esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Sensing Directional Hub MAC (VSDH-MAC) Protocol with Power Control
###### Abstract  
Medium access control (MAC) protocols play a vital role in making effective use of a multiple access channel as it governs the achievable performance such as channel utilization and corresponding quality of service of wireless sensor networks (WSNs). In this paper, a virtual carrier sensing directional hub (VSDH) MAC protocol incorporating realistic directional antenna patterns is proposed for directional single hub centralized WSNs. While in most instances, MAC protocols assume idealized directional antenna patterns, the proposed VSDH-MAC protocol incorporates realistic directional antenna patterns to deliver enhanced link performance. We demonstrate that the use of directional antennas with a suitable MAC protocol can provide enhanced communication range and increased throughput with reduced energy consumption at each node, compared to the case when only omnidirectional antennas are used. For the scenarios considered in this study, results show that the average transmit power of the sensor nodes can be reduced by a factor of two, and at the same time offer significantly extended lifetime.  
medium access control (MAC); wireless sensor network (WSN); wireless communication; directional antennas; energy efficiency; power control +
Footnote †: journal: Electronics Electronics
## 3 Results
### ERP Results
#### 3.2.2 ERP Mean Amplitude Analysis at Occipito-Temporal Electrode Clusters  
The grand averaged waveforms in occipito-temporal electrode clusters are shown in Figures 4 and 5. ANOVA revealed a significant interaction between Identity and Version, \(F\) (2, 34) = 4.61, \(p\) = 0.016, \(\eta_{\text{p}}{}^{2}\) = 0.21, for mean amplitude of N170. No other effects reached significance, \(Fs\) < 2.8, \(ps\) > 0.07.  
Figure 3: ERP waveforms recorded in each condition at right occipital sensor. The black and green lines represent ERP waveforms for self and other familiar faces, respectively. The rectangles drawn by dotted lines represent time windows used for mean amplitude measurement of P100.  
Figure 2: ERP waveforms recorded in each condition at left occipital sensor. The black and green lines represent ERP waveforms for self and other familiar faces, respectively. The rectangles drawn by the dotted line represent time windows used for mean amplitude measurement of P100.  
In order to clarify the source of the interaction, simple main effect analysis was conducted. Simple main effect analysis revealed significantly larger N170 in response to self than other familiar faces in the degraded version condition, \(F\) (1, 17) = 7.28, \(p\) = 0.015, \(\eta_{\text{p}}\)\({}^{2}\) = 0.29. Simple main effect of Identity did not reach significance in the other Version conditions, \(Fs\) < 3.4, \(ps\) > 0.08.  
P250 amplitude was significantly larger in the right than in the left hemisphere, \(F\) (1, 17) = 4.86, \(p\) = 0.041, \(\eta_{\text{p}}\)\({}^{2}\) = 0.22, and significantly larger in response to other familiar than self-faces, \(F\) (1, 17) = 7.81, \(p\) = 0.013, \(\eta_{\text{p}}\)\({}^{2}\) = 0.31. No other effects reached significance, \(Fs\) < 3.2, \(ps\) > 0.05.  
The ANOVA revealed no significant effects for LPC amplitude, \(Fs\) < 1.9, \(ps\) > 0.17.  
Figure 4: ERP waveforms recorded in each condition at left occipito-temporal electrode cluster. The black and green lines represent ERP waveforms for self and other familiar faces, respectively. The rectangles drawn by the dotted line represent time windows used for mean amplitude measurement of N170, P250 and LPC, respectively.  
