esa-sceva/satcom-chunk-collection huggingface dataset:
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 1 Introduction
The goal of contrastive learning is to improve the robustness of anomaly detection models by developing feature embeddings that optimize the difference between similar and dissimilar instances. Furthermore, to improve accuracy and adaptability, hybrid models that combine several detection methods, such as forecasting and reconstruction, have been introduced [10; 11]. Even with great advancements in this area, there are still a number of difficulties, especially when anomalies are not evenly distributed throughout a dataset. Instead of being frequent and widespread, anomalies are sparse and localized in many industrial applications. The efficacy of current models may be diminished by this non-uniformity, which may result in irregular anomaly scores and reconstruction errors. More adaptive and context-aware thresholding mechanisms must be developed because traditional thresholding techniques for anomaly detection frequently do not generalize across various operational conditions. To address these limitations, this study introduces an MSE-based feedback mechanism within an architecture that integrates both channel and temporal processing layers. Our approach leverages convolutional operations to effectively capture temporal dependencies while employing a dynamic thresholding method to enhance anomaly detection performance across different methodologies. Unlike static thresholding approaches that apply fixed anomaly detection criteria, our proposed dynamic thresholding method adjusts to variations in data distributions, reducing false positives and improving detection accuracy. Furthermore, we evaluate our method against existing techniques to assess its effectiveness in mitigating false alarms and improving anomaly detection robustness. The main contribution of this work is the introduction of an MSE-based feedback mechanism within an architecture that integrates both channel and temporal processing layers. In this design, convolution is utilized to capture temporal correlations effectively. The proposed dynamic thresholding method is not only applied within our architecture but also tested on existing methods from the literature to evaluate its effectiveness across different anomaly detection approaches. Additionally, we adapt the previously designed WaterLog dataset--originally developed for industrial control system security research--to the anomaly detection domain. The adapted dataset, WaterLog*, is now publicly available for academic use and serves as a valuable benchmark. Unlike many public datasets with frequent and densely packed anomalies, WaterLog* features sparse and isolated anomaly regions, closely reflecting real-world scenarios where anomalies are rare and localized. This makes it a more challenging and realistic benchmark for evaluating anomaly detection methods.
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 3 Design of Planning Algorithm for Cooperative Inspection
### Mission Planning Algorithm Based on ACO-GA
#### 3.2.4 Mutation Method  
Mutation is a crucial technique in GA to prevent falling into local optima, thereby enhancing the diversity of the population. Typically, this operation is to modify local genes. However, in this study, the solutions are encoded as integers, and no repetition of numbers is allowed. Hence, a form of gene exchange is chosen. For the chromosome to be mutated, two positions of data are randomly selected for exchange, as shown in Figure 5, thereby generating a new chromosome.  
In traditional GA, the probabilities of crossover and mutation are usually constant. At the beginning of iterations, the changes in chromosomes caused by crossover are evidently  
Figure 4: Detailed process of crossover.  
Figure 5: Chromosomal changes before and after mutation.  
more significant than mutation, which is more conducive to solving large-scale problems. However, as iterations progress, the solutions in the population tend to converge. The more similar the chromosomes subjected to crossover are, the less beneficial it is for the diversity of the population. Therefore, it is advisable to decrease the probability of crossover and increase the likelihood of mutation. Hence, the probabilities of both during iterations can be adjusted as follows:  
\[\left\{\begin{array}{l}P_{m}^{iter}=\frac{1}{2}P_{m}\cdot a\\ P_{e}^{iter}=\frac{1}{2}P_{e}(2-a)\end{array}\right. \tag{11}\]  
In Equation (11), \(P_{m}\) and \(P_{e}\) are the probability of crossover and mutation, respectively. In this setting, the probability of crossover and mutation will decrease and increase with the progress of iteration, respectively, which will have a more favorable impact on the search ability of chromosomes and the diversity of solutions.
## 2 Materials and Methods
### Antioxidant Capacity of Tuma Cheeses  
Analyses for antioxidant properties of cheeses were performed in duplicate on extracted samples by TEAC (Trolox equivalent antioxidant capacity) assay.  
