esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results: Numerical Analysis
### Numerical Simulation Results for Bilinear Spring Design  
To simulate the design of Figures 6a and 7, the following engine mount parameters, see Table 1, were used for the MATLAB simulations. Figure 9: Comparison of load–deflection plot of the bilinear spring vs. the cubic spring. To see the difference in dynamic stiffness between an engine mount with a linear spring (\(F_{\text{spring}}=Kx\)) versus a bilinear spring, a comparison was made using MATLAB R2023a. Figures 10 and 11 show a comparison of the dynamic stiffness of the hydraulic engine mount design in Figure 6a with a linear spring (\(F_{\text{spring}}=Kx\)) versus a bilinear spring in Figure 9 at different vibration amplitudes. From Figure 11, we can clearly see that at vibration amplitudes of \(\pm 0.1\) mm, the notch frequency is the same as the notch frequency of the hydraulic engine mount with a linear spring, but the peak frequency has been considerably attenuated (See Figure 10) by simply using a bilinear spring with no added damping. Elimination of the peak frequency is extremely desirable. The green color graph in Figure 10, near 35 Hz, may appear to show a sudden change in slopes. If one zooms to 35 Hz (see a smaller graph of Figure 10), the slope of the green-colored graph is continuous and not abrupt. It is important to mention that the frequency increments (\(\Delta\)f) chosen for the simulation can also make some of the graphs appear to have a sudden change in slopes. Since in this paper, we were dealing with nonlinear simulations and the run times were high, efforts were made to avoid using very small frequency increments (\(\Delta\)f). Because of \(\Delta\)f, some of the plots may appear to show sudden changes in slopes, but this is not the case. Figure 11 is zoomed to the notch frequency. If the engine is operating near the notch frequency (at 32 Hz), if the engine vibration amplitude is suddenly increased from \(\pm 0.1\) mm to \(\pm 0.4\) mm due to shock, the dynamic stiffness increases from 66,795 N/m to 192,851 N/m by a factor of 2.88 times without using any decoupler. Figure 11 clearly shows that at the notch frequency, when
###### Abstract  
Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis.  
Keywords:signal processing; condition monitoring; rolling bearing; linear predictive coding; neural networks +
Footnote †: journal: _Algorithms_
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.2.1 Contrastive Denoising with 95% High-Density Noise  
To visually illustrate the contrastive denoising effects, salt and pepper noise at a density of 95% was artificially added to exhibit the contrastive denoising effects. This was added using the code noisy_img = imnoise(img,'salt & pepper', 0.95) in MATLAB R2016b. As illustrated in Figures 3-5, the denoising of the Lena, Cameraman, and COVID images was conducted using the PDBTMF, MDBMF, and ADTMF algorithms and the results compared with the proposed improved TSETMF algorithm with 95% high-density noise. Figures 3a, 4a and 5a present the original Lena, Cameraman, and COVID images without noise, respectively. Figures 3b, 4b and 5b present the same images with 95% density noise, respectively. These were the input images for the denoising experiments using the various algorithms. Figure 3c-e, Figures 4c-e and 5c-e present the denoising effects of the PDBTMF, MDBMF, and ADTMF algorithms on the images, respectively. Figures 3f, 4f and 5f present the denoising effects of the proposed improved TSETMF algorithm on the three images, respectively. It should be noted that the PDBTMF algorithm removes noise by comparing the numerical relationships between the current pixel and the neighboring pixels [64]. The MDBMF algorithm is a non-linear salt and pepper filter able to maintain the signal edges [65].  
Figure 3: Contrastive denoising effects of the different algorithms for the Lena image. (**a**) Original image. (**b**) Salt and pepper noise with 95% density. (**c**) PDBTMF. (**d**) MDBMF. (**e**) ADTMF. (**f**) Proposed TSETMF algorithm.  
Figures 3-5 demonstrate that the PDBTMF, MDBMF, and ADTMF algorithms individually present a dramatic decline in denoising effects. For instance, the processing of the Lena image with 95% high-density salt and pepper noise displayed relatively distinct differences in the effects. Figure 3e shows the poor denoising effect achieved using ADTMF; however, the filter properties of PDBTMF with a significant amount of noise shown in Figure 3c are superior to ADTMF. Although the filter properties of MDBMF provide better  
Figure 4: Contrastive denoising effects of different algorithms for the Cameraman image. (**a**) Original image. (**b**) Salt and pepper noise with 95% density. (**c**) PDBTMF. (**d**) MDBMF. (**e**) ADTMF. (**f**) Proposed TSETMF algorithm.  
