esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 3 Results and Discussion
### Morphology Analysis  
Structural changes of plasma-treated black pepper seeds, as studied by scanning electron microscopy of their cross-section, are visualized in Figure 8. Interestingly, the samples radiated by plasma were characterized by a more compact outer layer of the structure than in untreated material. However, more cavities and open irregularities were present in irradiated material, which was especially visible for samples treated for 60 s. Opposite results were reported by [PERSON] et al. [55] who studied the effect of cold plasma on the morphology of allspice berries. In this study, the authors stated that plasma treatment had no impact on the surface of the material. However, the differences may be related to the fact that in the cited experiment, plasma was generated using a coplanar surface barrier discharge method. Despite the gentle character of the plasma treatment, the temperature of these samples also increased up to 40-45 \({}^{\circ}\)C.
## 2 Related Work
### Overview of Multivariate Time-Series Classification Methods  
The operation of power batteries in new energy vehicles generates large volumes of data across multiple dimensions, including voltage, current, temperature, internal resistance, and time. These data exhibit strong temporal dependencies and multivariate coupling characteristics, forming a typical multivariate time-series structure. For such complex data, traditional univariate models often fail to effectively capture inter-variable interactions and dynamic patterns, thereby limiting classification performance.  
In recent years, deep learning methods have demonstrated significant advantages in modeling multivariate time-series data. Various network architectures have been proposed to jointly extract temporal and spatial features. Originally designed for image processing, convolutional neural networks (CNNs) have proven effective in local pattern extraction and have been adapted for time-series tasks. By treating time-series data as pseudo-image structures and applying one-dimensional convolutions (1D-CNNs) along the temporal axis, CNNs can efficiently capture local dependencies and spatial features [24]. On the other hand, recurrent neural networks (RNNs) are more suitable for handling sequential data with strong temporal correlations. In particular, Long Short-Term Memory (LSTM) networks, with their gated mechanisms that selectively retain or forget past information, excel at capturing long-range dependencies. For instance, [PERSON] et al. employed a bidirectional LSTM architecture for multivariate time-series classification tasks such as ECG signal analysis and human activity recognition, achieving notable success in real-world applications [25].  
Therefore, in the context of power battery fault detection, the selected deep learning model must be capable of modeling the strong inter-feature coupling, temporal dependencies, and non-linear structure inherent in multivariate time-series data.  
Compared with traditional machine learning approaches and existing deep learning methods, our proposed approach addresses class imbalance by employing a GAN to generate minority class samples that better fit the true data distribution, rather than using simple interpolation methods. For the classifier, we adopt a hybrid CNN-LSTM structure, which enables effective extraction of both fine-grained local variations (via CNN) and long-term temporal dependencies (via LSTM) from raw battery time-series data, minimizing information loss while improving classification performance. Experimental results demonstrate that our method consistently outperforms baseline models--including Random Forest, Logistic Regression, and standalone CNN-LSTM--in terms of Precision, Recall, and AUC, showing superior accuracy and robustness.
## 3 Proposed Scheme
### Bearing Anomaly Identification and Crack Size Detection  
Based on Figure 4, after resampling and RMS feature extraction from the AE signal of bearing, the RMS signal in known conditions is modeled using the proposed autoregressive fuzzy Gauss-Laguerre algorithm. After that, to estimate the RMS signals in all conditions, the proposed smooth sliding digital twin based on the combination of the proposed autoregressive fuzzy Gauss-Laguerre algorithm and high-order sliding fuzzy observer is designed. In this part, the residual signal is generated based on the difference between the original RMS signal and the estimated RMS signal of the bearing. The residual signal of the proposed robust smooth sliding digital twin, (\(r_{SSDT}(k)\)), is obtained by the following definition.  
\[r_{SSDT}(k)=S_{AE}(k)_{rms}-\epsilon_{SSDT}(k) \tag{21}\]  
After generating the residual signal, SVM is used for bearing anomaly identification and crack size detection [8,9].