Figure 5: ERP waveforms recorded in each condition at right occipito-temporal electrode cluster. The black and green lines represent ERP waveforms for self and other familiar faces, respectively. The rectangles drawn by the dotted line represent time windows used for mean amplitude measurement of N170, P250 and LPC, respectively.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 3 Dynamic Multiple Junction Based Source Routing Protocol
### DMJSR Protocol Overview
#### 3.2.1 Protocol Assumptions  
DMJSR is a junction-based geographic routing protocol. Certain assumptions are made for the working of this protocol. Similar assumptions are made in [2; 31; 3]. GPS is utilized by a vehicle in order to locate its position. The destination vehicular node can be located using location services such as GLS [36]. Every vehicle contains an onboard navigation system, which provides the location of neighboring junctions and useful street level information by using preloaded digital maps. Moreover, it is also assumed that every vehicular node is familiar with its direction and velocity. We also assume that each vehicular node has information about vehicular traffic density between two junctions. This information can be made available by traffic sensors deployed beside junctions or simple distributed mechanism for road traffic estimation realized by all vehicles [41]. In the presence of aforementioned assumptions, the detailed description of our routing protocol is given below. It consists of two modules: (i) Dynamic multiple junction selection mechanism and (ii) improved greedy forwarding mechanism between the junctions on the basis of one-hop neighbor information.
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## 5 Conclusions  
In this study, a RMR topology is proposed for Bluetooth multi-hop networks. The RMR constructs a ring topology as a backbone subnet, which is then extended by a mesh-shaped topology. To maximize network capacity, three configurations (including piconet, scatternet, and hybrid) are examined and demonstrated to determine the optimal placement of mesh links. In addition, the number of mesh link is adjustable to achieve the optimal network configuration. In order to determine the optimum bridge of each mesh link, a heuristic peak-search method is presented to achieve the desired throughput performance of the RMR network. The peak-search method is implemented by three functional blocks: the scatternet formation, the network capacity and the optimum decision blocks. Simulation results demonstrate that the optimal Mesh-Ring configuration can be determined by the peak-search scheme and the scatternet case achieves better overall network performances than the other two cases. Moreover, that RMR demonstrates better network routing performance than the conventional Ring and the cluster-based Mesh schemes for Bluetooth sensor networks.  
[PERSON] designed the RMR formation algorithm, routing protocol, formulated the mesh connection models, performed the simulation, and wrote the paper; [PERSON] and [PERSON] investigated the topology, validated the algorithms, analyzed the results and cooperated in designing the simulation.  
This work was supported by the Ministry of Science and Technology of Taiwan, under Grants MOST 106-2221-E-216-002 and MOST 106-2221-E-009-050.  
The authors declare no conflict of interest.
## 3 Molecular Characterization of Coat Color Affecting Genes in Rabbits
### Molecular Characterization of a Biochemical Defect: The Yellow Fat Locus  
A Mendelian recessive allele at the _Yellow fat_ locus determines the excessive accumulation of xanthophylls in the fat, because these molecules cannot be degraded due to the absence of a key enzyme of this process. This biochemical defect has been reported in many rabbit breeds [128; 129], but its occurrence is becoming rarer nowadays due to the negative selection against meat rabbits that have yellow fat driven by a reduced acceptance of this carcass appearance by the consumers in most countries. To identify the causative mutation in rabbits, a candidate gene approach was used, considering that similar defects have been already characterized in other species. For exam ple, mutations in the beta-carotene oxygenase 2 (_BCO2_) gene have been already associated with yellow fat in cattle, sheep, and chicken [130; 131; 132; 133]. Then, [PERSON] et al. [134] investigated the _BCO2_ gene in normal rabbits and in rabbits with the Yellow fat phenotype. An in-frame deletion of three nucleotides in the coding sequence of this gene gene was reported to be the causative mutation of the _Yellow fat_ recessive allele [134]. Homozygous rabbits for this mutation have an increased accumulation of xanthophylls and beta-carotene in the liver, muscle, adipose tissue, and milk without any negative effects on growth and maternal performances compared to heterozygous or homozygous rabbits for the wild type allele [135; 136; 137].
## 4 Proposed Architecture  
This section introduces secure and scalable end-to-end architecture for IoT-based e-health monitoring systems. The proposed framework is designed to ensure data confidentiality and reliability across constrained environments by employing layered encryption mechanisms and a multi-broker MQTT infrastructure. It supports direct communication from IoT sensing nodes to cloud-based systems and ultimately to healthcare professionals, enabling real-time decision-making while maintaining strict privacy and security standards.
# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 4 Experiment
### Person Re-Identification Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline PersonNet [26] & 0.3721 & - & - & 0.1857 \\ BoW+kissme [8] & 0.4725 & - & - & 0.2188 \\ PAN [44] & 0.7159 & 0.8389 & - & 0.5151 \\ HydraPlus-Net [27] & 0.7690 & 0.9130 & 0.9450 & - \\ Part-Aligned [25] & 0.8100 & 0.9200 & 0.9470 & 0.6340 \\ SVDNet [9] & 0.8230 & 0.9230 & 0.9520 & 0.6210 \\ PDC [5] & 0.8414 & 0.9273 & 0.9492 & 0.6341 \\ AACN [29] & 0.8590 & - & - & 0.6687 \\ PSE [11] & 0.8770 & 0.9450 & 0.9680 & 0.6900 \\ \hline PADP (VGG19+BN) & 0.8821 & 0.9530 & 0.9697 & 0.7086 \\ PADP (Resnet18) & 0.8771 & 0.9510 & 0.9658 & 0.6902 \\ PADP (Resnet34) & **0.8895** & 0.9541 & **0.9742** & 0.7093 \\ PADP (Resnet50) & 0.8824 & **0.9561** & 0.9718 & 0.7028 \\ PADP (Densenet121) & 0.8881 & 0.9513 & 0.9685 & **0.7133** \\ PADP (Inception\_v3) & 0.8884 & 0.9543 & 0.9700 & 0.7056 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rank-1, rank-5, rank-10 and mAP of various methods on the Market-1501 dataset. From Tables 3-5, it can be seen that the proposed algorithm with different network architectures outperforms the current state-of-the-art person re-identification methods on average. Due to its simpler structure, 18-layer Resnet method performs worse than other networks, by 0.9% on Market-1501 dataset, 1.14% on the CUHK03 dataset, 0.76% on the DukeMTMC-ReID dataset at rank-1. However, it also outperforms the current state-of-the-art person re-identification methods on three datasets. On the Market-1501 dataset, our method with 34-layer Resnet outperforms the second best method by 1.25% at rank-1, and the Densenet121 based method outperforms the second best method by 2.33% at mAP. On the CUHK03 dataset, our method with 34-layer Resnet outperforms the second best method by 2.13% at rank-1. On the DukeMTMC-ReID dataset, our method with Densenet121 outperforms the second best method by 1.41% at rank-1, and 0.75% at mAP.
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 4 Discussion
### Inter-Day Analysis  
The present inter-day analysis significant differences were presented in external training load for TD, MRS, AvS, rHSR, HMLD, average sprint distance, DSL and DEC (Table 2). Our training data show a lower TD covered in MD-1 (moderate to strong effect). Our main findings seem to be convergent in a strategy tapering based on a gradual reduction until the last day before MD. Normally, the training load pattern presents a progressive increase up to MD-3 and/or MD-4 followed by a decrease until MD-1 [21; 54; 55; 56]. In what concerns youth football, the literature reported an unloading prior to the pre-match training session [19; 31; 56]. Our data show a higher AvS and MRS in MD-2. By contrast, MD-3 presented higher DSL and DEC. In the same line, previous studies reported a higher weekly training load in the middle of the week [31; 56]. Moreover, a large variation has been reported between training days and within training sessions [19; 56]. An unloading phase was normally adopted due to an optimal fitness recovery status for competition [57; 58]. For instance, the high values found in the accelerometer variables during MD-1 could be used to monitor neuromuscular fatigue [59].  
In the results of the present study, the internal training load and recovery status did not present significant differences for inter-day analysis. By contrast, previous studies reported an unloading phase in young players concerning RPE values [33; 45]. [PERSON] et al. [33] evidenced a tapering in U18 players. On the other hand, the U14 and U16 players presented relatively high training loads across the weekly microcycle. Indeed, it could be suggested that coaches opt for different tapering strategies when the age and competition focus increases.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 2 Topology Based Scalability
### Distributed Controller Designs
#### 2.2.2 Hierarchical Topology
However, most of existing approaches do not take into consideration the risk of having a single root controller, since controllers at the lower level can temporarily take over its tasks. Additionally, many hierarchical architectures do not tackle an important problem which is path stretch problems, leading to relatively high latency as the traffic take longer to pass through the network.
