esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermogravimetric Analysis  
Thermogravimetric analysis (TGA) was used to examine the thermal stability of the different wood samples: the TGA and differential thermogravimetric (DTG) curves are shown in Figure 10a,b, respectively. The curves could be divided into three regions, which was consistent with previous studies [33; 34]. Water evaporation occurred in the range of 50-150 \({}^{\circ}\)C and represented high-temperature evaporation of the bound water in the wood cells. The active pyrolysis zone was around 200-400 \({}^{\circ}\)C, and there were two peaks in this area, representing the degradation of cellulose and hemicellulose. The passive degradation zone, which represented lignin degradation, occurred in the range of 400-500 \({}^{\circ}\)C, and no peaks were observed.  
Figure 10: TGA (**a**) and differential thermogravimetric (DTG) (**b**) curves of the 10% DM + 10% MA-treated samples in different groups.  
Figure 9: The impact strength of the wood samples after the four treatments 103, ZC-103, 120, and ZC-120 (**a**); fracture surfaces of the samples after the impact test (**b**).  
In Table 2, the \(T_{onset}\) of the samples that contained ZnCl\({}_{2}\) was higher, which may be because of the brittleness of the wood samples. The addition of ZnCl\({}_{2}\) changed the particle size of the modified sample powder, and DM/MA had a smaller particle size and was more easily dispersed in the wood matrix: the good interface between the chemicals and the wood matrix made the samples more sensitive to temperature changes [35; 36]. The main degradation of carbohydrates in the wood occurred in the range of 200-400 \({}^{\circ}\)C, while the \(T_{max}\) of the untreated samples was 348.21 \({}^{\circ}\)C, indicating that the degradation occurred earlier than in the untreated group (Table 2). As the temperature continued to rise, the residual mass of each group changed little. The order of thermal stability from strong to weak was ZC-103 \(>\) ZC-120 \(>\) 103 \(>\) 120 \(>\) Ctrl: the two groups (ZC-103 and ZC-120) with ZnCl\({}_{2}\) exhibited better thermal stability, and the residual mass values were 27.65% and 26.97%, respectively. The untreated wood samples had the worst thermal stability, and the residual mass was 14.72%, which might have been caused by the proportion of water in the wood. The proportion of water in the mass lost was relatively higher than in the other samples. In general, these results indicated that the addition of ZnCl\({}_{2}\) improved the thermal stability of the modified samples and the quality of the residual mass because ZnCl\({}_{2}\) acted as a dehydrator at that time and caused the aromatization of carbon [37; 38]. In addition, ZnCl\({}_{2}\) affected the physical bonding and enhanced the interaction between the chemicals and the matrix, thereby forming a barrier that minimized the volatility of the pyrolysis process of the DM/MA-modified samples.
## 3 Best Relay Selection Design and Transmission Rate Analysis  
In this section, the design of the best relay selection and the analysis of transmission rate are given.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 5 Experiments and Results
### Comparison to Other Algorithms  
We compare CODB with other three competing algorithms (i.e., Brushfire, Dynamic Brushfire, and algorithms proposed by [PERSON] et al. [23] (we abbreviate it as BL below)) which are discussed in Section 2 in four typical scenarios as shown in Figure 9.  
We set aside four grid maps (from Figure 9a to Figure 9d) which have the same size (\(100\times 100\)) while allowing different proportions of the obstacles (i.e., fully reconstruction, 75%, 50%, and 25% respectively) to randomly change their positions or shapes. After each set of changes, we use the above mentioned four algorithms to construct or reconstruct the DMs. We run each algorithm on each scenario 100 times, and the comparisons of the performances among  
For the first scenario in which full reconstructions are inevitable since all the obstacles are moved or reshaped, the extra operations that enable local repairs make Dynamic Brushfire (8.61 ms) and BL (7.22 ms) slower than their static counterpart, Brushfire (6.34 ms). However, due to the integration of Canonical Ordering strategy, CODB can substantially prune the search space; thus, it still maintains an obvious advantage over the other three algorithms in time efficiency (4.26 ms), even in the case of full reconstructions. As for the other three scenarios, as the proportion of the dynamic obstacles decreases, dynamic algorithms gradually manifest their superiority in speed. Among the three dynamic algorithms, CODB is also faster than the others due to fewer cell visits, reducing the computation time by at least 50%.