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 5 Conclusions  
In this study, we introduced a face recognition scheme using the LZM transformation. The two most important issues for successful face recognition, especially for face images obtained from uncontrolled environments, are the used image descriptor and the designed face recognition scheme. In MS-LZM, which is proposed in this work, the descriptors are obtained using complex images generated by LZM transformation. First, an image pyramid is created for each face image to reveal both local and global properties of the face images. Then, a cascaded LZM transformation is applied to each image in this pyramid. With the first transformation, undesirable conditions such as illumination and blur effects are removed as much as possible. On the LZM images obtained as a result of the second transformation, phase-magnitude histograms (PMH) are calculated around the facial points.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **n1** & **n2** & **k1** & **k2** & **\#Scales** & **Time** \\ \hline FERET & 4 & 4 & 5 & 5 & 5 & 0.82 s \\ LFW & 4 & 4 & 7 & 7 & 5 & 0.95 s \\ SCface-Vis & 4 & 4 & 7 & 7 & 3 & 0.39 s \\ SCface-IR & 1 & 4 & 7 & 7 & 3 & 0.10 s \\ \hline \hline \end{tabular} (n1, k1) and (n2, k2) parameter pairs are for first and second LZM transformation, respectively. IR: infrared spectrum; Vis: visible spectrum.  
\end{table}
Table 5: Parameter values used for each dataset and feature extraction time for different settings.  
In previous face recognition studies performed with LZM transformation, only face identification or verification problem was taken into consideration. In this study, for both of these problem, we proposed an unsupervised face recognition scheme and achieved high performance on FERET, LFW, and SCface datasets. Each of the datasets used has different characteristics than the others. Since FERET contains a single image for each person in the gallery set, the results for this dataset showed the single sample per person success of MS-LZM. LFW is a popular dataset which consists of unconstrained face images and is used for the verification problem. The last dataset, SCface, was created using images taken from surveillance cameras. Using this dataset, MS-LZM was measured for both low-resolution images and for infrared images. Experimental results showed that our method outperformed state-of-the-art methods on FERET and SCface datasets, and the results obtained for LFW were comparable to the best known results. Taking these results into consideration, it can be easily concluded that MS-LZM is a powerful method that can be used to solve face identification and verification problems encountered in real-world applications.  
E.B. and M.G. conceived and designed the experiments; E.B. performed the experiments and wrote the paper; M.E.K. analyzed the results, guided the full text and revised the manuscript.  
This work was supported by The Scientific and Technological Research Council of Turkey with the grant number 112E201.  
The authors declare no conflict of interest.
## 1 Introduction  
Concrete is the primary material in construction worldwide. Additionally, the need for concrete will increase to almost 7.5 billion m\({}^{3}\) (approximately 18 billion ton) a year by 2050 [1, 2]. Particularly, the rapid development on large-scale infrastructures is causing the exhaustion of natural resources; several countries are facing the shortage of natural resources and the supply of aggregates is being exhausted [1]. Therefore, those countries are relying on imports to satisfy their needs. To handle this situation, a variety of studies are performed for the development of alternative resources for concrete [3, 4, 5]. Meanwhile, the recycling technologies of electric waste have been emphasized as a global issue, owing to its rapidly growing volume and complex nature [6]. Additionally, since 2012, when analog TV broadcasting ended, systems were converted to digital TV broadcasting in South Korea, and a large volume of cathode-ray tube (CRT) TVs and monitors were discarded. CRT glass products are classified into panels and funnels, wherein the panels may be reused as glass after washing; however, it is difficult to treat the funnels (heavyweight waste glass) using conventional recycling technology because they contain many heavy metals such as iron and lead [7,8]. Therefore, heavyweight waste glass has been studied extensively for many years. Conventionally, it is produced with crushed concrete materials. Using heavyweight waste glass as fine aggregate in concrete demonstrated some benefits such as improved durability [9,10].  
