esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 3 The NDMR Approach
### NDMR Operations
#### 3.2.1 Path Discovery  
The path discovery process of NDMR is shown in Figure 3. The path discovery can be either a two-way handshake or three-way handshake process depending on the paths received and found at the destination.  
The path discovery process is initiated whenever the source needs to communicate with a destination for which the source has no routing information in its route table. To discover and establish two node-disjoint paths to the destination, the source broadcasts a PREQ. When an intermediate node receives a PREQ for the first time, the node adds a potential reverse route to its buffer. The potential route contains the following information: _destination_, _source_, _next hop_, and _route_timeout_. The intermediate node then adds itself to the _node_list_ field of the received PREQ and rebroadcast the message. Since the PREQ was broadcast in the wireless network by the source, a node in the network may receive multiple copies of the PREQ from its various neighbors. The maximum number of PREQ copies received at a node is equal to the number of neighbors that the node has. When an intermediate node receives a duplicate of the PREQ, the node discards the duplicate and does not rebroadcast it.  
The destination node performs the selection of two node-disjoint paths, including the first and second paths. When the destination receives the first copy of the PREQ, it chooses the first path based on the _node_list_ of the received PREQ. The selection of the second path depends on receiving the next copies of the PREQ. At the destination, NDMR uses two parameters during the selection of the second path, including _Path_selection_timer_ and _Maximum_received_PREQ_. The _Path_selection_timer_ is a period in which copies of the PREQ received at the destination node are valid and processed. The _Maximum_received_PREQ_ value is equal to the number of neighbors that the destination has. After receiving the first copy of the PREQ, the destination starts the _Path_selection_timer_ and begins receiving duplicates of the PREQ. When the destination receives a duplicate of the PREQ, it forms a path candidate based on the _node_list_ of the received duplicate and checks if the path candidate and the first path are node-disjoint paths. The following two cases can occur:  
Figure 3: The process of path discovery under NDMR.
## 4 Fragility Analysis Based on IDA Method
### Definition of Limit States  
The failure mechanisms of seismically isolated structures are distinct from those observed in conventional aseismic structures. Owing to the relatively low stiffness and high elasticity of isolation bearings, the isolation layer serves as the primary energy dissipation mechanism during seismic events. Consequently, the displacement of the isolation layer is markedly greater than that of the superstructure, which remains relatively rigid. Isolation bearings, acting as the critical yet vulnerable components in nuclear power plant isolation systems, can precipitate the overturning or even collapse of the superstructure in the event of their failure.  
In ATC-40 [50], building damage is classified into six distinct levels: Immediate Occupancy, Damage Control, Life Safety, Limited Safety, Collapse Prevention, and Complete Collapse. GB/T 24335-2009 [51] categorizes building damage into five levels: Basically Intact, Slight Damage, Moderate Damage, Severe Damage, and Destruction. Similarly, GB/T50011-2010 [42] delineates building damage into five levels: Basically Intact, Slight Damage, Moderate Damage, Severe Damage, and Collapse.  
In accordance with the Chinese codes, this study establishes five damage states for seismically isolated structures, which are further refined into four limit states. The relationship between the damage states and the corresponding limit states is detailed in Table 4.  
According to HAD102/02-1996 [52], the diesel engine building of a nuclear power plant is classified as a Seismic Category I item related to nuclear safety, and its seismic design and analysis should be conducted based on SL-2. GB 50267-97 [53] stipulates that the peak acceleration of Operating-Basis Earthquake (SL-1) should not be less than half of the peak acceleration of the corresponding SL-2. The peak acceleration of the Safe Shutdown Earthquake is specified as 0.3 g, which corresponds to the rare earthquake level for a seismic fortification intensity of 7 degrees. GB/T50011-2010 [42] specifies that the tensile stress of rubber bearings under three-dimensional seismic action during rare earthquakes should not exceed 1 MPa. Furthermore, GB/T 51408-2021 [54] states that for structures with special seismic fortification requirements, the horizontal displacement limit of seismic isolation rubber bearings during extremely rare earthquakes can be taken as 4.0 times the total thickness of the rubber layer. Under SL-2, the maximum horizontal displacement of the isolation bearings should be less than 1.3 times the total thickness of the rubber layer and should not exceed 300 mm.  
