esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 3 Pose Estimation and Optimization of the Roadheader
### Pose Optimization
#### 3.4.1 Pose Graph Construction and Closed-Loop Detection  
The selection principle of key frames is based on the evaluation of the distance between frames. After \(\mathbf{R}\) and \(\mathbf{t}\) are given different weights, the motion transformation index representing the distance between frames can be calculated. If the index is less than a given threshold, the current frame is determined to be a key frame.  
After selecting the key frame, the pose graph of the motion transformation constraint can be constructed. To further modify the computed pose, closed-loop detection is needed and closed-loop constraints are added to the pose graph. The key of closed-loop detection lies in the judgment of the similarity between two frames of images, i.e., if the similarity between the current frame and the historical key frame exceeds a certain value of similarity between the current frame and the previous-moment key frame, and the consistency of space and time is satisfied, a closed loop can be determined.  
Based on the SLAM motion model of the roadheader, shown in Figure 3, the pose graph optimization model, which eliminates the waypoint variables and adds closed-loop constraints, is shown in Figure 6. The figure shows two processes of motion transformation and closed-loop detection, \(\mathbf{T}_{i(i-1)}\) is the motion transformation constraint between pose nodes \(x_{i-1}\) and \(x_{i}\), and \(\mathbf{T}_{(i+1)n}\) is the closed-loop constraint between pose nodes \(x_{i+1}\) and \(x_{n}\).
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results
### Comparision Schemes  
To highlight the advantages of the proposed MS-FL architecture, we benchmark it against two representative baselines:  
* Cloud-based FL (hierarchical FL)--following the layered design in [10], \(N\) edge servers serve as intermediaries between vehicles and a single cloud server. Each client trains locally for \(E\) epochs and uploads its parameters to its server; the server aggregates these updates, then forwards the result to the cloud. The cloud performs a second-level aggregation across all servers and broadcasts the global model back. Crucially, servers cooperate only through the cloud--no peer-to-peer exchange occurs;
* Isolated single-server FL at each RSU--here, every RSU acts as an independent FL server [5; 6]. It trains a model using only the clients in its own coverage area and never exchanges parameters with either peer servers or a cloud node. This baseline captures the performance of a non-collaborative multi-server deployment.  
By comparing MS-FL against these two schemes--one that centralizes coordination at the cloud and one that eliminates inter-server collaboration entirely--we can clearly quantify the benefits of direct, cooperative aggregation among servers in our proposed framework.
## 3 Proposed Method
### Edge Device Computing  
The pruned and quantized models are deployed on embedded systems, such as a Raspberry Pi, replacing traditional IPCs. This lightweight approach conserves resources, reduces costs, and facilitates a large-scale deployment. Edge devices can analyze data and promptly alert onsite maintenance personnel in industrial scenarios where certain equipment is located in hard-to-reach locations [10; 18]. This enables real-time responsiveness to the equipment conditions, allowing immediate intervention and preventing downtime.  
Benefits of edge computing are as follows:  
* Provides rapid real-time reflection of situations, enabling onsite personnel to detect anomalies promptly and take immediate action.
* Solves bandwidth issues in cloud and edge transmissions because edge devices only need to send inference results back to the control centers.
* Addresses cybersecurity concerns, protecting against network attacks that could lead to factory shutdowns.
* Reduces energy consumption because lightweight edge-computing models conserve power.
## 1 Introduction
### Research Objectives and Innovations  
This study develops deep learning-based urban road travel time prediction models and methods, and compares representative shallow learning models and methods from existing research from multiple perspectives including accuracy and stability, analyzing the applicability and characteristics of deep learning models and methods in the field of urban road travel time prediction.  
The main innovations of this study include the following:  
(1) A multi-run training method for hyperparameter optimization: To address the randomness problem in deep learning models, we propose a multi-run training method for hyperparameter optimization of long short-term memory with deep neural network (LSTM-DNN) deep learning models. This method trains multiple model instances for each hyperparameter configuration and evaluates the collective performance of each configuration based on statistical measures (such as median and standard deviation) to select the optimal hyperparameter configuration. This method effectively solves the randomness problem in deep learning model training, making model selection more reliable.  
(2) Ensemble learning prediction framework: We propose an ensemble learning framework suitable for urban road travel time prediction that integrates shallow learning and deep learning models to improve prediction performance. This framework selects baselearners with different characteristics (such as linear regression and LSTM-DNN models) and combines their prediction results through various meta-learners (such as linear regression, random forest, LSTM-DNN, and DNN models), achieving improved prediction performance.  