The total antioxidant capacity (TEAC) in extracted cheese samples was measured by TEAC assay as Trolox equivalent according to a published procedure [33], as described by [PERSON] et al. [36] (with some modifications). TEAC is a decolorization assay by which samples are evaluated for their radical scavenging ability using the ABTS radical cation (ABTS*) and Trolox as standard [37]. To obtained the ABTS radical cation, equal volumes of a 14 mM ABTS aqueous solution and 4.9 mM potassium persulphate were mixed and incubated in the dark for 16 h at room temperature. To perform the assay, the ABTS radical cation solution was diluted in 5 mM phosphate buffered saline (PBS, pH 7.40) until obtaining an absorbance of 0.795 (\(\pm\)0.020) at 734 nm by the Hach DR/4000 U spectrophotometer. The mixture of 150 \(\upmu\)L of PBS with 2850 \(\upmu\)L of a diluted ABTS radical cation solution was placed in a cuvette, and its absorbance was recorded at 734 nm immediately and after incubation for 6 min at 30 \({}^{\circ}\)C. At the same way, 150 \(\upmu\)L of extracted samples was mixed with 2850 \(\upmu\)L in the same diluted solution of ABTS radical cation, and their absorbance was read at 734 nm after a 6 min incubation at 30 \({}^{\circ}\)C; using the read absorbance, the percentage decrease of the absorbance due to decolorization was calculated in comparison with the absorbance obtained with PBS. Solutions of Trolox in PBS (0-2.5 mM) were used to construct a calibration curve (R\({}^{2}\) = 0.99), with results expressed as mmol Trolox/kg DM.  
The stability and susceptibility of cheese fat to oxidation were estimated by determining in duplicate the POV (peroxide value, mEq O\({}_{2}\)/kg fat) expressing the primary lipid oxidation [38], and TBARs (thiobarbituric acid-reactive substances, \(\upmu\)g of malonylaldehyde (MDA)/kg DM) as products of secondary lipid oxidation, as described by [PERSON] et al. [39] and slightly modified by [PERSON] et al. [40].  
Briefly, for TBARs analysis, 2 g of lyophilized cheese was mixed with 8 mL of aqueous solution of phosphate buffer (pH 7) and vortexed. Then, the sample was added to 2 mL of 30% (\(v/v\)) aqueous solution of trichloroacetic acid and vortexed for 5 s and filtered with Whatman filter paper No. 1. Then, 5 mL of filtrate was added with 0.02 M thiobarbituric acid aqueous solution and placed for 20 min in a hot water bath at 90 \({}^{\circ}\)C and then refrigerated. After centrifugation (4500 RPM for 5 min), the reading of the supernatant absorbance was performed at 530 nm by the Hach DR/4000 U spectrophotometer. Solutions of 1,1,3,3-tetramethyloxyp propane at concentrations between 0.016 and 0.165 \(\upmu\)g/mL were read to construct the calibration curve (R\({}^{2}\) = 0.99).
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 4 Dynamic Characteristics Analysis of Critical Disengagement of Slant Support Clutch
### Simulation Analysis of Critical Disengaging Speed  
In the overrunning state of the slanted support clutch, friction occurs between the outer ring and wedge before it is fully disengaged. The wedge accelerates to rotate in the same direction under the force of the outer ring. With an increase in the speed, the centrifugal force of the wedge increases, and the wedge further compresses the outer ring. As the wedge gradually rises, the friction force with the inner ring gradually decreases and,finally, completely disengages from the inner ring. Therefore, by simulating and reading the moment when the contact force between the clutch wedge and inner ring is zero, it can be determined that the clutch wedge and inner ring have already disengaged. At this point, the instantaneous speed of the clutch exceeds the critical disengagement speed. Using the dynamic model in Section 3.1, with the outer ring of the clutch as the active component, the loading curve of the model is displayed in Figure 13.  
Simulation analysis was performed on 15 wedges of six clutches, and the critical disengagement speed change rates of the different wedges were calculated based on the simulation results of the original wedges. The results are summarized in Table 7.  
By comparing and analyzing the data in Tables 6 and 7, it was found that the simulated critical detachment speed of the wedge was within the range calculated theoretically, with an error of no more than 4%. For every 0.005 mm increase in the length of the middle section of the wedge, the critical detachment speed of the wedge increased by approximately 2.2%.
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 3 Test and Result Analysis
### Performance Test of Filtering Algorithm for Invalid Targets
#### 3.1.1 Car at a Standstill  
For the test, the car was parked at a distance of approx. 100 m from the obstacle. The ARS 408-21 XX millimeter-wave radar data was received using a PC and CAN-USB device, and the messages were read using can-utils. Some of the data received at a standstill is shown in Table 5.  
