 s), this can improve protocol performance. The two combinations resulted in an average increase in throughput performance of 3.09%, a decrease in delay of 17.7%, a decrease in packet loss of 27.15%, a decrease in energy consumption of 19%, a decrease in routing overhead of 4.8%, and an increase in PDR of 4.8%. From these results, it can be concluded that reducing the combined value of RREQ_RETRIES and MAX_RREQ_TIMEOUT can improve the AODV protocol's performance; the AODV protocol performs better than OLSR due to the low number of generated routing packets.  
Regarding variations in node movement speed, performance increases as the speed increases and decreases as the node speed decreases. A speed of 20 m/s has the best performance, while 5 m/s has the worst performance. These results apply to all performance metrics, including PDR, throughput, packet loss, delay, and energy consumption. These results indicate that node movement speed affects the formation of new routes after the path is cut off. These results may not be valid under different scenarios, so further research is needed.  
[PERSON] contributed to conceptualization, validation, resources, writing--review and editing, supervision, project administration, and funding acquisition. [PERSON] contributed to conceptualization, methodology, software, formal analysis, data curation, writing--original draft preparation. [PERSON] contributed to validation, resources, and visualization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Rekognisi Tugas Akhir (RTA) project in 2020 by Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Universitats Gadjah Mada (UGM) (LOA number 2488/UN1.P.III/DIT-LIT/PT/2020, subnumber 213).  
The authors declare no conflict of interest.
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 4 Evaluation
### FIO Benchmark  
To clearly confirm the key features of ReZNS, we first begin with synthetic workloads from the flexible 1/O (FIO) benchmark [52], which includes parameters of libaio I/O engine, 1000 threads, 15 MB size, 1 queue depth, 4 KB block size, random writes, and 16/64/128 fsync. In order to naturally generate zone-reset commands while running the FIO benchmark, we first filled the dummy data up to 50% of total capacity, and then, we consecutively ran the FIO benchmark with the same experiment 3 times. This evaluation setup may trigger frequent zone-reset commands to reclaim free space as the FIO benchmark quickly consumes available free space inside the ZNS SSD.  
Figure 3 shows the performance results over varying the fsync interval. As shown in Figure 3, ReZNS shows the best performance in all cases, but the performance gap highly depends on the period of fsync; it outperforms the conventional ZNS SSD by up to 9%, 6%, and 1% for 16, 64, and 128 intervals, respectively. To understand the reason in detail, we monitored and quantified the number of the zone-reset commands on NVMeVirt [28]. Figure 4 shows our evaluation results where the number of zone-reset commands decreases as the fsync interval increases. In addition, the gap between ReZNS and the baseline narrows down as the period of fsync increases.  
This is no surprise because of two reasons. The first reason is that the long period of fsyncs in FIO may increase the opportunities to merge consecutive I/O requests. As a result, the merged I/O requests may be perfectly aligned with the boundaries of a single zone. In this case, ReZNS works in the same way as the conventional ZNS SSD because most zones are categorized as normal zones, not renewable zones; the _spare-list_ of ReZNS  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Specifications** & **Unit** \\ \hline Capacity & 40 GiB & - \\ Page Size & 32 KiB & - \\ Block Size & 2 MiB & - \\ Zone Size & 64 MiB & - \\ Number of Zones & 640 & - \\ Flash Blocks per Zone & 32 & - \\ Read Latency & 47.2 μs & Page \\ Write Latency & 533 μs & Page \\ Erase Latency & 96 ms & Zone \\ Energy Consumption for Read & 679 μs & Page \\ Energy Consumption for Write & 7.66 μs & Page \\ Energy Consumption for Zone-reset & 1.38 mJ & Zone \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of NVMeVirt [28; 51].  
Figure 3: Overall performance of FIO benchmark.  
becomes empty. For the second reason, it causes a noticeable delay when a fsync operation is overlapped with a zone-reset command inside ZNS SSD. As shown in Figure 4, the baseline performs more zone-reset commands than ReZNS due to the first reason as the period of fsync decreases; it has a higher possibility of suffering from a noticeable delay. As a result, Figure 3 shows the largest performance gap in 16 fsync intervals.
