esa-sceva/satcom-chunk-collection huggingface dataset:
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 2 Proposed Methods
### Proposed Classification Models
#### 2.3.2 CNN-Transformer-Based Algorithm (Proposed-II)  
To enhance sequential speech feature modeling, we propose a CNN-Transformer hybrid architecture, as shown in Figure 7, integrating a CNN feature extractor, a format conversion module, and a three-layer Transformer encoder to combine local and global feature modeling. The CNN module includes four convolutional layers with 24, 48, 24, and 4 filters, respectively, each using 3 \(\times\) 3 kernels, stride 1, padding 1, and batch normalization after each layer. A 2 \(\times\) 2 max-pooling layer after the second convolution reduces the feature map from 80 \(\times\) 39 to 40 \(\times\) 19, lowering computational complexity. The output is processed through Permute and Reshape layers into a sequence and projected to a 64-dimensional embedding space (\(d_{model}=64\)). The Transformer encoder, with three layers, each containing 8 attention heads (\(n_{head}=8\)) and a 128-dimensional feedforward network  
\begin{table}
\begin{tabular}{c c} \hline \hline  & Input Size: 80 Frames \(\times\) 39 Coefficients \\ \hline \multirow{3}{*}{Layer 1} & \multicolumn{2}{c}{Hidden Layer} \\  & Conv2D: filter: 32, kernel: 3 \(\times\) 3, strides: 1 \(\times\) 1, ReLU \\ Layer 2 & Conv2D: filter: 32, kernel: 3 \(\times\) 3, strides: 1 \(\times\) 1, ReLU \\ Layer 3 & Conv2D: filter: 64, kernel: 3 \(\times\) 3, strides: 1 \(\times\) 1, ReLU \\ Layer 4 & Conv2D: filter: 32, kernel: 3 \(\times\) 3, strides: 1 \(\times\) 1, ReLU \\ Layer 5 & Conv2D: filter: 16, kernel: 3 \(\times\) 3, strides: 1 \(\times\) 1, ReLU \\ Layer 6 & Conv2D: filter: 3, kernel: 3 \(\times\) 3, strides: 1 \(\times\) 1, ReLU \\ Layer 7 & Flatten \\ Layer 8 & Fully connected layer (3) \\  & Output: 16 Class, Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Layer information of the proposed CNN-based classification model.  
Figure 6: Architecture design of the proposed CNN-based classification model. (Yellow in the input represents features post-MFCC, blue indicates regions with particularly strong energy, light yellow denotes convolution operations, green signifies fully-connected layers).  
\(\left(dim_{feedforward}=128\right)\), applies a 0.1 dropout rate to prevent overfitting, capturing long-range dependencies to enhance speech pattern recognition. Finally, features are flattened and passed through a fully connected layer for 16-class prediction, optimized with Adam, and processed via a Softmax function to compute class probabilities, with the highest probability determining the result. The detailed layer structure is presented in Table 2.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.1 LSTM Results
The error rate increased to 50.4% since the increase in epochs with only one feature (TROPOMI) did not serve as sufficient predictors. Despite having the largest error rate, the MAE was slightly lower than LSTM experiment #2 (most optimal) in Table 4, indicating that individual predictions were closer to actual EPA NO\({}_{2}\) on average. Table 5 lists the actual EPA NO\({}_{2}\) values from the test set compared to predicted values for LSTM experiment #5 (Table 4). Figure 12 shows the model loss for the 6 th experiment with 430 epochs. It revealed certain converging points around 250 and 380 epochs, prompting additional experiments for the various feature sets listed in Table 4 with corresponding epochs. These epoch tests revealed that 380 was optimal across all experiments. Figure 11: Graph showing the LSTM model [430 epochs] capture of EPA NO\({}_{2}\) values over the year using a single power plant. LSTM experiment #20 (Table 4) produced the best results in this section based on the error rate for the first scenario. The addition of daily MERRA-2 and MCD19A2, as correlated predictors with the most optimal epoch, reduced the error rate to 39.9% average prediction error. Although this experiment produced the best results, LSTM experiment #10 (Table 4) came in a close second with an error rate of 41.9%. The tenth experiment utilized the same setup as the 20 th experiment but without MCD19A2 and TROPOMI, indicating that these datasets only offered a slight performance boost with this model. The same experiments were applied for the second scenario. Figure 15 corresponds to LSTM experiment #2 (Table 6). It displays the second scenario's train and test predictions over actual EPA NO\({}_{2}\) averaged by date. The experiment had a 63.71 % error rate, which is quite high and indicative of poor predictive ability. Despite the fact that the model in Figure 15 had been tested with