esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 4 Proposed Spreading Factor Allocation Schemes
### Proposed Distance-Based SF Assignment Algorithm (ED Sensitivity)  
During the initial deployment, each ED is assigned with an SF based on the ED sensitivity, as shown in Algorithm 1. In the proposed method, first, we find the distance between ED and GW using a similar approach (i.e., Euclidean distance), as presented in [7]. Secondly, we measure \(P_{rx}\) at GW (i.e., a GW would receive from ED), assuming a time-independent and symmetric link, where the channel uses the same path loss model for both UL and DL transmissions [5]. When the value of \(P_{rx}\) is measured, it is checked against \(S_{e}\), as specified in Table 2. If the condition holds, each ED picks an SF based on \(S_{e}\) using \(getSF\) function, to minimize the ToA and lower the probability of packet collisions. Furthermore, in a realistic environment, shadowing conditions change with time. However, in this work, the channel is time-independent, thus when SFs are assigned, no further adaptation is required.  
```
INPUT:\(S_{e}=\) ED sensitivities from OUTPUT:Assignment of spreading factors to \(N\) number of EDs for\(i\)in\(N\) Calculate\(P_{rx}\) (i.e., a GW would receive from ED) \(P_{rx}=getRecpvPower(ED_{i})\) if(\(P_{rx}\)>\(S_{e}\)) \(ED_{i}\gets getSF(P_{rx})\) end end
```  
**Algorithm 1** SF assignment based on ED sensitivity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Description** & **Value** \\ \hline \(ACK\_TIMEOUT\) & waiting time before retransmission & 1 to 3 s \\ \(ReTx\_Left\_CNT\) & number of retransmissions left to complete the packet transmission & multiple of 2 \\ \(Max\_Tx\) & maximum number of transmissions allowed & 8 \\ \(ACK\_CNT\) & number of DL ACK packets & 32 \\  & received & \\ \(SF\_CHANGE\_TRACK\) & number of times SF has been changed during the simulation & 6 \\  & time & \\ \(Max\_SF\) & maximum SF & 12 \\ \(Min\_SF\) & minimum SF & 7 \\ \(\alpha\) & \(ACK\_CNT\) threshold value & 32 \\ \(PER_{DL}^{UL}\) & DL ACK & - \\ \(PER_{DL}^{ACK}\) & _No. of UL packets transmitted_ & - \\ \(PER_{DL}^{ACK}\) & _No. of DL ACK packets transmitted_ & - \\ \hline \hline \end{tabular} The choice of \(ReTx\_Left\_CNT\) and \(Max\_Tx\) is based on [2,3]. Whereas the choice of \(SF\_CHANGE\_TRACK\) and \(\alpha\) is based on hit and trial method.  
\end{table}
Table 4: Symbols used in the typical SF and proposed channel-adaptive SF schemes.
## 2 Related Work
### MEC Business Discussion  
Research related to trend surveys on service use cases using MEC with players are described in this section. There are several elements to the service use case. For example, it involves efforts and business models, such as establishing a consortium and collaborating with several companies in order to verify new technologies with PoCs and submit/propose a requirement definition to a standardization body such as 3 GPP/ETSI/ITU-R. The 5 GPP (5G Infrastructure Public Private Partnership) established by the European Commission, which is the policy body of the European Union, has proposed use cases of MEC and described the advantages of MEC architecture and virtualization [56]. In addition, the open EDGE computing consortium has built the Living Edge Lab as a hands-on project focusing on new technology verification such as application and platform tool verification for Edge and architecture verification [57]. There is a movement to establish an open lab and prepare an environment where an open lab could perform various PoC verification immediately. In the past, it was a flow of conducting desk studies and simulations, design, and PoC. However, as of now, with the variety of tools (e.g., Open Source Software) and prototypes (e.g., Arduino) available today, software can quickly realize new ideas. Therefore, research has become more agile and development can repeatedly develop the prototypes of research ideas for verification [57; 58].  
