esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems**  
**[PERSON]**  
Department of Electronic Engineering, Korea Polytechnic University, Siheung-si, Gyeonggi-do 15073, Korea; [EMAIL_ADDRESS], Tel.: +82-31-8041-0489  
Received: 9 September 2019; Accepted: 7 October 2019; Published: 11 October 2019  
**Featured Application: The current work might be appropriate for fast detection and identification of unknown signals whose time of occurrence cannot be predicted, which can arise from diverse engineering problems such as fault detection and diagnosis of dynamic systems, maneuver detection and tracking of moving objects, etc.**  
**Abstract: In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.**  
**Keywords: chi-square test statistic; declaration rule; estimation filtering; temporary uncertainty; test variable**
# Waveform Design for Space-Time Coded MIMO Systems with High Secrecy Protection +
## 2 Related Works
### Alamouti Coded PLS with AN
On the other hand, the received signal at Eve will contain serious interference terms as follows:  
\[y_{\text{e1}} = g_{1}\left(s_{1}+\beta_{11}v\right)+g_{2}\left(s_{2}+\beta_{12}v \right)+n_{\text{e1}}, \tag{5}\] \[y_{\text{e2}} = -g_{1}\left(s_{2}+\beta_{21}v\right)^{*}+g_{2}\left(s_{1}+\beta_{ 22}v\right)^{*}+n_{\text{e2}},\]  
where \(g_{1}\) and \(g_{2}\) are the channel gains from the first and second virtual antennae at Alice to Eve, respectively. In addition, \(n_{\text{e1}}\) and \(n_{\text{e2}}\) are AWGN at Eve for the first and second symbol periods, respectively.
## 2 Materials and Methods
### Immunohistochemistryistochemistry  
Healthy, human gingival and periodontitis biopsies (Table 1) were fixed overnight in a 10% neutral-buffered formalin solution before being dehydrated and embedded in paraffin. Sections of a human hepatocellular carcinoma biopsy (courtesy of Professor [PERSON], Department of Surgery, LKS Faculty of Medicine, the University of Hong Kong) were used as the positive control for TLR3. Periodontal specimens were sectioned at a thickness of 4 um and mounted onto silicon-coated slides, followed by rehydration, then were immersedin 3% hydrogen peroxide at room temperature (r.t.) for 10 min for peroxididases blocking. For antigen retrieval, the sections were immersed in a 95 \({}^{\circ}\)C sodium citrate buffer (0.05% Tween 20-10 mM sodium citrate, pH 6.0) for 10 min, then cooled to r.t., followed by blocking with 2.5% horse serum at 37 \({}^{\circ}\)C for 30 min. The specimens were then incubated with anti-human TLR-1 to -8 and TLR10, NOD1/2 or NLRP1/2 primary antibodies, independently in 3% bovine serum albumin at 4 \({}^{\circ}\)C overnight (mouse anti-human: TLR1, TLR4 and NLRP2 (Santa Cruz Biotechnology, Santa Cruz, CA, USA), TLR2 (Imgenex, San Diego, CA, USA), TLR3 (eBioscience, San Diego, CA, USA), TLR5 (Abnova, Taipei, Taiwan), TLR8 (Enzo Life Science, Farmingdale, NY, USA); rabbit anti-human: TLR6, NLRP1 and NOD2 (Thermo Fischer Scientific, Waltham, MA, USA), TLR7 (LifeSpan BioSciences, Inc., Seattle, WA, USA), NOD1 (R&D systems, Inc., Minneapolis, MN, USA), TLR10 (Novus Biologicals, Littleton, CO, USA)). The optimal concentration of each TLR or NLR antibody was decided by pilot studies, and was 1:100 for anti- TLR1, TLR4, TLR6, TLR7/8/10, NOD1/2 and NLRP1/2 (1:100 was set as the highest primary antibody concentration, including antibody-elicited negative staining after pilot experiments), and 1:200 for anti- TLR2, TLR3 and TLR5. The negative control sections were incubated with thesame dilution of IgGs from the corresponding animal not immunized against the target human antigen. After incubation with secondary antibodies (anti-mouse/anti-rabbit IgG Peroxidase, 1:1000, ImmPRESS(r) Universal Reagent, MP-7500, Vector Laboratories, Burlingame, CA, USA) at 37 \({}^{\circ}\)C for 1 h, the specimens were stained with diaminobenzzidine (DAB) for visualization and semi-quantitative analysis. Stained sections were captured, analyzed with a digital imaging system (Leica DC 300 Ver 2.0; Leica, Wetzlar, Germany) and Leica Qwin Standard V 2.6 software (Leica, Wetzlar, Germany) [26].  