## 2 Materials and Methods
### Evaluation of Mesoporous Silica as Sorbents in Solid-Phase Extraction  
The functionalized mesoporous silicas prepared and characterized were tested as sorbents in SPE for TAs extraction before ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry (UHPLC-TQ-MS/MS) analysis. The SPE procedure was carried out by packing polypropylene cartridges with 50 mg of the prepared sorbents. Both ends were sealed with polyethylene frits. In addition, a nylon filter membrane (0.45 \(\upmu\)m) was incorporated at the bottom of the bed to prevent material loss during the process. First, the cartridges were conditioned with 3 mL of Milli-Q water, followed by equilibration with 3 mL of the same solvent used for the loading step. They were then loaded with the standard solution at 0.8 mL/min. The cartridges were then vacuum dried using a Supelco Visiprep SPE 12-port vacuum manifold (Sigma Aldrich, St. Louis, MO, USA) connected to a vacuum pump at 7.6 psi for 3 min. Elution of the analytes retained on the cartridge was performed with 5% ammonia solution in MeOH, followed by another vacuum drying process for 3 min. Finally, the eluate was evaporated to dryness using a vacuum/nitrogen manifold system (Schlenk line) at ambient temperature and reconstituted in 0.3 mL of MeOH for subsequent injection into the UHPLC-TQ-MS/MS. Several loading solvents, including EtOH, MeOH, H\({}_{2}\)O, and a mixture of MeOH:H\({}_{2}\)O (50:50, \(v/v\)), all acidified with 1% HCl (pH 1) and analyte concentrations, in the range of 0.1 to 30 \(\upmu\)g/L, were studied. In addition, sample loading volumes from 5 to 20 mL and analyte elution volumes from 3 to 6 mL, were studied (Figure 1). Studies were performed in triplicate, and the results were expressed as percentage recovery of atropine and scopolamine. The recovery was calculated by comparing the results obtained by UHPLC-MS/MS for a loading solvent spiked with the target analytes under the study conditions prior to the SPE with the result obtained by a loading solvent spiked with the analytes after the SPE (blank control).  
Figure 1: Schematic of the evaluation of mesoporous silicas as softens in the solid-phase extraction of tropane alkaloids (TAs).
###### Abstract  
The accurate assessment of anomalous head posture (AHP) is crucial for diagnosing, treating, and monitoring postural changes in individuals with ocular impairments. This study evaluated the accuracy of a digital goniometer and an iOS-based application by comparing their measurements to a gold-standard motion capture (MoCap) system. Additionally, it assessed cervical range of motion (ROM) limitations in children with AHP versus healthy controls. Fifteen pediatric patients with ocular-origin AHP and 20 age-matched controls participated. Head rotation and inclination were measured using a goniometer, the iOS app, and MoCap under static and dynamic conditions. Pearson's correlation coefficient (PCC), root mean square error (RMSE), and Bland-Altman plots assessed inter-system agreement, while MoCap analyzed cervical ROM. The results showed strong agreement between the ophthalmological tools and MoCap for head rotation (PCC = 0.86, RMSE = 3.43\({}^{\circ}\)) and inclination (PCC = 0.82, RMSE = 5\({}^{\circ}\)), with no significant inter-system differences (\(p\) > 0.05). AHP patients exhibited reduced head flexion (\(p\) < 0.05), suggesting long-term postural adaptations. Digital goniometers and smartphone applications provide accurate, cost-effective AHP assessment alternatives, particularly in resource-limited settings. Future research should expand cohorts and integrate multidisciplinary approaches to refine assessment and treatment strategies.  
anomalous head posture; functional assessment; ophthalmology; motion capture; cervical mobility
## 2 RPL in the Literature
### RPL and Related Routing Protocols  
A few papers focused on RPL comparison against other related protocols because RPL is a new standard IPv6 protocol designed especially for low and lossy networks and all research focuses on performance evaluation. In [18], the authors compared RPL and LOADng routing protocol as they explored that LOADng can operate in a better manner in WSN networks in terms of control message overhead, PDR, and latency.  