Meanwhile, almost all other studies focused on the removal of heavy metal in heavyweight waste glass. However, in our previous studies, we demonstrated the applicability of crushed heavyweight waste glass as fine aggregate in shielding concrete [11,12]. The results of those studies in indicated that, to improve the shielding performance, it is important to increase the density of the material in the radiation shielding concrete. Therefore, we did not perform any treatment to remove the heavy metals. Furthermore, we investigated the effect of heavyweight waste glass on the volume change properties of mortar according to the substitution ratio. The result indicated that it may be feasible to utilize heavyweight waste glass as fine aggregate in mortar specimens [1].  
Additionally, previous experimental studies have focused primarily on the mechanical properties or durability of concrete or mortar specimens [9,13,14,15,16]. Hitherto, structural behavior investigations are rarely conducted in RC members that use recycled heavyweight waste glass as fine aggregate. Namely, structural results regarding recycling heavyweight waste glass as fine aggregate are insufficient. Particularly, few have been investigated under the cyclic loading condition. The objective of this paper is to investigate experimentally the flexural behavior of RC members under cyclic loading, and the effect of the heavyweight waste glass substitution ratio.
## 3 Results and Discussion
### Development of the Hybrid Superhydrophobic/Hydrophilic (SHH) Surface  
The SHH surface was developed by using the best experimental conditions achieved in the methodologies previously discussed and by performing alternated layer depositions of OTS-treated (SHPho) and untreated (HPhi) SiO\({}_{2}\) NPs. The procedure consisted of depositing two layers of OTS-treated NPs over the clay substrate to create an SHPho surface. The corresponding layer depositions were conducted by following the same procedure previously described in Section 2.3. Subsequently, hexagonal segments containing HPhi NPs were deposited over the SHPho surface using a mask with hexagonal-patterned geometry, as shown in Figure 8a,b. The dimensions of such hexagonal patterns are 5 mm for both the distances across the corners and flats, considering a distance of 7.5 mm between each hexagonal pattern geometry as shown in Figure 8c. Eventually, the developed HPhi-/SHPho-layered depositions exhibited hybrid SHH surfaces as presented in Figure 9d.  
Figure 8: Schematic of the SHH surface creation process showing (**a**) the placing of the mask over SHPho surface, (**b**) deposition layer by layer by drop casting of HPhi layers assisted by a hexagonal-patterned mask, (**c**) top view of the finished SHH surface with the hexagonal-patterned geometries and (**d**) a close-up of the HPhi/SHPho layer depositions for the hybrid surface.  
Figure 7: TGA analysis and comparison of OTS, SiO\({}_{2}\) NPs and OTS-treated SiO\({}_{2}\) NPs.  
The surface roughness analysis of the developed hybrid SHH surface can be found in Supplementary Materials (Figure S2), and it obtained Ra values of 3.25 and 2.42 \(\upmu\)m for the HPhi and SHPho segments, respectively. As previously discussed in Section 3.2, there is a major dominance from the surface irregularities of the clay substrate, which allow one to achieve a better anchoring of the SHPho layers. However, the deposited untreated SiO\({}_{2}\) NPs can interact with the SHPho surface, which, in combination with the irregular surface, create the necessary conditions to form hexagonal HPhi segments.  
The wettability behavior of the hybrid SHH surface is shown in Figure 9. The hexagonal HPhi segments were dyed blue in order to distinguish them from the SHPho surface. The corresponding CA value measured for the SHPho surface is around 160\({}^{\circ}\) as shown in Figure 9d,e, and it is consistent with the value measured for a similar surface, such as sample S2. The relatively high CA values measured in the hexagonal HPhi segments, shown in Figure 9b,c, are around 94-112\({}^{\circ}\), and this is due to HPhi/SHPho layer interaction. The latter sets the optimal conditions to successfully achieve both water absorption avoidance and the progressive water accumulation to form large drops within the hexagonal HPhi segments, as shown in the sequenced pictures in Figure 9a.1-a.3 for the red-dyed water droplets. Such findings indicate that there is hybrid behavior in the developed SHH surface.