In contrast, regarding the business model, several kinds of research accompany the introduction of MEC. For example, ref. [59] deals with the conceptual level, including MEC handover to another MNO by having the MNO network hold MEC. Therefore, the existing operator holds MEC in many architectures [60; 61]. However, there is a problem in that these references have many conceptual levels and are not yet mature, because there are many uncertainties with the methods of deploying an application possessed by third parties. Among these, authors have discussed the business model of MEC, proposed a new local/private operator who should hold MEC, and performed numerical analysis by simulation [25; 26; 27; 28]. The present paper discusses who holds MEC/Cloud Orchestrator, which has not been discussed yet. In addition, the author proposes details regarding to the design of each holder based on the content of the discussion.
# External Field Response and Applications of Metal Coated Hemispherical Janus Particles
## 2 Experiment
### Measurement Methods  
#### 2.2.1 Reflectance Measurement of Single Particles  
Metal-coated hemispherical Janus particles have a planar metal surface unlike spherical Janus particles with a hemispherical metal surface [9]. Therefore, the metal plane functions as a mirror that reflects light. The reflection spectra of respective Ag-coated Janus particles on the cover glass were measured by microspectroscopy. The optical system is shown in Figure 2. A xenon lamp (LDLS, ENERGETIQ, Woburn, MA, USA) was used as a light source. Collimated white light from the lamp was half masked, and then incident through a half mirror on an objective lens (50\(\times\), NA = 0.7, CFI L Plan EPI CR, Nikon, Tokyo, Japan) to be focused on the sample. The reflected light from the sample was collimated with the objective lens, reflected by the half mirror, collected with an achromatic lens of f = 50 mm, and incident on a spectrometer (USB 2000, Ocean Optics, Largo, FL, USA) through a fiber to obtain spectra. The light was incident on the sample from either the spherical or the equatorial-plane side. The reflection spectrum of the silver thin film on the substrate was also measured with the same optical system. The measurement wavelength range was from 400 to 800 nm. This is due to the limitations of the spectral band of the light source (Xe lamp), and the spectral sensitivity characteristics of the spectrometer and the silicon detector that was being used.
In our prior work [35], we proposed a LiDAR-based ego vehicle localization approach using individually trained interaction dictionaries for each static track. While that framework demonstrated promising results in structured testing environments, it did not address scenarios involving simultaneous interactions, unobserved intervals, or the fusion of dictionaries across tracks. Our main contributions in this paper are as follows. * Innovative Classification Framework: We developed a robust method to classify static and dynamic tracks using LiDAR data, leveraging a combination of probabilistic models, including DBNs and interaction dictionaries. * Self-Aware Localization Strategy: We introduced a novel ego vehicle localization approach that does not rely on external odometry data during testing, enhancing autonomy in dynamic environments. Simultaneous Multi-Track Interaction Modeling: We proposed a combined interaction dictionary that enables simultaneous modeling of multiple track interactions and ensures continuity during missing observations. * Integration of Interaction Dictionaries: We designed a methodology that combines multiple static track interactions into a unified dictionary framework, ensuring robust localization even in the absence of continuous observations. * Hybrid Inference with MJPF: We implemented an MJPF filter that supports hybrid discrete and continuous inference for trajectory prediction. * Generative Localization Model: We developed a generative localization model capable of statistical filtering and divergence-based anomaly detection during trajectory estimation. * MJPF Enhancement for Ego Vehicle Localization: We incorporated MJPF into the localization process to refine ego vehicle positioning by continuously predicting and updating its estimated trajectory based on static track interactions. These enhancements distinguish the present work from our prior approach and contribute to a more robust data-driven localization strategy for AVs. The remainder of this paper is organized as follows. Section 2 presents a review of the related works in LiDAR-based classification and localization. Section 3 introduces the proposed methodology, including classification, interaction dictionaries, and ego vehicle localization using MJPF. Furthermore, it details the use of combined dictionaries for improving localization accuracy and describes the DBN modeling for track classification and localization. Section 4 presents experimental results, including classification accuracy, localization performance, and anomaly detection analysis. Finally, Section 5 concludes the paper with a summary of the findings and suggests future directions.