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 2 Design of the Digital ENFMS for APR1400
### Linear Power Signal Generation
#### 2.2.2 Hardware and Software for the Digital Signal Processing  
Digital signal processing for linear power signal generation requires filtering and arithmetic operations. Therefore, DSPs which have been utilized for safety I&C systems in nuclear power plants such as COMMON-Q and POSAFE-Q are also appropriate for this application. Analog input and processor modules shall be selected to meet response time and accuracy requirements of the instrumentation channel.
## 3 Results and Discussion
### Numerical Results  
The simulation results for the models with an interface friction angle of \(\varphi=0^{\circ}\) show that the analytical solutions can be reproduced numerically regardless of the considered cross sections acc. to Figure 5b. Differences to the analytical results for the strain distributions can only be recognized with respect to the results from the models with a higher interface friction angle of \(\varphi=15^{\circ}\).
## 4 Influence of Pile Length and Pile Diameter
### Pile Diameter  
To investigate the influence of the pile diameter on the SSI effect, the pile diameters of the 3-story, 9-story, and 15-story RC-frames considering SSI were, respectively, set as 0.25, 0.45, 0.65, 0.85, and 1.05 m for comparison. In this section, the seismic records RSN-587 and RSN-445 with a peak acceleration of 100 gal were used as seismic inputs, and the values of vs. were, respectively, set as 150 and 250 m/s to fully investigate the influence of the pile diameter. Figure 14a,b show that the maximum and minimum values of \(e_{bs}\) for F9 built on soil with vs. of 250 m/s are \(-\)25.0% and \(-\)46.0%, and those of \(e_{sd}\) for F9 built on soil with vs. of 150 m/s are 37.5% and \(-\)4.8%, respectively. The maximum influences of the pile diameter on \(e_{bs}\) and \(e_{sd}\) are, respectively, up to 21% and 42%, indicating that the influence of the pile diameter on the SSI effect is significant in some cases. Moreover, the influences of the pile diameter on the values of \(e_{bs}\) and \(e_{sd}\) with different structure frequencies, soil shear wave velocities, and seismic records are different, which is related to the soil shear wave velocity, structure frequency, and spectral characteristic of the seismic record. In  
Figure 12: Comparisons of \(e_{sd}\) under seismic records with different spectral characteristics.  
addition, Figure 14 shows that with the increase in the pile diameter, the values of \(e_{bs}\) and \(e_{sd}\), respectively, increase and decrease in most cases, indicating that in general, the maximum base shear and story drift of RC frames, respectively, increase and decrease with a larger pile diameter. The reason for the above phenomenon is that in general, a larger pile diameter can transmit a bigger seismic force to the superstructure and produce a more significant inhibitory effect on the rocking of the superstructure.  
Figure 14: _Cont._  
Figure 13: Comparisons of \(e_{sd}\) and \(e_{bs}\) under different pile lengths.
# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
## 2 Related Work
### Personalized Federated Learning  
PFL is a method to reduce the effects of data heterogeneity [17]. It maintains different models on different clients, achieving higher accuracy on their respective datasets. Previous methods in pFL encompass fine-tuning [18; 19], federated meta-learning [20], federated multi-task learning [21; 22], model mixup [23], and federated clustering [24; 25]. Recent pFL methods involve FedBN [26], which similar to FedAvg [2], keeps batch normalization layers local. FedPer [27] divides the model into a global layer and a personalized layer, with only the global layer being aggregated. FedRep [23] builds upon FedPer by further analyzing the effectiveness and rationality of local training and separately trains global and personalized layers. FedBABU [28] adopts a similar neural network partitioning approach by aggregating only the global layer. The difference lies in that FedBABU does not train the personalized layer but fine-tunes it after training has been completed. FedFomo [29] enables each client to obtain the complete models from other clients and choose those that offer greater advantages for local aggregation to update their local model, but the communication cost increases exponentially.  