Based on the above codes and standards, which define the limits for structural seismic response parameters under different seismic intensities, the limit values for different limit states TP and \(\gamma\) defined in this paper are shown in Table 5.
## 7 Conclusions  
A five-node simplified lumped parameter network model was proposed and the multiple linear regression algorithm was applied to identify parameters of the state equation. The temperature estimation algorithm based on the state equation and Kalman filter algorithm was proposed to predict the motor temperature. It can be concluded that:  
1. The proposed lumped parameter network model is simple, and the thermal model order is low. Therefore, it is easier and faster to implement online estimation in an embedded system. Besides, both the radial and axial heat transfer paths inside the motor were considered, and minimum thermal nodes were applied to model the complete thermal circuit. In this way, the accuracy of thermal model was improved.
2. The multiple linear regression algorithm was utilized to identify the parameters of the state equation. This method does not depend on the structural parameters of the motor. It is not necessary to identify each specific parameter in the thermal model, and only the parameters in the state equation need to be identified.
3. The temperature estimation algorithm based on the state equation was used to predict the motor temperature. However, the disadvantage of this method is that error easily accumulates over time based on an open-loop structure. Thus, the Kalman filter algorithm was proposed to estimate the motor temperature online. The experimental results showed that the temperature estimation error does not exceed \(\pm 5\,^{\circ}\mathrm{C}\) in most cases, which indicates that the proposed model can accurately predict the stator, rotor, and end cap temperature of the motor. The experimental results also verified the accuracy and effectiveness of the parameter identification algorithm and temperature estimation algorithm.  
In the future, other methods for measuring the rotor temperature will be studied due to the high cost of the special rotor temperature acquisition equipment, so that the proposed thermal model has a wide application range.  
Conceptualization, K.L. and Y.Z.; methodology, Z.W.; software, M.X.; validation, K.L., Y.Z. and M.X.; formal analysis, M.X.; investigation, B.T.; resources, Z.W.; data curation, K.L.; writing--original draft preparation, B.T.; writing--review and editing, M.X.; visualization, L.K.; supervision, Z.W.; project administration, Y.Z.; funding acquisition, L.K.  
This research was funded by National Key Research and Development Program of China (2016 YFB0100804).  
The authors declare no conflict of interest.
# ITOC: An Improved Trie-Based Algorithm for Online Packet Classification
## 2 Related Work
### Classification Based on Trie  
Similarly to decision tree structure, trie structure is widely used in packet classification, especially in longest prefix matching. Trie structure is a kind of storage structure, which means that the content of rules is saved in the nodes of tries. A popular trie-based packet classification instance is the Access Control List library (ACL library) provided by Data Plane Development Kit (DPDK) [21]. DPDK is a widely used set of packet processing libraries and drivers, and DPDK ACL library applies an offline packet classification method. In DPDK ACL library, the data structure build-up process has the following steps.  
First, the method gets all the contents of rules in the ruleset. Then the method optimizes the ruleset. After the optimizations, the method transforms every rule into a single trie. The transformed tries then are merged into a total trie. When all tries of rulesare merged into the total trie, an array is generated from the total trie, and the array is the result of DPDK ACL buildup process. Figure 2 shows the brief buildup process of DPDK ACL.  
The lookup process of DPDK ACL is quite simple. The key structure is the array generated in the buildup process. When we search in a table of trie-based methods, we only need to traverse the array and do some simple operations, such as adding, comparing, shuffling, and data reading. The total number of operations is a fixed value, so it is easy to implement a batch lookup interface. The lookup way is the key of trie-based methods to achieve high lookup speed.  
In addition to the excellent performance in ruleset lookup, DPDK ACL library has no requirement for the match fields of rulesets. So this trie-based packet classification algorithm is the choice when we implemented the software switches for the SDN environment with protocol-oblivious forwarding.
## 2 Materials and Methods
### Modeling Method
#### 2.2.2 SE Attention Mechanism and Residual Convolutional Network
Given that the two residual blocks produce feature maps with different channel dimensions, separate SE modules--each matched to its respective dimensionality--are deployed, thereby ensuring dimensional compatibility and preventing cross-interference among the attention weights.
