esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Variational Analysis Method (VAM)
#### 3.3.2 VAM Wind Field Retrieval Method Considering Only SAR DCA  
An empirical GMF, named CDOP, was developed by analyzing the wind measurements from the ASCAT scatterometer with DCA derived from Envisat ASAR, radar incidence angle, and the wind direction relative to the radar viewing direction. The DCA caused by ocean surface wind can be expressed as follows [14]  
\[\Delta f_{pp}=\alpha_{pp}F[X(\theta,\varphi,V,pp)]+\beta_{pp} \tag{14}\]  
where \(\theta\) is the incidence angle (in degrees), \(\varphi\) is the angle between the wind direction and the radar-looking direction, \(V\) is the wind speed at 10 m above the ocean surface, and \(pp\) represents the polarization mode. \(\alpha_{pp}\) and \(\beta_{pp}\) are two coefficients that depend on polarization. Additionally, although both Envisat ASAR and Sentinel-1 are operated in C-band, there are certain differences in system parameters, measurement modes, and data acquisition methods. Therefore, the applicability of CDOP to Sentinel-1 data still requires further investigation.  
Incorporating the DCA information observed by SAR, the observation field term can be expressed as  
\[\begin{split} J_{O\_DCA}(V,\varphi)\\ =\left(\frac{\text{CDOP}(V,\varphi,\theta)-f_{DCA}^{phys}}{\Delta f _{DCA}^{phys}}\right)^{2}\end{split} \tag{15}\]  
where \(f_{DCA}^{phys}\) represents the preprocessed geophysical DCA derived from Equation (3), and \(\Delta f_{DCA}^{phys}\) is the standard deviation of DCA, taken as 10 [2].  
By substituting Equations (11) and (15) into Equation (9), the total cost function can be obtained  
\[J_{DCA}=J_{O\_DCA}+J_{B}=\left(\frac{\text{CDOP}(V,\varphi,\theta)-f_{DCA}^{ phys}}{\Delta f_{DCA}^{phys}}\right)^{2}+\left(\frac{V-V_{B}}{\Delta V_{B}} \right)^{2}+\left(\frac{\varphi-\varphi_{B}}{\Delta\varphi_{B}}\right)^{2} \tag{16}\]
## 5 Case-Study: Resource Fitting
### Experiment 2: FL Clients' and Server Collocated vs. Isolated
#### 5.2.1 Collocation Impact on Training Round Duration  
We found that the difference in per-round duration between the co-located and isolated configurations was negligible. For TensorFlow, the co-located execution took about 40.7 s per round, while the isolated execution took 41.4 s per round, resulting in a mere 1.6% difference. Similarly, for PyTorch, the co-located execution took 90.8 s per round, and the isolated execution took 92.4 s per round, resulting in a 1.7% difference. Hence, _the duration of a training round is not impacted by the collocation of the FL server and clients_.
## 3 Finite Element Formulation  
### Spatial Discretization: NIURBS Basis Functions  
In general, the dynamics equilibrium equations of the geometrically exact beam can be transformed into a set of nonlinear algebraic equations by finite element spatial discretization and time discretization.  
In the spatial domain, the linearized governing equations described above are discretized by adopting the IGA; namely, both the geometry and the unknown variables are approximated by NURBS [24].  
Denote \(\boldsymbol{\
abla}\) as a non-decreasing knot vector, giving:  
\[\boldsymbol{\
abla}=\left\{\Psi_{1},\cdots,\Psi_{n+\varsigma+1}\right\} \tag{34}\]  
with the degree of basis functions \(\varsigma\) and the number of control point \(n\). According to the Cox-de Boor recursion formula, a B-spline basis function can be obtained from a linear combination of two B-spline basis functions of degree \(\varsigma\)-1, giving:  
\[\chi_{i,\varsigma}(\Psi)=\frac{\Psi-\Psi_{i}}{\overline{\Psi}_{i+\varsigma}- \Psi_{i}}\chi_{i,\varsigma-1}(\Psi)+\frac{\Psi_{i+\varsigma+1}-\Psi}{\overline {\Psi}_{i+\varsigma+1}-\overline{\Psi}_{i+1}}\chi_{i+1,\varsigma-1}(\Psi) \tag{35}\]  
with  
\[\chi_{i,0}(\Psi)=\left\{\begin{array}{rl}1&\text{if }\Psi_{i}\leq\Psi<\Psi_{i+1} \\ 0&\text{otherwise}\end{array}\right. \tag{36}\]  
when \(\varsigma=0\).  
