esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study investigated the performance of an optimal control and operation strategy for PMSG wind turbines to improve their frequency support capability in a power system. The PMSG wind generation system was constructed including PMSG modeling and integrated MSC and GSC controls. The PMSG wind turbine control is implemented using relevant direct and quadrature component controls on the rotor voltage, implemented using converters. The active power control in this study focused on the MPPT control to extract optimal power and the de-loaded control to obtain the power margin. The KE reserve control was also applied to enhance the frequency support capability. To improve the contribution of a wind turbine to support stable frequency regulation via the temporary release of more KE, the KE optimization method was applied using the PSO algorithm. It can be observed from the case study that the results validate the proposed strategy for the PMSG given the modeled frequency variations. In the proposed optimal strategy, the frequency regulation capability of the PMSG wind turbine depends on the additional KE reserve generated from the optimization process and the power margin. De-loaded control prevented the wind system instability that may have resulted from excessive KE discharge. The results demonstrate that the proposed strategy can restrict the frequency drop while decreasing the disturbances across the overall operational scale of PMSG wind turbines. Consequently, the proposed optimal strategy can significantly enhance the system contribution to the frequency regulation capability of wind turbines. Further research work will concentrate in solving optimization problem about the control strategy to account for PMSG wind turbines, compared with the benchmark optimization algorithms.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03029308). This research was also supported by Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute (grant number: R15 XA03-55).  
[PERSON] contributed this manuscript solely.  
The author declares no conflict of interest.
## 3 Materials and Methods
### Unimodal-Based DL Systems  
After data preprocessing, each modality is then processed by a dedicated CNN-based model to extract feature vectors or matching scores, which are subsequently fused. To identify the most effective pretrained CNN architecture for biometric recognition, two unimodal models were initially developed--one for FP and one for FV. Each model was trained and evaluated independently using five well-known CNN architectures: MobileNet, DenseNet121, InceptionV3, VGG16, and VGG19. The unimodal model that achieved the highest recognition performance was selected as the foundation for the proposed multimodal biometric system.  
A brief description of each pretrained CNN is provided.  
* VGG16/VGG19: Deep CNNs comprising sequential convolutional layers with small receptive fields (3 \(\times\) 3) and ReLU activation. Known for simplicity and effective feature extraction, they are computationally expensive [49; 50].
* ResNet: Residual connections were introduced to mitigate the vanishing gradient problem in deep architectures, thereby allowing effective training of networks exceeding 50 layers [49; 50].
* DenseNet: Dense connectivity across layers by encouraging feature reuse, enhancing gradient flow, and reducing the number of parameters compared to traditional CNNs [49; 50].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attribute** & **Details** \\ \hline Participants & 140 subjects (108 males, 32 females) aged 16–29 years \\ Collected Fingers & Index, middle, and ring fingers from both hands \\ Total Fingers & 840 fingers \\ Total Images & 33,600 images (20 per finger) \\ Collection Sessions & 2 sessions with a time span of 1 week between sessions \\ Environmental Variations & Indoor and outdoor sessions; some subjects had ink, oil, \\  & or dust \\ Preprocessing & Grayscale conversion, size normalization, and \\  & pixel normalization \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main characteristics of the NUPT-FPV dataset.  
* InceptionV3: Uses multi-scale convolutional filters, the \"Inception modules\", to extract features at various levels of abstraction while maintaining computational efficiency [49; 50].
* MobileNet has a lightweight architecture and suitability for real-time resource-constrained biometric applications. It employs depth-wise separable convolutions to reduce computational complexity while maintaining accuracy, which is particularly advantageous for mobile and embedded authentication systems [50].
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 6 Conclusions  
This paper starts with the important feature of space-time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm [14] from the dynamic sparsity adaptive and structural aspects. The SSA-CoSaMP algorithm is proposed. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, saving spectrum resources and energy consumption. The simulation result shows that the proposed algorithm has obvious performance gain compared with the traditional pilot-based channel estimation algorithms [9; 14; 15] in both low SNR and smaller number of pilot conditions. In the wireless communication environment, the structural characteristics are not only in the actual delay multipath domain but also in the virtual angle delay domain. Therefore, the next research work is mainly for massive MIMO antenna arrays  
Figure 8: Bit error rate (BER) performance comparison of the proposed algorithm with other existing algorithms [9; 14; 15] for different SNR levels.  
where the problem of sparse structuring in the virtual angle domain enables the structural improvement scheme to be applied in the virtual angle domain, deeply exploring the scope of structured use and improving the applicability of the scheme.  
