esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
From the proposal in [2], where the model was only applied to a one machine test case, the present work models multimachine EPS in unbalanced conditions, then generalizes the small signal stability analysis, and finally, compares the results obtained with a monophasic model used in the classical literature. The simulation tools are DigSilent and Matlab to obtain the unbalanced three-phase power flow, as well as the linear equivalent model. To the best of our knowledge, this kind of study cannot be performed in any available commercial software for large-scale systems. To name a very popular software solution, DigSilent PowerFactory is based on simulation and is not prepared for an eigenvalue analysis with unbalanced conditions [17]. In this work, we propose and validate an analytic derivation for the generalization of a small signal stability study. Thus, the solution of algebraic differential equations is not required. The methodology we introduce in this article covers an area that has not been deeply developed and aims at analyzing unbalanced systems with a large number of machines and buses, considering an analytical model of the entire system. Furthermore, there is no analytically-oriented model for controller tuning in unbalanced systems, and the proposed approach to small signal stability analysis opens the way for the adjustment of the controller parameters under unbalanced conditions. This work is at the cutting-edge of research in small signal stability analysis. The present report is structured as follows: Section 2 presents the description of the problem and how it is approached. Section 3 describes the modeling and linearization results. Section 4 explains the case study and results obtained. Finally, Section 5 presents the conclusions and proposed future work.
## 3 Background
### Long Short-Term Memory (LSTM) Networks  
Autoencoders LSTM network is a type of recurrent neural network (RNN) with controllable memory units that enable the network to learn when to forget previous hidden states and when to update hidden states with new information [44]. They have been proved to be very effective in many long-range sequential modeling applications such as machine translation, speech and action recognition [45; 46; 47; 48]. Generally, LSTM recursively maps the input data at the current time step to a sequence of hidden states, and thus the learning process of LSTM should be in a sequential manner.  
LSTM networks address the vanishing gradient problem commonly found in ordinary RNNs by incorporating the concept of gating into their state dynamics. The inputs provided to the LSTM memory unit are fed through gates which are the input, output, and forget gates. At each time step, an LSTM unit updates a hidden state vector \(h_{t}\) and a cell state vector \(c_{t}\) responsible for controlling state updates and outputs using current input, previous cell state and hidden state values. Specifically, given a sequential data of T timestamps \((x_{1},\ x_{2},\ \ldots,\ x_{T})\), an LSTM memory unit maps the inputs to an output sequence \((y_{1},\ y_{2},\ \ldots,\ y_{T})\) by computing the gate functions recursively for each step from \(t=1\) to \(t=T\) with the following equations:  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+W_{ci}\,c_{t-1}+b_{i}), \tag{5}\]  
\[f_{t}=\sigma\Big{(}W_{xf}x_{t}+W_{hf}h_{t-1}+W_{cf}\,c_{t-1}+b_{f}\Big{)}, \tag{6}\]  
\[o_{t}=\sigma(W_{x0}x_{t}+W_{ho}h_{t-1}+W_{co}c_{t}+b_{o}) \tag{7}\]  
\[c_{t}=f_{t}\,o\,c_{t-1}+i_{t0}\,\text{tan}\,\text{h}(W_{xc}x_{t}+W_{hc}h_{t-1} +b_{c}), \tag{8}\]  
\[h_{t}=o_{t}\,o\,\text{tanh}\,(c_{t}), \tag{9}\]  
where \(x_{t}\,\) and \(h_{t}\,\) are the input and hidden state vectors, the notations \(i_{t},f_{t},o_{t},c_{t}\) are the activation vectors of the input gate, forget gate, output gate, and memory cell, respectively. \(W_{ab}\) is the weight matrix between \(a\) and \(b\), and \(b_{a}\) is the bias term of \(a\), \(\sigma\) is the sigmoid function. \(o\) denotes the element-wise product. The memory cell unit \(c_{t}\) is the sum of two terms: the previous memory cell unit modulated by forget gate and a function of the current input and previous hidden state. Input and forget gates can be thought of as knobs that control the learning process: the LSTM learns to selectively forget its previous memory through \(f_{t}\) and considers its current input and previous hidden state through \(i_{t}\). The output gate \(o_{t}\) learns how much of the current memory cell to transfer to the hidden state. This gated structure seems to enable the LSTM to learn complex and long-term temporal dynamics for contextual data.