various tunings and epochs, the LSTM model failed to predict EPA NO\({}_{2}\) in this scenario adequately. Additionally, due to the averaging necessary to avoid EPA NO\({}_{2}\) data overlap across date features, this model was biased toward a narrow range of EPA NO\({}_{2}\) values between 0.08 and 0.10. Figure 16 shows the model loss over 1000 epochs. This was conducted to verify if the model had any further convergence points. This figure represents the lack of convergence points and shows that the model achieved the lowest error on a single sample of the data at 800 epochs. This led to an 800 epochs test in Table 6 that yielded the lowest error rate of 55.79% in the second scenario of the experiments. Figure 16: Graph of model loss through a 1000 epoch training cycle for the LSTM model all coal-fired power plants second scenario. Figure 15: Graph showing the general model capture [430 epochs] of averaged EPA NO\({}_{2}\) values split by train and test sets for all coal-fired power plants second scenario.
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 3 Dataset
### Data Preparation
#### 3.2.1 Remove Punctuation and Stop Words  
We conducted punctuation and stop word removal as part of our data preprocessing. By eliminating frequently used stop words and punctuation marks from the features, which are non-informative, we enhanced the efficiency of our models. Additionally, we manually added extra stop words based on their frequency in the dataset. Following feature engineering, we visualized the top 15 common words in emails. The most prevalent words were \"com,\" \"company,\" and \"please.\" Furthermore, we examined the top 15 frequent adjectives and adverbs. The top adjectives included \"new,\" \"financial,\" and \"free,\" while the top adverbs were \"also,\" \"forward,\" and \"well.\" These findings align with the typical characteristics observed in spam emails, such as web-based content and widespread fake financial offers. The process of explicitly managing stop words yields multiple benefits during the detection of spam emails. Removing common stop words helps clear data noise, which results in better classification accuracy because the model focuses on important words rather than generic text. To improve model performance, it is beneficial to manually tag high-frequency but non-informative words as stop words because this action prevents the system from placing importance on irrelevant terms. The improved method produces better representations of features that result in increased generalization and enhanced results when working across multiple spam email datasets.
## 4 Comparisons of Results for DFE, MLD Algorithms  
The DFE and MLD algorithms are first compared regarding their complexity levels in time and space. Time complexity describes the duration an algorithm requires to be executed as a function of the input size. Within the framework of DFE and MLD, it provides an estimate of the duration required for the system to process a set of signals or symbols. DFE complexity grows linearly over time since the time complexity of the DFE is reliant upon the number of symbols in the input signal and the number of taps or delays in the feedback filter. For a signal of N symbols, the total time complexity is O(N) because DFE processes each symbol in sequential order. If T feedback taps are present in an algorithm, processing the feedback taps for each symbol might be necessary for the DFE, leading to a time complexity of O (N \(\times\) T) [48; 49; 50; 51].  
In MLD, each possible symbol sequence is compared with the received symbol sequences. An exhaustive search needs to be applied to examine all the feasible transmitted symbol sequences; the time complexity is O(2N) \(\times\) T, as MLD necessitates a comparison of each possible symbol sequence across all symbols in the signal. In 64-QAM, there are 64 potential symbols; the time complexity is O (264) \(\times\) T, indicating significant computational expense [49; 50; 51].  
Space complexity describes how much memory or storage is utilized by an algorithm as a function of the input size. The space complexity of the DFE is dictated by the number of prior symbols stored in the feedback filter (e.g., past decisions) and the magnitude of the current input data. The space complexity of DFE is O(L), as it necessitates the storage of the past L symbols and processes each symbol independently.  
In the case of MLD, for each symbol in the sequence, the space complexity for MLD is O(M\({}^{\text{K}}\)), as it retains all potential combinations of the symbols for K received symbols and M potentially transmitted symbols, resulting in an exponential increase relative to the number of symbols.  