Conceptualization, I.K. and S.K.; data curation, I.K., Q.A., and S.A.; formal analysis, I.K. and S.K.; funding acquisition, I.K. and S.K.; investigation, I.K., O.A.S. and Q.A.; methodology, O.A.S., Q.A., and S.K.; project administration, I.K. and O.A.S.; software, O.A.S., I.K., and S.A.; writing--original draft, S.A., I.K., and S.K.; writing--review and editing, I.K., S.K., and S.A.  
This research was supported by the Research Program through the National Research Foundation of Korea (NRF-2016R1D1A1B03934653, NRF-2019R1A2C1005920).  
The authors declare no conflict of interest.
## 5 Use of IRT in Livestock Production
### Detection of Lameness in Dairy Cows
The thermograms showed 97% of dairy cows affected by lameness, at least on one limb, more on the hind limbs, using a threshold temperature of 27 \({}^{\circ}\)C (139 animals out of 142 had a temperature greater than 27 \({}^{\circ}\)C on at least one limb) [168]. In the study by [PERSON] et al., the limbs of dairy cows were diagnosed by a veterinarian and thermographic measurements. The results showed a sensitivity of 93% and specificity of 38% for hind limbs and a sensitivity of 50% and specificity of 93% for forelimbs [122]. [PERSON] et al. proved that thermography using a handheld laser thermometer has the ability to detect the presence of elevated temperature associated with extremity lesions. Monitoring of foot temperature over a period of time showed an elevated temperature six weeks prior to lesion diagnosis and also a noticeable decrease in the average foot temperature six weeks after treatment, following lesion removal [169]. [PERSON] et al. evaluated the effectiveness of IRT in dairy cows with different mobility scores and found that higher hoof temperatures measured with a thermal imaging camera were only recorded in cows with a locomotion score (mobility score) of 3 or higher [170]. [PERSON] et al., using IRT, examined healthy and affected dairy cows suffering from white line disease, sole hemorrhages, foot ulcers, horizontal and axial lacerations. In both healthy and affected dairy cows, higher temperature was noted at the mid- and interdigital region of the hind foot. Dairy cows with affected limbs showed higher temperatures additionally at the lateral and medial hoof of the hind leg [171].
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 2 Bone Formation  
In its initial phase mesenchymal stem cells and osteoblasts occupying the surface of cortical bone, secrete receptor activator of nuclear factor kappa B ligand (RANKL) which upregulates osteoclast differentiation by binding to RANK receptors on the surface of osteoclast precursors. This process is counterbalanced by osteoprottegerin (OPG), a soluble decoy receptor that inhibits osteoclastogenesis by binding RANKL. In the second phase, newly evolved osteoclasts secrete hydrochloric acid and acid phosphatases that degrade surface bone and the bone matrix, releasing imbedded growth factors, including bone morphogenic proteins, transforming growth factor-\(\beta\) and insulin-like growth factor-\(1\). Osteoclasts form [PERSON]'s lacunae in trabecular bone and a cutting zone in cortical bone; once these cavities reach a certain size, osteoclasts undergo apoptosis, terminating bone resorption. In the third phase of bone remodeling, the newly liberated growth factors stimulate osteoblasts to evolve from their mesenchymal stem cell precursors to control bone mineralization and secrete collagen to form the bone matrix. In the fourth phase of bone remodeling, osteoblasts trapped in the bone matrix evolve into osteocytes, cells that connect with one another, osteoblasts, and osteoclasts through a myriad of dendritic processes that constitute the lacunar-canalicular network (see Figures 1 and 2). Osteocytes are the most abundant cell type in bone (90-95% of all bone cells); they respond to hormonal and mechanical signals and are the primarily cell responsible for the control of bone homeostasis [7; 8].  