# Spectrophotometric Determination of Biopolymers in Alabama Benchmark Soils
## 3 Results
### Soil Chemical Properties  
A summary of the chemical properties of the nine Alabama soils is shown in Table 2.  
Most of the soils were acidic except the Houston, Vaiden, and Sumter soils, which were alkaline clay soils. The soil organic matter (SOM) content ranged from 1.11 to 6.85%, where the lowest values were recorded for sandy soils and the highest values for clay soils.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Soil** & **SOM** & **SOC** & **TN** & & **POxC** & **PMN** & **P** & **ECEC** & **CEC** & **EA** & **pH** & **pH** & **EC** \\
**Code** & & **\%** & & & **g kg\({}^{-1}\)** & **ppm** & & & **meq/100 g** & & **Water** & **CaCl\({}_{2}\)** & **dS/m** \\ \hline MSL & 1.11 & 0.68 & 0.05 & 13.87 & 288.3 & 9 & 55.6 & 2.57 & 3.7 & 5.81 & 4.89 & 4.07 & 0.02 \\ ASL & 4.31 & 1.75 & 0.14 & 12.15 & 334.9 & 18.5 & 38.3 & 9.25 & 13.79 & 14.77 & 5.35 & 4.88 & 1.93 \\ LSL & 2.68 & 1.02 & 0.13 & 7.96 & 389.3 & 21 & 27.4 & 4.35 & 6.44 & 8.89 & 5.02 & 4.36 & 0.05 \\ CLS & 2.29 & 1.03 & 0.07 & 14.71 & 218.3 & 20 & 38.7 & 5.32 & 7.92 & 7.62 & 5.85 & 5.23 & 0.05 \\ DSL & 4.56 & 1.44 & 0.18 & 7.87 & 288.3 & 51 & 13.5 & 10.2 & 14.79 & 14.52 & 5.53 & 5.02 & 0.14 \\ CSL & 4 & 1.51 & 0.13 & 11.98 & 412.7 & 47 & 31.3 & 7.76 & 12.04 & 11.90 & 5.63 & 5.04 & 0.09 \\ HCL & 5.9 & 2.48 & 0.17 & 14.42 & 340.5 & 34.5 & 0.4 & 41.4 & 44.83 & 6.06 & 7.44 & 7.29 & 0.518 \\ VCL & 6.85 & 2.88 & 0.26 & 11.08 & 316.0 & 64.5 & 6 & 44.2 & 48.49 & 9.86 & 7.32 & 6.74 & 0.581 \\ SCL & 2.35 & 1.13 & 0.11 & 10.56 & 227.4 & 3 & 0.3 & 16.2 & 16.9 & 0 & 7.71 & 7.41 & 0.385 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical properties: soil organic matter (SOM), soil organic carbon (SOC), total nitrogen (TN), carbon-to-nitrogen ratio (C:N), permanganate oxidizable carbon (POxC), potentially mineralizable nitrogen (PMN), soil available phosphorus (P), cation exchange capacity (CEC), effective cation exchange capacity (ECEC), extractable acidity (EA), potential of hydrogen (pH), and electrical conductivity (EC).  
Figure 4: Principal component analysis (PCA) of cellulose, acetyl bromide lignin, and autoclaved citrate extractable protein content in soils used.  
The same trend was recorded for soil organic carbon (SOC), total nitrogen (TN), cation exchange capacity (CEC), and electrical conductivities (EC). Marvyn sandy loam had the lowest SOM content and the lowest CEC, while Vaiden clay had the highest SOM content and the highest CEC. The highest values for soil available phosphorus (SAP) were recorded for sandy soils.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 2 Conventional Sound Event Detection
### U-Net for Sound Event Detection  
We first describe the weakly supervised U-Net [26] for the sound event detection (SED) task. U-Net was proposed for medical image segmentation and has shown good performance in image segmentation tasks in various fields. This model has a convolutional encoder-decoder structure and shows high reconstruction performance thanks to the skip connection between the encoder and decoder. We decided to use this model because this kind of structure is good at solving the problem of the baseline model, and then, we used this model to implement a system that trains the SED task in a weakly supervised manner.  
