esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Modeling Methods on Sweetness Prediction  
In this study, the spectral reflectance at the range of 400-2500 nm was used to select the optimal model combination. Both classical linear models of MLR and PLS, as well as nonlinear models of LSSVM and BP based on SPA and CARS algorithms for processed spectra were compared. [PERSON] et al. (2017) previously employed multiple linear regression (MLR), partial least squares regression (PLS), and least squares support vector machine(LSSVM) for estimation of kiwifruit internal qualities [53], achieving accuracy greater than 0.94. Similarly, [PERSON] et al. (2021) utilized hyperspectral data to estimate plum properties and achieved accuracy exceeding 0.93 using SPA and CARS algorithms with MLR and BP [25]. In line with these findings, our results showed that better accuracies were observed by the nonlinear models SPA-LSSVM and CARS-BP, which achieved accuracy of 0.79 and 0.77, respectively. In contrast, MLR and PLS models achieved the highest accuracy at 0.59, indicating that the relationship between kiwifruit spectra and sweetness extends beyond a typical linear correlation.  
From the perspective of agricultural research, the physicochemical parameter model constructed during the whole growth period of crops often had high precision, and could explore the change law of properties from the growth cycle [54]. The study declared that the estimation model was robust only when the sample had a sufficiently wide maturity range [55]. For kiwifruit, the distribution of sweetness was concentrated in each harvest period. The estimation model at whole harvest stage gave the best expression of sweetness changes. This was consistent with the previous research. Numerous researchers in non-destructive detecting of fruit internal qualities had consistently reported that sweetness content estimation models outperformed those for other quality attributes such as pH and firmness [25; 35; 56; 57]. This research could partially provide a theoretical basis for the development of portable non-destructive monitoring equipment for kiwifruit sweetness.
### Background and Literature Review
According to the nature of the evaluation methods, the DR flexibility potential evaluation can be divided into qualitative and quantitative evaluation. The qualitative evaluation of the DR flexibility potential can only reflect its general behavior and not provide accurate values [20], which is rarely applied practically. The quantitative evaluation of the DR flexibility potential uses the available statistical data to obtain the adjustable capacity of different flexible resources, which would be aggregated to respond to the system operator. The indicatorsand definitions of the DR flexibility potential are reviewed in [21; 22]. In [23], the electricity flexibility potential of HVAC and a storage tank is quantified via their physical operational mechanism. The electricity flexibility potential of price-based and incentive-based DR programs are calculated in [24], based on their technical flexibility characteristics. In [25], the flexibility potential of various flexible generation resources is evaluated based on their operational mechanism, which are further priced to participate in local flexibility markets. In [26], the DR flexibility potential within the multi-energy system is evaluated from five dimensions and graded into four levels. A generic simulation-based method is adopted in [27] in order to quantify the DR flexibility potential of zero-energy buildings. However, the existing DR flexibility potential studies [19; 20; 21; 22; 23; 24; 25; 26; 27] are simply from the aspects of the inherent operational mechanism or its external characteristics, which ignores the fluctuations in the real-time surrounding supply-demand environment. The DR potential of diverse flexible multi-energy resources cannot fully be utilized practically.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 4 Experimental Setup
### Experimental Details  
Before performing deep learning, it is necessary to convert audio into time-frequency domain by using short-time Fourier transform (STFT) to obtain the spectrum as input feature. The formula for time-frequency transformation is as follows:  
\[STFT(t,f)=\int_{-\infty}^{\infty}x(\tau)h(\tau-t)e^{-j2\pi f\tau}d\tau \tag{15}\]  
where \(t\) represents the duration of the audio, \(f\) represents the number of frequency points in the spectrum, and \(h(\tau-t)\) represents the analysis window function used in this STFT. The window function used in this paper is a Hann window of length 32 ms (512 frames) with a frame shift of 16 ms.  
The formula for the discrete short-time Fourier transform is as follows:  
\[X(t_{d},f_{d})=\sum_{n=0}^{N-1}x(n+t_{d}H)w(n)\exp\left(\frac{-2\pi if_{d}n}{N}\right) \tag{16}\]  
where \(x(n+t_{d}H)\) represents the input discrete-time audio signal, \(H\) represents the hop size, which indicates the length of window function movement on the signal, \(w(n)\) represents the window function, \(t_{d}\) represents the time frame of the spectrum, and \(f_{d}\) represents the frequency bin of the spectrum.  
