esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of FBG Categories  
The results of the RIA and RIS evaluations supported that there was no statistically relevant correlation between attenuation or Bragg wavelength and integrated dose or dose rate. As such, it may further be concluded that there was no statistically significant difference in the behavior of the three FBG categories: UV, FS and RAD. Previously published research, including the work by [PERSON] et al., indicated that the Ge-dopant found in the UV and FS specimens would react when exposed to gamma radiation in a manner that increases the photosensitivity of the fiber [18]. This reaction would particularly impact the UV gratings as the UV inscription process relies on the photosensitivity of the fiber. As such, it would be expected that with a high enough dose, the UV specimens would see RIS. In conclusion, there is no discernable difference in RIS between the three grating categories as seen in Figure 5. This would suggest that either the accumulated doses were not high enough to cause a  
Figure 5: Radiation Induced Wavelength Shift as observed for test specimens exposed to proton radiation in tests Set 3–7. Left-side y axis lists Radiation Induced Shift (RIS) measured in nanometers as measured by the OGA. The approximate temperature shift that would be equivalent to the observed RIS if the FBGs were reacting only to a change in temperature is approximately 1 \({}^{\circ}\)C for a wavelength shift of 0.01 nm. The dotted lines denote the OGA measuring uncertainty.  
noticeable RIS or the expected RIS from gamma irradiation does not correlate with the observed RIS from proton or neutron irradiation.
## 6 Recent Studies on CRSN Routing
### Joint Route and Spectrum Brokering with PU Awareness  
Basically, routes in any CR network must provide a measure of protection for the ongoing communication of the PUs. This protection can be implemented either by avoiding the regions known to have high PU activity entirely or by jointly allocating transmission power to incur a greater number of hops and to minimize the probability of interfering with the primary receivers. Protection can also be implemented using underlay or overlay cognitive radio technologies.  
In an underlay system, secondary users are allowed to share the channel simultaneously with primary users. Underlay systems protect primary users by enforcing a transmission power constraint on the secondary signals so that the interference generated by the secondary devices is below an acceptable noise floor for the primary users of the spectrum. The interference power constraints associated with underlay systems allow only short-range communication, which can be suitable for CRSNs. Overlay systems, on the other hand, allow concurrent transmissions of primary and secondary users with the condition that the secondary users can use part of their power for secondary communication and the remainder of the power to assist (relay) primary transmissions. Thus, a careful choice of the power split can precisely offset the decrease in the primary user's SNR due to the interference caused by the secondary transmission with the increase in a primary user's SNR due to the assistance from secondary relaying [35, 36].  
Thus, it can be concluded that underlay CR can only be used when there is a strong primary signal, whereas overlay CR is more appropriate for weak primary signals.  
In line with this approach, distributed best-route selection for multipath routing (DBMR) [37] investigates multipath routing selection in performance optimization under energy-constrained CRAHNs via distributed and heuristic routes selected by secondary users (SUs) with the aim to improve end-to-end delay while taking energy into consideration. Multipath routing is modeled as a restless bandit stochastic process optimization problem that allows secondary users to select routes considering the dynamic occupancy of a licensed spectrum and energy based on a finite-state Markov chain (FSMC) model. To protect the PU transmission, the SU's transmission power in each hop varies with the PU's occupancy in each channel. Again, as noted in SER [33], the energy required for frequent spectrum sensing in addition to the increased energy demand of maintaining multipath capability still remains a critical issue.  
Other related works in this regard include [38], which only incorporates the channel selection metric into the cluster head and does not introduce a routing strategy. [39, 40] propose a routing method for cognitive sensor networks based on a high capacity cluster head or gateways, which is inconsistent with ideal scenarios. In contrast, [41] details the process of collecting relevant routing parameters, such as estimates of neighboring PUs and SUs and channel usage statistics, without providing a clear routing framework.