Figure 3 illustrates the U-Net architecture for the sound event detection model. In the left, the encoder part, a number of convolutional and pooling layers are repeatedly applied to reduce the input size from \(311\times 64\) to \(39\times 8\), with appropriate selections of the number of convolutional kernels. In the right, the decoder part, deconvolution is applied to restore the input size, \(311\times 64\), so that the final segmentation masks are of the same size as the input spectrogram. Note that the number of masks equals the number of classes. The category predictions are obtained by global pooling and used in computing the classification loss, and the detection results are obtained by averaging the frequency components. We used supervised cross-entropy loss to train the whole network [30], which is suitable for classification tasks with their targets expressed by a one-hot representation [31].  
The final output of the conventional U-Net [26] is a event segmentation map of the detected classes with the same dimension as the input. Training U-Nets requires a true segmentation map, to minimize the amount mismatch between the predicted segmentation result and the ground truth for each sample. When the complete ground truth map is not available, but only the class labels that exist in the input sound are given, the connection from the segmentation layer of U-Net to the class label output layer is added. The size of the segmentation map is much larger than the number of classes, so we applied GWRP [18] to reduce the map size. The resultant dimension of the GWRP output is [batch_size, number_of_classes], so that audio tagging in time domain can be achieved in a supervised manner.  
Figure 3: U-Net architecture for weakly supervised sound event detection with full upsampling in the deconvolutional layers. The left encoder part reduces the input size to a smaller size with a number of convolutional and pooling layers. The right half, the decoder part, restores the bottom output map of a reduced size to the original input size. The number of channels at the final output layer is the same as the number of classes (a positive integer “C” in this example). For each output channel, a global pooling layer is applied to derive the audio tagging outputs, so that the number of target nodes is the same as the number of tagging classes.
## 3 System Methodology
### Markovian Decision Processes-Based Optimized Policy for Multimodal Transportation  
In the realm of multimodal transportation, the decision-making process involves two crucial aspects: evaluating the efficiency of transportation modes and devising an optimal policy for mode selection. This mathematical model seamlessly integrates data envelopment analysis (DEA) for efficiency assessment and a Markov decision process (MDP) for policy optimization. The decision variables used in this process are \(\theta_{r}\), which is efficiency score for mode \(r\), and weight assigned to each mode \(k\) denoted by \(\lambda_{k}\). The objective is to maximize \(\theta_{r}\) with respect to following constraints.  
Ensure that the weighted sum of infrastructure costs for all modes is less than or equal to \(\theta_{r}\) times the infrastructure cost of mode,  
\[\sum\
olimits_{k}\lambda_{k}IC_{k}\leq\theta_{r}IC_{r} \tag{32}\]  
Ensure that the weighted sum of passenger and freight capacities for all modes is greater than or equal to the respective capacities of mode,  
\[\sum\
olimits_{k}\lambda_{k}PC_{k}\geq PC_{r} \tag{33}\]  
\[\sum\
olimits_{k}\lambda_{k}FC_{k}\geq FC_{r} \tag{34}\]  
\(\lambda_{k}\) values are non-negative: \(\lambda_{k}\geq 0\) for all k.  
The MDP is employed to formulate an optimal policy for mode selection over time, considering rewards associated with efficiency, cost, and emissions.  
\[Maximize{\sum\
olimits_{t=0}^{\infty}\gamma^{t}\cdot R(s_{t},\pi(s_{t}))} \tag{35}\]  
where \(R(s,a)\) is the reward function for state \(s\) and action \(a\), \(P(s^{\prime}\mid s,a)\) is the transition probability from state \(s\) to state \(s^{\prime}\) given action \(a\), \(V(s)\) is the value function for state \(s\), and \(\pi(s)\) is the policy function indicating the recommended action for state \(s\). The rewards are computed based on DEA efficiency scores and associated costs and emissions. The Bellman equation, which can also be considered as policy improvement, is mentioned in (35) and (36).  