Then, a NURBS basis function can be derived through introducing the weight factors \(w_{i}\), giving:  
\[N_{i,\varsigma}(\Psi)=\frac{\chi_{i,\varsigma}(\Psi)w_{i}}{\sum_{j=1}^{m}\chi _{j,\varsigma}(\Psi)w_{j}} \tag{37}\]  
in which \(\chi_{i,\varsigma}\) is the B-spline basis function. Obviously, the NURBS basis function degenerates into the B-spline basis function when all weight factors are equal to one. The position coordinate vector of any point can be calculated from interpolation and written as:  
\[\mathbf{r}(\Psi)=\sum_{i=1}^{m}N_{i,\varsigma}\mathbb{P}_{i} \tag{38}\]  
where \(\mathbb{P}_{i}\) is the control point.  
The continuity of the NURBS basis function is determined by the multiplicity of a knot. Given multiplicity k of a knot, the continuity of the basis function is C\({}^{\infty}\) between knots, while it is C\({}^{\varsigma-k}\) at the knot. Thus, NURBS can accurately describe various types of curves and surfaces, such as circles, hyperbolas, and elliptic surfaces.
# An Adversarial Deep Hybrid Model for Text-Aware Recommendation with Convolutional Neural Networks
## 4 Experiment
### Experimental Setting
#### 4.1.3 Evaluation Methodology  
We test the models on held-out datasets for both warm start and cold start recommendation, with the evaluation settings described as follows.  
**Warm Start.** We adopt 5-fold cross-validation. For each user, we do a 5-fold split of their interactions. We always assign items with less than 5 interactions to training set. This ensures that there are no cold start items in the test set. The removed interactions simulate the unobserved data (zero entries in the matrix) in the real scenario. After learning, we predict interactions across all unobserved items for each user.  
**Cold Start.** We again adopt 5-fold cross-validation. We consider the cold item recommendation setting and the situation is similar to recommending for cold users. We evenly split the set of all items into 5 folds. Similarly, we always assign items with less than 5 interactions to training set. We fit the models on the training set items and form predictive per user ranking of items in the test set.  
**Metrics.** After training, a top-\(N\) ranked list of unobserved items can be returned for each user, which are then compared to the test data for performance evaluation. We adopt two widely-used performance metrics: Recall@\(N\) and truncated Normalized Discounted Cumulative Gain (NDCG@\(N\)). Formally, let \(T\) represent the binary user-item interaction matrix of the test data and define \(\pi_{u}\) as the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#Records** & **\#Users** & **\#Items** & **Sparsity** & **Avg.** \\ \hline MovieLen-1m & 993,482 & 6040 & 3544 & 95.36\% & 97.09 \\ MovieLen-10m & 9,945,875 & 69,878 & 10,073 & 98.59\% & 92.05 \\ Amazon & 135,188 & 29,757 & 15,149 & 99.97\% & 73.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the evaluation datasets.  
column indexes of items in the recommended ranking list for user \(u\). \(\pi_{u}(k)\) returns the item column index in the \(k\)-th ranking position for user \(u\).  
Recall@\(N\) for user \(u\) is defined as:  
\[\text{Recall@}N(u,\pi_{u})=\frac{1}{min(N,\sum_{j=1}^{u}T(u,j))}\sum_{k=1}^{N}T (u,\pi_{u}(k)),\]  
where the denominator calculates the minimum between the truncation number \(N\) and the number of items that user \(u\) has interacted with. We use Recall@\(N\) to denote the average of Recall@\(N(u,\pi_{u})\) over all users. Recall@\(N\) considers whether the recommended items are in the user list.  
DCG@\(N\) for user \(u\) is computed by:  
\[\text{DCG@}N(u,\pi_{u})=\sum_{k=1}^{N}\frac{2^{T(u,\pi_{u}(k))}-1}{log(k+1)}.\]  
NDCG@\(N(u,\pi_{u})\) is the normalized DCG@\(N(u,\pi_{u})\) with respect to all possible rankings. We use NDCG@\(N\) to denote the average of NDCG@\(N(u,\pi_{u})\) over all users. NDCG takes into account the ranking positions of the recommended items and similarly, we report NDCG@\(N\) as the average measure over all users.