(3) Comprehensive empirical research: Based on actual urban road travel time data from Shenzhen, China, extensive experiments were conducted comparing the performance of different models under various prediction scenarios. The research results provide valuable empirical reference for the field of urban road travel time prediction.
# Coproduction as an Approach to Technology-Mediated Citizen Participation in Emergency Management
## 3 Design Case Study: The Participative Roles of Citizens in EM
### Identifying the Different Roles Citizens Can Play in EM Coproduction
These five roles were discussed and further explored in a following focus group with professionals and policy-makers in EM from Madrid 112, the operation center where the different corps and agencies in charge of Madrid region collaborate to respond to emergencies. A focus group is a research technique used to discuss topics that require a broad range of opinions with a number of participants that range among five to seven [43], since the goal is to gather different perspectives more than interviewing large populations. In our study, six participants took part in the focus group, representing the police, fire service, forest service, ambulances service, and the 112 operation center. All of them had more than five years of experience in managing EM services for the whole region of Madrid, which has a population of 6.3 million residents. The goal of the focus group was to analyze whether the roles identified by a different EMA could make sense also for the participants to advance towards a coproduction process. The focus group started with a presentation about citizen participation in EM that ended up describing the five roles. Participants filled a questionnaire where each role was assessed by asking them whether the distinction between one role and the precedent was real and whether they could provide real examples of such roles from their experience as EM managers or first responders. An example of a question is shown in Figure 1.  
This comparison among roles and the requirements participants set to move from one role to the other made it possible to identify a potential trajectory of progressive roles that could help to understand the criteria that might be used to discriminate among them. Figure 2 illustrates the results of these discussions. This representation does not aim at representing a potential structure of participation--not even a unique or rigid path on the evolution of roles--it is just a design artifact used to gather more information on the kind of skills professional EM workers will expect from each kind of participant compared to the previous level. This simplistic representation helped to avoid the initial bias of thinking on citizens as a homogeneous crowd and focus the discussion on what makes a difference from one role to the other. For instance, analyzing the migration path from _trusted sensor_ to _node_ helped to establish a discussion to identify ways in which some citizens have provided valuable information, particularly in rural areas, for which participants highlighted the need to have previous experience in the kind of hazard being addressed. These trajectories illustrate potential evolutions in a coproduction process where the explicit recognition of the citizens' contributions and their interaction with the EM workers could act as an intrinsic motivation to try to exert more advanced roles and, hence, become more engaged in the EM process.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 3 Background
### Extreme Learning Machines
To further decrease the weights of the majority class data, the weighted ELM2 (W-ELM2) is more suitable, which considers the golden ratio. The trade-off between the ELM and W-ELM1 is given by the W-ELM2. Numerous techniques have been developed to properly solve the unbalanced data classification in ELMs such as improved weighted ELM [49], improved neutrosophic weighted ELM [50], dual activation function-based ELM [51], weighted regularized ELM [19], among others. In terms of simplicity and improvement, the decay weighted ELM (DW-ELM) must be highlighted [20]. For balance and optimization learning, an extra degree of freedom is inserted to the weighted ELM, which is known as the decaying parameter \(d\). The weighted matrix may be written as [20]:  
\[\text{Decay weighted ELM:}\quad W_{ii}=\frac{\sqrt[4]{N(t_{i})/ max[N(t_{i})]}}{N(t_{i})}. \tag{6}\]  
As the decaying parameter increases, the minority class is more relevant than the majority class. Namely, by varying this parameter, the classifier would get better boundary positions. If \(d=1\), the DW-ELM converges to the original ELM. Note that any weighted ELM increases the computational cost respect to the standard ELM, comparing Equations (2) and (3). Finally, the training stage of any version of the ELM has the following steps (Algorithm 1):  
Given the training set \(\Omega=\{(x_{i},t_{i})\mid i=1,..,L\}\), activation function \(g(\cdot)\), regularization parameter \(C\), and hidden neuron number \(N\). ```
1:Arbitrary generate the input weights \(w_{j}\) and biases of the hidden nodes \(b_{j}\). 2:Determine the hidden layer output matrix \(H\) for \(x_{i}\), refers to the first matrix of expression (1). 3:Calculate the output weights of \(\beta\). For the basic ELM employs Equation (2). Instead, the weighted ELMs requires the use of expression (3) where the elements of the weighted matrix \(W\) are given by Equations (4)-(6). In particular, the DW-ELM demands the establishment of the decay parameter. ```  
**Algorithm 1** ELM learning procedure.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 6 Conclusions  
In this paper, we have presented a cooperative transmission mechanism based on revenue learning with a kind of traditional double link technology for vehicular networks. Under the premise of combining algorithms for a joint interference mechanism and the income from the learning mechanism of multi-point cooperation transport, the proposed strategy improved the average throughput and fairness for the scheduling vehicular users. Simulation results show that our operative transmission mechanism based on revenue learning significantly obtains better performance compared to the conventional existing schemes, and the low-complexity suboptimal approaches can adequately balance the performance and complexity. Our study still has some limitations, e.g., the static models were randomly generated by the on-board network users. The dynamic revenue learning model of in-vehicular networks will be investigated in the future.  