Figure 1: The cycle of bone formation. RANK and macrophage colony stimulating factor (M-CSF) signaling initiates osteoclast differentiation from hematopoietic stem cell precursors. Mature multilobed osteocytes resorb bone, releasing growth factors (IGF-1, TGF-\(\beta\), and BMPs) that initiate osteoblast differentiation from mesenchymal stem cell precursors. Osteoblasts form and mineralize bone matrix, and eventually evolve into osteocytes—the master regulators of bone homeostasis.
# High-Precision Combined Tidal Forecasting Model
## 2 Related Concepts
### The Harmonic Analysis Method  
Tidal forces can be regarded as the sum of forces during different periods; in other words, as the sum of many simple harmonic oscillations. The harmonic analysis method separates the harmonic constants (including amplitudes and phase lags) of each tidal component from the continuous observation data of tide heights [6]. Tidal height is calculated by summing the \(m\) tidal components as follows:  
\[h(t)=A_{0}+\sum_{i=1}^{m}R_{i}\cos(\omega_{i}t-\theta_{i})=A_{0}+\sum_{i=1}^{m }\left(\alpha_{i}\cos\omega_{i}t+\beta_{i}\sin\omega_{i}t\right) \tag{7}\]  
where \(\alpha_{i}=R_{i}\cos\theta_{i}\); \(\beta_{i}=R_{i}\sin\theta_{i}\); \(A_{0}\) is the mean sea level; \(R_{i}\) is the component amplitude; and \(\omega_{i}\) and \(\theta_{i}\) are the angular velocity and initial phase of the tidal components, respectively.
## 3 Methodology
### Pseudo-Label Generation
The jointly trained network optimizes the parameters \(\mathbf{\theta}_{\mathcal{F}}\) and \(\mathbf{\theta}_{\mathcal{C}}\) using the following loss function:  
\[\mathcal{L}_{joint}=\frac{1}{L}\sum_{i=1}^{L}\mathcal{L}_{cc}(y_{i}^{s}, \mathcal{C}(\mathcal{F}(\alpha(\mathbf{x}_{i});\mathbf{\theta}_{\mathcal{F}});\mathbf{ \theta}_{\mathcal{C}}))+\frac{1}{U}\sum_{j=1}^{U}\mathbb{1}\left(y_{j}^{\mu} \geq 0\right)\mathcal{L}_{cc}\Big{(}y_{j}^{\mu},\mathcal{C}\big{(}\mathcal{ F}\big{(}\beta\big{(}\mathbf{x}_{j}\big{)};\mathbf{\theta}_{\mathcal{F}}\big{)};\mathbf{ \theta}_{\mathcal{C}}\big{)}\Big{)} \tag{9}\]  
where the first part is calculated using \(L\) reliable samples \(\left(x_{i},y_{i}^{s}\right)\) from \(\mathfrak{X}^{r}\), and the second part is calculated using \(U\) pseudo-labeled samples \(\left(x_{i},y_{i}^{\mu}\right)\) with pseudo-labels \(y_{j}^{\mu}\). These pseudo-labels \(y_{j}^{\mu}\) are assigned to the classes predicted by the network with the highest probability and exceeding a certain threshold. \(\alpha\) and \(\beta\) respectively denote the operators for weak and strong transformations on the input image. \(\mathcal{L}_{cc}\) is the cross-entropy loss function. After three stages of clustering, the input unlabeled images \(\mathfrak{X}=\left\{\mathbf{x}_{i}\right\}_{i=1}^{N}\) are divided into \(K\) clusters with clustering labels \(\left\{y_{i}^{s}\right\}\). The probability matrix \(\mathbf{P}=\left[\mathbf{p}_{1},\mathbf{p}_{2},\ldots,\mathbf{p}_{N}\right]^{\mathrm{T}}\in \mathbb{R}^{N\times K}\) is generated for the image set for each clusters. The probability matrix \(\mathbf{P}\) represents the probabilities of each image belonging to specific clusters.
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 2 Proposed Methodology
### Text Representation Using Bi-LSTM  
The sentence is fed through a word embedding layer followed by Bi-LSTM. Note that Bi-LSTM [40] is an extension of conventional LSTMs, while an LSTM network is a modified version of RNNs.  
Figure 4: Demonstration of various normalization techniques.  
Figure 3: Architecture of Big transfer (BiT) model.  