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Performance Verifications
#### 4.3.5 Enhanced Data Plane  
Although OVS-DPDK eliminates the context switch in the kernel-user space, the performance is still affected by the configurations of OVS-DPDK. Applying OVS-DPDK requires CPU resources for lcore and pmd-cpu. The lcore is used by non-datapath threads to remove the idle and wrong flows while the pmd-cpu needs to poll the queues of physical NICs and virtual NICs. If the number of pmd-cpu is too small, the lack of pmd-cpu will decrease the throughput. Therefore, this experiment is to verify the performance impact of lcore and pmd-cpu to enhance the data plane.  
As shown in Figure 18, the throughput increases when the number of lcore and pmd-cpu grows. When the packet sizes are 64, 512, and 1518, the throughputs of 2 lcores and 2 pmd-cpus are 1.4 Gbps, 5.9 Gbps, and 9.9 Gbps. Under the same packet sizes, the throughputs of 4 lcores and 4 pmd-cpus are 1.9 Gbps, 9 Gbps, and 9.9 Gbps. Therefore, the throughputs of 4 lcores and 4 pmd-cpus are improved by up to 50%. Furthermore, the enhancement of increasing lcore and pmd-cpu is even more obvious than increasing the VM size. The reason is that each VM consumes the virtual NIC resource and increases the workload of pmd-cpu. When the number of pmd-cpus is small, the pmd-cpu may not process so many packets. Thus, the packets are dropped if pmd-cpus are not affordable.  
Figure 17: Throughput of different technologies in PVVP scenario when packet size is 1518.  
Figure 18: Throughputs of OVS-DPDK when lcore and pmd-cpu increase.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 3 Method Description
### Edges' Detection  
Achieving a great accuracy in the detection of the edges of the signal \(x_{n}\), corresponding to the switching instants of logical levels, provides a better robustness in the demodulation.  
Figure 11: Representation of the normalized extracted signal boundaries.  
Figure 10: Representation of the zeros of first-order derivative.  
Thus, as an edge represents the tipping point between an high and low level, or vice versa, its position is defined as the equidistant point along the signal \(x_{n}\) between the associated boundaries (Figure 12). To do so, the average value of boundaries from a same edge is considered as the ordinate of the corresponding switch point, while its abscissa is determined by computing the intersection point between the rising/falling edge and the constant straight line of the _switch_ value (Figure 12).  
Thus, the slope \(a\) of the straight line passing through \(P_{h}\) and \(P_{l}\), the first points of each edge higher than and lower than the associated _switch_ value, respectively, and the corresponding y-intercept \(b\) are given by  
\[a(j)=\frac{y_{P_{h}}-y_{P_{l}}}{x_{P_{h}}-x_{P_{l}}} \tag{7}\]  
\[b(j)=y_{P_{h}}(j)-a(j)\times x_{P_{h}}(j) \tag{8}\]  
Then, from the equation of a first-order function \(y=ax+b\), the abscissa of the switch _switch_idx is given by  
\[switch\_idx(j)=\frac{switch(j)-b(j)}{a(j)} \tag{9}\]  
As _switch_idx_ is determined by the intersection of two straight lines, the position of the switch is therefore more accurate than just selecting the point of the edge closer to the _switch_ value. As clearly shown in Figure 13, the switch points represented by red triangles oriented according to the rising or falling edges are indeed located halfway between the boundary points of the edges illustrated with green diamonds.  
Figure 12: Representation of switch points’ detection (_only two switches are plotted for better readability_).  
Figure 13: Representation of the switch points detected from the normalized extracted signal.