## 4 Technological Innovations
### Gesture Recognition  
Gesture recognition, like BMW's gesture control [30], allows drivers to interact with their vehicle using simple hand movements, making tasks such as volume adjustment or call answering more intuitive and safer. Auditory-supported air gestures allow drivers to look more at the road screens [31]. This technology reduces physical interaction with controls, which not only adds convenience but also addresses hygiene concerns, particularly in contexts where minimizing physical touch is crucial. Gesture recognition reduces the reliance on traditional physical controls, thus improving both ease of use and hygiene in automotive settings [32; 33]. For example, the gesture control recognition proposed for the first time by Aptiv, originally launched on the BMW 7 Series in 2015, is now being deployed across a wide range of models. A diagram showing the gesture-based control system is shown in Figure 5.  
Figure 5: Diagram showing gesture-based control system minimizing physical interaction with interface (Aptiv company, available online: [[https://www.aptiv.com](https://www.aptiv.com)]([https://www.aptiv.com](https://www.aptiv.com)) (accessed on 26 January 2025)).  
As illustrated in Figure 4, gesture recognition technologies directly contribute to improving multimodal interaction by enabling intuitive non-verbal controls. In addition, by minimizing physical contact and visual distraction, they also enhance driving safety.
## Results  
### VIS/NIR Spectrometer for TSS Detection of Orange  
#### 3.1.1 Sampling Position Comparison  
To compare the influence of sampling positions of 'Aiyuan 38' orange on detection ability, four positions, namely the stem, calyx, equator, and between the equator and stem were tested in this study. The average spectral reflectivity of different sampling positions of orange are shown in Figure 1. The shape of the response curve for the stem position is obviously different from the other three positions due to the effect of stem. In addition, the absorption peak and reflectivity value for the stem position is obviously less than the other three sampling positions, that is, readings from the stem position acquire less spectrum information than readings from other positions do, which should therefore exclude methods which measure from the stem position. The shape of the response curves from the calyx position are slightly different with those from the equator position or the position between the equator and stem, especially on the curve slope from about 500 to 900 nm, due to the structure of calyx itself being relatively different from the surface at the equator or between the equator and stem. Further analysis is still needed to ensure the optimal detection position.  
To further select the optimal sampling position, PLSR was applied to detect the TSS of orange based on full spectral data from different sampling positions. There were 117 orange samples in total, with 92 samples were randomly selected as a calibration set, and the remaining 25 samples forming the validation set. The same calibration and validation sets were used throughout the research to better compare the detection ability. For PLSR results, fitting the correlation coefficient (R\({}^{2}\)) is the key parameter in evaluating the correlation between the predicted value and the actual value. The range of R\({}^{2}\) is from 0 to 1, where the greater the R\({}^{2}\), the better the predictive ability. Besides, the root mean squared error (RMSE) and the slope are other subsidiary parameters for detection effect evaluation. The closer the RSME value is to 0, the better the prediction effect. And the closer the slope value is to 1, the better the prediction effect. The PLSR detection results of orange TSS based on full spectral data from stem, calyx, equator, and between the equator and stem positions are shown in Figure 2a-d, respectively. As can be seen, measuring from the calyx position results in better TSS detection ability than other sampling positions, with the R\({}^{2}\), RMSE, and slope for calibration set of 0.9571, 0.2147, and 0.9571, respectively, for validation set of 0.5650, 0.7927, and 0.8348, respectively. Thus, the spectral data of calyx position was used for next TSS detection modeling.  
Figure 1: Average of spectral response of different positions on orange.  
Figure 2: Partial least squares regression (PLSR) detection results of total soluble solid content (TSS) based on full spectral data of (**a**) stem position; (**b**) calyx position; (**c**) equator position; (**d**) position between equator and stem. RMSE: root mean squared error.
## 2 Methods
### Artificial Neural Network  
Here we use a single hidden layer model for the convenience of notation. The framework can be expanded to arbitrary number of hidden layers. A single hidden layered neural network is defined as  
\[\widehat{\mathbf{y}}=\mathbf{f}^{\mathbf{W}_{1},\mathbf{W}_{2},\mathbf{b}_{1 },\mathbf{b}_{2}}(\mathbf{x}):=S_{2}(S_{1}(\mathbf{x}\mathbf{W}_{1}+\mathbf{ b}_{1})\mathbf{W}_{2}+\mathbf{b}_{2}).\]  
Here \(\widehat{\mathbf{y}}=(\widehat{y}_{1},\widehat{y}_{2})\in\mathbb{R}^{2}\), \(S_{1}\) and \(S_{2}\) are activation functions and \(\boldsymbol{\omega}=\{\mathbf{W}_{1},\mathbf{W}_{2},\mathbf{b}_{1},\mathbf{b}_{ 2}\}\) are the parameters of network model. In particular, \(\mathbf{W}_{1}\in\mathbb{R}^{m\times k}\), \(\mathbf{W}_{2}\in R^{k\times 2}\), and \(\mathbf{b}_{1}\in\mathbb{R}^{k}\), and \(\mathbf{b}_{2}\in\mathbb{R}^{2}\). In this study we use convolutions to transform the input to the hidden layer. This means that we restrict the structure of the weight matrix \(W_{1}\) to be a convolution and instead of learning the best fit over all possible weight matrices learn the best fit over all weight matrices that are convolutions. Using convolutions in this transformation is what is referred to as a convolutional layer.  