Next, the received signal of the used setup shown in Figure 2 is processed offline using the three proposed equalization algorithms and applying different modulation formats in the OFDM subcarriers to minimize the ISI caused by the dispersive transmission.
## 7 Evaluation Results
### Maximum Covered Area Data Size  
We measured the maximum covered area data sizes required by our proposed routing using the circular covered area model and the same routing using the vectorial covered area model. The result is shown in Figure 28. The covered area data size of the routing using the circular covered area model was fixed at 40 KBytes. In contrast, the routing using the vectorial covered area model had a covered area that was made up of 928 vertices, requiring a covered area data size of 14.86 KBytes.  
Although the routing using the vectorial covered area model requires an enormous amount of covered area data, covered areas do not change frequently, and covered area information is held only by relay nodes. Therefore, we consider that the routing using the vectorial covered area model is superior to the same routing using the circular cover area model because its bundle arrival rate is higher, as shown in Figure 22.  
Figure 27: Distribution of the maximum required buffer sizes.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Control Models
#### 5.3.2 Packet vs. Flow Control
In flow-based decision making, the overhead for control plane communication is low. In most cases, only one packet of the flow may be required to be inspected, thus reducing the complexity of the overall system. The network is easier to manage and control, as scalability is high with this approach. Furthermore, security is high, as traffic from compromised sources can be quickly identified and isolated. The following are some disadvantages of coarse-grained flow control. This approach is not as precise as fine-grained packet control and is less flexible, as it limits the ability of the network to adapt to changing traffic patterns or respond to unexpected events. Furthermore, it has less visibility of the network, as individual packets within a flow are not monitored, which can increase difficulty in troubleshooting problems. Admission control with flow aggregation for QoS provisioning, which uses a model to analyze the required amount of bandwidth and buffer space at OpenFlow switches to meet requirements in delay and packet loss, has been utilized [348]. A local fast rerouting technique in case of link failures that aggregates traffic flows affected by the failure to compute a local reroute path by the controller for the aggregated flow that reduces the number of flow operations between the controller and the switch has been studied in [349]. A flow control mechanism that manages QoS and best-effort flows based on network slices and schedules, which uses multi-objective optimization for priority forwarding, multipath forwarding for best-effort flows, and an algorithm for flow allocation and slice adjustment, is presented in [350]. The controller can utilize traffic flows for intrusion detection by gathering statistical information about the flows from OpenFlow switches and analyzing the flows by aggregating a set of features [351]. A framework known as software-defined label switching combined central control with label switching to reduce storage burden while maintaining per-flow control, is a hybrid approach that incorporates part of the control plane into switches and has resulted in a lower number of flow entries and overflows [352]. A framework for minimizing the number of flows while maintaining end-to-end delay as a QoS parameter that aggregates flows using an algorithm for flow control has been studied in [353]. A framework to mitigate the denial of service attack on the controller by effectively rerouting malicious traffic, adjusting flow timeouts, and aggregating flow rules has been achieved in [354]. Considering the fact that fine-grained traffic flow measurement is challenging due to a lack of monitoring resource constraints, a traffic aggregation and measurement paradigm that aggregates flows and uses them to estimate network flows using optimization under the constraint of flow table size has been studied in [355].
## 4 Materials and Methods
### SLC10A1 Promoter-Reporter-Gene Constructs  
Two promoter fragments were constructed: A 2296 bp construct, containing the whole analyzed promoter region and the beginning of exon 1 up to the translational start site (\(-\)2173 up to +123). The other construct of 1015 bp contained the end of the promoter and the beginning of exon 1 (\(-\)892 up to +123). The primers used contained tags with recognition sequences for restriction enzymes: forward with KpnI, reverse with BgIII. (SLC10A1 Prom_long_f: gctg-ggtacc-GGGTGTCAGGTTCCTGGAAGA, SLC10A1 Prom_short_f: gctg-ggtacc- ATGCCCCACCTCTCTCTGT, SLC10A1 Prom_r: gccagatct-GCAGTGGAAAGACCACTCCT).  