## 2 Theory and Principle
### Lines Loaded with a Pair of Resonators (Frequency-Splitting Structures)  
In lines loaded with a pair of resonant elements (e.g., Figure 1a), the symmetric configuration leads to a single transmission zero, since the fundamental resonance frequencies of the two resonators degenerate to the same one. However, if the whole structure is asymmetric, in general there are two transmission zeros because two different resonances arise. This is here referred to as frequency splitting, meaning that the electromagnetic structure exhibits two split or separated resonance frequencies. It should be also stressed that the two resonance frequencies depend on the resonance frequencies of the two resonators, as well as on the coupling between the resonators, if present. It should be noted that in transmission lines loaded with pairs of resonators (transversally arranged), line-to-resonator coupling for both resonators cannot be canceled simultaneously. Therefore, resonance condition always occurs (as long as the resonator is in close proximity to the transmission line, which is obvious).  
Figure 3: Illustration of symmetry rupture by square-shaped inclusions in the approach based on a single resonator, where a CPW is loaded with a symmetrically oriented SRR.  
Consequently, in this approach based on pairs of resonators, a high controllability in regard to the number and frequency of resonances as a function of symmetry may be achieved (the bandwidth and depth of the notches, however, may also depend on symmetry). Figure 4 illustrates the above-mentioned assertions where symmetry is disrupted by inclusions. As can be seen, an interesting aspect of this approach is that there is no restriction in the orientation of the individual resonators in regards to the line. This is important for cancel mixed-coupling effects in certain topologies [24]. For example, if symmetric SRRs are considered, their symmetry planes are not needed to be oriented longitudinally and the SRRs are coupled only magnetically to the line.  
Like in the strategy based on single resonators, symmetry rupture by adding inclusions to the structure using paired resonators may be useful for the implementation of differential sensors and comparators [34]. Potential applications are dielectric characterization, quality control (e.g., defect detection in samples by comparison to a reference), biosensors, _etc._ It is also possible to use this sensing principle based on frequency splitting for the implementation of spatial sensors [35].
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 3 Contamination Test in Wind Tunnel
### Experimental Devices  
The tests were carried out in a circulating wind tunnel, as shown in Figure 6. The test power is provided by the YDTW-50/50 kVA test transformer. The range of wind speed in the wind tunnel is 0-7 m/s and the adjustable accuracy is 0.1 m/s. It is mainly composed of contaminated section, fan section and test section. The front end of the test section is equipped with a six angle cellular devices to reduce the air flow turbulence, a deflector for equalizing airflow is installed at the corner of the wind tunnel. Other equipment includes frequency converter, anemometer, electronic balance of 0.1 mg accuracy, temperature hygrometer, air particulate matter collection instrument, scrubbing tools, Ultrasonic fog generator, etc.  
Figure 5: SiO\({}_{2}\) powder samples used in the tunnel test: (**a**) optical microscope; (**b**) 250 and 2000 mesh powder specimen enlargement.  
Figure 6: Wind tunnel test device. (**a**) Diagram of wind tunnel; (**b**) Wind tunnel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Powder Specification (mesh)** & **d (10\%)** & **d (50\%)** & **d (90\%)** & **D\({}_{\textbf{50}}\) (\(\mu\)m)** \\ \hline
250 & 35.3 & 46.5 & 57.8 & 46.5 \\
400 & 22.3 & 27.7 & 35.2 & 27.7 \\
800 & 11.8 & 15.9 & 17.6 & 15.9 \\
2000 & 2.8 & 4.2 & 5.8 & 4.2 \\ \hline \hline \end{tabular} Note: d (10\%), 10\% particle size is smaller than this value; d (50\%), 50\% particle size is smaller than this value; d (90\%), 90\% particle size is smaller than this value, D\({}_{\textbf{50}}\), average particle size, cumulative probability distribution of particle size is 50%.  
\end{table}
Table 3: Diameter situations of tested SiO\({}_{2}\) powder.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Packet Size
#### 4.1.2 Simulation Results of Three-Log Distance Propagation Model regarding Packet Size  
In the three-log distance propagation model, the mobile RSU has a much smaller delay than the sRSU because the distance is limited; hence, the mRSU packet arrives at the node earlier. The mobile RSU also has a better packet delivery ratio because the possibility of packet dropping is reduced. After all, the RSU is in motion with the vehicles.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### Network Model Architecture
#### 3.1.3 Data Plane  
The data plane is the network topology that enables video transmission between end devices.
## 3 Results
### ESs Trade-Off/Synergy
#### 3.2.2 ESTD Results  
The area and spatial distribution of ES changes were investigated using the dynamic change values [28] of each ES in 2000 and 2018 to calculate the ESTD of the five ES changes.  