After performing STFT, we obtain the phase spectrum and magnitude spectrum of the speech signal, as well as the real spectrum and imaginary spectrum. This paper mainly focuses on the first two.  
Programming environment: In our study, we employed the Python programming language for implementing all models on a Windows 10 system. During the training phase, a NVIDIA Tesla V100S GPU with 32 GB of memory was utilized to enhance computational performance.  
Consistency was maintained in the training parameter configuration across all speech data. We employed the Adam optimizer with a learning rate of \(1\times 10^{-3}\). To promote effective learning, exponential decay with decay rates of \(\beta_{1}=0.9\), \(\beta_{2}=0.999\) was applied once per epoch. The batch size for training was set to 32. Additionally, to ensure reproducibility, the random seed values for numpy, pytorch-gpu, and pytorch-cpu were set to 0.  
The baseline model comprises a full-band unit and a sub-band unit. For the baseline, a sub-band width of 15 was employed. To capture future information, we set the Lookahead value to 32.  
In our proposed method, we introduced a pMix unit alongside the full-band and sub-band units. The pMix unit consisted of four unidirectional Magphase units. The number of hidden units in the LSTM layers for the full-band and sub-band units remained consistent with the baseline, specifically 512 and 384, respectively. In our specific model configuration, the sub-band width was set to 10, while the Lookahead value was set to 2.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 4 Simulations and Results
### Mask R-CNN Results  
In the backbone comparison study for Mask R-CNN, different CNN architectures were implemented in our study as the feature extraction layers. The traditional ResNet50-FPN network, along with a ResNet18-FPN and VGG19-FPN network, was trained and compared to perform the WTB fault localization [34,35]. Table 6 shows the parameters that were used to train each variant of the Mask R-CNN backbone. Data augmentation schemes were not deployed with Mask R-CNN.  
Each of the modified backbone Mask R-CNN models was trained using a distinct set of parameters identified through the hyperparameter tuning procedure. Subsequent to training, each model was subjected to the test dataset to conduct an evaluation, yielding the following metrics: box mAP 50, box mAP 50-95, segmentation mAP50, and segmentation mAP50-95. The segmentation scores were instrumental in assessing the overall performance  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Parameter** & **Value** \\ \hline Epoch & 25, 50 \\ Optimizer & Adam, AdamW, RMSprop, SGD \\ Image Size & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training parameters for the Mask R-CNN.  
Figure 8: YOLO models’ best training run compared.  
of a Mask R-CNN model, given its unique capability to generate both bounding boxes and segmentation masks. The overall loss scores for each of the backbone variants are plotted against epochs in Figure 9. It is observed that the ResNet-50 backbone attains the lowest loss after epoch 25; however, the other two variants exhibit similar trends and are in close proximity. The results of the best evaluation metrics for each backbone variant are shown in Table 7. In terms of combined box mAP 50, the ResNet18-FPN backbone managed to perform the best, while the ResNet50-FPN backbone outperformed all the other backbones in the combined box mAP50-95 metric. The opposite trend occurs when it comes to the segmentation scores. The ResNet50-FPN backbone was the best-performing model in terms of combined segmentation mAP50, while ResNet18-FPN performed the best in terms of segmentation mAP50-95.
## Appendix D Evaluation Resources  
### First Week Evaluation  
1. What are the main functions of the Copernicus Atmospheric Monitoring Service (CAMS)? * Monitor and analyze air quality at global and regional levels * Directly measure emissions from specific sources like factories or vehicles * Analyze and forecast atmospheric variations due to greenhouse gases, pollutants, aerosols, and ozone * Control air quality at the local level * All of the above 2. The following gases are measured by Sentinel-5's TROPOMI, EXCEPT: * Methane (CH4) * Sulfur Dioxide (SO2) * Xenon (Xe) * Ozone (O3) * None of the above 3. What types of phenomena and gas emissions can be monitored with CAMS? * Smoke from campfires or barbecues * Flows and concentrations of greenhouse gases (GHGs) * Indoor gas levels like in offices, homes, or factories * Emissions of ash and sand * None of the above 4. What are the functions of the Copernicus Land Monitoring Service (CLMS)? * Continuously monitor the Earth to identify environmental changes and phenomena * Carry out physical actions on the ground, such as reforestation, infrastructure construction, or direct management of natural resources * Provide real-time data useful for monitoring land processes * Supply rapid and accurate data on land distribution and usage, as well as various biophysical parameters at different levels * None of the above 5. Briefly list 3 applications of Sentinel-2 for land monitoring applications 6. The following are some of the challenges of CLMS, EXCEPT: * Manage and analyze large volumes of satellite data with high accuracy and create reliable predictive models * Manage regulations, funding, user education, and cooperation among various disciplines * Provide free global access to commercial satellite information and other paid data * Deliver comprehensive documentation on the usage and methodology employed in their productsWhat functions does the Climate Change Service (C3S) perform?