## 3 Optimal Dispatching Model of MGs and ESP
### Basic Optimal Dispatching Model of MGs and ESP  
During optimal scheduling of multi-microgrids in coalition, we assume that all MGs agree to cooperate with one another in order to minimize the total operation cost including purchasing electricity cost, energy storage cost and the cost caused by power loss. MGs and ESP make optimal scheduling strategies with the consideration of purchasing electricity cost, the discharging cost of BESS and the power loss cost. Consider a coalition consisting of \(N\) MGs through a communication network and a power interconnection infrastructure; the optimal dispatching problem can be described as [38; 39]:  
\[min\qquad\sum_{n=1}^{N}C_{n}(\mathbf{x}_{n})+g(\mathbf{w}) \tag{14}\]  
\[s.t.\qquad\sum_{n=1}^{N}(x_{gridin}+x_{BESSn}+P_{uc_{n}}-P_{lossn})=\sum_{n=1 }^{N}d_{n} \tag{15}\]  
where \(C_{n}\) is the cost function of in MG \(n\); \(g(\mathbf{w})\) is the power loss cost, which is the optimal objective function of ESP; \(P_{lossn}\) is the power loss borne by MG \(n\); \(\mathbf{w}\) is the vector composed of tie-line power \(P_{nm}\), which is transferred from MG \(n\) to MG \(m\) and tie-line power \(P_{n0}\) of transferring power from MG \(n\) to the main grid.
## 4 Results
### Remote Sensing-Derived Forest Carbon Sinks Estimates in the Qinba Mountain Region
3.3 Relationship Between Remote Sensing Inversion Results of Forest Carbon Sinks and Elevation in the Qinba Mountain Area  
Based on the vertical zonal distribution characteristics of vegetation on the southern slope of Mount Taibai in the Qinling Mountains [47], the DEM of the study area was reclassified into nine elevation groups: \(<\)750 m, 750-1000 m, 1000-1500 m, 1500-2350 m, 2350-2650 m, 2650-3000 m, 3000-3300 m, 3300-3700 m, and \(>\)3700 m. The grid data for these nine groups were then intersected with the grid data from the remote sensing inversion results of forest carbon sinks in the Qinba Mountain area to obtain the forest carbon source/sink results for each elevation group. Figure 11 shows the average remote sensing inversion results of forest carbon sinks in the Qinba Mountain area for the different elevation groups. It can be observed that as the elevation increases, the forest carbon sinks values also tend to increase. By summarizing the forest carbon sinks grids within each elevation group and dividing the number of forest carbon sinks grids by the total number of grids in that group, we can calculate the ratio of forest carbon sinks grids to the total number of grids within the same elevation group, as shown in Figure 12.  
Figure 11: Average forest carbon sinks values across different elevation ranges. Note: The blue dots represent the average carbon sinks values across different altitude groups. Figure 12 shows that as elevation increases, the proportion of forest carbon sinks also rises. This indicates that at higher elevations, forests predominantly act as carbon sinks, while the proportion of forests functioning as carbon sources decreases with increasing elevation. Additionally, this study has analyzed the changes in source/sink status for the same forest grids at different elevations from 2011 to 2018. It calculated the number of forest grids whose source/sink status either remained stable or changed only once during this period for each elevation group. By dividing these numbers by the total number of grids in each group, the proportion of stable source/sink grids was derived, as shown in Figure 13.  