In [19], they raise a new hybrid approach of reactive and proactive and comparing LOADng and RPL protocols. The results of [19], showed that RPL can support better performances in terms of overhead, delays and memory. In [20], the authors' shows that however LOADng protocol is originally designed for lossy and low power networks, it cannot perform well comparing to RPL in terms of E2E delay and the total time of route formation.  
In [21], [PERSON] et al. made an evaluation study of a recently developed RPL for its validation for large-scale smart meter networks. They used a realistic and detailed standard packet-level simulator called Qualnet to show that RPL outperforms an ad hoc well-known routing protocol named Ad-hoc On-demand Distance Vector protocol (AODV) especially when congestion is highly probable. The results showed a lower delay and higher Packet Delivery Ratio (PDR) for RPL compared to AODV. AODV is proven to be more vulnerable than RPL; from a scalability point of view, RPL seemed to be the best choice.
## 2 Numerical Simulation Methodology  
The numerical methods of aerodynamic noise are categorized into direct noise simulation and hybrid method. Due to the large difference in scale and energy between the acoustic and flow field, a discrete format with high order accuracy, low dissipation, and low dispersion is needed to improve the solution accuracy, which greatly reduces the feasibility of the direct method. Particularly for HSTs and their components with complex geometry, the hybrid method is usually used to predict the aerodynamic noise. The hybrid solution divides the numerical simulation of aeroacoustic performance into near-field and far-field solutions, i.e., sound source simulation and sound propagation solution.  
In the present study, CFD simulations are proceeded by ANSYS Fluent software. The LES turbulence model is used to solve the flow-field structure near the pantograph first, and then the FW-H analogy equation is applied to predict the far-field noise of the pantograph area.
## 3 Methodology
### Matrix of Cross-Impact Multiplications Applied to Classification  
The matrix of cross-impact multiplications applied to classification (MICMAC) is a matrix multiplication system developed by [PERSON] and [PERSON], who introduced an operational method for ranking the elements of a system [49]. The main purpose of MICMAC is to evaluate the significance of the dimensions impacting the system and categorize them based on their influence and dependency within digital dimensions [50]. [PERSON] et al. emphasized that the MICMAC framework is used to explore the main drivers and dependencies of research system dimensions, based on the interaction between rows and columns [51]. Based on their characteristics, these variables are categorized into four classes.  
* Cluster I (Linkage variables): Variables with both high influence and high dependence, playing a central role in system interactions.
* Cluster II (Independent variables): Variables that strongly influence others but are minimally affected by them.
* Cluster III (Autonomous variables): Variables with low influence and low dependence, having limited interaction within the system.  
Cluster IV (Dependent variables): Variables highly influenced by others but with low ability to influence in return.  
To address the limitations of the ISM method and to better understand the interactions between factors, further analysis is conducted using MICMAC. This method helps to simplify and standardize the often prevalent unstable behavior in the process [52]. The specific steps are as follows:  
Step 1: Calculate the driving force \(D_{i}\) and dependency \(R_{j}\) using the sum of the values in the rows and columns of the initial reachability matrix.  
\[D_{i}=\sum_{j=1}^{n}M_{ij}\;(i=1,2,\cdots,n) \tag{3}\]  
\[R_{i}=\sum_{i=1}^{n}M_{ij}\;(i=1,2,\cdots,n) \tag{4}\]  
In this formula, \(M_{ij}\) represents an element in the reachability matrix \(M\), where \(i\) is the row of the element, and \(j\) is the column of the element. \(D_{i}\) reflects the extent to which the resolution of factor \(C_{i}\) can drive the resolution of other factors, while \(R_{j}\) reflects the extent to which the resolution of factor \(C_{j}\) depends on the resolution of other factors.  
Step 2: Create a four-quadrant matrix with \(x\)-\(y\) axes, where the \(x\)-axis represents the dependency of each influencing factor, and the \(y\)-axis represents the driving force of each influencing factor.  