To obtain the probability of \(\mathbf{x}\) being classified with a label \(\mathbf{y}\), we use a SoftMax activation function, i.e.,  
\[S_{2}(\mathbf{z})=\frac{\exp(\mathbf{z})}{\exp(z_{1})+\exp(z_{2})}.\]  
In classification the cross entropy loss function is the natural choice, and minimizing the cross entropy is equivalent to minimizing the negative log likelihood  
\[E^{\omega}(\mathbf{X},\mathbf{Y})=-\sum_{n=1}^{N}\mathbf{y}_{n}\log(\widehat{ \mathbf{y}}_{n})=-\log p(\mathbf{Y}|\boldsymbol{\ell}^{\omega}(\mathbf{X})).\]  
To avoid the model overfitting on the training set, which typically results in a failure to generalize the model, regularization is often added. One of the choices is \(L_{2}\) regularization, which penalize the model parameters with squared \(L_{2}\) norm and gives the following objective function  
\[\mathcal{C}(\boldsymbol{\omega})=-\log p(\mathbf{Y}|\boldsymbol{\ell}^{ \omega}(\mathbf{X}))+\lambda_{1}||\mathbf{W}_{1}||_{2}^{2}+\lambda_{2}|| \mathbf{W}_{2}||_{2}^{2}+\lambda_{3}||\mathbf{b}_{1}||_{2}^{2}+\lambda_{4}|| \mathbf{b}_{2}||_{2}^{2}, \tag{1}\]  
where \(\lambda_{i}>0\), \(i=1,\ldots,4\) are regularization parameters. The importance of \(L_{2}\) regularization will become evident in Section 2.4. Minimizing the objective Equation (1) with respect to the parameters \(\boldsymbol{\omega}\), trough the techniques of back-propagation and stochastic gradient decent is the core task in machine learning. This process will give a point estimate for each model parameter. In many situations, it is important to quantify the uncertainty of the prediction outcome. In this case a Bayesian take on the problem is suitable.
# A Phased Array Antenna with New Elements Designed Using Source Transformations
## 4 Conclusions  
In conclusion, it has been shown how the transformation electromagnetics/optics technique can be employed to transform a linear dipole array into an array of complex geometry antennas, where the individual elements of the transformed array are \"pinwheel\"-shaped antennas transformed from dipole antenna elements. The transformed parameters are derived, and through full-wave simulations, it is shown that the original linear dipole array and the transformed \"pinwheel\" linear array have the same behaviors in some specified region, verifying the correctness and effectiveness of the proposed array. It is believed that the proposed array will have tremendous potential for future applications in structurally integrated and conformal phased arrays for wireless communications, radars, and sensing where structural and mechanical constraints do not align with antenna performance.  
Figure 6: The electric fields for the proposed TO-based “pinwheel” array for different values of loss factor (\(tan\delta\)) (**a**) \(tan\delta=0.01\); (**b**) \(tan\delta=0.1\); (**c**) \(tan\delta=0.3\); (**d**) \(tan\delta=0.5\).  
**Author Contributions:** Conceptualization, D.M. and B.D.B.; methodology, D.M.; software, D.M. and J.L.; validation, D.M., J.C., S.D. and J.W.A.; formal analysis, D.M., J.L. and J.C.; investigation, D.M., B.D.B., S.D. and J.W.A.; resources, B.D.B.; writing--original draft preparation, D.M.; writing--review and editing, D.M., S.D., J.W.A., M.S.A. and B.D.B.; supervision, B.D.B., M.S.A. and J.W.A.; project administration, B.D.B., M.S.A. and J.W.A.; funding acquisition, B.D.B., M.S.A. and J.W.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the US Air Force Research Laboratory Munitions Directorate under Grant DSC6511-01.  
Not Applicable.  
**Data Availability Statement:** The data presented in this study are available on reasonable request from the corresponding authors.  
