esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 3 Algorithm Design
### Proposed Optimization Algorithm for Link Weight
So, we set two objective functions as follows:  
\[fitness1=\sum_{\mathbf{e}\in\mathbf{p}}\psi(\mathbf{e}) \tag{13}\]  
\[fitness2=\sum_{\mathbf{e}\in\mathbf{p}}{(u(\mathbf{e})-\overline{u})}^{2}/h(\mathbf{p}) \tag{14}\]  
As SDN has a global view of the network resource, it is able to get the global network traffic to formulate a better strategy for network optimization. Under the condition of a given traffic demand matrix from the SDN controller, link weight determines the shortest path between the source node and the destination node and further influences the path cost and load distribution in the network. Therefore, the optimization of link weight has become an effective way to realize traffic engineering. In this paper, we use the path cost and load balance of the network as the two optimization objectives for the MOPSO algorithm and improved MOPSO algorithm. The improved MOPSO algorithm for optimization of link weight is summarized in Algorithm 3. The traffic matrix G1 can be updated by the controller after each routing request. ```
1. Input: objective function fitness1, fitness2, traffic matrix G, source node and destination node. 2. Initializing the population size, the positions and velocity of particles, the flight parameters, the maximal size of archive, and maximal iterations. 3. While iteration <= maximal iterations
4. Calculate the value of fitness1 and fitness2. 5. Calculate the non-dominated solutions and update the archive. 6. If the number of archive > maximal size
7. Pruning the archive
8. End
9. The external archive is mapped to PCCS. 10. Calculate the entropy and the difference of entropy. 11. Choose the _gbest_ according to the difference of entropy. 12. Update the flight parameters. 13. Update the positions and velocity of particles. 14. End
15. Output: the best position, namely weighted matrix G1
```  
**Algorithm 3**Improved MOPSO algorithm  
Through the MOPSO algorithm or improved MOPSO algorithm, the path cost and load balance are optimized. After calculating the weight of the traffic matrix, the first \(k\) shortest paths from the source and the destination node are calculated. This reduces the length of the sub-list to decrease the consumption of the packet header to a certain extent. Then, the optimal path is selected from the first \(k\) shortest paths according to the comprehensive evaluation model.
## 2 Navigation Model Based on GNSS
### State Equation of the Navigation System  
In the Earth's inertial coordinate system, in order to analyze the navigation model, we define the state variable as  
\[\mathbf{X}=\begin{bmatrix}\mathbf{r}^{\mathsf{T}}&\mathbf{v}^{\mathsf{T}}\end{bmatrix}^{ \mathsf{T}}\]  
where the \(\mathbf{r}=\begin{bmatrix}x&y&z\end{bmatrix}^{\mathsf{T}}\) is the position vector of the high-orbit satellite in the Earth's inertial coordinate system and the \(\mathbf{v}=\begin{bmatrix}v_{x}&v_{y}&v_{z}\end{bmatrix}^{\mathsf{T}}\) is the corresponding speed vector. Therefore the state equation of the orbital motion for the high-orbit satellite can be expressed as  
\[\mathbf{\dot{X}}=f\left(\mathbf{X}\left(t\right),t\right)+\mathbf{w}\left(t\right). \tag{1}\]  
where \(f\left(\mathbf{X}\left(t\right),t\right)\) is the perturbation acceleration of the high-orbit satellite, the specific form can be found in the reference [36], and \(\mathbf{w}\left(t\right)\) is the error vector of the perturbation model.
# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 3 Results  
### Weather and Management Effect on Cover Crop Biomass  
During the 2021 cover crop cycle, the temperature dropped below freezing (0 \({}^{\circ}\)C) first on 30 November 2020, and stayed above freezing after 22 February 2021. During the 2022 CC cycle, the temperature dropped to freezing on 19 November 2021, and it stayed above freezing after 28 February 2022. Growing degree days based on 0 \({}^{\circ}\)C base temperature during the 2021 cover crop season were 1908, 1749, 1749 and 1805 for studies 1, 2, 3 and 4, respectively, while they were 2201, 1852, 1852 and 1792 during 2022 (Table 1). The overall relationship between GDD and cover crop biomass across all studies, years, and cover crop species/mixes was very weak (R\({}^{2}\) < 0.02), regardless of the GDD base temperature, i.e., 0 vs. 4 \({}^{\circ}\)C. However, the seeding rate showed a strong linear relationship (R\({}^{2}\) = 0.79; Figure 3A) with cover crop biomass. Cover crop biomass increased with a higher seeding rate. Biomass return for each unit of seeding rate (Kg biomass per Kg seeding rate, Figure 3B) decreased with higher seeding rate and became stable at around 20-30 kg biomass for each kg of seeds after 135 kg ha\({}^{-1}\) seeding rate. Biomass also increased with higher number of pure live seeds (PLS) per square meter (Figure 3C). Biomass return per square meter of PLS decreased with a higher number of PLS per square meter and became stable at 3.5 kg m\({}^{-2}\) once PLS reached approximately 1300 (Figure 3D).
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.5 User Perception and Acceptance
Novice drivers preferred AVs due to their safety and reliability, while experienced drivers favored manual control, citing greater enjoyment. [PERSON] et al. (2023) [38] examined AV adoption in small towns with limited public transit through an interview-based study and found that AV services, such as ENDEAVRide in Texas,improved access to essential services for older adults and those with disabilities. However, safety concerns and job losses in driving-related fields persisted. [PERSON] and [PERSON] (2023) [39] further identified rural concerns about AV reliability on rural roads, safety in various weather conditions, and limited internet infrastructure through an online survey of 1247 rural and non-rural respondents. Trust levels were similar across both groups, though older individuals and those with lower education exhibited less trust. Key concerns among rural residents included technology failures, hacking, and the lack of human interaction in AVs. Hesitation was also linked to unfamiliar terrain and limited internet infrastructure. [PERSON] et al. (2023) [58] used the recent household survey data to analyze the impact of CAV adoption on residential location choices, finding that high CAV adoption encourages suburban and rural growth by enhancing travel efficiency, while high AV penetration alone might worsen network conditions, potentially diminishing rural living's appeal. [PERSON] et al. (2022) [61] surveyed rural and urban participants and found that rural respondents prioritized performance and availability, while urban respondents focused on convenience and travel time. Both groups valued mobility benefits and enjoyment, suggesting AV development and policymaking must address distinct rural needs. A qualitative study by [PERSON] and [PERSON](2020) [97] in Carinthia, Austria, explored public perceptions of AVs after real-life rides in an autonomous shuttle (SAE Level 3) within mixed traffic. Interviews with 19 participants revealed that AVs are seen more as a complement than a replacement for current transport options, especially in rural areas. Participants generally held positive attitudes, with safety perceptions influenced by AV experience and speed. [PERSON] (2019) [26] and [PERSON] (2017) [8] highlighted AV solutions to improve mobility for rural seniors and non-drivers, emphasizing infrastructure upgrades like improved road markings, wheelchair ramps, and audible stop announcements. AVs could complement volunteer driver programs, but challenges such as safety concerns, legal liability, and costs persist, especially on poorly structured roads. Tailored AV solutions are essential in rural areas due to their reliance on private vehicles and limited public transport. Legal liability in accidents was identified as a critical factor for successful implementation.