# Investigation on Sensorless Estimating Method and Characteristics of Friction for Ball Screw System
## 3 Sensorless Friction Estimation  
### Equation of Motion for the Feed Drive System  
Here, we use the feed drive system of the z-axis worktable as an example to estimate the friction force. As shown in Figure 4, this feed drive system consists of an a.c. servo motor, a coupler, a pair of linear ball guides, a ball screw and nut, bearings, a lathe bed saddle, and a turret. The ball screw on the z-axis is supported by a pair of angular contact ball bearings at each end. The inertia of the motor armature, the coupler, and the ball screw are lumped together into an equivalent inertial term, \(J\). The viscous damping of the bearings is lumped into an equivalent term, \(B\). Neglecting the deformations of the lead screw bar and the screw-nut contact, the equation of motion for the motor shaft can be expressed as follows:  
\[k_{\text{e}}i=J\theta^{\prime\prime}+B\theta^{\prime}+T_{\text{d}} \tag{1}\]  
where \(k_{\text{e}}\) is the motor constant of the servo motor; \(i\) is the SMTC; \(\theta\), \(\theta^{\prime}\) and \(\theta^{\prime\prime}\) are the angular displacement, the angular velocity and the angular acceleration of the motor shaft, respectively; and \(T_{\text{d}}\) is the torque with which the screw-nut drives the \(z\)-axis worktable.  
Let \(p\) denote the lead of the screw; then, the relationship between the angular displacement, \(\theta\), and the displacement of the worktable, \(z\), can be expressed as follows:  
\[z_{t}=\frac{1}{2\pi}\theta p, \tag{2}\]  
and the force, \(F_{\text{d}}\), with which the screw nut drives the worktable can be expressed as  
\[F_{\text{d}}=\frac{2\pi T_{\text{d}}}{p}. \tag{3}\]  
Then, the equation for the worktable is expressed as  
\[F_{\text{d}}=m_{t}z^{\prime\prime}+F_{f} \tag{4}\]  
Figure 4: Servo feed drive system for a lathe along the z-axis.  
where \(m_{t}\) denotes the mass of the worktable; \(F_{f}\) denotes the friction force of the worktable; and \(z^{\prime\prime}\,_{t}\) denotes the acceleration of the worktable along the z-direction.  
Substituting Equations (2)-(4) into Equation (1) and rearranging them yields the following equation of motion for the feed drive system:  
\[\left(m_{t}+\frac{4\pi^{2}}{p^{2}}J\right)\!\!z^{\prime\prime}\,_{t}+\frac{4\pi ^{2}}{p^{2}}Bz^{\prime}\,_{t}=\frac{2\pi}{p}k_{\rm e}i-F_{f}. \tag{5}\]
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 2 Materials and Methods
### Meteorological Variables and Flux Observations  
An automatic weather station (AWS) was utilized to observe meteorological variables, which was mounted on the AR site flux tower platform and could observe several meteorological variables including air temperature (Ta), barometric pressure (p), relative humidity (Rh), precipitation, wind speed/direction, and photosynthetically active radiation (PAR); measurements were recorded every 10 min [48]. Half-hourly averaged meteorological data were obtained by averaging multiple 10-min data over a half-hour period, and daily averaged meteorological data were obtained by re-averaging daily half-hourly averaged data.  