\[V(S)=max_{a\in A}\Big{(}R(s,a)+\gamma{\sum\
olimits_{s^{\prime}\in S}P\big{(} s^{\prime}\mid S,a\big{)}\cdot V\big{(}s^{\prime}\big{)}}\Big{)} \tag{36}\]  
\[\pi(S)=arg\;max_{a\in A}\Big{(}R(s,a)+\gamma{\sum\
olimits_{a^{\prime}\in S}P \big{(}s^{\prime}\mid S,a\big{)}\cdot V\big{(}s^{\prime}\big{)}}\Big{)} \tag{37}\]  
We repeatedly evaluate and refine the policy until convergence. This model essentially balances the short-term efficiency of transportation modes through DEA with the long-term benefits derived from the MDP. It offers a comprehensive approach to decision-making in multimodal transportation, aligning efficiency considerations with strategic, forward-looking policy choices.
# Harnessing Artificial Intelligence for Wildlife Conservation
## 4 Results and Discussion
### Challenges and Limitations  
Despite its successes, Conservation AI faces several challenges and limitations. One of the primary challenges lies in the quality of data collected. Camera traps, more so than drones (although they have their own problems, i.e., distance and scale), often capture low-quality images and videos under a wide range of adverse weather and environmental conditions. Acquiring sufficient region-specific data to effectively train our models--especially when dealing with rare or critically endangered animals--can be particularly difficult. This limitation can affect the accuracy of the AI models and, in some cases, slow down progress.  
Even when data are available, the tagging process is time-consuming and crucial to the overall pipeline (tagging is performed during the data preprocessing stage). If tagging is not performed meticulously, it can significantly degrade the quality of the models. Maintaining high standards in data management is both time-consuming and costly, and as a non-profit platform, ensuring the necessary data management capacity remains an ongoing challenge.  
Additionally, deploying and maintaining camera traps and drones in remote areas presents logistical challenges and requires considerable resources. While transfer learning helps mitigate some data collection issues and contributes to our strong results, it does not eliminate the need to create high-quality, balanced datasets for model training. Features  
Figure 11: Precision–recall curve for the Indomalayan Mammals model with an average of 0.977 [EMAIL_ADDRESS].  
Figure 12: Precision–recall curve for the Central Asian Mammals model with an average 0.989 [EMAIL_ADDRESS].  
present in the deployed environment may not be included in the initial base models, necessitating the need to deploy hardware in model-specific regions to create custom datasets. This can be likened to overfitting models to regions of intended use.  
Another limitation is the need for continuous training and updating of AI models. As new species are added and environmental conditions evolve, base models must be retrained from scratch to maintain their accuracy. It is not sufficient to simply add new data or classes to an existing model, as transfer learning requires fine-tuning earlier layers in the network. Consequently, the entire dataset, including the new information, must be passed through the training cycle again, which demands significant computational resources--an expensive requirement over time.  
Moreover, the deployment of AI technology in conservation efforts necessitates collaboration with local communities and authorities. This collaboration can be challenging due to cultural and logistical barriers, as well as general mistrust in AI solutions.
## 2 Background  
### Intelligent Transportation Systems  
In the past decade, intelligent transportation systems (ITS) have received a lot of attention [1; 2; 5; 14; 37; 38; 39; 40]. The sophistication of the technology behind intelligent transportation systems is ever-improving. The advancements in processing capabilities, communication technology, and sensors now provide vehicles and drivers with the opportunity to figure out the conditions that exist on the road [2]. As a result, there is a high possibility of increasing the efficiency of the system. For example, drivers are given the opportunity to determine the best available route to reach their destination in minimum time. However, traffic congestions are extremely dynamic. For instance, even a small accident suffices to instantly create significant traffic congestion on the road. Despite that fact,if vehicles are provided with technology to gather relevant data and process them in real time, drivers will be able to immediately identify the congestion and look for alternative routes.  