# A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition
## 2 Related Work
### Convolution Neural Networks  
CNN is known to be the most active field of research in deep neural networks [51, 52] and was first introduced in the study \"Backpropagation applied to handwritten zip code recognition\" published by LeCun in 1989 [53]. LeCun later proposed the first CNN, a network called LeNet, in 1998 [54]. CNN is mainly used to solve difficult pattern recognition tasks, mainly focusing on images, and consists of an accurate but simple architecture [52]. Recently, CNN has been recognized as a powerful tool that can be used in various areas such as face, image, video, and voice analysis [51]. CNN is similar to traditional artificial neural network (ANN), which are composed of neurons that self-optimize through learning [52]. However, CNN is more proficient at reducing the number of ANN parameters. This allowed researchers and developers alike to solve tasks that could not be solved with a classic ANN, allowing them to access larger models. CNN assumes that features do not have spatial dependence in problem solving [51]. For example, in face recognition, a face is recognized even if that face exists in an arbitrary position in a given image. Another important aspect of CNN is that abstract features are extracted when an input propagates to a deeper layer; as layers are stacked, local features are advanced to global features, thus solving the problem of not reflecting the entire relationship of the image, which is a disadvantage of fully connected neural networks. As a result, it has robustness in the transformation of input data. NN creates a feature map from an input image through a convolution filter. If it extracts several different features, it can set the number of convolution kernels to extract different features. Sub-sampling reduces the size of the feature map and, through it, topology invariance can also be obtained. After several stages of convolution and sub-sampling, the size of the feature map decreases, leaving only the robust features that can represent the whole. The global feature obtained in this way is connected to the fully connected network (FCN) input. Like the feature of ANN, it is possible to produce an optimal recognition result through learning. In this study, the user's face image is extracted and represented as a matrix in 3D. The difference between the current image matrix and the previous image matrix is obtained, and this is defined as a face change image. We developed a CNN-based rating prediction model trained on the obtained face change image over time and predicted a rating with FCN.
## 4 Discussion
Although, when compared with other model variables, our estimated county-level mean SIF showed relatively poor ability to capture spatial variations of corn and soybean yields, it could act as effective supplement information of EVI and climate for better estimating crop yields. DNN models inputting both SIF and EVI data performed very close to climate-based DNN models. Additionally, DNN models integrating EVI and climate data outperformed either EVI or climate-based models (Figures 4 and 5, and Figure 6; Tables 1 and 2). However, the addition of data from the same source could not constrain yield estimate. From the statistical point of view, DNN models with SIF, EVI, and climate data as inputs performed even slightly poorer than models with EVI and climate (Table 1). The spatial patterns of discrepancies between yields estimated by two models and census data were also similar (Figure 5). Nevertheless, satellite SIF data is useful for estimating crop yields even if current SIF products have some limitations. The launching of satellite with finer resolution and broader spatial coverage than OCO-2, such as FLEX [93] and GeoCARB [94], would pave the way for further testing to what degree SIF might contribute to crop yields estimation from space. Currently, one possible solution for estimating crop yield using SIF data is to downscale satellite SIF data. For example, several studies have proposed methods to produce spatially continuous high spatial resolution SIF datasets using OCO-2 SIF measurements [95; 96; 97]. It is worthy of investigating the applicability of such downscaled SIF in estimating crop yields. Furthermore, it should be noted that when predicting crop yield empirically, especially when employing sophisticated machine learning techniques such as DNN, it would be beneficial to collect more training data. In addition, information other than EVI or SIF could also be useful in predicting corn and soybean yield and boosting the model performance as additional predictors. For example, vegetation optical depth (VOD) derived from the satellite passive microwave bands could indicate canopy biomass and water content condition of the plants [35; 98; 99]; evaporative stress index (ESI) retrieved from satellite thermal bands and leaf area index is desirable for assessing the crop moisture status as an indicator of agricultural drought [100; 101]. Furthermore, it is expected that with more data and more comprehensive predictors, the model could extract information more effectively and better discover non-linear relationships between dependent and independent variables. Therefore, our future work would further test the usefulness of SIF signals in crop yield predicting when more data are available. We would also employ multiple sources of data for crop yield predicting via the DNN technique, aiming to build a high-performance crop yield forecasting system at the regional level.