Figure 5: Contrastive denoising effects of different algorithms for the COVID image. (**a**) Original image. (**b**) Salt and pepper noise with 95% density. (**c**) PDBTMF. (**d**) MDBMF. (**e**) ADTMF. (**f**) Proposed TSETMF algorithm.  
denoising effects, some noise still exists, as shown in Figure 3d. By comparison, the proposed improved TSETMF algorithm exhibits little observable noise and a high degree of image smoothing and the denoising effects of this algorithm are superior to the other three. In general, the proposed improved TSETMF algorithm exhibits a relatively good filtering effect under high-density salt and pepper noise.
## 4 Case Study
### Data Preparation  
#### 4.2.1 Road Network Topology Data  
This study utilizes OpenStreetMap (OSM) data to establish a topological model of the road network in the research area. To simplify the modeling and analysis of the sparse road network, highways, national roads, provincial roads, and county roads were selected (Figure 7). The modeling elements include geographic information of road segments and nodes, as well as static traffic data of the road segments.
## 5 Discussion  
### Sedimentary Environment Analysis  
Based on the comprehensive analysis of rock cores, sedimentary structures, grain size characteristics, and seismic data, the overall sedimentary environment in the study area is a composite sedimentary system of shore-shallow lake delta fronts. The overall color of the rock core is relatively dark, and the well-preserved plant debris in the mudstone layer (Figure 2d) indicates that sedimentation occurred in an underwater hypoxic environment, transitioning from mainly mudstone sedimentation in the lower section to sandy sedimentation in the upper section, reflecting the overall environmental evolution of the study area's water body to become lighter. Layered fragmented carbon debris (Figure 3e) and highly rounded mud gravel (Figure 3f) indicate that sediments have undergone long-distance transport and strong wave or coastal current alteration. The presence of massive bedding, parallel bedding, and low-angle cross-bedding, combined with the bidirectional current flow characteristics of local water movement and dual saltation of grain sizes reflecting scouring and backflow features, further supports a lacustrine shoreface wave-dominated sedimentary environment [20]. In the phase sequence combination analysis, it is shown that the circular mud gravel commonly distributed at the bottom of the sand body (S29, S39, S212 wells) is a stagnant sediment at the bottom of the river channel, reflecting the scouring effect of water flow on the underlying mudstone layer. The weak reverse rotation of the S39 well sand body also conforms to the sedimentary characteristics of sand bars in the shore-shallow lake facies.  
The multiple sets of medium-thick sand bodies developed in the X1 and X2 wells in the eastern part of the study area have the characteristics of good vertical continuity and matched lithological combinations. Seismic slices show the reflection characteristics of the north-south branching river channels in the study area (Figure 9), and the logging curves are box-shaped, consistent with the sedimentary characteristics of the underwater distributary river sand bodies [21; 22; 23; 24]. The continuous and stable reflection characteristics of earthquake amplitudes in the western and central-eastern regions are highly consistent with the spatial distribution of medium-thick sand bodies, suggesting that ancient rivers formed a bifurcated river system by flowing into the lake from north to south. Based on the comprehensive characteristics of lithology, structure, and seismic response, the sedimentary evolution of the study area can be divided into two stages: in the early stage, shallow lake sedimentation was the main form, with shallow lake mudstone mainly developed; in the later stage, the uplift of the lake basin edge or the advancement of material sources led to the shallowing of the water body, resulting in the development of strong hydrodynamic transformation of sandy sediments and presenting an overall composite sedimentary system of shore-shallow lake delta fronts, which is jointly controlled by river input and wave action.
## 4 Results  
The comparisons and analyses of the satellite and GPS IWV datasets are performed in several ways. First, direct comparisons of the matched and valid satellite-GPS IWV data pairs are shown. However, in many if not most studies IWV climatologies are used, often in the form of monthly means. Therefore, also comparisons of satellite and GPS IWV monthly means are presented. Last, the impact of temporal, spatial and retrieval-specific sampling on the various IWV datasets is investigated to assess and quantify the effect of various sampling issues on resulting aggregated IWV as well as IWV frequency distributions.
## 4 Overview of GNSSs
### Glonass
#### 4.3.3 GLONASS Ground Segment  
The GLONASS ground segment's main components include the GLONASS system control center (SCC) and central clocks (CC), telemetry, tracking and command stations (TT and C), and uplink stations, as well as unidirectional monitoring stations and satellite laser ranging (SLR) stations. All major ground segment assets are located on Russian territory [84].  
Plans for the development of the ground control segment (GCS) before 2020 include all core elements of the GCS to improve its performance, including upgrading passive measurement and computing stations, the main master clock, inserting measurement stations and expanding the network of laser measurement stations. The upgraded GCS will additionally include the following [85]:  
* Onboard Inienatellite Measurement Equipment (OIME) ground control loop that provides orbit and clock data input to the navigation satellite;
* A network of passive measurement stations (PMS) for operational bits and clock data as part of the GCS to improve accuracy and integrity.