Upon preliminary experiments, no TLR9 mRNA could be detected from HGK/HGF explants nor human gingival tissue (HGT) biopsies (c.f. Section 3.4); the immunohistochemistry detection of TLR9 did not follow. All immunohistochemical investigations were repeated thrice on separate occasions with different sections from the same tissue blocks.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 4 Experiments
### DTS-Net and DTS-Net-Lite  
DTS-Net is the proposed deep architecture for semantic segmentation and employs Xception as the feature extractor CNN. In the experiment, we used relatively high-resolution images to obtain a segmentation mask with a fine border and high mean intersection over union. DTS-Net-Lite is the fast, lightweight version of the proposed method and employs MobileNetV2 as the feature extractor. In the experiment, we used approximately half the image size for DTS-Net to gain more processing speed; thus, some accuracy was sacrificed.
## 4 Multi-Objective Optimization for Edible Oil Transportation Scheduling Method
### [PERSON]'s Path Planning Algorithm  
[PERSON]'s algorithm is a greedy-based pathfinding method that iteratively selects the node with the smallest current distance for expansion. It is applicable to graph structures without negative edge weights. The computational procedure of the algorithm is as follows [29]:  
1. Distance Initialization: Set the distance of the initial node A to zero, and assign all other nodes a distance of infinity. Mark all nodes as unvisited: \[d(\text{A})=0;\] (1)
2. Current Node Selection: Select an unvisited node n with the smallest tentative distance \(d(\text{n})\) and mark it as visited;
3. Distance Update: For each neighbor node m adjacent to node n, if the distance \(d(\text{m})\) from the starting node A to m via n can be shortened, update it as follows: \[d(\text{m})=d(\text{n})+w(\text{n,m})\] (2)  
where \(d(\text{n})\) represents the current shortest distance from node A to node n, \(d(\text{m})\) is the currently known shortest distance to node m, and \(w(\text{n,m})\) denotes the edge weight (distance) between nodes n and m;
4. Mark node n as processed. Repeat steps 2 and 3 until all nodes have been visited.
## 1 Introduction
### Contributions and Impacts  
The major contribution of the paper is the model to steer the receiver clock of a low-cost receiver with the precision time information from labs. By doing so, it enhances the stability of the low-cost receiver and thus the positioning accuracy will be improved. As the proposed model is based on satellites and no physical connections are required, the advantage of the model is not only limited to improving the stability of a GPS receiver but any device that requires timing calibration can be benefited from the model. The proposed model has the capability of remotely transferring the time and frequency information.  
Figure 1: Scatter plot of the position recorded by Ublox for a randomly chosen day in a static condition for single point positioning (SPP). The latitude and longitude are in meters. The red circle represents a radius of 10 m error in 2D positioning.  
In today's world of digitization and digitalization, such remote calibration capabilities are vital.  
The proposed approach specifically targets low-cost receivers equipped with lower-grade clocks. By steering these clocks based on the devised model, the performance of such receivers can experience substantial improvement. This in turn will improve vertical positioning and the accuracy of overall positioning in general. The implications of this correction extends to applications such as autonomous vehicles, where precise positioning is imperative. Moreover, the correction of the receiver's clock performance can have a direct impact on the estimation of total GNSS signal delay. As the clock bias errors are improved, the signal delay estimation is expected to have less error. This is beneficial in climate-related studies like rainfall prediction, which relies on accurate estimation of atmospheric delay [17]. This enhancement is also particularly advantageous for studies requiring numerous GPS stations. For instance, GPS-based rainfall prediction, characterized by lower spatial resolution, necessitates the installation of multiple GPS receivers at diverse locations to enhance coverage. The current limitations posed by the high cost of receivers hinder this deployment strategy. However, the proposed methodology offers a solution by enhancing the performance of low-cost receivers, facilitating their cost-effective deployment across numerous locations.