According to the results shown in Figure 13, it can be observed that as elevation increases, the stability of forest grids in the Qinba Mountain area also improves, with a decrease in the frequency of transitions between carbon sinks and carbon sources. In contrast, at lower elevations, forest grids are more prone to switching between carbon sinks and carbon sources, indicating lower stability. Figure 12: Distribution of forest carbon sinks across different elevation ranges. Note: The blue dots represent the ratio of carbon sinks grids to total grids within different elevation groups. Figure 13: Distribution of stable carbon source/sink grids across different elevation ranges. Note: The blue dots represent the ratio of grids that are consistently carbon sources or carbon sinks to the total number of grids at different elevation levels.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results
### Per-IoU AP Analysis  
To gain deeper insight into the localization capability of different fusion strategies under varying spatial precision requirements, we conducted a per-IoU AP evaluation on both the FLIR and M\({}^{3}\)FD datasets. Following the COCO-style evaluation protocol, we  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Variant** & **GFLOPs** & **Parameters (M)** \\ \hline AEFF only (M4) & 216.794 & 120.762 \\ C3-PSA only (M5) & 203.738 & 108.778 \\ Full model (M6) & 227.896 & 120.979 \\ ICAFusion (SOTA) & 192.636 & 120.3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of model complexity (FLOPs and parameters).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **M1 (Vis)** & **M2 (Inf)** & **M3** & **M4** & **M5** & **M6** \\ \hline AEFF & ✗ & ✗ & ✗ & ✓ & ✗ & ✓ \\ C3-PSA & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ \\ \hline Precision & - & - & 87.8 & 86.8 & 88.7 & 89.1 \\ Recall & - & - & 73.4 & 79.5 & 77.7 & 76.8 \\ mAP0.5 & 81.7 & 79.8 & 80.3 & 84.4 & 83.5 & 84.2 \\ mAP0.5:0.95 & 50.1 & 49.2 & 48.9 & 51.6 & 51.3 & 52.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation findings on the M\({}^{3}\)FD dataset. ✓ indicates the component is enabled; ✗ indicates it is disabled.  
report the Average Precision (AP) scores at discrete IoU thresholds ranging from 0.50 to 0.95.  
As shown in Figure 7, YOLO-MEDet consistently outperformed the baseline fusion method across intermediate IoU thresholds (0.75-0.85), highlighting its enhanced localization precision under moderately strict conditions. Although ICAFusion slightly exceeded YOLO-MEDet at the highest threshold levels (IoU \(\geq\) 0.90), the overall trend indicates that YOLO-MEDet maintains more stable and robust localization across the entire IoU range. This helps explain the observed 2.9% decrease in the overall [EMAIL_ADDRESS]:0.95, which is largely attributable to performance at stricter thresholds rather than a uniform decline.  
A similar pattern is observed on the M\({}^{3}\)FD dataset, as presented in Figure 8. YOLO-MEDet consistently surpassed the baseline across all IoU thresholds, with particularly strong advantages in the 0.60-0.85 range. While the performance outcomes of YOLO-MEDet and ICAFusion converged at higher thresholds, the proposed model demonstrates a more balanced and resilient performance under varying spatial overlap constraints.  
These findings confirm that YOLO-MEDet achieves reliable localization across a spectrum of IoU conditions, with notable strengths in medium to strict overlap scenarios.  
Figure 8: Comparison of per-IoU AP scores on the M\({}^{3}\)FD dataset.  
Figure 7: Comparison of per-IoU AP scores on the FLIR dataset for YOLO-MEDet, a baseline additive fusion model, and the state-of-the-art ICAFusion.  
This contributes to its practical applicability, even in cases where the overall [EMAIL_ADDRESS]:0.95 appears marginally reduced.
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 3 Modeling of HES Components
### The ESU  
The charging and discharging process is controlled by estimating the SOC of batteries. The model presented in [34] is used to calculate the SOC of ESU, i.e.,  
\[SOC(t+1)\ = SOC(t)+\eta_{Bat}\bigg{(}\frac{P_{bat}(t)}{V_{bus}}\bigg{)}\times\Delta t \tag{4}\]  
where \(\eta_{Bat}\) refers to battery efficiency and is assumed 100% for both the charging and discharging process. \(V_{bus}\) denotes the voltage of a DC bus (480 V), and \(\Delta t\) is the time step set equal to 1 h. The \(P_{bat}\) represents the charging and discharging power of battery according to its required (charging) and available (discharging) power, respectively. The power required by ESU (\(P\_ESU\_Req\)) is the minimum amount of power that, if applied  
Figure 5: PV single diode circuit model.  