## 6 Evaluation and Performance
### Efficiency in Resource-Constrained Environments  
The efficiency of the scheme in resource-constrained environments was tested using an ARM Cortex-M4 processor integrated on an STM32F407 VG board (STMicroelectronics, Geneva, Switzerland). used by one of our beneficiaries. Key operations were evaluated in terms of execution time, memory usage, and power consumption.  
The results show that the scheme works efficiently, with a response time suitable for real-time applications, as shown in Table 2. Also, the memory usage is minimized due to simple arithmetic operations.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Operation** & **Mathematical Description** & **Complexity** \\ \hline Message blinding & \(M^{\prime}=M\cdot r\ mod\ n\) & \(O(k^{2})\) \\ Signing the blinded & \(\sigma^{\prime}=(M^{\prime})^{K_{priv}}\ mod\ n\) & \(O(k^{3})\) \\ Unblinding & \(\sigma=\sigma^{\prime}\cdot r^{-1}\ mod\ n\) & \(O(k^{2})\) \\ Signature verification & \(\sigma^{\prime}\ mod\ n=M\cdot T\) & \(O(k^{3})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity.
# Studies on Influencing Factors of Ammonium
## 1 Introduction  
Superalloy is widely used in the aerospace, oil, metallurgy, and chemical industries, automobile manufacturing, and so on [1, 2]. With the rapid consumption of resources, the recovery and utilization of waste superalloys have become important [3, 4]. Rhenium is a kind of rare metal resource that has low global reserves and a stiff price [5]. Therefore, extracting rhenium from waste superalloys is both economically and environmentally significant.  
There have been many reports about extraction processes of rhenium in recent years. [PERSON] et al. proposed a process for recovery of molybdenum and rhenium from molybden concentrate by optimizing roasting-acid and leaching-ion exchange, and that process has been applied practically [6, 7]. [PERSON] et al. contrived a new technology for extracted NH\({}_{4}\)ReO\({}_{4}\) with an ion exchanging method [8, 9]. [PERSON] et al. recovered rhenium from a molybden roaster fume as high-purity ammonium perrhenate [10, 11, 12, 13]. However, there are few reports about influencing factors of the morphology, crystallinity, and purity of NH\({}_{4}\)ReO\({}_{4}\) crystals in the recovery process, which leads to different batches of products having large differences in purity and microscopic morphology. Furthermore, it makes optimization of the production process difficult.  
According to the industrial process of rhenium recovery from waste superalloy, the electrolytes of waste superalloy are recrystallized to recover ammonium rhenate. However, it is difficult to determine the ammonia content and centrifugal speed as the important process parameters in this process, which leads to different batches of the products having large differences in purity, crystallinity, and microscopic morphology, and the products generally include W, Mo, and other elements with lower purity. This study innovatively analyzed the influence of ammonia content and centrifugal speed on crystal growth direction selectivity, microscopic morphology, aging time, and purity ofammoniumhenate, to provide theoretical experiences for determination of the process parameters in industrial recovery.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Model-Based Intent Recognition Methods  
Model-based intent recognition relies on predefined frameworks, combined with adaptive parameter adjustments, to create deterministic models. These methods are grounded in structured representations and domain knowledge, offering the advantage of interpretability and robustness. Common techniques are described below.
## 2 Corrosion Mechanism and Its Detection Techniques  
### Corrosion Mechanism in Steel in RC and PSC  
Pore water in concrete has various ions such as Na (OH) and CaCl\({}_{2}\), and the pH in pore water is over 12.0 due to the high alkalinity of cement hydrates. Under high-alkalinity conditions, the surface of steel can be protected by a passive film from external acid and ions. However, steel corrosion easily occurs owing to the consumption of hydrates, decrease in pH, and intrusion of halogen ions. Among the anions, the chloride ion (Cl\({}^{-}\)) easily penetrates into concrete and yields local corrosion, so-called pitting. The anodic and cathodic reactions can be expressed as Equations (1)-(3) [4; 23].  