## 2 Materials and Methods  
### Data Used for Analysis  
We employed an open database from the National Institute of Advanced Industrial Science and Technology [30], where the motion data of gait are recorded for 300 healthy Japanese individuals with the ethical approval of the local review committee. Thus far, the same database has been employed in many studies, e.g., [17; 31; 32; 33]. For our analysis, we selected data from 60 individuals, comprising 30 males and 30 females randomly chosen, all above the age of 60 years. The means and standard deviations of their height and weight were 159.62 \(\pm\) 7.58 cm and 60.57 \(\pm\) 9.33 kg, respectively. They walked in a 10 m straight line in bare feet with a mean (\(\pm\)standard deviation) stride length and step width of 1.26 \(\pm\) 0.089 m and 0.15 \(\pm\) 0.027 m, respectively [17]. Each sample included two succeeding steps, and five samples starting with left heel contact were used for each individual, with 300 samples (60 people \(\times\) 5 samples) in total. As elaborated in Section 2.4, our analysis focuses on the correlation coefficient between the observed and estimated MoS values. With a hypothetical correlation coefficient of 0.5 [26] and setting \(\alpha\) and \(\beta\) values at 0.05 and 0.20, respectively, a power analysis [34] indicates a required sample size of 28.  
The database recorded the three-dimensional coordinates of various body features using an optical motion capture system at 200 Hz. The time-series coordinates were smoothed using a Butterworth low-pass filter with a cutoff frequency of 6 Hz.  
As shown in Figure 1, we selected the ten feature points for the analysis. They included the vertex of the skull, C7, T10, sacral crest, xiphisternum, right shoulder (acrominon), right elbow (lateral epicondyle), right great trochanter, right knee (femoral lateral epicondyle), and right toe (center of second and third metataralsals). The criteria of selection were as follows. First, the coordinates close to the center of body mass and toe were selected because they were used to compute the MoS. Points close to the center of body mass, i.e., the sacral crest, T10, and xiphisternum, are available in the database. Second, as the points between the center of body mass and toe, the great trochanter and knee were covered. Commercial smartwatches are usually equipped with IMUs; hence, the forearm is an easy-to-access measurement point, and we selected the elbow. Unfortunately, the records for the wrist were missing for several individuals; hence, we did not select the wrists. We also included the vertex of the skull considering that an IMU can be fixed to a cap. The C7 and shoulder were selected mainly for reference, although these points are not practical body features for fixing IMUs. For the shoulder, elbow, knee, and toe, the laterality, that is, the right or left side, did not substantially influence the final results.