These last are based on the idea of connecting current and previous information, which enables understanding of the sequence of data [41]. RNNs tries to remember information they learned during the training procedure as well as what they learned from the previous input. This is achieved by repeatedly applying transformations to the input sequence data. After the output has been generated, it is copied and returned to the recurrent network.  
Although RNNs have been used in various tasks, they encounter major problems such as gradient vanishing and exploding. To cope with these limitation the LSTM network [42] has been introduced as an alternative solution. The LSTM network depends on the so-called memory cell, which can learn (make decisions) how to allow data to be entered, left, or removed from the cell state through an iterative process. This is done at a time step through special structures called gates. The LSTM has three gates, i.e., input, forget, and output gates. The input gate controls how the input data would change the state of the memory cell. The forget gate controls the cell of the previous state, whether it has to remember or forget it. The output gate enables the memory cell to influence the outputs. The equations for the gates are as follows:  
As mentioned previously, in this work, we use Bi-LSTM which comprises two LSTMs. During the training process, one of the LSTMs is trained on the input sequence and the other one is trained on the reversed copy of the input sequence. In other words, the input sequence is processed in both forward (past to future) and backward (future to past) directions by using two recurrent networks (two separate hidden layers, a forward state sequence and a backward state sequence). Both of the networks then connect to the same output layer to generate the output. Similar to the image branch, the output of Bi-LSTM is fed to a fully connected layer followed by \(l_{2}\)-normalization yielding a feature representation \(f(Y_{i})\) for the sentence \(Y_{i}\).
## 3 Results
### Survivin Inhibition with YM-155  
As the effect of IDA on the panel of AML cell lines was heterogeneous, we further targeted the survivin expression with 200 nM YM-155, a small molecule which binds to the survivin promoter and regulates its transcription [40]. Again, as the HL-60 cell line was reported to be extremely sensitive to YM-155 [46; 53], we used 10 times lower concentration for this line. Interestingly, YM-155 attenuated both mRNA and protein survivin expression in the majority of AML cell lines except MV4-11 (Figure 5a,b and Figure S2). Similarly, as with IDA, a viability drop was detected in all YM-155-treated cells despite the variability in survivin expression changes (Figure 5a). Combination with Ara-C did not significantly alter these effects. We also investigated the expression of the other proteins and we found marked downregulation of XIAP in the majority of cells except OCI-AML3. p53 expression was mostly not affected by the YM-155 treatment and Mcl1 and p21 were differently regulated in individual cell lines (Figure 5b).  
Figure 4: Immunofluorescence detection of survivin expression (Alexa Fluor555, red) in control, 5 mM Ara-C and/or 100 nM IDA-treated AML cell lines. Bars represent 10 μm.  
Decreased survivin expression in the nuclei of interphase cells was confirmed by immunofluorescence (Figure 6 and Figure S3).  
Contrary to Ara-C or IDA treatment, YM-155 did not affect the ability of cells to enter mitosis. As we reported earlier [49], a combination of epigenetic drugs 5-aza-2\({}^{\prime}\)-deoxycytidine (decitabine) and suberoylanilide hydroxamic acid (SAHA) affected the centromere-related position of survivin in the mitotic cells; however, the localization of survivin was not altered by YM-155 treatment suggesting that YM-155 has no impact on the cell-cycle-related survivin function (Figure 7). Nonetheless, addition of Ara-C to YM-155 resulted in loss of the mitotic fraction in all cell lines.  
Figure 5: Effect of 200 nM (20 nM for HL-60) YM-155 and its combination with 5 uM (0.5 uM for HL-60) Ara-C on AML cell lines after 24 h treatment. (**a**) Viability (estimated by PI exclusion test) and survivin mRNA and protein expression in control (white), Ara-C (gray), YM-155 (white, dotted), or Ara-C + YM-155 (gray, dotted)-treated cells. Values represent an average of at least three independent measurements \(\pm\) SD. Significance of the difference is marked by asterisks (difference vs. control is indicated above the appropriate bar): * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\), *** \(p<0.0001\). (**b**) Expression of apoptosis-related proteins. Blots are representatives of at least three independent experiments. Original blots with MW markers are provided in the Supplementary Materials, Figure S2.  