Step 3: Allocate each influencing factor to different categories.  
Step 4: Interpret and apply the results. Based on the classification of each factor, analyze their roles and significance within the system. MICMAC analysis helps to identify key drivers and dependent factors in the system, providing a basis for further decision making and strategy development.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 4 Experiments
### Implementation Details  
To evaluate the performance of our point cloud keypoint-based pose estimation algorithm, experiments were conducted on the LineMod dataset. The hardware platform used includes a Windows \(11\) operating system and an NVIDIA GeForce RTX 4090 GPU with 24 GB of VRAM. Our framework was implemented using PyTorch 2.2.2. All experiments were performed on an Intel i9-14900K 3.60 GHz CPU and an NVIDIA GeForce RTX 4090 GPU.  
We fine-tuned the PIDNet architecture [28], which was pre-trained on ImageNet [39], to obtain mask images for different categories of target objects. Point cloud keypoints were extracted from RGB-D data using the RSCS method and used for pose estimation. We then trained our improved pose estimation network based on G2L-Net [27]. The network architecture is illustrated in Figure 2.  
In the experimental section, we indeed adjusted the network's hyperparameters to optimize its performance and enhance the precision and efficiency of pose estimation. During this process, we referred to hyperparameter settings from other mature methods in related fields and made targeted adjustments based on the specific needs of our network architecture and task objectives.  
For pose prediction, we used mean squared error (MSE) as the loss function, with units in millimeters. The Adam optimizer [40] was employed to train the RSCS6D network. The initial learning rate was set to 0.001 and halved every 50 epochs, with a maximum of 200 epochs. This setup ensures efficient training and convergence of the network.
# Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon
## Appendix A  
Proof of Lemma 1.: For simplicity, let \(\alpha=r\times\delta\times s\). The initial amounts of fuels are equal for all the \(n\) vehicles at the beginning of time slot 0, i.e., at \(t_{0}\). Thus, the claim holds at time \(t_{0}\).  
Assume that the claim holds at \(t_{k}\). Now, let us compute the difference at \(t_{k+1}\). We will exploit the assumption that \(g(i)\leq g(j)\) for any two positions \(i\) and \(j\) where \(i<j\). Let \(a_{i}\) and \(a_{j}\) be the amounts of fuels at the \(i\)th and \(j\)th positions (\(i<j\)) in the platoon at \(t_{k}\). Since \"TTH\" sorts the vehicles based on the remaining fuels, it holds that \(a_{i}\geq a_{j}\), i.e., \(0\leq(a_{i}-a_{j})\). Furthermore, since the claim holds at \(t_{k}\), we have  
\[0\leq(a_{i}-a_{j})\leq\alpha\times(g(n)-g(1)) \tag{1}\]At the time \(t_{k+1}\), the \(i\)th vehicle of the time slot \(k\) has the fuel amount of \(a^{\prime}_{i}=a_{i}-\alpha\times(1-g(i))\) and the \(j\)th vehicle of the time slot \(k\) has \(a^{\prime}_{j}=a_{j}-\alpha\times(1-g(j))\). Then, \((a^{\prime}_{i}-a^{\prime}_{j})=(a_{i}-a_{j})+\alpha\times(g(i)-g(j))\). If we put this equation to Equation (A1) and revise it, we have  
\[\alpha\times(g(i)-g(j))\leq (a^{\prime}_{i}-a^{\prime}_{j})\leq \alpha\times(g(n)-g(1))+\alpha\times(g(i)-g(j))\] \[\alpha\times(g(i)-g(j))\leq (a^{\prime}_{i}-a^{\prime}_{j})\leq \alpha\times(g(n)+g(i)-g(j)-g(1))\]  
Considering the second inequality in the above equation, since \(g(j)\geq g(i)\), we have \((a^{\prime}_{i}-a^{\prime}_{j})\leq\alpha\times(g(n)-g(1))\). Furthermore, since \(g(j)\leq g(n)\) and \(g(1)\leq g(i)\), we have \((g(i)-g(j))\geq(g(1)-g(n))\geq-(g(n)-g(1))\). Thus, the first inequality becomes \(-\alpha\times(g(n)-g(1))\leq(a^{\prime}_{i}-a^{\prime}_{j})\). Combining the two inequalities, we have the following inequalities at time \(t_{k+1}\).  