Conceptualization, M.C. and M.N.; methodology, H.C., M.C., and Y.D.; software, Y.D. and M.C.; validation, Q.C. and S.Y.; formal analysis, Q.C.; investigation, S.Y.; resources, H.C.; data curation, M.C. and M.N.; writing--original draft preparation, Y.D., M.C., M.N., Q.C. and S.Y.; writing--review and editing, H.C. and M.C.; visualization, F.D.; supervision, F.D.; project administration, F.D.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (grant numbers 61871433, 61828103), the South China Normal University National Undergraduate Innovation and Entrepreneurship Training Program (grant number S202210574115), and the Research Platform of South China Normal University and Foshan.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors would like to thank the supervisor, committee, and colleagues who provided help throughout this work and made this work possible. Furthermore, the authors would like to thank financial support from the National Natural Science Foundation of China and the South China Normal University.  
The authors declare no conflict of interest.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 4 Conclusions  
This research focused on analyzing four design parameters to improve the design of the vein visualization system: the evaluation of NIR wavelength, the LEDs arrangement for light illumination purposes, the effect of the diffuser and the number of LEDs. Light illumination with a wavelength of 850 nm is found to provide a better vein visualization due to its excellent light scattering characteristics in human tissue. This study also shows that the square LED arrangement of LEDs as a light source provides excellent uniformity of light radiation.  
In this research, the light diffuser was not considered one of the key parameters for the light illumination process. Although the noise removal of the image makes the image processing performed faster, it does not contribute to improving the vein count. Increasing the square LED arrangement will increase the illumination area, but the vein's absorption did not show much improvement. Nevertheless, a physical response derived from a human's vein after being irradiated depends on the tissue's biological properties and the LED's features as a light source. The contrast method shows a more promising vein image enhancement than the Hessian method.  
These evaluations are crucial for developing a novel vein visualizing system, particularly in vein image acquisition techniques that have not yet been investigated thoroughly in the literature. The solution suggested in this current research can help provide useful inputs on developing imaging devices for medical procedures.  
Investigation, A.B.A.R. and F.J.; Supervision, F.P.C. and A.B.; Validation, F.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Malaysia Sabah with project no. SDN0018-2019 and GUG0318-2019.  
Not applicable.  
The authors of this paper would like to thank Universiti Malaysia Sabah for supporting this research by providing research grants.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Characterization of Adhesives  
The characteristics of adhesives used in LVL production have an important role in the quality of the final product, which can be established based on the physical and mechanical properties of the boards [43].  
The characterization of the adhesives used is presented in Table 3. According to the viscosity values of each adhesive, it can be seen that ER and PVAc viscosity values are very similar and around 75% lower than PF viscosity. These results are related to the penetration depth that each adhesive can achieve [44]; in this case, PF-wood penetration was more limited, which can also be related to a great bond quality.  
The pH has a significant influence on the glue-line formation as it affects the rate of adhesive curing [45]. Regarding the pH level results, PF and ER adhesives are in an alkaline condition, while PVAc is in an acidic condition. Anyways, the pH of the adhesives should not exceed the range of 2.5-11 because, beyond these limits, the resin causes degradation of the fibers of the wood, especially in the presence of moisture [46], a situation that could happen to veneers glued with PH adhesive. Normally, a low pH affects predominantly holocellulose, while a high pH affects mainly the lignin content of the wood. Therefore, a low pH may have a more negative effect on wood strength than a high pH.  
PF adhesive is more conductor electrically than PVAc adhesive; this is probably because PF adhesive has more additives like fillers, which offer benefits like increased viscosity, better mechanical properties, increased electrical conductivity, achievement of higher thermal and dimensional stability, and others [47; 48].  