\[Fe\ \rightarrow\ Fe^{2+}+2e^{-}\ (\text{Anodic reaction}) \tag{1}\]  
\[H_{2}O+\frac{1}{2}O_{2}+2e^{-}\to 2(OH)^{-}\ (\text{Cathodic reaction}) \tag{2}\]  
\[Fe+\frac{1}{2}O_{2}+H_{2}O\to Fe(OH)_{2}\ (\text{Overall reaction}) \tag{3}\]When oxygen is sufficient in the anodic areas, ferrous hydroxide, \(Fe(OH)_{2}\), can be further oxidized to other corrosion products, and this is accompanied by increasing volume, expressed as Equations (4)-(6).  
\[\text{Ferrous hydroxide}:\;Fe^{2+}+2 OH^{-}\;\rightarrow\;Fe(OH)_{2} \tag{4}\]  
\[\text{Ferric hydroxide}:\;4 Fe(OH)_{2}+O_{2}+2H_{2}O\;\rightarrow\;4 Fe(OH)_{3} \tag{5}\]  
\[\text{Hydrated ferric oxide}\;(\text{rust}):\;2 Fe(OH)_{3}\;\rightarrow\;Fe_{2}O_{3} \cdot H_{2}O+2H_{2}O \tag{6}\]  
Volume swelling at the steel is reported to range from a factor of two to ten, and this leads to cracking and spalling of concrete. In addition to the corrosion mechanism mentioned above, crevicle corrosion and macro-corrosion can easily occur in steel reinforcement or PS (Pre-Stressing) tendons close to corrosion positions [23,24]. For PS tendons, in particular, a complex corrosion mechanism occurs as the macro-cathode area and small macro-anode areas form by the cavities in the duct or grout quality degradation unlike RC structures. There are various causes of corrosion, such as sulfur ions in cement, the movement of deicing agents and moisture from the upper slab, and the influence of chalky grout. Macro-micro-complex corrosion mechanisms simultaneously occur as the strands are very adjacent to each other [23]. Figure 1 shows schematics of various corrosion mechanisms.
## 4 Approach
### Architecture Overview
#### 4.1.1 Partition Network \(\mathcal{N}_{g}\)  
Partition network \(\mathcal{N}_{g}\) is a fully connected network contains two hidden layers of size 64 and 128, and a 30% dropout layer is introduced between them to prevent overfitting. As shown in Figure 2, we embed each operation with four attributes. The following Table 1 lists the four parts of the operator features:  
1. Runtime (time). Designates the runtime of executing the operator on the specified device, in microseconds. To ensure algorithm convergence, standardization (Z-score) processing is performed on the runtime.
2. Structure (structure). Use Node2 vec to learn and generate graph embedding vectors. Node2 vec is a common graph feature extraction method in graph representation learning. It combines the RandomWalk and SkipGram models to learn the co-occurrence relationship between nodes, and generates dense vectors for each node.  
Figure 1: The architecture of Trinity.  
Figure 2: The double-layer network of agent.  
Node type (type). This includes the calculation time of the node, the memory size of the node, and the operator type of the node. We use natural language processing methods; collect 200 commonly used operator words in the TensorFlow API, such as Conv2D, MaxPool, or MalMul; and build a vocabulary, such as Conv2D, MaxPool, or MalMul.
4. Output shape (out). Accumulate all output tensor dimensions of the current operator. Accumulate the dimensions of all output tensors of the current operator. For example, if the existing convolution operator outputs a four-dimensional tensor with shape \((2,2,1,64)\), the output shape is \(256=2\times 2\times 1\times 64\). The output size of an operator can not only represent the maximum traffic that the operator may generate, but also reflect the memory overhead that the operator may generate.  
After the embedding of all operators, the embedding is input to the partition network to generate groups, and the operators in the group are merged into group embedding and output to the schedule network as follows, as shown in Table 2.  
1. Group type. Take the average of all operator type embeddings in the group as the first part of the group embedding.
2. Group outsize. The output tensor size of all operators in the group is averaged as the second part.
3. Relationship Between Groups. This represents the connection relationship between groups. The length of the embedding represents the number of groups (for example, if the operator is divided into 256 groups, the vector length is 256). If an operator in the current group is connected to an operator in the \(i\)th group, the \(i\)th position of the vector is set to 1, otherwise, it is 0.