[PERSON] [30] presented the fast weighting concept in hypernetworks, wherein one network can generate context-dependent weight adjustments for another network. The first attempt to apply hypernetworks to pFL was in pFedHN [31]. The pFedHN deploys a hypernetwork on the server that is larger than the local target model. The hypernetwork acquires parameters for local models and creates personalized models for each client, though the communication expenses are approximately equivalent to those of FedAvg. Paper [32] implements separate hypernetworks for each user on the server. Additionally, during the model aggregation phase, the server assesses the significance of each layer across various networks. Paper [33] applies the hypernetwork to the local deployment and trains the hypernetwork, local personalization layer, and embedding vector, respectively, reducing communication overhead and increasing accuracy. In the Fed-RoD [34], each client's architecture includes a feature extractor and two separate headers. The global header collaborates with the feature extractor to aggregate data and update models, while a personalized header is produced by the local hypernetwork.
## Appendix A Adaptive Tracking Technique Using Particle Filter  
[PERSON] et al. (2005) proposed an adaptive tracking technique employing particle filter [PERSON] et al. (2005). The following equations describe an appropriately modified version of the UKF-based tracking technique:  
\[\mathbf{V}_{k+1}=\left[\rho\mathbf{V}_{k}+\frac{1}{2n_{\mathbf{x}}+1}\sum \
olimits_{i=0}^{2n_{\mathbf{x}}}\!\!\left[\zeta_{k+1|k}^{i}-\mathbf{z}_{k+1} \right]\!\left[\zeta_{k+1|k}^{i}-\mathbf{z}_{k+1}\right]\!\right]\!/\left(1+\rho\right) \tag{1}\]  
\[\mathbf{M}_{k+1}=\frac{1}{N_{k+1}}\sum\
olimits_{k=1}^{k+1}\!\left[\frac{1}{2 n_{\mathbf{x}}+1}\sum\
olimits_{i=0}^{2n_{\mathbf{x}}}\!\!\left[\zeta_{k+1|k}^{i}- \mathbf{\hat{z}}_{k+1|k}\right]\!\left[\zeta_{k+1|k}^{i}-\mathbf{\hat{z}}_{k+ 1|k}\right]\!\right] \tag{2}\]  
\[\lambda=\frac{\text{tr}[\mathbf{V}_{k+1}]}{\text{tr}[\mathbf{M}_{k+1}]},\ \lambda_{k+1}=\begin{cases}\lambda&\text{if}\ \ \lambda>1\\ 1&\text{if}\ \ \lambda\leq 1\end{cases} \tag{3}\]  
where \(\mathbf{V}_{k+1}\) and \(\mathbf{M}_{k+1}\) are the covariances of the actual and theoretical residual; \(\rho\) is the forgetting factor ranging between 0 and 1 (for which \(\rho=0.95\) is often selected); and \(\text{tr}[\cdot]\) is the trace of the matrix. The calculation of Equations (1)-(3) is performed in the measurement-update part of UKF. The key idea of the above equations is that if there are considerable sudden changes in the state vector, the actual-residual covariance \(\mathbf{V}_{k+1}\) becomes different from the theoretical-residual covariance \(\mathbf{M}_{k+1}\). Then, the covariance of the artificial process noise increases rapidly in the next time-step to track the sudden decrease of states, i.e., \(\mathbf{Q}_{k+1}=\lambda_{k}\mathbf{Q}_{k}\). At this calculation, the whole elements in the covariance of artificial process noise are increased with the same ratio. Instead, to consider general cases where each system parameter usually decreases at different time-points with different level of damage, the Equation (A3) can be modified to:  
\[\Lambda_{ii}=(\mathbf{V}_{k+1})_{ii}/(\mathbf{M}_{k+1})_{ii},\ (\mathbf{ \Lambda}_{k+1})_{ii}=\begin{cases}\mathbf{\Lambda}_{ii}&\text{if}\ \ \mathbf{\Lambda}_{ii}>1\\ 1&\text{if}\ \ \mathbf{\Lambda}_{ii}\leq 1\end{cases}\] (A4)  
which is a simple modification from the original version to consider the decreases in each system parameter, separately. The limitation of this adaptive technique is that the inaccurate changes in state vector may cause severe bias or unstable estimation. Additionally, the increasing factor can be calculated when the corresponding responses are measured, which violates the condition of sparse measurements.