Figure 6: Immunofluorescence detection of survivin expression (Alexa Fluor555, red) in control, 5 uM Ara-C and/or 200 nM YM-155-treated AML cell lines. Bars represent 10 \(\upmu\)m.  
Figure 7: Effect of YM-155 on survivin (AlexaFluor555, red) localization in mitotic cells. Chromosomes are stained with Hoechst 33342 (blue). Bars represent 2 \(\upmu\)m.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 2 Background
### Remote Sensing and Digital Twins  
Satellite and UAV technologies are good for monitoring large-scale agricultural systems and supporting the creation of digital twins (DTs). Remote sensing (RS) data are essential for virtualization and offer valuable insights into crop conditions and resource needs. However, the effectiveness of satellite imagery in agriculture is often limited due to spatial and temporal resolution constraints. UAVs, equipped with advanced sensors such as RGB, multispectral, hyperspectral, and LiDAR, provide a complementary approach, offering higher-resolution data tailored for precision agriculture [34] where traditional low-resolution RS data may fall short. High-resolution RS data are beneficial; however, they significantly increase data volume. Traditionally, RS has been used for generating land use land cover (LULC) maps, analyzing soil properties, assessing crop health, and optimizing irrigation. Recent advancements in data fusion techniques allow for the combination of high-spatial- and low-temporal-resolution data from multiple satellite sources, enabling more comprehensive and timely information [35]. Integrating satellite data with DT models advances agricultural monitoring capabilities by providing real-time insights and detailed, three-dimensional virtual representations of agricultural landscapes, including terrain and canopy structures [27]. These virtual models enable improved decision-making by offering a more in-depth view of environmental interactions.  
DTs are also increasingly applied in water resource management, with notable implementations for flood and hazardous assessments [36]. For instance, [PERSON] et al. [37] developed a DT for flood prediction using high-resolution satellite data, while [PERSON] et al. [38] utilized digital elevation models (DEMs) to construct a DT for wetland environmental studies. Such applications highlight the versatility of DT technology across various environmental domains. In specific cases, like modeling the Poyang Lake wetland, DTs simulate typical wetland environments, showcasing how these virtual models can support both environmental conservation and resource management in agriculture, offering a robust foundation for sustainable practices.
# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 3 Background
### Evaluation Metrics
#### 3.5.2 AP and mAP Metrics  
Average precision (\(AP\)) is a measurement of accuracy on each class commonly used in classification problems with networks such as SSD and Faster R-CNN. This metric is originally proposed in [47] and it is later used in many studies [48; 49]. The AP measurement is calculated using Equation (3). It performs an 11-point interpolation to summarize the shape of the Precision x Recall curve by averaging the precision at a set of 11 evenly spaced recall levels [0, 0.1, 0.2,..., 1] (Figure 5). The precision \(\rho_{interp}(r)\) at each recall level \(r\) is interpolated as the maximum precision \(\rho(\bar{r})\) at recall value \(\bar{r}\) greater than or equal to recall level \(r\).  
\[AP=\frac{1}{11}\sum_{r\in 0,0.1,...,1}\rho_{interp}(r)\qquad\qquad\qquad\qquad with \quad\rho_{interp}(r)=\underset{r:\bar{r}\geq r}{max}\rho(\bar{r}) \tag{3}\]  
We use \(mAP\) (mean average precision) to calculate the average accuracy of all classes to evaluate the general accuracy of the five experimental network models in this study. The \(mAP\) measurement is calculated using Equation (4) after obtaining the \(AP\) measurement. \(AP_{i}\) is the \(AP\) of class \(i\), \(N\) is the number of classes.  
\[mAP=\frac{1}{N}\sum_{1}^{N}AP_{i} \tag{4}\]  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Predicted Class Actual Class** & **P** & **N** \\ \hline
**P** & TP & FP \\ \hline
**N** & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
3.5.3. Intersection over Union (IoU)  
The intersection over union (_IoU_) [50] or Jaccard Index is a measure to represent the similarity between the ground truth bounding box and the predicted bounding box of the model. The _IoU_ is calculated as Equation (5).  
\[IoU^{truth}_{pred}=\frac{truth\cap pred}{truth\cup pred} \tag{5}\]