## 2 Intensity-Based pH Indicators
Some pH-sensitive indicators have an excitation and emission shifted toward red spectra. These red-shifted FPs are particularly practical for multicolor labeling in combination with green or yellow FPs. Thus far, only pHTomato [24] and mNectarine [25] have been used for pH analysis in living cell imaging. The emission intensities of pHTomato are highly pH-dependent with a pKa of 7.8. Along the mtAlpHi [20], this is the highest determined pK\({}_{\mathrm{a}}\) among intensity-based pH sensors, thereby making the sensor suitable for following mitochondrial pH [24]. The pH influences the fluorescence intensity of pHTomato, but not the excitation and emission waveforms. Conversely, the absorbance spectra of mNectarine [25] recorded at various pHs reveal complex changes in profile that appear to include a mixture of anionic cyan-absorbing (489 nm) and orange-absorbing (558 nm) forms of the protein at high pH, and a mixture of protonated violet-absorbing (387 nm) and blue-absorbing (453 nm) forms of the protein at low pH [25]. The apparent pKa of mNectarine is 6.9, with excitation at 558 nm and emission measured at 578 nm. Recently, a new generation of pH-sensitive FPs has been developed with pK\({}_{\mathrm{a}}\) values ranging from 5.3 to 6.5; however, they have not been tested as pH sensors in live cell experiments. The absorption/excitation spectrum for green FP mWasabi is narrower than that of EGFP with the same emission spectra; therefore, less cross-talk is expected when used in combination with blue- and cyan-shifted FPs [16]. The Clover is very bright yellow FP, with an acidic pK\({}_{\mathrm{a}}\) of 6.2, thereby making it suitable for imaging pH changes of Golgi-network and secretory vesicles [21]. The FPs derived from the _Discosoma_ mRFP are mainly red-shifted [23, 24, 25, 26]. Further, [PERSON] _et al._[22] developed two highly photostable mOrange variants by random and iterative mutagenesis. mOrange M163K and mOrange2-- with pK\({}_{\mathrm{a}}\) 7.5 and 6.5, respectively--exhibit enhanced photostability, efficient maturation, and increased pH sensitivity [22,23]. The other pH-sensitive red-shifted FPs are mRuby2, mKate2, mTangerine, and mKate. Their pK\({}_{\mathrm{a}}\) varies from 5.3 to 6.2, which makes them suitable for imaging pH changes from <5.0 to 6.5 [21,26,27]. For example, the maturation of endosomes coincides with a pH reduction from 6.8 to <5.5 [1,28,29]. A drawback for mKate, mRuby2, and mTangerine pH sensors is that they are not very bright fluorophores with low quantum yield compared to other FPs. The problem can be elegantly solved by tethering two FPs, as done for mOrange [23].
# Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
## 4 Results and Discussion
### Effects of Heat Treatment on Residual Stress  
The residual stress produced by laser welding of TC4 titanium alloy near the weld and fusion line was about 50% of the yield strength of the base material. Therefore, it was necessary to adopt laser welding and post-weld heat treatment to control the residual stress. In order to avoid oxidation and pollution of titanium alloy in air, a vacuum furnace with a precision of \(\pm\)5 \({}^{\circ}\)C was used for heat treatment. The specimen was clamped with fastening bolts to eliminate its warp. In general, the heating temperature and the holding time determine the extent to which residual stresses are removed during heat treatment. In the experiment, the national standard GJB3763A was adopted, the heat treatment temperature was 650 \({}^{\circ}\)C, and the holding time was 120 min. Then, the residual stress of the laser welding specimens after heat treatment was measured with an ultrasonic laser.  
As shown in Figure 11, the longitudinal residual stress was greatly reduced. The decrease in the maximum of residual stress was particularly obvious. The maximum of the longitudinal residual stress was reduced from 423 to 92 MPa, a decrease of 78%, but the maximum residual stress after heat treatment was still larger. Due to the redistribution of the residual stress caused by heat treatment, partial tensile stress and compressive stress in the distant weld area were converted to each other. Although heat treatment can reduce residual stress to a certain extent, it was still necessary to control the residual stress by optimizing the welding process parameters.
# Seamless Human-Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices
## 1 Introduction
* Identification of tasks being executed by human operators and validation of the assembly progress. Recent research has mainly focused on tracking the position and posture [19] and even fewer approaches perform intention prediction [20; 21]. This work advances further by exploiting human and environment tracking to identify the workflow execution status which allows for the triggering of task-optimized support strategies. * Online calculation of ergonomic conditions for each task as it evolves. Tools for online assessment of ergonomics using depth sensors and wearable devices have been provided [22] to support workplace layout optimization and operator support [23]. Their use is extended in this paper to feed the robotic cognition module with the ability to consider ergonomic aspects when devising operator support actions. * Adaptation of robot posture to both human actions (direct input to the robot) and needs (ergonomics assessment) using learning strategies and trajectory optimization techniques [24]. The approach extends beyond the switching between predefined states by real-time adaptation of the robot-provided support to each operator. The novelty of the proposed solution lays in the fact that it enhances the cognitive capabilities of the robots, performing real-time tracking as well as optimization of ergonomics by automatic adjustment of the robot pose, going one step forward from existing implementations that focus on providing suggestions for ergonomics improvement. Moreover, it allows automatic adaptation of the robot behavior to the needs and preferences of the operators. In a nutshell, it promotes the seamless coexistence of humans and robots. In this work, the combination of these advancements has been applied for the first time in the case of high payload collaborative manipulators which, unlike cobots, are much more capable to supplement humans in performing strenuous tasks. Section 2 describes the approach and its architecture, detailing their functionality and interrelationships; in Section 3 the implementation of the approach is presented and its application on an elevator assembly case study is outlined in Section 4. Section 5 discusses the results and outlines future work.
