esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluations
### Simulation Results
The simulation results verify that LoMoB is more tolerant to estimation errors of the distances between the projected beacon points and a sensor node. Subsequently, LoMoB is more promising than LDB in harsh underwater environments.
### Motivation  
In recent years, power electronics-based compensation has become essential to solving the problems of shunt capacitor-based compensation devices [8; 9]. These devices are known as static power compensators (D-STATCOMs). The main characteristics of these compensators include the injection of variable reactive power due to the variation in amplitude and phase angle regarding the connected network. All of this is associated with the behavior of the demand [10]. These devices are placed at the network nodes to significantly reduce network losses. However, proper integration is necessary when determining their sizing and location in order to reduce network losses. Some of the advantages of implementing D-STATCOMs in a distribution network are: (i) low cost, (ii) high reliability, and (iii) long lifespans (between 5 and 15 years) [11]. For this reason, in this document, D-STATCOMs are integrated into AC distribution networks with radial and meshed topologies, aiming to reduce the operating costs of the system, i.e., the costs of purchasing power at the slack node or substation, together with the costs of installation, operation, and maintenance of the D-STATCOMs.  
Similarly, to find a solution, this research proposes a reduction in the total operating costs of AC distribution systems with radial and meshed topologies when integrating D-STATCOMs, which is achieved through the application of a discrete version of the sine-cosine algorithm (DSCA), in conjunction with the BONMIN solver, considering the behavior of the end users' demand curve. Most distribution networks have radial topologies, except for a small percentage with meshed topologies, which implies large voltage drops and power losses due to their high resistance/reactance ratio [12]. This, together with the fast-growing demand and the slow expansion of the electricity system, has generated a series of inconveniences in distribution systems as a sector, as well as for the end users of the system [13; 14]. To find an adequate solution to these problems, it is of vital importance that, in addition to the existing infrastructure, control devices can be integrated to offer a technical-economic solution via the implementation of a structured system, as is the case of the incorporating D-STATCOMs into distribution systems. This arises from the need to increase efficiency through effective control of network power, as the optimal allocation of these devices maximizes load capacity, minimizes power losses, compensates for reactive power, and improves stability and power quality [15].  
Integrating D-STATCOMs is a key solution to improve the performance of distribution systems with radial and meshed topologies. However, the best location for installing these devices must be first identified, as well as the right size, in order to reduce the existing issues of distribution networks. This makes the network more effective by increasing the network capacity and system reliability.
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Implementation Details  
The methods mentioned before were trained on a laptop with an NVIDIA GeForce GTX 1060 GPU (CUDA 9.0 and CUDNN v7), an Intel(r) CoreTM i7-8750H CPU, and 16 GB RAM, using the _Tensorflow_ framework and tested on MATLAB R2017b. The training set of each MS dataset consists of 12,800 patches with the size of \(33\times 33\) and partial overlaps, which are cropped from a selected sub-scene. Analogously, the validation set consists of 3200 \(33\times 33\) patches, which are cropped from another different sub-scene. In addition, a sub-image different from the training/validation set is selected for testing. For the HS dataset, the process of building the training/validation set is the same as that on the MS datasets except the size of the patches, which is only \(11\times 11\), limited to the small size of this dataset. The sizes of convolutional kernels are empirically set to \(7\times 7\) for the detail branch while are \(7\times 7\times 3\) for the approximation branch. Also, the number of layers in our SA-PNN is empirically set to 4. The batch size using in the training phase is 64. The initial learning rate is set to 0.00005, and it is reduced to half every \(10^{5}\) iterations. The mean squared error (MSE) is chosen as the loss function and optimized with the Adam algorithm. The training process terminates at \(5\times 10^{5}\) iterations. Table 1 shows the average training time of our SA-PNN on four datasets under GPU mode. Note that the train time on the Pavia dataset is less than that on the other three datasets due to the smaller size of the training patches.