## 2 Literature Review
### Collaboration in IVR  
A highly immersive, interactive, and authentic virtual space is created by IVR technology, which completely separates users from the physical world using devices such as HMDs or mobile viewers (e.g., Google Cardboard) [44]. IVR may benefit collaboration by enabling higher levels of representational fidelity and embodiment, (non-)verbal communication, and avatar realism [45]. Several studies have explored collaboration effectiveness in an IVR environment; the results indicate that IVR can support communication, understanding, and teamwork for medical training and education [46] and that it can be used to effectively assess social communication skills [47]. Other benefits include enhanced creativity compared with conventional tools and systems [48], empowered empathetic behavior [49], and improved knowledge acquisition [50].  
According to the computer-supported cooperative work time-space matrix, most existing collaboration types in IVR environments are categorized as asynchronous co-located, synchronous distributed, and asynchronous distributed collaboration; there has been relatively less research conducted on synchronous co-located collaboration [13]. In the first three collaboration types, individuals typically use different devices and engage with the virtual content at different times [11; 51]. These patterns of IVR collaboration present unequal perspectives that can lead to communication difficulties and poor collective efficacy, especially when different devices are used. Contrarily, synchronous co-located collaboration enables the use of the same devices and equal interaction among peers, which is essential for enhancing presence and facilitating seamless communication [11; 52]. For example, [PERSON] et al. [53] developed a co-located multiplayer game and found that co-located collaboration in VR could evoke a strong sense of presence. [PERSON] et al. [11] obtained similar results, finding that symmetric and co-located collaboration led to higher presence and immersion than other collaboration types and also positively influenced the cognitive load.  
However, limited research has been conducted on synchronous co-located IVR collaboration, particularly involving more than three users. Possible reasons for this include the requirement for additional technology and space, as well as the absence of design principles for symmetric IVR collaboration [12; 21].
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results
### Evaluation Metrics  
A reliable way to evaluate objective FR-IQA methods is based on measuring the correlation strength between the ground-truth scores of a publicly available IQA database and the predicted scores. In the literature, [PERSON]'s linear correlation coefficient (PLCC), [PERSON]'s rank-order correlation coefficient (SROCC), and [PERSON]'s rank-order correlation coefficient (KROCC) are widely applied to characterize the degree of correlation. PLCC between vectors \(\mathbf{x}\) and \(\mathbf{y}\) can be expressed as  
\[PLCC(\mathbf{x},\mathbf{y})=\frac{\sum_{i=1}^{m}(x_{i}-\overline{x})(y_{i}- \overline{y})}{\sqrt{\sum_{i=1}^{m}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^ {m}(y_{i}-\overline{y})^{2}}} \tag{3}\]  
where \(\overline{x}=\frac{1}{m}\sum_{i=1}^{m}x_{i}\) and \(\overline{y}=\frac{1}{m}\sum_{i=1}^{m}y_{i}\). Furthermore, \(\mathbf{x}\) stands for the vector containing the ground-truth scores, while \(\mathbf{y}\) vector consists of the predicted scores. PLCC performance indices are determined after a non-linear mapping between objective scores (MOS or differential MOS values) and predicted subjective scores using a 5-parameter logistic function, as recommended by the authors of [54].  
SROCC between vectors \(\mathbf{x}\) and \(\mathbf{y}\) can be defined as  
\[SROCC(\mathbf{x},\mathbf{y})=PLCC(\textit{rank}(\mathbf{x}),\textit{rank}( \mathbf{y})) \tag{4}\]  
where the \(\textit{rank}(\cdot)\) function gives back a vector whose \(i\)th element is the rank of the \(i\)th element in the input vector. As a consequence, SROCC between vectors \(\mathbf{x}\) and \(\mathbf{y}\) can also be expressed as  
\[SROCC(\mathbf{x},\mathbf{y})=\frac{\sum_{i=1}^{m}(x_{i}-\hat{x})(y_{i}-\hat{y })}{\sqrt{\sum_{i=1}^{m}(x_{i}-\hat{x})^{2}}\sqrt{\sum_{i=1}^{m}(y_{i}-\hat{y })^{2}}} \tag{5}\]  
where \(\hat{x}\) and \(\hat{y}\) stand for the middle ranks of \(\mathbf{x}\) and \(\mathbf{y}\), respectively.  
KROCC between vectors \(\mathbf{x}\) and \(\mathbf{y}\) can be determined as  
\[KROCC(\mathbf{x},\mathbf{y})=\frac{n_{c}-n_{d}}{\frac{1}{2}n(n-1)} \tag{6}\]  
where \(n\) is the length of the input vectors, \(n_{c}\) stands for the number of concordant pairs between \(\mathbf{x}\) and \(\mathbf{y}\), and \(n_{d}\) denotes the number of discordant pairs.