Regarding the content of solids, PF adhesive has the least solids in its composition, PVAc adhesive has almost half of the solids in its composition, while almost the entire composition of ER adhesive corresponds to solids; in other words, it has almost no liquid in its composition. When the panel is subjected to hot pressing, evaporation of the liquid  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Viscosity (cP)** & **pH** & **Electrical Conductivity (mS)** & **Solid Content (\%)** \\ \hline PF & 4827 & 12.53 & 24.23 & 42.27 \\ ER & 1200 & 7.50 & - & 93.88 \\ PVAc & 1240 & 2.76 & 13.89 & 52.06 \\ \hline \end{tabular}
\end{table}
Table 3: Characterization of adhesives.  
component (cure), that is, the solidification of the adhesive, forms the glue line that is responsible for the bond between the substrates [49], so as the epoxy resin has a higher content of solids, in comparison to the other adhesives characterized, it may present a better bond quality.
## 1 Introduction
Based on a sensor or actuator's priority, channel conditions and power requirements, it can empower low-power communication to conserve energy and extend the battery life with a less frequent need to replace it. Figure 1: Illustrating of OMA and NOMA schemes.
## 3 Results and Discussions
### Determination of the Mean Particle Size and the Apparent Mean Shape
To demonstrate this effect, an optical spectrum after a residence time of 24 h is depicted in Figure 2(b) (solid line). The photon energy where the plasmon resonance has its maximum, _i.e._, at \(h\
u=1.87\) eV, will be considered as the resonance frequency \(\bar{\Omega}\) of the nanoparticle ensemble. In addition to the measured spectrum, an absorption cross section in arbitrary units of a single gold nanoparticle with the same resonance frequency as the nanoparticle ensemble is depicted. Since the generated nanoparticles are well separated and exhibit radii smaller than \(R=15\) nm, the center frequency of its plasmon resonance depends only on the nanoparticle shape and the dielectric surrounding. Hence, performing a simple quasistatic modeling, taking the inhomogeneous environment into account by an effective dielectric constant [14,15], we obtain the apparent mean axial ratio of the nanoparticle ensemble, which amounts to \(\langle\widetilde{a/b}\rangle=0.14\). Note, the observed broad size distribution (Figure 1(b)) results in a broad shape distribution, which has significant consequences. The plasmon mode is inhomogeneously broadened (cf. Figure 2) and, most importantly, the spectrum is dominated by large nanoparticles [15]. As a consequence, neither the mean size of the nanoparticles in the ensemble nor their mean shape can be extracted solely from the extinction spectrum. Nevertheless, as we will demonstrate in Section 3.3 the strong correlation between the shape, size, and plasmon resonance position of single nanoparticles in the quasistatic regime can be exploited to characterize precisely the morphology of the nanoparticles.
# Magnetooptics in Cylindrical Structures
###### Abstract  
Understanding magnetooptics in cylindrical structures presents interest in the development of magnetic sensor and nonreciprocal devices compatible with optical fibers. The present work studies wave propagation in dielectric circular cylindrical structures characterized by magnetic permeability and electric permittivity tensors at axial magnetization. The Helmholtz equations deduced from the Maxwell equations in transverse circularly polarized representation provide electric and magnetic fields. With the restriction to terms linear in off-diagonal tensor elements, these can be expressed analytically. The results are applied to magnetooptic (MO) circular cylindrical waveguides with a step refractive index profile. The nonreciprocal propagation is illustrated on waveguides with an yttrium iron garnet (YIG) core and a lower refractive index cladding formed by gallium substituted yttrium iron garnet (GaYIG) at the optical communication wavelength. The propagation distance required for the isolator operation is about one hundred micrometers. The approach may be applied to other structures of cylindrical symmetry in the range from microwave to optical frequencies.  
magnetooptical effect; Faraday effect; nonreciprocity; isolator +
Footnote †: journal: Applied Physics Letters
## 2 Results and Discussion
### Conclusions  
An optimized, rapid thin-film microextraction protocol using sorbent sheets (SPMESH) followed by DART-MS for trace level analyses of a key odorant (IBMP) in grape macerates below its sensory threshold was described. The improved method utilized a new SPMESH device holder design to prevent well-to-well leakage, increased sample size, and optimized DART and HRMS/MS settings. The new method achieved excellent agreement with a gold standard approach (SPME-GC-MS) at IBMP concentrations \(<\) 5 ng/kg, below the sensory threshold of IBMP. Low detection limits, \(<\)2 ng/kg, could also be achieved with a lower cost DART-QqQ-MS/MS instrument. Control of the X-Y positioning stage with JumpShot-HTS improves throughput by allowing for automated movement from row to row, and to sample only from regions containing extracted volatiles. The improvements described here for SPMESH-DART-MS should be expandable to rapid analyses of other trace level volatiles.