Figure 6: Stateflow chart implementation of the P&O algorithm.  
continuously, increases the state of charge of ESU from the initial to the upper limit (i.e., \(SOC_{U}\)) in the time-step \(\Delta t\). The \(P\_ESU\_Req\) is given as  
\[P\_ESU\_Req(t) =\frac{(SOC_{U}-SOC(t))\times C_{bat}\times N_{bat}}{\Delta t} \tag{5}\]  
Likewise, the power available with ESU (P_ESU) is the maximum amount of power that ESU can deliver continuously before reaching the lower limit (i.e., \(SOC_{L}\)) in one time-step. The \(P\_ESU\) is given as  
\[P\_ESU(t) =\frac{(SOC(t)-SOC_{L})\times C_{bat}\times N_{bat}}{\Delta t} \tag{6}\]  
Here, \(C_{bat}\) is the nominal battery capacity for a single unit in kWh. Whereas, \(N_{bat}\) represents the total number of batteries in the ESU. To ensure batteries durability, their SOC should be confined within \(SOC_{U}\) and \(SOC_{L}\). The \(SOC_{U}\) and \(SOC_{L}\) are set as 90% and 10%, respectively [35]. It means that the energy is stored in the ESU up to 90% of total rated capacity (\(SOC_{U}\)) to avoid overcharging. Similarly, ESU stops discharging when the SOC level reaches 10% of its rated capacity (\(SOC_{L}\)) to avoid over-discharging.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 3 Proposed Scheme
### Initialization Process  
In this procedure, the trusted authority (\(TA\)) chooses a publicly available one-way hash function \(\{H(.)\}\) and \(MSK\in\mathcal{Z}_{p}\) a private master key.
## 1 Introduction
proposed the principle of framewise tampering blind detection [12], which is a previous work presented at a conference by the first author of this paper. However, the method is so simple that complete reversibility is not guaranteed. Therefore, this paper extends this method to be entirely reversible. In addition, we investigated the most inaudible way of embedding the information. This paper proposes an entirely reversible audio information-hiding method based on intDCT in a framework to achieve blind tampering detection imperceptibly with high capacity. Since the proposed method enables blind detection, tampering detection is achieved without sharing the original data in advance. Figure 1 illustrates a flow diagram, or block diagram, to present the insertion and extraction algorithms. Integrity and reliability can be verified without the original data. On the verification side, the information for verifying the reconstructed original data is compared to the extracted information for verifying the original data. This makes blind verification possible. The novelty of the proposed paper is to propose a reversible audio information-hiding method based on intDCT in framewise to achieve blind tampering detection imperceptibly with high capacity. A location map is proposed to explore and record the appropriate locations for embedding, which can be extended and applied to hiding location optimization as further work. Location maps can determine whether or not to embed data into segments that may overflow after expansion. This is superior to the conventional works [12; 20], which may have data loss when overflow occurs since the data is discarded if overflow, which harms reversibility. Blind tampering detection is also achieved without sharing original data in advance, since the payload for verification can be calculated from the reconstructed original data. This paper is organized as follows. The approaches taken here and those of previous studies are discussed in Sections 1 and 2. Section 3 describes the conventional information-hiding method and its problems and proposes the proposed method in detail. Section 4 summarizes the experimental evaluation results. We discuss the results in Section 5 and conclude the paper in Section 6.
# A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems
###### Abstract  
The precoding scheme based on codebooks is used to save the same set of codebook in advance at the transmitter and the receiver, then, the receiver selects the most appropriate precoding matrix from codebooks according to different channel state information (CSI). Therefore, the design of codebook plays an important role in the performance of the whole scheme. The symmetry-based hybrid precoder and combiner is a highly energy efficient structure in the millimeter-wave massive multiple-input multiple-output (MIMO) system, but at the same time, it also has the problems of high bit error rate and low spectral efficiency. In order to improve the spectral efficiency, we formulate the codebook design as a joint optimization problem and propose an iteration algorithm to obtain the enhanced codebook by combining the compressive sampling matching pursuit (CoSaMP) algorithm with the dictionary learning algorithm. In order to prove the validity of the proposed algorithm, we simulate and analyze the change of the spectral efficiency of the algorithm with the signal-to-noise ratio (SNR) and the number of radio frequency (RF) chains of different precoding schemes. The simulation results demonstrate that the spectral efficiency of the algorithm is obviously outstanding compared with that of the OMP-based joint codebook algorithm and the hybrid precoding algorithm with quantization algorithm under low SNR and different numbers of RF chains. Particularly, when SNR is lower than 0 dB, the proposed algorithm performs very close to the optimal unconstrained precoding algorithm.  