## 2 Materials and Methods  
### Materials  
Steam-distilled Lemon essential oil was purchased from Green Andina (Bogota, Columbia). Natural waxes were obtained from L&F (Medellin, Columbia). Absolute ethanol (lot SHBK8928), sodium carbonate (Lot 94F-0313), sodium acetate trihydrate (Lot SLBX1773), 2,4,6-tri-(2-pyridyl)-1,3,5-triazine (lot BCBW9015), 2,2-diphenyl-1-picrylhydrazil (lot BCBQ6979), nitroblue tetrazolium (NBT) (lot BCBQ6979), and nicotinamide adenine dinucleotide (NAD) (lot BCBQ6979), were obtained from Sigma-Aldrich (St. Louis, MO, USA). Ferric chloride.6H\({}_{2}\)O (lot 176057) and dimethyl sulfoxide (lot 183521) were purchased from Fisher Chemical (Sunnyvale, CA, USA). In addition, (\(\pm\))-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox(r) (97%, lot A0405014) and gallic acid (A0267865) were acquired from Acros Organics (Morris Plains, NJ, USA). Further, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) (lot p31b064) was purchased from Alfa Aesar (Haverhill, MA, USA). Methanol (lot 1823009611), Folin-Ciocalteu phenol reagent (lot hc43368401), and tetrahydrofuran (lot dg643), were obtained from Merck (Darmstadt, Germany). 2,2'-Azino-bis(3-ethylbenzothazoline-6-sulfonic acid), 2, 2-diphenyl-1-picrylhydrazil (067k lot 1154), and diammonium salt (lot slbp9592v) were obtained from Sigma-Aldrich (St. Louis, MO, USA).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Wavy** & **Aliphatic** & **Hydroxy** & **Diesters (\%)** & **Hydrocarbon** & **Free Fatty Acids** & **Fatly** & **Alohol** & **Ref.** \\
**Emulsifier** & **Ester (\%)** & **Ester (\%)** & **Diesters (\%)** & **Diesters (\%)** & **Diesters (\%)** & **Diesters (\%)** & **Diesters (\%)** & **Diesters (\%)** \\ \hline Beewax & C\({}_{40;6}\) 35 & C\({}_{24;34}\): 12 & C\({}_{54;68}\): 17 & C\({}_{21;35}\): 14 & C\({}_{16}\)C\({}_{24;C34}\): 14 & C\({}_{24;36}\): 1 & [22; 23] \\ \hline Carnauba & C\({}_{50;}\) 38–40 & 13 & * 4-HCA: 21 & C\({}_{27;28}\): 1 & C\({}_{16}\): 5 & C\({}_{32;34}\): 12 & [24; 25] \\ \hline \multirow{4}{*}{Candleilla} & Simple & & & & nonacosane: & & & \\  &
\begin{tabular}{c} Simple \\ esters-lactones \\ 21 \\ \end{tabular} & 6–8 & 0 & 2.5 & & & & & \\  & & & & 46–46.5 & & & & \\  & & & & triacontane: & & & \\  & & & & 2.5 & & & \\ \hline \multirow{4}{*}{Ozokerite} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{[28]} \\  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \hline \end{tabular}  
* \(p\)—hydroxycinnamic aliphatic di-esters, **\(p\)—methoxycinnamic aliphatic diesters.  
\end{table}
Table 1: Chemical composition of waxy emulsifiers.
## 4 Discussion
In the future, an integrated diagnostic framework that combines acoustic sensing with complementary modalities such as vibration sensing or thermal imaging could enhance fault localization accuracy. While this study focused solely on acoustic signals, the underlying principles are not exclusive. For instance, accelerometers could provide finer measurements of low-frequency structural oscillations, while acoustic signals excel in capturing higher-frequency resonance and wave interference. Combining such data through sensor fusion techniques may allow for more robust and explainable diagnostic models. These approaches would be particularly beneficial in heterogeneous robot fleets or systems operating in unpredictable environments, where relying on a single sensing modality may prove insufficient.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Electrolysis  
The electrolysis process occurs within an electrolytic cell, composed of two electrodes, an ion-conducting electrolyte, and a separator/diaphrag. There are currently three main types of electrolysis technologies used to produce hydrogen, namely polymer electrolyte membrane water electrolysis (PEMEL), alkaline water electrolysis (AEL), and solid oxide water electrolysis (SOEL) [43]. These technologies differ in their electrolyte, ionic agent, operating conditions, and stage of development [44]. In addition, anion exchange membrane water electrolysis (AEMEL) is a developing technology, not yet scaled up to commercial applications, that presents good potential to be used as a way to produce hydrogen due to its low cost and high performance when compared with other electrolysis technologies.
## 3 Proposed Methodology
### Applications  
In MANETs, it is assumed that there are some malicious nodes present within the network. Attacker (malicious) nodes seek to steal information from nodes and disturb the network. In some hostile environments, like communication between military troops, the information is very confidential and important in estrangement situations. Stealing or fabricating this kind of information is very sensitive because it directly involves human life and safety. An attacker could hack the node that holds important information, aiming to sniff the communication of the opposite side and steal important information. An attacker node always takes advantage of this open medium communication to hear all of the information.
# Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network
###### Abstract
The low-level feature map of the inpainting network exhibits a high degree of similarity to the ground truth, as depicted in Figure 1b,c. Conversely, the high-level feature map in Figure 1e appears to be deficient in crucial inpainting traces, necessitating the application of an effective module to optimize it. Initially, we attempt to optimize the high-level  
Figure 1: An example of applying AFPN. features through the utilization of channel attention (CA). However, it was observed that despite the increased visibility of the previous inpainting traces, a significant number of inpainting traces remained undetected, as depicted in Figure 1f. Subsequently, considering the peculiarity of the inpainted image, MSCA was employed to extract multi-scale contextual information ranging from local to global information in the feature map. The results in Figure 1g indicate that MSCA is an effective module for enhancing the existing inpainting traces and detecting previously indiscernible inpainting traces. Additionally, we also attempt to employ SA to handle low-level features, yet it is observed that the manipulation adds noise to the low-level features, as depicted in Figure 1d. Following different operations, the high- and low-level features exhibit a complementary relationship, and fusing them by direct addition can result in an inpainting trace-detection map that closely approximates the ground truth, as depicted in Figure 1b,h. In summary, AFPN effectively achieves a high detection accuracy by appropriately processing the low- and high-level features. Our major contributions can be summarized as follows:  
1. We use a forensic network to detect traces left by deep learning-based inpainting methods. The network employs a feature pyramid to extract multi-scale inpainting features. To fully utilize multi-scale feature information, we employ MSCA to optimize high-level features and fuse the optimized high- and low-level features for inpainting forensics. The efficacy of the attention module and feature fusion module is verified through ablation analysis. 2. We design a fusion loss function to assess the quality of not only the fused feature maps but also the high-level feature maps. Experimental results demonstrate that the fused loss function can optimize the training process and enhance the performance of our network. 3. To indicate the generalization performance of our network, we employ six state-of-the-art deep learning-based image inpainting methods to set up a diverse inpainting test dataset. Extensive experiments show that the employed AFPN can achieve good detection performance across diverse inpainting test datasets. Furthermore, we assess the robustness of the proposed methods on JPEG compression and additive noise attacks. The rest of this paper is organized as follows. Section 2 summarizes the related work on inpainting forensics methods and attention mechanisms. Section 3 presents our network. The experimental results are presented in Section 4, while Section 5 concludes the paper.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Decoding  
Using GSJE and BSJE, we can successfully estimate the jamming with a noise vector \(\widetilde{\mathbf{e}}\). Then, Bob can obtain the jamming-mitigated complex-valued codeword \(\widetilde{\mathbf{x}}\) by subtracting the estimation of \(\widetilde{\mathbf{e}}\) from \(\mathbf{y}\). The corresponding real-valued codeword \(\widetilde{\mathbf{c}}\) is obtained by cascading the real and imaginary parts of \(\widetilde{\mathbf{x}}\). Decoding the original codeword \(\mathbf{m}\) from \(\widetilde{\mathbf{c}}\) is typically performed using MAP estimation, which is a procedure to obtain the most likely input for a given output, i.e.,  
\[\hat{\mathbf{m}}=\underset{\mathbf{m}}{\text{argmax}}\ p(\mathbf{m};\widetilde{ \mathbf{c}}). \tag{22}\]  
Assuming a message \(\mathbf{m}\) has a uniform distribution, the MAP estimator is equivalent to the ML estimator [33]. The ML estimator of \(\mathbf{m}\) is defined by \(\hat{\mathbf{m}}\), which maximizes the likelihood function. This relationship is represented as follows:  
\[\hat{\mathbf{m}}=\underset{\mathbf{m}}{\text{argmax}}\ p(\hat{\mathbf{c}}^{ \prime};\mathbf{m}). \tag{23}\]