**Acknowledgments:** S.D., M.S.A. and J.W.A. would like to thank the Chief Scientist office of AFRL's Munitions Directorate for their support.  
The authors declare no conflict of interest.
# SimCDL: A Simple Framework for Contrastive Dictionary Learning
## 4 Algorithms
### Incoherent Dictionary Learning via Distance Barrier (IDB)  
Incoherent Dictionary Learning via Distance Barrier (IDB) introduces an Incoherent Distance Barrier (IDB) function to discriminate between class dictionaries. This formulation, inspired by work on incoherent frames [17], aims to promote incoherence between classes for classification tasks. The IDB optimization problem is expressed as  
\[\begin{array}{ll}\underset{\mathbf{D}_{i},\mathbf{X}_{i}}{\text{min}}&\sum_{i=1}^{C }\|\mathbf{Y}_{i}-\mathbf{D}_{i}\mathbf{X}_{i}\|_{F}^{2}+\gamma\sum_{i=1}^{C}\sum_{j\
eq i }f\big{(}\mathbf{D}_{i},\mathbf{D}_{j}\big{)}\\ \text{s.t.}&\left\|\mathbf{x}_{i,\ell}\right\|_{0}\leq s,\ell=1:N\\ &\left\|\mathbf{d}_{i,j}\right\|=1,j=1:n\end{array} \tag{14}\]  
where \(f(\cdot)\) is a function that enforces incoherence.  
The first version of IDB utilizes a distance barrier function:  
\[b(\mathbf{D}_{i},\mathbf{D}_{j})=\sum_{k}\sum_{l}\Bigl{[}\max(0,M-\|\mathbf{d}_{k}^{(i)}- \mathbf{d}_{l}^{(j)}\|^{2})+\max(0,M-\|\mathbf{d}_{k}^{(i)}+\mathbf{d}_{l}^{(j)}\|^{2}) \Bigr{]}, \tag{15}\]  
where \(M\) is a soft margin that indicates the minimum allowable distance between atoms from different classes. This function considers scenarios where atoms are either aligned or opposed. The soft margin \(M\) is derived from the mutual coherence constraint \(\mu\) between atoms:  
\[|\mathbf{d}_{i}^{T}\mathbf{d}_{j}|\leq\mu,\;\forall i\
eq j, \tag{16}\]  
which is equivalent to  
\[\left\{\begin{array}{ll}\|\mathbf{d}_{i}-\mathbf{d}_{j}\|^{2}\geq M\\ \|\mathbf{d}_{i}+\mathbf{d}_{j}\|^{2}\geq M\end{array},\;\forall i\
eq j,\right. \tag{17}\]  
where \(M=2(1-\mu)\).  
To further enhance incoherence, the function \(f(\cdot)\) in (14) includes terms like  
\[f_{j}(\mathbf{d}_{j})=\|\mathbf{W}_{j}\mathbf{D}_{j}^{T}\mathbf{d}_{j}\|^{2}+\lambda b(\mathbf{d}_ {j}), \tag{18}\]  
where \(\bar{\mathbf{D}}_{j}\) concatenates all dictionaries except the one corresponding to class \(j\), and \(\mathbf{W}_{j}\) is a diagonal weighting matrix defined as  
\[w_{ij}^{2}=\max(|\mathbf{d}_{i}^{T}\mathbf{d}_{j}|/\mu,1). \tag{19}\]The first term discourages coherence across all atoms, while the barrier function \(b(\cdot)\) prioritizes updates for nearby atoms. The optimization is carried out using block coordinate descent, updating one atom at a time with a gradient descent approach:  
\[\mathbf{d}_{j}\leftarrow\mathbf{d}_{j}-\gamma_{k}g_{j}(\mathbf{d}_{j}), \tag{20}\]  
where the gradient \(g_{j}(\mathbf{d}_{j})\) accounts for both representation error and inter-class incoherence. This equation represents an update step in the optimization procedure, which adjusts \(\mathbf{d}_{j}\) to minimize the objective function; the left side is the new value of the atom, and the right side uses the old value.
## 3 Evolution of Wireless Communication Technology (1G-6G)
### Sixth Generation (6G) Technology  
The Sixth Generation (6G) technology was envisioned in 2019-2020 to transform the \"Internet of Everything\" into an \"Intelligent Internet of Everything\" with more stringent requirements in terms of a high data rate, high energy efficiency, massive low-latency control,high reliability, connected intelligence with Machine Learning (ML) and Deep Learning (DL), and very broad frequency bands [19]. Three new application services are proposed; Computation Oriented Communications (COC), Contextually Agile eMBB Communications (CAeC), and Event-Defined uRLLC (EDuRLLC) [19]. COC will enable the flexible selection of resources from the rate-latency-reliability space depending on the available communication resources to achieve a certain level of computational accuracy for learning approaches. CAeC will provision eMBB services that would be adaptive to the network congestion, traffic, topology, users' mobility, and social networking context. 6G-EDuRLLC targets the 5G-URLLC applications that will operate in emergency or extreme situations having spatial-temporal device densities, traffic patterns, and infrastructure availability.  