The unfiltered resolution data of the millimeter-wave radar includes a large number of empty targets, spurious targets and non-threatening targets. For empty targets, target IDs with X = 0 or Y = 0 are filtered out in the algorithm based on the horizontal and vertical coordinates of the target; for spurious targets, the cumulative number of detected targets C\({}_{\text{D}}\) is set to three, and the number of lost targets C\({}_{\text{L}}\) is set to five in the algorithm based on the effective life cycle of the radar; for non-threatening targets, a threshold of horizontal and vertical coordinates is set to filter them out. When the vehicle is at a standstill, a person in the test environment moves 1 m, 2 m, and 3 m from the left front to the right rear of the tractor, the vehicle and the trolley position remain unchanged, and the target information is output using the millimeter-wave radar, and then the invalid targets are filtered out using the algorithm. The results before and after processing in the stationary state are shown in Figure 7.  
Figure 5: Installation of ARS 408-21 XX millimeter-wave radar in a tractor.  
Figure 6: Experimental environment.  
The targets in the test environment were processed by the algorithm to obtain valid target information for three types of obstacles: people and trolleys, while the rest of the spurious information was effectively filtered out. After three trials, the average filtering rate of the invalid target filtering algorithm for the radar data applied in this paper was more than 87% in the stationary state.
## 2 Methods
### Route Planning Model  
#### 2.2.1 Mathematical Model  
This study employs the bidirectional A* algorithm to solve the route planning model. By improving the cost function and adding constraints to the bidirectional A* algorithm, the problem of integrating the IMO guidelines, risk, sailing time, and fuel consumption is solved, ultimately achieving the goal of obtaining the optimal route.
## 4 Remote Base Unit
### Hardware Description of the Remote Base Unit
#### 4.1.2 Satellite Network  
In the proposed system, wireless long range monitoring was desired. To achieve this, it has been chosen Ku band transmissions over a satellite network.  
Figure 6: Embedded HTTP Server of base terminal unit.  
Figure 7: Connections between the Embedded HTTP Server and the RF Module.
## 4 Results
### Phantom Experiment  
#### 4.2.1 Experimental Phantom with Point Targets  
The first experimental phantom contains point targets, a massive cyst, and a speckle background. Figure 8 presents images of the experimental phantom with point targets beamformed by various beamformers. Images obtained by adaptive weighting methods show dark artifacts beside point targets compared to CPWC, CPWC+SVD, and CPWC+NSVD. GCF and SLSC provide fewer artifacts than CF. From Figure 8i-k, these dark artifacts become more visible through the use of the NSVD (\(\beta=2\)) filter. However, in adaptive weighting images, NSVD (\(\beta=3\)) results in reduced artifacts  
Figure 6: Lateral profiles of the simulated cyst targets centered at (**a**) \((x,z)~{}=~{}(0~{}\text{mm},18~{}\text{mm})\), (**b**) \((x,z)~{}=~{}(0~{}\text{mm},42~{}\text{mm})\).  
Figure 7: (**a**) CR, (**b**) CNR, (**c**) gCNR, and (**d**) sSNR for each beamforming method in images of simulated phantom with cyst targets. The first and second column contain the results of anechoic cysts centered at (x, z) = (0 mm, 18 mm) and (x, z) = (0 mm, 42 mm), respectively.  
compared to NSVD (\(\beta=2\)). Images of GCF+NSVD (\(\beta=3\)) and SLSC+NSVD (\(\beta=3\)) show fewer dark artifacts than other adaptive weighting methods.  
Figure 9 plots the lateral profiles of two experimental point targets positioned at \(p_{1}(x,z)=(-0.4\) mm, 18 mm) and \(p_{2}(x,z)=(-0.4\) mm, 38 mm). As seen in Figure 9, CPWC+SVD and CPWC+NSVD have slightly lower sidelobes than CPWC. Experimental point targets of adaptive weighting methods show narrowed mainlobes and suppressed sidelobes compared to CPWC, CPWC+SVD, and CPWC+NSVD. Figure 10 shows the barplot of the quantitative lateral FWHM of the experimental point targets. Adaptive weighting methods can significantly improve the lateral resolution. The NSVD filter has only a slight effect on lateral FWHM and increased \(\beta\) results in decreased lateral FWHM, which is consistent with the simulation results.  
Figure 8: B-mode images of experimental phantom with point targets formed by different methods. (**a**) CPWC, (**b**) CPWC+SVD (\(\beta=2\)), (**c**) CPWC+SVD (\(\beta=3\)), (**d**) CPWC+NSVD (\(\beta=2\)), (**e**) CPWC+NSVD (\(\beta=3\)), (**f**) CF, (**g**) CCF, (**h**) SLSC, (**i**) CF+NSVD (\(\beta=2\)), (**j**) GCF+NSVD (\(\beta=2\)), (**k**) SLSC+NSVD (\(\beta=2\)), (**l**) CF+NSVD (\(\beta=3\)), (**m**) GCF+NSVD (\(\beta=3\)), (**n**) SLSC+NSVD (\(\beta=3\)).  