massive MIMO; millimeter wave; hybrid precoding; codebook +
Footnote †: journal:  
Article
_Applied_  
_sciences_  
_Article_  
**Chemical Analysis of Fluorobenzenes via**  
**Multinuclear Detection in the Strong Heteronuclear**  
_J-Coupling Regime_  
**[PERSON] 1,*\({}^{*}\)\({}^{\Box}\), [PERSON] 2, [PERSON] 1, [PERSON] 3, [PERSON] 3, [PERSON] 2\({}^{\Box}\), [PERSON] 4, [PERSON] 5 and [PERSON] 1  
Bioenergy and Biome Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; [EMAIL_ADDRESS] (R.K.F.); [EMAIL_ADDRESS] (R.F.W)  
Inorganic, Isotope, and Actinic Chemistry Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA;[EMAIL_ADDRESS] (M.T.J.); [EMAIL_ADDRESS] (R.J.B.)  
Physics and Chemistry of Materials Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; [EMAIL_ADDRESS] (T.N.); [PERSON]ADDRESS] (G.A.-T.)  
Quantum Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; [EMAIL_ADDRESS]  
Non-Destructive Testing and Evaluation Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
Received: 29 April 2020; Accepted: 29 May 2020; Published: 31 May 2020  
**Featured Application: Herein, we extend the application of low field nuclear magnetic resonance in the strong heteronuclear J-coupling regime to a series of fluorobenzene compounds. The results indicate that this spectral regime may be used for detailed spectroscopic analysis of small molecules.**  
Chemical analysis via nuclear magnetic resonance (NMR) spectroscopy using permanent magnets, rather than superconducting magnets, is a rapidly developing field. Performing the NMR measurement in the strong heteronuclear J-coupling regime has shown considerable promise for the chemical analysis of small molecules. Typically, the condition for the strong heteronuclear J-coupling regime is satisfied at \(\mu\)T magnetic field strengths and enables high resolution _J_-coupled spectra (JCS) to be acquired. However, the JCS response to systematic chemical structural changes has largely not been investigated. In this report, we investigate the JCS of C\({}_{6}\)H\({}_{6-x}\)F\({}_{x}\) (\(x=0,1,2,\ldots,6\)) fluorobenzene compounds via simultaneous excitation and detection of \({}^{19}\)F and \({}^{1}\)H at 51.5 \(\mu\)T. The results demonstrate that JCS are quantitative, and the common NMR observables, including Larmor frequency, heteronuclear and homonuclear J-couplings, relative signs of the J-coupling, chemical shift, and relaxation, are all measurable and are differentiable between molecules at low magnetic fields. The results, corroborated by _ab initio_ calculations, provide new insights into the impact of chemical structure and their corresponding spin systems on JCS. In several instances, the JCS provided more chemical information than traditional high field NMR, demonstrating that JCS can be used for robust chemical analysis.  
low field NMR; J-coupled spectroscopy; strong-heteronuclear J-coupling regime; fluorobenzene; Pople notation; strong coupling
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 3 Experimental Evaluation
### Data Set  
In this paper, the real aircraft track data is used to verify the performance of the proposed method. The flight data of CCA1315, CCA1369, CCA1883 and CCA1803 within a month are extracted in this paper, and each datum includes the information of flight time, height, speed, heading, longitude and latitude. However, the distribution of positive and negative samples is unbalanced in the data set. In this paper, the data set is randomly divided into a training set and test set. The combination of an undersampling method and oversampling method is used to balance the category distribution of training data. The Synthetic Minority Oversampling Technique (SMOTE) is a kind of oversampling method. Its main idea is to form new minority samples by interpolating between several minority samples. However, SMOTE will produce noise samples during the interpolation process. This problem can be solved by using the Edited Nearest Neighbor (ENN) method to clean up the interpolation results. Any sample that is different from its k nearest neighbor categories will be removed, thus generating a class-balanced training set.