## 2 Materials and Methods
### Osteoarthritis Rodent Animal Model and Treatment for In Vivo Study  
The surgical procedure was executed as follows: The OA treatment group underwent an open surgical procedure, which entailed the transection of the anterior cruciate ligament and a partial medial meniscectomy. For this procedure, an incision was made on the medial aspect of the joint capsule, anterior to the medial collateral ligament. In the postoperative phase, the incision was sutured in two layers, starting with the peripheral tissues, followed by the skin closure with interrupted sutures, utilizing dissolvable 3-0 catgut (D78532, Braun, Melsungen. AG, Germany). This surgical intervention triggered the onset of OA pathogenesis, denoting it as the surgically induced side. The right knee joint, which remained nonoperated and intact, served as the contralateral control. Conversely, in the second group, the rats were subjected to a sham operation, wherein a similar incision was made in the joint capsule; however, the transection of the anterior cruciate ligament and partial medial meniscectomy were not performed, adhering to our previously established protocols [29; 30; 31].  
Randomization was utilized to assign the rats into distinct treatment groups, aiming to minimize selection bias and ensure group comparability. On the 13 th day post-surgery, a total of 60 rats were selected for the study, with 10 rats assigned to each group based on body weight and knee deviations: sham, OA, OA + diclofenac sodium (DS), OA + NEM100, OA + NEM50, and OA + NEM25. To preserve the integrity of NEM and DS (Wako Pure chemical Industries, Ltd., Tokyo, Japan) against light and moisture exposure, these were stored at \(-20\)\({}^{\circ}\)C. Two weeks after the OA surgery, NEM was administered orally to the rats via gastric gavage, employing a stainless-steel gavage needle attached to a 5 mL syringe, with NEM dissolved in distilled water at concentrations of 5, 10, or 20 mg/mL. The administration volume was set at 5 mL/kg, corresponding to doses of 25, 50, and 100 mg/kg/day, administered once daily for 56 days. DS was dissolved in physiological saline to a concentration of 0.4 mg/mL and administered subcutaneously on the dorsal back skin, using a 23-gauge needle attached to a 5 mL syringe, once daily for 56 days at a volume of 5 mL/kg (equating to a 2 mg/kg dose). For the sham and OA groups, only distilled water was administered orally at a volume of 5 mL/kg. Body weight and knee thickness measurements were conducted weekly, starting from the day before OA surgery and continuing until sacrifice (24 h post the final 56 th administration). Three days prior to euthanasia, bromodeoxyuridine (BrdU) (Sigma-Aldrich) was administered intraperitoneally at a dose of 50 mg/kg in saline.
# VFLD: Voxelized Fractal Local Descriptor
## 2 Related Works  
Computer vision and robotics use 3D point descriptors, which are vector encodings of 3D points. These descriptors play a key role in many applications, such as object recognition, tracking, and reconstruction. However, developing accurate and efficient 3D point descriptors presents several challenges. One of the primary challenges in developing 3D point descriptors is the high dimensionality of the data. Three-dimensional point descriptors typically require the calculation of several mathematical functions and estimations, leading to high computational complexity. Additionally, 3D point descriptors need to be robust to noise and occlusions. Thus, the descriptors must be designed to handle such variations while maintaining accuracy. These descriptors have a wide range of applications, including 3D object recognition and tracking, navigation, robotics, and augmented reality. Accurate and efficient 3D point descriptors can enable robots and autonomous vehicles to navigate complex environments, recognize objects, and detect obstacles. For example, autonomous cars use 3D point descriptors to detect pedestrians and other vehicles on the road. Similarly, drones use 3D point descriptors to navigate through complex environments and avoid obstacles. Developing accurate and efficient 3D point descriptors is crucial to enable robots and autonomous vehicles to navigate complex environments, recognize objects and detect obstacles.  