Alpine meadow and atmospheric exchange of energy, carbon dioxide, and water were measured by an eddy covariance (EC) system, which was also mounted on the tower-based platform around three meters from the ground [48]. The EC system includes a three-dimensional acoustic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) and an open path gas analyzer (Li-7500, Li-Cor, Lincoln, NE, USA) [48; 49]. The data sampling frequency is 10 Hz. We filled in the gaps in the data with the online resource tool ([[http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/)]([http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/)) (accessed on 24 September 2021)) supplied by the Max Planck Institute for Biogeochemistry (MPI-BGC). Based on data for the measured CO\({}_{2}\) net ecosystem exchange flux (NEE) and meteorological variables, a division of inter-day NEE into GPP and ecosystem respiration was performed using the inter-day division method proposed by [PERSON] et al. [50]. The final separation was obtained for half-hourly GPP data. The daily averaged GPP data were obtained by re-averaging daily half-hourly averaged data.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.1 Keypoint Detection  
The YOLOv11-pose model provided by the Ultralytics framework detects 17 anatomical keypoints per person, including the nose, shoulders, hips, knees, and ankles. Each keypoint is defined by 2D spatial coordinates (x\({}_{i}\), y\({}_{i}\)) and an associated confidence score c\({}_{i}\)\(\in[0,1]\). Only keypoints with confidence c\({}_{i}\) > 0.5 are considered reliable and used in further computations. Table 3 summarizes the selected keypoints and their corresponding indices used in the fall detection algorithm.  
Threshold values were empirically selected based on preliminary tests. These thresholds rely on specific anatomical keypoints extracted by the YOLOv11-Pose model. These thresholds can be adapted to different environments or user profiles through calibration modules, which will be explored in future versions of the system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Body Part** & **P (Keypoint Index)** \\ \hline Nose & 0 \\ \hline Left Shoulder & 5 \\ \hline Right Shoulder & 6 \\ \hline Left Hip & 11 \\ \hline Right Hip & 12 \\ \hline Left Knee & 13 \\ \hline Right Knee & 14 \\ \hline Left Ankle & 15 \\ \hline Right Ankle & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Keypoint index mapping for human pose estimation.
## 6 Solutions to Optimization Problems  
In UAV-enabled, RIS-assisted communication, various objectives are considered, such as UAV placement and trajectory design, UAV energy consumption for communication and beamforming, BS power transmission, RIS ON/OFF status, phase shifts, and resource allocation. These objectives are typically formulated as optimization problems. Convexprogramming is the most widely used solution due to its optimality and reduced time complexity. However, due to the complexity of the multilateral optimization problem, real-time solutions may not always be feasible, and the optimization process may converge to a sub-optimal solution after applying multiple convex relaxations. In such cases, heuristics and machine learning methods can be employed
## 3 Results and Discussion
### Topology Optimization of Bone Plate  
Topology optimization model was considered to tailor the strength-to-weight ratio by maximizing the stiffness while reducing 25, 50, and 75% of the initial volume of a custom-fit distal tibia bone plate 'INT-BP'. The aim is to reduce as much volume as possible without risking plate instability to minimize the overall equivalent stiffness. Topology optimization for each screw configuration resulted in three different optimal designs, as shown in Figure 4. Consistent with other findings, the resulting designs align with the stresses distributed from the considered loading and boundary conditions, hence presenting non-uniform designs [20; 37]. The results reported an acceptable volume from all obtained designs within less than 5% of the user-defined volumes. Designs considering higher volume reductions have been shown to remove the entire connection between some of the screw holes (e.g., TO75-BP), thus decreasing the number of screws in the plate.