* Monitor and thoroughly examine climate trends through satellites and ground stations
* Directly implement climate change mitigation or adaptation projects
* Provide climate information and forecast models to guide adaptation policies
* Conduct rescue operations or immediate response to climate emergencies
* None of the above
* What is CMIP?
* It is an international collaboration that compares climate models from different institutions to better understand and predict climate changes.
* It is an organization that conducts primary climate research and also develops its own climate models.
* It is an acronym for implementing climate policies and direct intervention in climate change mitigation or adaptation issues.
* It is an entity that collects climate data through observations or measurements.
* None of the above
* Indicate the possible emission scenarios according to CMIP6:
* Inequ
* Sustainability
* Fossil-fuel development
* Regional rivalry
* None of the above
* Which of the services we covered in the first three days of the workshop (CAMS, CLMS, and C3S) interested you the most and why? What do you think is necessary for your colleagues to be able to use them?
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
## 3 Methodology
### Time-Aware Attention
#### 3.2.1 Time Importance by Function-Based Representation  
From each timestamp in the sequence \(S\), we design a time distance matrix, \(T\in\mathbb{R}^{n\times n}\), by calculating the time difference between each timestamp in the sequence. We can observe relative time distance in the sequence from \(t_{ij}\), which indicates time difference between \(s_{i}\) and \(s_{j}\).  
\[T=\begin{pmatrix}t_{11}&t_{12}&\cdots&t_{1n}\\ t_{21}&t_{22}&\cdots&t_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ t_{n1}&t_{n2}&\cdots&t_{nn}\\ \end{pmatrix} \tag{4}\]  
In the n by n symmetric matrix \(T\), all of the diagonal elements are zero and others have as larger values as far from each element. We redefine the scaled \(T\) as \(T^{\prime}=\frac{1}{\log(e+T)}\)[40] to consider a global time relationship and represent time values into the range \([0,1]\). Because of scaling, elements of \(T^{\prime}\) are inversely represented by a time interval. We estimate the time importance, \(\alpha_{t}\), from \(T^{\prime}\). \(T^{\prime}\) is transformed by the function representation and a softmax function. For the function representation, we use the sigmoid function, which transforms from scaled time interval to time importance. The sigmoid function consists of learnable parameters, gradient \(a\) and constant \(b\).  
\[f(x)=\frac{1}{1+\exp^{-ax+b}},\quad\text{s.t.}\quad a>0 \tag{5}\]  
In Equation (5), we force the range of the gradient \(a\) to be positive values. This is because we assume time importance would increase as the time interval decreases. Furthermore, the scaling process makes \(T\) have an inverse relationship between time interval and time importance. Thus, the assumption can be satisfied by forcing the range of \(a\). Finally, we express the time importance with softmax, \(\alpha_{t}=softmax(f(T^{\prime}))\).
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
## 3 Heavy Metal Ion Detection Platforms Using Glutathione
### HMI Detection Combined with Electrochemical Techniques  
Based on our survey of the field, most HMI detection techniques utilized optical techniques to extract the sensing signal. These optical methods have obvious advantages, such as visualization, but may be less advantageous when it comes to field deployability and affordable readout systems. As a result, HMI detection platforms by electrochemical techniques are also worth investigating. Among the electrochemical techniques, field effect transistors (FETs) and working electrodes are the two representative devices.
## 2 Circuit Description  
TIAs are very crucial for analog front-end (AFE) receivers, as they determine the system's overall performance. It is well known that conventional voltage-mode TIAs face limitations, such as a gain-bandwidth design tradeoff and a narrow input dynamic range, which might result in severe signal distortions, especially when large input photocurrents are entered. Although AGC circuits can be equipped in these voltage-mode TIAs, the signal recovery of a narrow LiDAR pulse is still very challenging. In addition, the delays introduced by feedback loops in traditional AGC mechanisms should be either avoided or reduced to enhance TIA performance; otherwise, this would lead to another challenge for circuit designs.