Traditionally, 3D object registration has been carried out with local feature-based descriptors. Local methods describe a single point and are based only on local geometric features around the points of interest. However, they require methods to select which points are in fact keypoints, and specialized methods to match points from both clouds. In this section, we review traditional local descriptors, new deep learning methods for 3D registration, and the application of these local descriptions to 3D object recognition. Additionally, we briefly describe the use of a fractal dimension as a descriptor to put our approach into context.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 5 FSS Measurements  
Transmission and reflection are the main characteristics evaluated for an FSS, and there are only few common methods to measure them. In order to measure the transmission of an FSS, it is placed between two directive antennas, one acting as a receiver while the other acting as a transmitter. The FSS blocks the path of propagation and filters the wave radiated from the transmitter on its way to detection by the receiver antenna. The FSS plane can be rotated while the antennas are fixed for measuring the oblique incidence performance. As a reference, the power transmission between the two antennas is measured while the FSS is removed. This response is kept as a calibration, and the FSS transmission response is obtained by normalizing the measured power of the FSS to the calibration response [118].  
In order to simulate a free space environment, the experiment should be performed in an echoic chamber. However, this method may not be able to measure the characteristics accurately, specifically the reflectivity. In the measurement of reflection, the undesired scattering can dominate the reflected signals. Also, the diffraction from edges of FSSs become strong and shadows the actual reflected signal because the FSS is of a finite size  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Shape of** & **Cross** & **Angular** & **Band** & **Larger** \\
**Elements** & **Polarization** & **Stability** & **Separation** & **Bandwidth** \\ \hline Jerusalem Cross & 3 & 2 & 2 & 2 \\ \hline Dipole & 1 & 4 & 1 & 4 \\ \hline Tripole & 3 & 3 & 2 & 3 \\ \hline Ring & 2 & 1 & 1 & 1 \\ \hline Square Loop & 1 & 1 & 1 & 1 \\ \hline Cross Dipole & 3 & 3 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of FSSs with different element shapes (based on the freestanding FSS Performance) [113].  
and usually resides a distance from the antennas. Edge scattering also increases due to the large beamwidth of antennas [119].
## 9 Results and Discussion
### Skewness Score Performance Analysis  
In this section, we compare the Hybrid Model's skewness-aware scheduling approach (Skewness Score) against utilization-driven strategies--specifically, CPU Utilization, which routes requests to the invoker with the lowest CPU usage, and Memory Utilization, which selects the invoker with the lowest memory usage. We also include Round Robin as a baseline that represents a lack of resource awareness. Figure 15 shows that relying on single-resource metrics (CPU or Memory) while ignoring the interdependence between resources leads to inefficiencies. For example, the Memory Utilization method underutilizes the CPU (62%), while the CPU Utilization method causes CPU overloading (88%). In contrast, the Skewness Score approach accounts for multiple dimensions--CPU, memory, and execution  
Figure 14: (**a**) Number of active invokers used by each method. (**b**) Comparison of skewness scores. (**c**) Overall resource utilization across active invokers.  
duration--resulting in more balanced usage, with CPU and memory utilizations of 78% and 75%, respectively. This helps avoid both resource underutilization and bottlenecks. Another factor used to evaluate these strategies is load imbalance, measured by the standard deviation of CPU and memory usage. The skewness-aware approach significantly reduces load imbalance for both CPU and memory compared to the severe imbalance caused by Round Robin's static distribution. A low imbalance indicates that the workload is evenly distributed across invokers, preventing any single invoker from being overwhelmed or forming performance hotspots. This improvement in resource utilization directly impacts the number of active invokers required to handle incoming requests. As shown in Figure 16, the Hybrid Model requires the fewest invokers--only 14--demonstrating the effectiveness of skewness-aware routing in maximizing resource efficiency. In contrast, Round Robin requires nearly double the number of invokers, while CPU and Memory Utilization strategies need 4 to 8 additional invokers due to the bottlenecks they introduce.