# New Challenges in (Bio)Analytical Sample Treatment Procedures for Clinical Applications
## 3 Sample Preparation Techniques for Diagnostic Purposes
### Fabric Phase Sorptive Extraction (FPSE)
It is worth mentioning that, unlike other sample preparation techniques, FPSE did not require any kind of pretreatment of urine samples prior to the extraction of analytes on the FPSE membrane. Another interesting study is presented by [PERSON] et al. [66], where the researchers investigated the presence of traces of six beta-blockers possessing widely varied logP values (from 0.1 to 3.1) in human serum and urine using FPSE as the sample preparation technique followed by UHPLC-ESI-MS/MS as the chromatographic technique. Sol-gel CW 20M sorbent coated on 100% cotton cellulose was identified as the most efficient FPSE membrane for the target analytes. After optimizing all the FPSE parameters, the FPSE-UHPLC-ESI-MS/MS composite method provided LOD values for the selected beta-blockers from 0.3 to 2 ng/mL, and the LLOQ values were calculated as 50 ng/mL in both serum and urine samples. The method was linear in the broad range of 50-5000 ng/mL. [PERSON] et al. [61] described an FPSE method followed by HPLC-UV analysis designed to monitor favipiravir, a promising antiviral agent for the treatment of coronavirus, in human plasma and breast milk samples. The overall chromatographic run was only 5 min. The LOQ values of the validated method were 0.2 ug/mL in plasma and 0.5 ug/mL. By eliminating the protein precipitation and other sample pretreatment steps from the sample preparation workflow, FPSE has established itself as a rapid, simple, and green sample preparation technique for forensic toxicology. Recently, [PERSON] et al. [67] proved the operational simplicity and performance superiority of FPSE in processing post-mortem samples, including whole blood and cerebrospinal liquor collected during autopsy to monitor seven common antidepressant drugs. A sol-gel CW20M-coated FPSE membrane was identified as the best FPSE sorbent for the selected drugs with an optimum extraction time of 20 min, back-extraction time of 5 min, and 150 \(\upmu\)L of methanol as the eluent. The overall chromatographic run time was 20 min. The LOQ values were 0.1 \(\upmu\)L/mL for all drug compounds except Venlafaxine, which was 0.2 \(\upmu\)L/mL. The performance superiority, simplicity, and easiness in the application of this novel technique have been demonstrated in many articles and reviews, in particular, in extractions from complex sample matrices [32; 63; 64].
## 2 Mathematical Representation of Multirotor UAV Configurations
### Results of the Tilt Angle Analysis  
The results of the analysis of the rotors' tilt angles, and their influence on the distribution of thrust forces to the control vector, are presented. In order to perform the analysis, it is necessary to define the input configuration parameters, which are considered symmetrical, consisting of an even number of passively tilted rotors. All rotor arms are of the same length, and all rotors consist of the same components. The input parameter is the characteristic of the propulsion unit, more precisely the maximum thrust force. The results for the two configurations are presented, first for PTX6 consisting of setup 3 EPUs, and then for PTX8 consisting of setup 5 EPUs. Given the selected geometric arrangement, the motion of the aircraft in 3D space can be realized by generating vertical and horizontal forces in the Body frame. Fully actuated configurations with passively tilted rotors have the margins of forces in the form of a polyhedral. In the six-rotor configuration (PTX6), the polyhedron is bounded by six rhombuses (Figure 8), while in the eight-rotor configuration (PTX8) it is bounded by sixteen triangles (Figure 9). Figures 8 and 9 show the results of the performed analysis for the amount of tilt angles from 10 to 40\({}^{\circ}\), where forces are expressed in newtons.  
horizontal movements without the need to change orientation. Performed analysis can be further expanded and used to investigate the energy consumption, agility, the effects of the size (power) of the aircraft, the rejection of disturbances, among others.
## 7 Classification of the Surveyed AI Techniques
### Metaheuristics
#### 7.2.1 Single Solution Based Metaheuristics
channel characteristics could be modeled in the solution space while the candidate solutions are sought, selected and evaluated for their optimality. In an attempt to promote self-optimization and reduce end-to-end communication delay in an IoT dynamic multi-user mission-critical system, authors in ref. [160] proposed a tabu search-based algorithm to facilitate the placement and deployment of service chained virtual network functionalization in a network cloud infrastructure. A similar TS algorithm is used in ref. [161] for balancing network traffic between cloud and fog nodes. The work uses a convex combination technique to combine the multiple objectives into a single objective to simplify the optimization task. Further, authors in ref. [162] used simulated annealing to study how scheduling optimization techniques can be adapted to a workload of processes with low parallelism but with high arrival rates and highly variant run-times in a multi-cloud system. Using a discrete event simulator, the performance and the cost of the system was evaluated with results proving the viability of the algorithm. Moreover, the authors in ref. [163] proposed an SA-based load-balanced clustering algorithm for maintaining adequate sensing coverage in unbalanced data traffic with an end goal of increasing network lifetime. The authors further introduced a novel clustering cost function that can account for the sensor node traffic load and the communication cost over physical distances. The performance of the algorithm was compared with leading state-of-the-art clustering approaches via simulations with results showing that the algorithm can improve the network lifetime and coverage by keeping more sensor nodes alive for longer periods at a reduced computational cost.