## 3 Controller Design and Stability Analysis  
### Control Design Objective  
A majority of research on control strategies for WECs has focused on the first stage of the system [17; 34; 47]. Here, we will only focus on that of the turbine stage. Previous studies have dealt with control strategies of an OWC-WEC with the turbine module [9; 35; 36]. These papers assume that blade inertia is large enough for the turbine angular speed to remain constant. However, these studies do not cover the effects of electric generator load on the turbine system in the time domain.  
In the previous section, our turbine modeling provides the input states (\(\Delta p_{t}\) and \(b_{g}\)) and the outputs (\(\Omega\) and \(V_{x}\) (or \(\dot{x}\))). Here, we emphasize that \(b_{g}\) is the only controllable parameter. Thus, this study presents control strategies for the impulse turbine where the control input is \(b_{g}\).  
Two major issues arise when considering the turbine's characteristics and power quality. These issues relate to (1) the maximum turbine efficiency and (2) the constancy of the output voltage. As shown in Figure 2, the efficiency of the turbine is a function of \(\phi\) and has a single peak point. This optimal point will herein be referred to as \(\phi_{o}\). For the first issue, it can be determined from (7) that the reference rotor angular velocity (or desired angular velocity) \(\Omega_{d}\) becomes a function of time varying state \(V_{x}\) as:  
\[\Omega_{d}=\phi_{o}/r_{t}\cdot V_{x}. \tag{18}\]  
Consequently, the control objective for the maximum turbine efficiency is to make \(\Omega\) track the time-varying desired angular velocity \(\Omega_{d}\).  
Furthermore, a constant supply of voltage is important for the quality of generated electric power. It is hugely advantageous to provide a constant voltage in terms of usability and workability while providing irregular current. Whether charging a battery directly or connecting it to the converter, electricity with a constant voltage is more utilizable than that with a constant current. Equation (14) states that the output voltage is proportional to the rotor speed. As such, generating a constant output voltage relies heavily on maintaining a constant rotor speed. Thus, for the second issue, the control objective is to make \(\Omega\) converge to the constant desired angular velocity \(\Omega_{d}\).  
It should be noted, however, that maximum turbine efficiency and constant output voltage cannot be simultaneously achieved. Hence, upon reaching maximum turbine efficiency, the output voltage must be swung according to \(V_{x}\). Conversely, if a constant voltage output is achieved, the turbine efficiency will subsequently be sub-optimal. Nonetheless, both parameters are determined by \(\Omega\), and consequently, choosing \(\Omega_{d}\) will determine the control purpose. From this point, the former will be referred to as the i-MET (instantaneous-maximum efficiency tracking) control and the latter as the CAV (constant angular velocity) control, respectively.
## 3 Radiometric and Geometric Calibration and Corrections
### Geometric Corrections
The availability of near-coincident, overlapping PS images also permits the application of photogrammetric, stereo-vision techniques to reconstruct three dimensional (3D) features [26]. [PERSON] and [PERSON] [27] describe a methodology for incorporating DEMs derived from stereo imagery into the georectification process. Using a Rational Function Model, the authors generated DEMs and achieved a standard deviation of ~3.9 m and ~7 m when compared to reference DEMs generated using airborne lidar and GeoEye/WorldView 2 data, respectively. The results demonstrate the ability for PS data to measure topographic changes at a high spatial and temporal resolution. After reviewing Planet's geometric processing procedures alongside exemplar studies using these data, it is clear that more research is needed into when and how to best perform additional processing. First, it would be beneficial to know the true distribution of geometric errors across the different constellations so that researchers can better understand how these errors might be impacting their analyses. Second, greater attention is needed into developing and testing ancillary processing techniques to improve the geometric error distribution. These techniques will be particularly useful if they can help align PS data with higher fidelity satellites such as Landsat and Sentinel-2.
# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 4 Experiment and Performance Evaluation
### Further Considerations of Loss Function in the STFT and STDCT Domains
Following the expression outlined in Equation (13), we formulate the composite loss function in the STDCT domain as follows:  
\[\mathbf{\mathcal{L}}_{comp}\big{(}\hat{Y}_{DCT}[k],X_{DCT}[k]\big{)}=\alpha\mathbf{ \mathcal{L}}_{MSE\_Mag}\big{(}|\hat{Y}_{DCT,\beta}[k]|,\big{|}X_{DCT,\beta}[ k]\big{|}\big{)}+(1-\alpha)\mathbf{\mathcal{L}}_{MSE\_Polar}\big{(}\hat{Y}_{DCT,\beta}[k],X_{ DCT,\beta}[k]\big{)} \tag{14}\]  
with \(\hat{Y}_{DCT,\beta}[k]\) and \(X_{DCT,\beta}[k]\) defined as follows:  
\[\hat{Y}_{DFT,\beta}(k)=\text{sgn}\big{(}\hat{Y}_{DCT}[k]\big{)} \cdot|\hat{Y}_{DCT}[k]|^{\beta};\] \[X_{\beta}(k)=\text{sgn}(X_{DCT}[k])\cdot|X_{DCT}[k]|^{\beta}.\]  
where \(\text{sgn}(\cdot)\) denotes the sign function. Our experiments assessed the effects of four combinations of \(\alpha\) and \(\beta\). Specifically, the combination (\(\alpha\), \(\beta\)) = (0, 1) directly applies MSE to the target coefficients. The setting (\(\alpha\), \(\beta\)) = (0.5, 1), which concurrently minimizes the magnitude and phase spectra, replicates the original parameters used in prior studies [9]. The combination (\(\alpha\), \(\beta\)) = (0, 0.5) solely considers power compression factors, whereas (\(\alpha\), \(\beta\)) = (0.5, 0.5) engages both magnitude estimation and phase recovery with power compression integrated. The choice of \(\alpha\) and \(\beta\) at 0.5 reflects their proven efficacy in STFT-based speech denoising applications, anticipating similar results with the STDCT sequences. Both classical and advanced U-Net models were retrained and evaluated under the above four settings. As shown in Tables 2 and 3, trends in response to adjustments in \(\alpha\) and \(\beta\) were similar across both U-Net configurations. Modifying either \(\alpha\) or \(\beta\) independently showed minimal impact on all evaluated metrics. Metrics from the advanced U-Net, which incorporates GLFBs, generally surpassed those from the classical U-Net. Our experimental results indicate that simultaneously optimizing phase and magnitude spectra without considering power compression actually lowered perceived quality. Also, applying power compression alone (\(\alpha\), \(\beta\)) = (0, 0.5) can only yield minor improvements. However, combining power compression with balanced magnitude and phase adjustments significantly enhanced speech quality, with improvements of 0.093 and 0.102 in PESQ for the classical U-Net in the STFT and STDCT domains, respectively.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Model Evaluation and Performance  
Evaluation Metrics for Anomaly Detection  
* **Precision [32]:** Represents the proportion of truly anomalous samples among all the samples predicted as anomalous by the model. A higher precision indicates that the model is reliable in detecting anomalies, minimizing false positives.
* **F1 Score [33]:** The weighted average of precision and recall, particularly useful for imbalanced data situations. A higher F1 score indicates the model's stronger stability in anomaly detection.
* **Accuracy:** Measures the proportion of correctly classified samples in the entire dataset. However, for anomaly detection tasks, accuracy is not the best evaluation metric as normal data usually dominate the dataset.
* **AUC-ROC (Area Under the Curve of Receiver Operating Characteristic) [34]:** Measures the model's ability to distinguish between normal and anomalous data. An AUC close to 1.0 indicates that the model has strong discriminative power and can effectively detect water quality anomalies.  
The Isolation Forest algorithm demonstrated a high F1 score of 98.56%, which shows its effectiveness in detecting anomalies. The model's precision of 97.77% indicates that it reliably detects true anomalies with minimal false positives, and its AUC-ROC score of 0.85 suggests that the model performs well in distinguishing between normal and anomalous data points.  
Random Forest achieved an accuracy of 98.10%, an F1 score of 98.99%, and an AUC-ROC of 0.87. These high values indicate that the model is highly effective in classifying water quality samples, confirming that the dataset provides meaningful high-quality data for machine learning tasks.  