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 3 Autonomous Orbit Determination Algorithm  
### Orbital Dynamics Model  
Assuming \(\mathbf{Y}(t)=\big{(}\mathbf{r}(t)^{\rm T},\mathbf{v}(t)^{\rm T}\big{)}^{\rm T}\) represents the position and velocity state of the satellite at time \(t\), the orbital dynamics equation is as follows [17]:  
\[\begin{array}{ll}\frac{{\rm d}\mathbf{Y}(t)}{{\rm d}t}&=f[\mathbf{Y}(t),\mathbf{u}(t),t] \\ &=\begin{bmatrix}\mathbf{v}\\ \mathbf{a}_{\rm E}+\mathbf{a}_{\rm M}+\mathbf{a}_{\rm S}+\mathbf{a}_{\rm SRP}+\mathbf{u}\end{bmatrix} \end{array} \tag{7}\]  
where \(\mathbf{a}_{\rm E}\) and \(\mathbf{a}_{\rm M}\), respectively, represent the gravity acceleration of the Earth and the Moon; \(\mathbf{a}_{\rm S}\) represents the sum of the gravity perturbation accelerations of the three bodies of the  
Figure 4: Schematic diagram of transponder ranging system.  
Sun, Mars, Jupiter, and Saturn; \(\mathbf{a}_{\text{SRP}}\) is the acceleration of solar radiation pressure; and \(\mathbf{u}\) is the process noise, modeled as Gaussian white noise.  
The orbital state at the initial time \(t_{0}\) is denoted as \(\mathbf{Y}(t_{0})\), and the state transition matrix from time \(t_{0}\) to time \(t\) is represented as:  
\[\mathbf{\Phi}_{\text{Y}}(t,t_{0})=\frac{\partial\mathbf{Y}(t)}{\partial\mathbf{Y}(t_{0})} \tag{8}\]  
The transition matrix from process noise to track state is \(\mathbf{\Gamma}_{\text{Y}}\), calculated as follows:  
\[\mathbf{\Gamma}_{\text{Y}}(t,t_{0})=\frac{\partial\mathbf{Y}(t)}{\partial\mathbf{u}(t_{0} )}=\begin{bmatrix}\frac{\Delta t^{2}}{2}\times\mathbf{I}_{3\times 3}\\ \Delta t\times\mathbf{I}_{3\times 3}\end{bmatrix} \tag{9}\]  
where \(\Delta t=t-t_{0}\) is the time step and \(\mathbf{I}\) is the identity matrix.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 4 Experiments
### Tasks and Evaluation Metrics  
We evaluated the baselines and our model on a binary classification task and a multi-class classification task.  
**Binary Classification Task:** We transformed the 5-star ratings to binary labels, where ratings of 4 and 5 are viewed as positive while the rest are viewed as negative, and demanded the recommendation model to predict the correct label. We used predictive probability 0.5 as the threshold to assign the label. We measured the performance of the models by the classification accuracy and the mean squared error (MSE) loss. Classification accuracy was acquired by calculating the percentage of the correct prediction number to the total prediction number. The MSE loss can be represented by Equation (5):  
\[\text{MSE}(\Theta)=\frac{1}{N}\sum_{k=1}^{N}\,(y_{k}-\hat{y}_{k})^{2} \tag{5}\]  
Here, \(\Theta\) is used to parameterize the deep recommendation model. Suppose \(N\) pairs of the input user-item data can be represented by \(\{u_{k},i_{k}\}_{k=1}^{N}\); then, \(y_{k}\) represents the corresponding ground truth label, whose value can be 0 or 1. \(\hat{y}_{k}\) represents the probability of label to be 1, which is predicted by the recommendation model.  
**Multi-Classification Task:** We viewed the 5-star ratings as 5 classes and made the model predict the correct ratings. In this task, we evaluated the model according to the classification accuracy and the cross-entropy (CE) loss. The calculation method of the classification accuracy is the same as that described in the Binary Task section, and the CE loss value can be represented by Equation (6):  
\[CE(\Theta)=-\frac{1}{N}\sum_{k=1}^{N}\sum_{m=0}^{M-1}y_{km}\log p_{km} \tag{6}\]  
where \(N\) represents the mini-batch data's size, and \(M\) represents the number of data classes. \(y_{k}m\) represents the ground truth label which shows whether one pair of the data \(\{u_{k},i_{k}\}\)belongs to class \(m\). Similar to \(y_{k}m\), \(p_{k}m\) shows the probability that the data \(\{u_{k},i_{k}\}\) belong to class \(m\).  