Figure 9: The lateral profiles of two experimental point targets positioned at (**a**) \(p_{1}(x,z)=(-0.4\) mm, 18 mm), (**b**) \(p_{2}(x,z)=(-0.4\) mm, 38 mm).
## 4 Performance of SERENA and TDMA/CA
### Simulation Setup  
We undertook the simulations using NS2, Network Simulator 2. We completely redesigned layers 1 to 3 of the simulator in order to implement the synchronization module detailed in [24], the time segmentation access method and the physical phenomena of wireless networking such as the capture effects, collisions, interferences, _etc_. In addition, we used a hierarchical routing algorithm based on the hierarchical addresses proposed by ZigBee [25]. Simulation parameters are specified in Table 6. The reception threshold denotes the minimum received signal power needed to decode the received frame. The capture threshold is the minimum ratio power between two signals allowing the receiver to capture the strongest one and lock on it. The capture threshold and the reception threshold are used to assess the quality of links, as shown in the next section.
# Retrofit of Existing Reinforced Concrete (RC) Buildings: Steel vs. RC Exoskeletons
## 2 Case Study Building
### Application of RC Exoskeleton  
Successively, the RC exoskeleton, as shown in Figure 9, was taken into account for the proposed structural reinforcement strategy and applied to the case study RC frame.  
In this case, the exoskeleton is assumed to consist of an RC frame with beams and columns characterized by a rectangular cross-section of 0.60 \(\times\) 0.60 m that are connected to the bare RC frame by a set of rigid links.  
The efficiency and validity of such a retrofit intervention can be addressed by PO analyses. In this case, the RC retrofit qualitatively produces the same effects of the steel exoskeleton, but with less marked advantages (see Figure 10).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Push-Over** & \(\mathbf{F_{max}}\) & \(\mathbf{\Gamma}\) & \(\mathbf{F^{*}_{max}}\) & \(\mathbf{\alpha_{u}}\)/\(\mathbf{\alpha_{1}}\) & \(\mathbf{S_{e}(  
In fact, it is easy to verify that the increase in the overall lateral stiffness for the composite \"bare frame + RC exoskeleton\" system is lower than in the case of the steel exoskeleton, and this should possibly be considered with additional seismic devices [35]. Most importantly, it is possible to observe that the corresponding \(C/D\) safety indexes in Table 4 are greater than the unit, and no critical situations are found in terms of seismic performance, confirming the effectiveness and adequacy of the intervention.  
Table 4 shows the results of the examined (sixteen) load combinations, provided by PO analyses, for the bare RC bare frame retrofitted with the RC exoskeleton.  
Figure 10: PO analysis results: comparison of bare RC building and existing building retrofitted by RC exoskeleton.  
Figure 9: Retrofit intervention for the bare RC building with RC exoskeleton: (**a**) plan and (**b**) axonometric views.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 5 Experimental Results
### Data Sets and Simulation Environment  
Four primary user signals including BPSK, QPSK, 64 QAM and 16 QAM are obtained from RML [32]. The signal length is 512 and the background noise is additive white Gaussian noise. In the training set, the \(SNR\) of signal samples ranges from \(-20\) dB to 20 dB with an interval of 2 dB. Each signal has 250 samples per dB, totaling 21,000 signal samples, and 21,000 noise samples. In fine-tuning data set and the test data, the \(SNR\) of signal samples ranges from \(-20\) dB to 10 dB with an interval of 2 dB. A total of 30 signal samples per dB of each signal and 1920 noise samples are generated as the fine-tuning data set. A total of 150 samples of each signal per dB are generated as the test set.  
The MATLAB software was used to obtain datasets and PyCharm was used to train the defense models. The experimental environment and computer configuration are shown in Table 3.