Logistic Regression achieved a precision of 96.76%, an F1 score of 98.36%, and an AUC-ROC of 0.84. These results show that the dataset is well structured and that even a simple model can make accurate predictions regarding water quality states.  
The AUC-ROC score for LOF was 0.75, which is slightly lower than the other models but still indicates that LOF is effective in detecting local anomalies in water quality data.  
As shown in Table 5, the performance metrics for the different models demonstrate their effectiveness in detecting anomalies in water quality data.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 3 Directivity Measurements  
To confirm the validity of the proposed antenna, we fabricated the proposed design and performed basic experiments using it. In this section, two types of directivities are presented: One is \(E_{\Delta}\) with an AOA network and \(E_{\Omega}\), which are employed to estimate the angle of the incoming wave, as described in Section 2.1. The other is beam steering for enhancing the reliability of communications.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 1 Introduction
As the emerging concept of network slicing is central to realizing 5G-6G networks [18], we advocate for network slicing as a potential solution to manage and achieve the s-health application and service requirements (e.g., high-reliability and low-latency). Network slicing employs virtualization techniques (Network Function Virtualization--NFV, Software Defined Networks--SDN) for heterogeneous environments, encompassing cellular network and IoMT, once it allows the same physical network to be split into isolated virtual subnetworks [5; 6]. The benefits of network slicing for s-health include autonomous traffic analysis and network resources adaptation to specific requirements [10]. Therefore, we propose an architecture for the PeRformance management of smart healthcare applIcations requireMents based on network resoUrec Slicing (PRIMUS) that manages traffic and adapts network resources to support s-health. PRIMUS creates network slices for the applications. It adapts the network resources by creating and selecting dynamic routes for s-health traffic. The creation of these routes has metrics the flow latency and packet loss rate, being based on the application traffic identification, proposed in the previous work [20; 21]. The PRIMUS architecture was evaluated on Mininet-WiFi Network Emulator [22] that supports virtualization techniques since network slicing requires such technologies. The simulation comprises an emulated wireless network infrastructure containing two switches, one access point, three wired devices, three wireless devices, and three servers. The network was virtualized and sliced based on Openflow rules by Libera Hypervisor [23], and managed by ONOS. We have considered three slice types involving s-health synthetic traffic and Best Effort (BE). Each device generated traffic simulatingeach slice type and behavior to a server by the iPerf tool. Results show the feasibility of achieving high-reliability and low-latency in the s-health context by performance management. In the network slices created for s-health, the latency has reached values around 10 ms. This article proceeds as follows. Section 2 overviews the related works. Section 3 details the background on virtualization technologies and network slicing. Section 4 presents the proposed performance management architecture, network model, and assumptions. Section 5 presents the performance evaluation and discusses results. Finally, Section 6 concludes the article.
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 3 Results and Discussion
### Benchmarking the Performance of the ZIF-67@BNT Nanocomposite with Its Constituents  
The results depicted in Figure 7 contrast the MO adsorption performance of ZIF-67@BNT nanocomposite with its parental materials (i.e., ZIF-67 and BNT). As shown in Figure 7, ZIF-67 exhibits an MO adsorption capacity of 222.9 mg/g. The ZIF-67@BNT nanocomposite, on the other hand, shows the highest MO adsorption capacity with a value of 275.3 mg/g. The enhanced performance of ZIF-67@BNT can be attributed to a synergistic effect between ZIF-67 and BNT, where the integration of BNT with ZIF-67 results in improved structural stability, increased active sites, or optimized interaction with the targeted pollutant (MO in this case). This superior capacity highlights the potential of nanocomposite materials to outperform their individual components by leveraging the strengths of their constituents. The superiority of nanocomposites generated from different materials has also been demonstrated in the literature [10; 32; 55; 56; 57; 58]. Such superiority underscores the importance of material hybridization in developing advanced materials with enhanced properties, paving the way for further exploration and optimization in the wastewater treatment field.  