The test difficulty of the binary classification task is relatively small, and the tolerance for the prediction error of the model is relatively large. During this task, the prediction accuracy of the model for a single evaluation is investigated. The multi-classification task requires our model to capture the implicit relationships in user-item interactions, and the capture of nonlinear and linear relationships is more demanding. By carrying out tasks one by one and improving the requirements step by step, the performance of our model for single tasks and complex tasks can be tested more intuitively.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 2 Physical Layer Cooperative Mechanisms
Despite a two-hop communication makes the overall channel effect partially attenuated with respect to a direct link case, it is still important to perform the most convenient processing at the relay node, that is AF or DF, based on the quality of the received signal. Moreover, due to the typical broadcast signal emission of underwater nodes, the destination may receive a copy of the same message from both the source and the relay. So, another issue to be deepened when dealing with cooperative communication is the synchronization of nodes transmission, in order to let the destination receive interference-free signals. The potential availability of multiple copies of the same message allows also to achieved diversity both in the time and spatial domain. Well known mechanisms such as maximum ratio combining (MRC) and equal gain combining (EGC) can be implemented, as for instance described in [25]. So, cooperation offers different solutions to improve the link performance, as long as nodes synchronization and signal processing are managed properly [26; 27].
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 4 Experiment
### Visualization Results  
In Figure 7, we show the visual quality comparison of SR results with 3 \(\times\) SR under the BD model. Taking \"img_99\" as an example, the architectural meshes reconstructed by some methods, such as IRCNN [54] and VDSR [14], have serious artefacts and do not match the reality. The methods with better performance are RDN [50], RCAN [15], and SAN [20], which produce natural overall mesh reconstruction results but still have the  
Figure 6: Comparison with visualization results of the BI model on BSD100, Urban100, and Manga109. The best performances are **highlighted** in bold.  
problem of detail texture blurring. Compared with these classic methods, our LNLCN can not only remove image texture blur but also restore high-frequency details, reconstructing relatively sharp edges and clear images. This is because we incorporate learnable nonlocal attention into each DFFAG module, which captures important features which are useful for image texture. IRCNN [54] and RDN [50] are specifically designed to handle image reconstruction tasks with the BD model, and our LNLCN achieves a numerical increase of nearly 1 dB PSNR compared to these models, which fully demonstrates the advantages of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & **Set5** & **Set14** & **BSD100** & **Urban100** & **Manga109** \\  & & **(PSNR/SSIM)** & **(PSNR/SSIM)** & **(PSNR/SSIM)** & **(PSNR/SSIM)** & **(PSNR/SSIM)** \\ \hline Bicubic & & 28.78/0.8308 & 26.38/0.7271 & 26.33/0.6918 & 23.52/0.6862 & 25.46/0.8149 \\ SRCNN [13] & & 32.05/0.8944 & 28.80/0.8074 & 28.13/0.7736 & 25.70/0.7770 & 29.47/0.8924 \\ FSRCNN [40] & & 26.23/0.8124 & 24.44/0.7106 & 24.86/0.6832 & 22.04/0.6745 & 23.04/0.7927 \\ VDSR [14] & & 33.25/0.9150 & 29.46/0.8244 & 28.57/0.7893 & 26.61/0.8136 & 31.06/0.9234 \\ IRCNN [54] & & 33.38/0.9182 & 29.63/0.8281 & 28.65/0.7922 & 26.77/0.8154 & 31.15/0.9245 \\ RDN [50] & & 34.58/0.9280 & 30.53/0.8447 & 29.23/0.8079 & 28.46/0.8582 & 33.97/0.9465 \\ RCAN [15] & & 34.70/0.9288 & 30.63/0.8462 & 29.32/0.8093 & 28.81/0.8645 & 34.38/0.9483 \\ SAN [20] & & 34.75/0.9290 & 30.68/0.8466 & 29.33/0.8101 & 28.83/0.8646 & 34.46/0.9487 \\ HAN [52] & & 34.76/0.9294 & 30.70/0.8475 & 29.34/0.8106 & 28.99/0.8676 & **34.56**/0.9494 \\ LNLCN (Ours) & & **34.81/0.9301** & **30.73/0.8482** & **29.35/0.8109** & **29.17/0.8692** & 34.55/**0.9506** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of quantitative results obtained with the BD degradation model. The best and second-best performances are **highlighted** in bold and underlined, respectively.  
Figure 7: Comparison with visualization results of the BD model on Urban100. The best performances are **highlighted** in bold.