## 1 Introduction
Nevertheless, it has also been demonstrated in the literature, that the thermal performance of the wicking mechanism within micro/nanosized pores were improved by utilizing a highly conductive material such as sintered copper mesh integrated with carbon nanotubes, titanium base heat pipes, and oxygen-free copper, which facilitated the capillary pressure in the pores, driving the working fluid, and achieving improved thin-film evaporation [16; 17; 18]. All of the previous studies were important to highlight pore transport mechanisms and to provide directions for high-performance membrane design. However, although important, very little has been performed in the literature to demonstrate those effects experimentally in a continuous flow system [19; 20; 21; 22]. All of the previously reported solar-assisted evaporation experiments [23; 24; 25; 26; 27; 28; 29; 30] involved batch-mode setups. The batch mode setups do not involve the flow of water; therefore, we suspect that the accumulation of heat in the fluid over time is likely to occur and thus the ability to obtain a real correlation between physical membrane properties and the actual evaporation flux can be influenced. Herein, we fabricate polyvinylidene fluoride (PVDF) membranes containing variable amounts of fumed silica (FS) additive to achieve membranes for solar-assisted thin-film evaporation with different properties. Our approach had two important advantages: (1) the achievement of negligible change in thermal and solar absorbance properties across different membranes and (2) the achievement of variable pore sizes, volume porosities, and wettability. Moreover, we performed all of our membrane testing investigations in a continuous flow setup (as opposed to a batch/stagnant mode setup), which was important to eliminate heat accumulation in the bulk of the water and provide realistic correlations between the variables and the actual evaporation fluxes achievable. Thus, the mass transport properties of our fabricated membranes were the main variables in our study, and we show their direct effects on the evaporation mass flux experimentally.
## 2 Materials and Methods
### Preparation of Chiral Selectors
#### 2.1.2 Synthesis of Eremosaminyl Aglycon of Eremomycin and Eremomycin Aglycon  
Synthesis of eremosaminyl aglycon of eremomycin (EEA) and eremomycin aglycon (EAg) was performed simultaneously. For this, 3.28 g of eremomycin was dissolved in 65 mL of concentrated HCl and cooled down to 4 \({}^{\circ}\)C for four hours. Following the  
Figure 2: The synthesis and structures of eremomycin aglycons used as chiral selectors in CSPs. (CBZ), benzoyl-, _tert_-butyloxycarbonyl- (BOC) derivatives [36]: 2-phenylpropionic acid derivatives known as profens [31; 33; 37]: -hydroxy acids [33; 38]: pyrroloquinolones [39], chemotherapy drug pemetrexed [40], and others. Significantly less has been reported on the enantioseparation of -blockers using this subclass of CSPs. [PERSON] et al. [19] studied enantioseparation of propranolol, atenol, alpenolol, alkenenol, alkenenol, and pindolol (see structures in Figure 1) with Chirobiotic V and Chirobiotic V2 columns containing different amounts of bonded vancomycin. Separation of all enantiomers of studied -blockers was obtained under conditions of reversed-phase and polar organic HPLC modes. In the reversed-phase mode, the retention of -blockers increased with an increase of methanol concentration in the eluent for both of the above CSPs. Optimal separations were observed when 90% methanol with 0.5% triethylammonium acetate at pH 5 was used as an eluent; however, in this case, separation was achieved in 30 to 60 min. Under equal separation conditions, -blockers were retained for a longer period on a column with a higher concentration of vancomycin in the stationary phase, but as the amount of methanol in the eluent increased this difference in retention levelledthe eluent. The \(R\)s value improved slightly, up to 1.0, with a decrease in flow rate from 1.0 to 0.5 mL/min. Microspherical polystyrene-divinylbenzene macroporous particles with \(\mathrm{eremomycin}\)-stabilised gold nanoparticles (Eremo@GNP@PS-DVB) demonstrated improved enantioseparation of pindolol as compared with \(\mathrm{eremomycin}\) bonded silica (E-CSP) [42]. The baseline separation with - 1.5 of enantiomers was obtained in polar organic mode with 100% methanol containing 0.1% TEAA as an eluent. Probably the changes in \(\mathrm{eremomycin}\) conformation on GNP surface are favourable for \(\mathrm{eantioseparation}\)[43]. Thus, according to the literature, the separations of -blockers are the most selective in a polar organic mode of HPLC. Both \(\mathrm{vancomycin}\)in and \(\mathrm{eremomycin}\)-containing CSPs demonstrated superior \(\mathrm{eantioselectivity}\) in methanol with added ammonium acetate or \(\mathrm{triethylammonium}\) acetate (TEAA) at low (0.05-0.5%) concentrations. A dilution of methanol-based eluent with \(\mathrm{acetonitrile}\) increased the retention of -blockers. Based on this analysis of the literature 100% methanol containing 0.1% TEAA was selected as the initial eluent in this study. However, very weak or no retention was observed for -blockers in the eluent, so 50% methanol-50% acetonitrile containing 0.1% TEAA eluent was used instead.