Based on the results presented in Figure 7, the ZIF-67@BNT nanocomposite offers a greater performance, making it a promising candidate for applications in water decoloring such as the MO removal reported herein. Thus, subsequent studies will focus on optimizing the performance of ZIF-67@BNT nanocomposite using RSM  
Figure 7: Benchmarking the adsorption of MO onto ZIF-67@BNT with its constituents (i.e., ZIF-67 and BNT). The adsorption took place from 100 mg/L MO solution containing 50 mg/mL of each adsorbent.  
adsorption kinetics and adsorption isotherm of MO onto this nanocomposite, and developing predictive machine learning models. Plausible adsorption mechanism(s) will also be highlighted wherever applicable throughout the discussion in the subsequent sections.
## 4 Computational Experiments and Results Analysis
### Case Study Description  
Tests were conducted to evaluate the performance of the proposed algorithms in various models. The computational program and data can be consulted in [45]. The proposed models were tested on simulated data constructed from realistic data for four non-interconnected zones in Colombia: San Andres, Providencia, Puerto Narino, and Leticia. The first two are insular areas, while the latter two are located in the Colombian Amazonas region. The demand data were obtained from the official report of the UPME (Unidad de Planeacion Minero Energetica) of Colombia, where they provide the load profile for these areas, specifically for the period from January to December 2018. The availability of renewable resources was estimated using measurement tools such as NASA, PVGIS, and Solcast. Additionally, the portfolio of generators and batteries, along with their technical and cost parameters, was sourced from commercial catalogs.  
The models primarily provide information on which generators to install, the percentage and amount they contribute to the network for each hour, the LCOE, and the required area.  
Initially, the differences in performance and quality of the sizing solutions provided by the algorithms will be evaluated. Following this, important characteristics of the model will be assessed, such as the utilization of fiscal incentives for renewable energy and the parameter that regulates compliance with the level of service in sets of periods within the planning horizon. Finally, a comparison among the different proposed models will be conducted.  
Figure 8: Complement 1.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 2 Low-Resource Language Speech Recognition
### Methods of Low-Resource Language Speech Recognition
#### 2.2.4 Unsupervised Learning  
Unsupervised learning is another important method to solve the data scarcity. The basic idea of this method is to learn the shared representations through a large number of unlabeled data, thus reducing the data requirement of acoustic modeling.  
Semi-supervised training is a typical unsupervised learning approach. In this training scheme, an initial model is firstly trained with a small amount of annotated data, and the model is used to decode the unlabeled data. Once completed, the resultant transcripts are treated as pseudo-labels and used to retrain the model [80]. [PERSON] et al. [81] recently found that semi-supervised learning can improve the performance of low-resource language speech recognition even with data from mismatched domains. More aggressively, semi-supervised training can be conducted in a cross-lingual manner, which decodes unlabeled data by an off-the-shelf model of a rich-resource language and, then, translates the resultant transcripts to the target language, e.g., by phoneme mapping [82; 83].  
Self-supervised training is perhaps the most-popular unsupervised learning approach currently. For example, [PERSON] et al. [84] trained a Wav2 Vec 2.0 model with 800 h of speech data in 10 languages. After fine-tuning on the BABEL data, this model achieved remarkable performance improvement in multilingual tests. [PERSON] et al. [85] reproduced this result on a database containing 40 Indian languages from a wide variety of domains including education, news, technology, and finance. [PERSON] et al. [86] also adopted the multilingual self-supervised training scheme in low-resource language settings, though their model was based on a teacher-student learning scheme.  
Besides pre-training, unsupervised learning also can be used as regularization. For example, [PERSON] et al. [83] proposed a multi-modal data augmentation (MMDA) method, which feeds text to the encoder of a sequence-to-sequence model and, then, lets the decoder reconstruct the input text. In this setting, the model is trained with extra text data. This extra training is unsupervised and plays the role of regularization. [PERSON] [87] improved this approach by passing the text through a pre-processing network before feeding it to the encoder. [PERSON] et al. [88] proposed a UniSpeech model, which treated the model training of Wav2 Vec 2.0 as an auxiliary task to obtain robust speech representations at the encoding layers.