It can be seen that the high trade-off between WY and CS was mainly distributed in the northeastern and southeastern parts of the study area, while the synergy between WY and SC was as high as 69.35% of the total area; this is consistent with the distribution of change values in Table 2. The synergy between CS and SC was also relatively significant, accounting for 53.28% of the total area in Figure 6. The trade-off between FP and SC was relatively large, accounting for 47.92% of the total area; the trade-off between SC and HQ is also relatively significant. As a whole, the trade-off/synergy between WY and SC, WY and CS, and SC and CS are spatially clustered over a large area, while the trade-offs and synergies of the remaining ESs are more well-dispersed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **WY-FP** & **WY-CS** & **WY-SC** & **WY-HQ** & **FP-CS** & **FP-SC** & **FP-HQ** & **CS-SC** & **CS-HQ** & **SC-HQ** \\ \hline Trade-off & 50.02\% & 45.23\% & 30.65\% & 51.05\% & 49.23\% & 52.08\% & 50.89\% & 46.72\% & 51.46\% & 51.86\% \\ Synergy & 49.98\% & 54.77\% & 69.35\% & 48.95\% & 50.77\% & 47.92\% & 49.11\% & 53.28\% & 48.54\% & 48.14\% \\ \hline \end{tabular}
\end{table}
Table 2: The area of synergy of trade-offs between the value of change in ecosystem services. WY-FP is the ESTD of WY and FP. Same for the rest. ESTD < 0 indicates a trade-off between the two ESs and ESTD > 0 indicates a synergistic relationship.  
Figure 5: Correlations between category five ecosystem services in 2000 and 2018. WY2000: Water Yield in year 2000; WY2018: Water Yield in year 2018. Same for the rest.  
Figure 7 shows the distribution of ES change data in a reciprocal trade-off/synergistic relationship with the data normalized in advance. Each violin plot represents the data distribution; most data distributions are relatively concentrated, with discrete values for some FP and CS. Similar to the kernel density plot readings, the peak heights represent the probability density of the data, with higher peaks being denser. In Figure 7a, taking WY in WYFP as an example, it is obvious that the first orange box is wider at y > 0 than at y < 0, i.e., it indicates that most of the WY data are positive when WY presents a trade-off with FP. Another example is in Figure 7a,b, where SC in WYSC shows a different form of data distribution in a trade-off or synergistic relationship with WY. When synergistic relationships are present Figure 7b, SC data are more dispersed. In Figure 7a,b, the probability density of FP in FPSC in the trade-off/synergy relationship with SC is larger for FP < 0 data under the trade-off relationship than for FP < 0 data under the synergistic relationship.
## 1 Introduction
The presence of instrumental noise regarding optics and electronics can lead to ATN values remaining unchanged or even slightly decreased when sampling at 1 s or in very low BC concentration environments, while ATN should always be increasing [1]. Thus, corrections for scattering, filer loading, sampling spot size variation, and decrease in ATN are encouraged. Even though corrections are required, many studies associated with mobile monitoring of BC and personal exposure studies often report no correction for filter loading of the microAeth(r) AE51 [8; 9; 10; 11; 12; 13; 14; 15; 16]. The purpose of this study, however, is not to develop a new correction algorithm or procedure but to show the potential users of aethalometer-type instruments the state of the instruments as it is. There is anecdotal information that noise is amplified when the microAeth(r) AE51 is plugged to an AC-DC converting adaptor. This noise amplification has been known in the community though not confirmed in academic literature. The author has struggled to find examples in the peer-reviewed literature where researchers have presented a noise issue through monitoring, and this seems unusual when compared with other aethalometer-type instruments. Whilst academic usage has been modest, public uptake of BC sensors has a potential to grow. Motivated by the above discussion, we aim to investigate the errors regarding microAeth(r) AE51 by assessing its ability to reduce bias caused by the alteration of power source during a measurement.
# Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset
## 2 Materials and Methods  
### Dataset Description and Labeling  
The dataset used in this work is an annotated dataset that includes tightly aggregated nuclei of multiple tissues suitable for the training of machine learning-based nuclear segmentation algorithms. The dataset is publicly available and deals with sample preparation methods generally used in quantitative immunofluorescence microscopy. The dataset includes \(N=79\) fluorescence images of immuno and 4',6-Diamidino-2-phenylindole dihydrochloride (DAPI) stained samples containing a total of 7813 nuclei. More specifically, 41 images were derived from a human keratinocyte cell line (normal tissue), 10 images from one Schwann cell stroma-rich tissue cryosection (from a ganglionneuroblastoma patient), 19 images from seven neuroblastoma patients, 1 image from a [PERSON] patient and 8 images from two neuroblastoma patients. From the data description, it is noteworthy that there is extensive heterogeneity in the dataset in terms of magnification, vendor, signal-to-noise ratio, image size, and diagnosis. More information about the dataset can be found in the work of [PERSON] et al. in [23].  
The dataset contained 41 images of a normal cell's nuclei from a human keratinocyte cell line and 38 images of pathological nuclei from three different malignant pediatric tu-mors, neuroblastoma-Schwannian stroma\(-\)poor, ganglioneuroblastoma-Schwannian stroma-rich (and specifically from the Schwannian stroma-rich component of the tumor), and [PERSON]'s tumor. Neuroblastoma and ganglioneuroblastoma belong to the quite heterogeneous in terms of biologic, genetic and morphologic features group of peripheral neuroplastic tumors which evolve from immature sympathetic neuroblasts during development and constitute one of the commonest childhood extra-cranial solid tumors. Microscopically, the Schwannian stroma-poor tumors are composed of neuroblastoma cells forming groups or nests separated by delicate, often incomplete stromal septa (neuropil) without or with very limited Schwannian proliferation, while ganglioneuromas are characterized by two distinctive components: (i) a mature Schwannian stromal component with individually scattered mature and/or maturing ganglion cells and (ii) a neuroblastic component. [24]. [PERSON] tumor (nephroblastoma) is a malignant embryonal neoplasm which affects 1: 8000 children, mainly aged <10 years, and originates from nephrogenic blastemal cells and mimics the developing kidney, showing divergent patterns of differentiation [25].  
Thus, in terms of classification, the 41 images of the nuclei from the human keratinocyte cell line were labelled from an expert pathologist as \"normal\", the 10 images from the Schwann cell stroma-rich component of ganglioneuroblastomas as \"benign\" as they consisted exclusively of nuclei of the mature and maturing ganglion cells scattered in between the mature Schwann cell stroma, and the remaining 28 belonging to neuroblastoma and [PERSON]'s tumor categories, as \"malignant\".
## 5 Pattern Recognition and Quantitative Diagnosis
### Pattern Recognition of RC Valve Leakage
#### 5.1.3 Pattern Recognition Based on System CIs  
Contrast experiment I and contrast experiment II show that both response-based and excitation-based pattern recognition cannot effectively classify the failure modes of RC valve while using simple signal processing methods. The system-based diagnostic method proposed in this paper exerts the system transfer characteristic relationship between excitation and response. The indicators that can best characterize the fault characteristics in response and excitation were extracted as the system CIs to identify the fault pattern of RC valve. The K and RMS of excitation CIs and the ERF and EDRE of response CIs were selected as system CIs to classify different RC valve fault nodes. Figure 14 shows the classification effect.  
It can be seen from Figure 14 that the classification effect of using K and ERF, EDRF on fault modes is better than that of using RMS and ERF, EDRF. In Figure 14b,d, RMS and ERF, EDRF do not completely separate the different RC fault modes. Although the four types of feature points in  
Figure 14: SVM classification effect of system Cis; (**a–d**) are the classification effect of K and ERF, RMS and ERF, K and EDRF as well as RMS and EDRF as characteristic indicators, respectively.  
Figure 13: SVM classification effect of response Cis; (**a–c**) are the classification effect of ERF and EDRF, ERF and ETRF as well as ETRF and EDRF as characteristic indicators, respectively.  
Figure 14a,c are separated, the data points in Figure 14c are more concentrated. Therefore, K and EDRF have a better classification effect on faults.  
The comparative experimental analysis results proved the superiority of the diagnosis method that is based on system characteristics. The system-based diagnostic method can extract richer and more comprehensive fault information, has lower requirements on the complexity of data processing methods, and is more suitable for online fault diagnosis.