# A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats
###### Abstract  
This article presents a feasibility analysis to remotely estimate the geo-location of a femto-satellite only using two station-CubeSats and the communication link between the femto-satellite and each CubeSat. The presented approach combines the Time Difference Of Arrival (TDOA) and Angle Of Arrival (AOA) methods. We present the motivation, the envisioned solution together with the constraints for reaching it, and the best potential sensitivity of the location precision for different (1) deployment scenarios of the femto-satellite, (2) precisions in the location of the CubeSats, and (3) precisions in each CubeSat's Attitude Determination and Control Systems (ADCS). We implemented a simulation tool to evaluate the average performance for different random scenarios in space. For the evaluated cases, we found that the Cramer-Rao Bound (CRB) for Gaussian noise over the small error region of the solution is highly dependent on the deployment direction, with differences in the location precision close to three orders of magnitude between the best and worst deployment directions. For the best deployment case, we also studied the best location estimation that might be achieved with the current Global Navigation Satellite System (GNSS) and ADCS commercially available for CubeSats. We found that the mean-square error (MSE) matrix of the proposed solution under the small error condition can attain the CRB for the simulated time, achieving a precision below 30 m when the femto-satellite is separated by around 800 m from the mother-CubeSat.  
CubeSat; femto-satellite; Cramer-Rao Bound (CRB); hybrid measurements; passive source localization 2022141101. [[https://doi.org/10.3390/rs14051101](https://doi.org/10.3390/rs14051101)]([https://doi.org/10.3390/rs14051101](https://doi.org/10.3390/rs14051101))  
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Footnote 1: [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Image Augmentation  
In convolution, neural network data quantity limitation is a major problem in the predictive model performance. Also, imbalanced classes can prevent the model from understanding and treating the classes properly [22]. To solve this problem using a convolution neural network, image augmentation is the best and most useful method. Image augmentation is used to artificially increase the number of images in the dataset. It creates many forms of images by applying different kinds of variation to a single image. This allows the model to learn and understand features from many image variants and leads the model to provide good prediction quality. The augmentation technique is applied to the training set to effectively train the model. The testing set is used to test the efficiency of the model, so artificially duplicating the image is not useful here [22].  
Image augmentation techniques that we used in our experiment are presented below.  
Figure 5: Sample image segmentation results.  
Rotation is an augmentation technique that duplicates the images by rotating them from the value zero up to 360 degrees. To obtain the correct rotation result, rotation must be performed with uniform random generation. In our experiment, we used random rotation within the range of the images in the range of \([-45,45]\).  
Flipping is a technique that mirrors the images with uniform random generation values.  
Stretching is an augmentation technique used to create a scale transformation that uses a scale factor chosen randomly in the [1.2, 1.5] range to resize the input image. In the horizontal and vertical directions, this transformation resizes the image by the same element.  
Translation creates a translation transformation that shifts the input image horizontally and vertically by a distance selected randomly in the range of \([-50,50]\) pixels.  
Shearing is used to create a shear transformation, with the shear angle selected randomly in the range of \([-30,30]\).
###### Abstract  
Millimeter-wave (mmWave) bands is considered for fifth-generation (5G) cellular systems because abundant spectrum is available for mobile broadband communications. In mmWave communication systems, accurate beamforming is important to compensate for high attenuation in the mmWave frequency band and to extend the transmission range. However, with the existing beamformers in mmWave cellular systems, the mobile station (MS) cannot identify the source (base station; BS) of the received beam because there are many neighboring BSs transmitting their training signals, requiring a large overhead. This paper proposes a new beam weight generation method for transmitting (Tx) beamformers at the BS in mmWave cellular systems during a beam training period. Beam weights are generated for Tx beamformers at neighboring BSs, so that a mobile station (MS) can estimate the source (cell ID; CID) and angle of departure (AoD) for each BS in multi-cell environments. A CID and AoD estimation method for mmWave cellular systems in a line-of-sight (LOS) dominant condition is presented using the beam weights generated by Zadoff-Chu sequence. A simulation is conducted in a LOS dominant condition to show that the performances of CID detection and AoD estimation are similar for both the proposed and conventional methods. In the conventional methods, the DFT-based beamforming weight is used for Tx beamformer at the BS and orthogonal matching pursuit (OMP) algorithm is used for AoD estimation at the MS. The proposed method significantly reduces the processing time (1.6-6.25%) required for beam training compared to the conventional method.  
m 2020, 9, 335; doi:10.3390/electronics9020335