## 3 Methods  
### Supervised Classification  
In agriculture, the RF algorithm is commonly used for classification and regression problems [40]. This classifier is a decision tree (DT)-based ensemble machine learning classifier. The bootstrap with replacement technique generates multiple sample data sets and, as a result, individual and independent DTs, the results of which are combined for the majority decision [40]. The expected generalization error based on \"out-of-bag\" (OOB) data is considered as one of the primary advantages derived from the RF algorithm. Other advantages include the internal relevance measure of the input features and the ability to adapt to a large number of input features. Many previous studies have demonstrated the high efficiency of feature importance in selecting the most relevant variables for crop type/LC classification in order to minimize over-fitting, and for well-organized computations [25; 41; 42; 43].  
For this research, the \"Mean Decrease in Accuracy\" (MDA) measure was employed for a recursion feature selection, as in earlier studies [11; 44; 45]. It should be noted that the derived feature significance was calculated in R version 4.2.2 using the random forest package [46].  
The RF classification model requires two hyper-parameters, which are the number of randomly nominated features for each node (mtry) and the number of trees (ntree). In particular, we implemented ntree setting from 50 up to 1500 with the default of the mtry  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature Data**} & **2001** & **2006** & **2011** & **2016** & **2021** \\ \cline{2-6}  & **L5 \& L7** & **L5** & **L5** & **L8** & **L8 \& L9** \\ \hline Spectral bands & 12 & 6 & 6 & 6 & 12 \\ Vegetation indices (VIs) & 18 & 9 & 9 & 9 & 18 \\ Topographic & 2 & 2 & 2 & 2 & 2 \\ \hline Total (Full Set of Features) & 32 & 17 & 17 & 17 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: All features (i.e., the spectral bands, vegetation indices (VIs) and topographical variables) were used for agricultural fields/LC classification in 2001, 2006, 2011, 2016 and 2021.  
(the square root of the total number of input variables). Subsequently, we selected the best parameter as 1000 trees based on the OOB result for all five base years.  
Based on the 2303 sampling points, RF models were trained to categorize eight crop type/LC classes for each of the five base years (Table 1). The feature variables, comprising spectral bands, derived VIs, and supplementary geo-data, were used for all time steps. Appendix A lists the top ten most important features for each year (details in Table 2).  
The MDA and OOB findings for the five base years were examined for evaluating the efficiency of the optimized RF classification models, and the final RF models were evaluated from the independent data set using the GEE and utilized to predict the entire data set.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 1 Introduction
### Background
While artificially simulated head movements could reduce the localization error [6], such systems require access to a motorized dummy-head microphone [17] or necessitate the incorporation of a spatial audio renderer, essential for a dynamic synthesis of binaural stimuli with simulated head rotation [6]. The adverse influence of reverberations on performance of binaural models could be partially reduced by processing direct sounds and room reflections separately. However, a decomposition of audio recordings into their prime and ambient components still constitutes a challenging task [18]. The other solutions involve a so-called multi-conditional training [6] or incorporation of a precedence effect model [19]. In summary, over the past two decades, substantial improvements have been made with regards to binaural modelling of human hearing, in particular in relation to localization of multiple speakers in binaural speech signals [6; 7; 8; 9]. However, to the best of the authors' knowledge, no formal studies towards the automatic characterization of complex audio scenes in binaural recordings of music have been reported yet, with the exception of the paper summarizing the pilot experiment undertaken by the present authors [20]. The real-world binaural recordings, acquired predominantly from the publicly available Internet repositories, were exploited in the aforementioned pilot experiment [20]. The number of excerpts used in the previous study was relatively small (600), making it difficult to train the model and then to reliably validate it. Moreover, these recordings were annotated manually by the first author, potentially introducing some level of researcher bias. Furthermore, an analysis of the importance of the extracted features revealed the surprising prevalence of spectro-temporal metrics over the binaural cues, which might have been caused by genre imbalance (spectral cues are known to be instrumental in the area of music genre classification [21]). For example, one of the scenes was overrepresented by environmental recordings (soundscapes), whereas another scene was dominated by pop music excerpts. Therefore, it could be hypothesized that the classifier used in the pilot experiment might have worked, to some extent, as a genre recognizer rather than as a spatial scene classifier. In order to circumvent the drawbacks of the pilot test, it was necessary to repeat the experiments using a large number of contrived excerpts (as opposed to a small set of real-world recordings), with an equal number of genre types for each spatial scene and with a controlled horizontal distribution of audio sources around a listener. The above considerations underlay the work described in this paper.