\[-\alpha\times(g(n)-g(1))\leq(a^{\prime}_{i}-a^{\prime}_{j}) \leq\alpha\times(g(n)-g(1))\] \[|a^{\prime}_{i}-a^{\prime}_{j}|\leq\alpha\times(g(n)-g(1))\]  
So the claim holds at time \(t_{k+1}\). Since the claim holds at \(t_{0}\), the claim holds for each time slot \(k\) by induction.
## 5 Experimental Results Analysis  
In this section, the training and validation results for the proposed system are presented. Additionally, the predicted outcome of the proposed system is illustrated in Figure 8. Furthermore, we explain our research findings in this section. The end-to-end system's average response time is 50 ms, which indicates superior real-time performance. The average response time of the detection and sentence-generation models is approximately 30 ms and 20 ms, respectively. These results were obtained by averaging 100 trial tests.  
Figure 8: Predicted labels of characters and associated text.
# A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling
###### Abstract  
Radiation mapping is a desirable task to automate because of the inherent risks involved and its tedious nature. A novel system was designed to address this by combining various existing technologies, utilizing behavior-based robotics and Bayesian optimization. The system uses a quadruped robot equipped with a manipulator and gamma detector to take measurements at locations that are selected based on the uncertainty of a surrogate model used to estimate the true radiation field. The robot uses input from the world with depth cameras to avoid collisions with the robot's body, and unreachable points for the end effector are addressed by both allowing for a soft collision with the environment to occur, prompting the system to abandon that point, and varying the exploration tendency of the optimization based on consecutive collisions. This approach provides unique traversability and adaptability over other strategies in the literature. Experiments were performed by placing a Cesium-137 source on the ground and varying geometric setups and an optimization parameter demonstrating the adaptability to diverse environments and the increased robustness resulting from the designed behavior. The results additionally demonstrate that dynamically adjusting the optimization algorithm's exploration tendency based on the arm's collision history improves the system's ability to navigate cluttered environments and construct accurate radiation maps without getting stuck in unreachable areas.  
Keywords:behavioral robotics; radiation mapping; Bayesian optimization; adaptive sampling; manipulators +
Footnote †: journal: Applied sciences
## 3 The SensoTube Architecture
### Data Gateway Layer (DGL)  
A WSAN gateway should be able to bridge the local wireless network (e.g., based on ZigBee, etc.) with other communication networks, using proper RF communication modules (e.g., WiFi, GSM/GPRS, GSM 3G/4G, etc.). In contrast to the existing architectures, SensoTube-based WSAN systems can have more than one data gateway channel in the same system through the use of many DGL shields. Thus, SensoTube can effectively support the general domain of the interconnections to external networks [121]. For example, one DGL shield is used for the Internet access while another DGL shield provides Bluetooth connectivity for local user-interface (Human-Machine Interface--HMI), and another DGL shield provides a wired interface via USB or RS-485 data busses for, e.g., local configurations and reporting. The master MCU of the SensoTube-based WSAN system (e.g., the MCU of the DCL, or the MCU of some other layer's shield) can direct the operations of the WNL and DGL shields through their MCUs, in order to achieve any data interconnection scenario. Furthermore, as in the case of WNL shields, the DGL shields can fully integrate and exploit the inherent capabilities of the modern integrated communication modules (see Section 3.2).  
Moreover, the DGL entity can support research and experimentation in the challenging application areas such as the Internet-of-Things (IoT) [33], which at least for now practically appears to be an Internet-of-Gateways (IoG). Similarly, the cyber-physical space (CPS) [122] research field could also be facilitated in SensoTube-based systems. Towards this direction, the processing and memory resources, required for local embedded web servers and other web technologies, can be exclusively designed in DGL shield without disturbing the operations of the other functional layers of the system.