The communication connectivity that clearly exists between vehicles benefits intelligent transportation systems [2; 39]. The acquisition of data can be considered to be one of the principal requirements behind intelligent transportation systems, since data provide the resource that is needed to make intelligent decisions. Imagine that an intelligent transportation system is assisting drivers in taking the best available route. One aspect that could be considered is the \"greedy-routing issue\" that can be solved with the assistance of centralized architecture. This is in contrast to a decentralized system, where all the information is being locally gathered and the data are gathered by sensors that are located on the road in the centralized architecture. Then, the data are centrally processed, and the resulting information is provided in a meaningful manner [1; 38].
## 2 The Experimental Platform
### Manufacturing and Mounting of the Instrumented Fuel Assembly  
The manufactured fuel assembly consists of a hexagonal bundle of 127 cylindrical fuel elements, surrounded by a hexagonal shroud or wrapper, and mounted on 13 support plate rails at the fuel assembly inlet. These support rails run horizontal over the fuel assembly and are shown in Figure 3. Each rail can slightly rotate about its mounting, allowing for some translation at the fuel pin inlet. The individual fuel pins are unrestrained along the length of the fuel assembly, but helical wire-spacers with a diameter of 1.8 mm wound around the outer surface of each fuel pin with a pitch of 265 mm keeps them separated from one another. The fuel pins were made of stainless steel cylindrical hollow tubes (made from 316 L steel) with an outer diameter of 6.55 mm, and a length of 1400 mm. In this manner, the geometry, restriction, and support of the manufactured fuel assembly matched the MYRRHA design. In addition, we filled each pin with representative internals, such as the gas plenum, a spring to accommodate the thermal expansion of the internals with respect to the fuel pin, and fuel pellet dummies. The resulting fuel pin weight was close to 900 g.  
Due to the limited space between the individual fuel pins (only 1.8 mm) and the anticipated small vibration amplitudes, optical fibre sensors are one of the very few options to measure vibrations in the fuel assembly. In our previous work [5; 17; 20] we demonstrated that FBGs are sufficiently sensitive to record the fuel assembly vibrations and that their useful time in a LBE environment is sufficient to carry out meaningful measurements. We shortly recall that a FBG consists of a periodic variation of the refractive index of the core of the optical fibre and is characterized by a resonance optical wavelength at which it reflects light, referred to as the Bragg wavelength \(\lambda_{\mathrm{B}}\)[21; 22]. Tracing the position of \(\lambda_{\mathrm{B}}\) as a function of time allows using the FBG as a sensor since the resonance wavelength changes with applied mechanical strain or changes in temperature. In this work, we took advantage of the multiplexing capabilities of FBGs, meaning that multiple FBGs can be combined into a single optical fibre to conduct quasi-distributed measurements. This feature was particularly useful when instrumenting the fuel pins with a high sensor density, which was desired to resolve fuel pin length-dependent phenomena, such as variations of the vibration amplitudes and associated strain levels along the fuel pin.
## 3 Discussion
### Principal Component Analysis and Cluster Analysis  
The acquired e-tongue data was evaluated using principal components analysis (PCA). PCA was performed with means centering and Pareto scaling to account for the variation of peak intensities within the chromatograms [41]. This is a convenient statistical method and it can identify and express the data in such a way that highlights their differences and similarities. It reduces the amount of data to a smaller number of new derived variables which represent the original data adequately [42]. Classical 90% confidence ellipses were used in determining the sample populations in the scores plots. In the experiment of e-tongue analysis, except the CK, RM was taken for analyzing and comparing to explain fully variability among different processing samples and find out the optimal processing conditions which made the samples more perfected.  
Cluster analysis (CA) is a generic method for a wide range of exploratory multivariate techniques. It is used for classifying a data set in homogeneous groups and represented in dendrogram which provides a simple way of visualizing the hierarchical structure of the clustering and the level at which each cluster is formed [43]. In order to diagnose and characterize the correlation among samples of different treatments, the resulting dendrogram was used for showing the similarity of the properties of e-tongue.  
From Figures 3 and 4, the main differences are found between samples processed with different temperature and for different times, respectively. Besides, CK presents the largest difference compared to samples of preheating as shown in Figure 3b, while in Figure 4b, the difference of CK is not direct. Based on the diversity of preheating temperature and preheating time on CK, it could be concluded that the effects of preheating temperature is significant compared to preheating time.