Table 2 summarizes the characteristics of the wireless communication networks from 1G to 6G [16; 32].
## Results and Discussion
### Benefits of Storage Additives  
The results of this laboratory study showed the potential of adding biochar and clinoptilolite in order to reduce NH\({}_{3}\) emissions in the storage of pig slurry, without increasing the GWP, and avoiding \"pollution swapping\" between NH\({}_{3}\) and N\({}_{2}\)O emissions. However, no advantages were gained from the combination of these two additives on gaseous emissions. In addition to the gains on the reduction of environmental impacts to the atmosphere during the storage stage, the treatment of slurry either by biochar or clinoptilolite will increase the N availability for crops or consequently increase yields. Moreover, biochar will improve the soil biological activities, nutrient retention, water-retention capacity, increase of pH value and amount of soil organic matter [21], whereas clinoptilolite will increase moisture retention in the soil due to increased soil surface area and cation exchange capacity [37].  
Although caution must be exercised when extrapolating laboratory studies to farm-scale conditions, the inclusion of the additives with the slurry at the storage stage and prior to soil application represent an efficient method because of reduced environmental impacts from these two stages of manure management (storage and soil application). Therefore, integrated studies are necessary to evaluate the impact of biochar and clinoptilolite on all stages of animal manure management, namely feeding, housing, storage and soil application.
## 3 Proposed Methodology
### Proposed Architecture Using BERT and Neural Networks
#### 3.5.3 ELECTRA + CNN  
We proposed an architecture following Algorithm 7, which integrates a 1D CNN with an ELECTRA-BERT encoder. The proposed architecture begins with an input layer that expects variable-length text inputs and which incorporates a pre-trained text preprocessing layer and handles initial text processing steps such as tokenization and other necessary preprocessing steps tailored to the selected pre-trained model [62]. The next layer is the encoder layer, named Electra, which is applied to the preprocessed text inputs to generate contextualized word embeddings that help the model to capture semantic information and understand the meaning and context of the text.  
```
0: Preprocessed data
1. Load the pre-trained ELECTRA model
2. Create 1D CNN Model (a)  Add Conv1D (activation = relu) (b)  Add Conv1D (activation = relu) (c)  Add Conv1D (activation = relu) (d)  Add Global Max Pooling layer (e)  Add Flatten Layer (f)  Add dense layer (activation function = ReLU) (g)  Add dense layer (activation function = ReLU) (h)  Add dropout layer (rate = 0.1) (i)  Add dense layer (activation function = softmax)
3. Loop for \(n\) epochs: (a) ELECTRA Encoder  i.  Tokenize data into sequences of subwords tokens using ELECTRA tokenizer  ii.  Add special Tokens (e.g., [CLS],[SEP]) (b)  Encoded data  i.  Pass the tokenized sequence through ALBERT to obtained contextualized word embeddings.  ii.  Extract the final hidden state representation for each sequence from Electa output. (c) Train the model  i.  compile the model with categorical cross-entropy loss function and Adam optimizer.  ii.  fitted the model by feeding the ELECTRA embeddings as input (d) Evaluate the model  i.  Assessed the model performance using metrics like precision, recall and F1 score.
```  
**Output**Classification results (hate, offensive, neither hate nor offensive) ```  
**Algorithm 7** Hate Speech Classification Using ELECTRA Encoder and 1d CNN.  
The proposed architecture then employs two 1-dimensional convolutional layers with ReLU activation. These layers capture local patterns within the text and extract meaningful features that contribute to the classification. Following the convolutional layers, a global max pooling layer is used to aggregate the extracted features. This layer selects the maximum value within each receptive field, reducing the sequence length to a fixed-size representation. This pooling operation ensures that the model focuses on the most salient features across the entire input sequence. The pooled features are then flattened into a one-dimensional vector to facilitate further processing. This is followed by fully connected layers, including a dropout layer for regularization purposes, to process the flattenedfeatures and learn complex patterns within the data. Finally, the model includes a dense output layer with softmax activation that produces class probabilities for the target classes.