One part of the promoter fragments was methylated using CpG methyltransferase M.SssI (New England BioLabs, Ipswich, MA, USA) in the presence of S-adenosylmethionine (SAM), the other part was mock-methylated under equal conditions in absence of CpG methyltransferase M.SssI. The promoter fragments were ligated into the pGL4.10 vector (Promega). HuH-7 cells were used for transfection with the constructs and the pRL-TK control vector (Promega) in a ratio of 30:1. Luciferase activity was measured using the Dual-Luciferase(r) Reporter Assay System (Promega) according to manufacturer's instructions.
## 2 Development of a Local Shielding Gas Concept
### Implementation of the Shielding Gas Concept  
In addition to the design of the inner nozzle geometry with regard to laminar flow conditions, further requirements had to be addressed to employ the shielding gas device in the demanding environment of the LMD process (see Section 2.1). Figure 4 shows the final design of the entire assembly as a sectional view.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Unit** & **Adapter** & **Nozzle** \\ \hline Inlet radius \(r_{1}\) & mm & 47.1 & 18.0 \\ Outlet radius \(r_{2}\) & mm & 18.0 & 7.5 \\ Length \(L\) & mm & 61.0 & 30.0 \\ Contraction ratio \(\sigma\) & – & 6.85 & 5.76 \\ Maximum angle \(\alpha_{\mathrm{max}}\) & \({}^{\circ}\) & 42.9 & 34.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters describing the geometry of the contraction.  
Figure 3: Outlet geometry of the shielding gas nozzle with the circular channels acting as flow straighteners.  
During the deposition process, both the thermal radiation emitted from the process zone as well as the laser radiation that is partially reflected at the melt pool cause the nozzle to heat up. This is especially relevant for the continuous deposition of large parts or coatings. For this reason, the nozzle was machined from brass (CuZn39 Pb3), which exhibits a low absorption coefficient at infrared wavelengths as well as an excellent thermal conductivity. The nozzle was attached to the adapter via an M44 fine thread. Moreover, an additional thread was included at the end of the nozzle, enabling additional features for specialized applications, such as a trailing nozzle, to be attached.  
In order to protect the sensitive optical components of the wire deposition head from overheating due to heat conduction, water cooling was integrated into the adapter. Additional pins were introduced into the cooling channel, which improved the heat transfer by increasing the wetted surface as well as the turbulence in the cooling water flow [56]. The inlet of the shielding gas was implemented using a single central connection in conjunction with an internal distribution to the five inlets of the adapter. To ensure uniform flow conditions at the five inlets, the internal channels were designed to be of equal length.  
The complex internal geometry of the part was realized by manufacturing using PBF-LB/M, whereby the limitations of conventional manufacturing processes could be circumvented. As material, the aluminum alloy AlSi10 Mg was employed. It is worth mentioning that the realized design represents a perfect example for the possibilities of additive manufacturing, whereas the part could not have been produced in this form using traditional manufacturing methods.
# What Is a Causal Graph?
## 2 Probabilistic DAG
### Structural Probabilistic Model  
Suppose we are given the conditional distribution \(p(Y\mid X)\). It is then possible to construct (albeit non-uniquely) a fictitious \"error variable\" \(E\), having a suitable distribution \(P\), and a suitable deterministic function \(f\) of \((X,E)\), such that the distribution of \(f(x,E)\) is just \(p(Y\mid X=x)\). For example, using the probability integral transform, if \(F_{x}\) is the cumulative distribution function of \(p(Y\mid X=x)\), we can take \(f(x,e)=F_{x}^{-1}(e)\) (using a suitable definition of the inverse function \(F_{x}^{-1}\) when \(F_{x}\) is not continuous) and \(E\) uniform on \([0,1]\). However, such a representation is highly non-unique. Indeed, we could alternatively take \(f(x,e)=F_{x}^{-1}(e_{x})\), where \(e\) is now a vector \((e_{x})\) and the multivariate distribution of \(E\) is an arbitrarily dependent copula, such that each entry \((E_{x})\) is uniform on \([0,1]\).  
Given any such construction, for purely distributional purposes we can work with the functional equation \(Y=f(X,E)\), with \(E\sim P\) independently of \(X\). This operation can be extended to apply to a complete Bayesian network by associating with each domain variable \(V\) a new error variable \(E_{V}\), with a suitable distribution \(P_{V}\), all these being independent, and with \(V\) being modelled as a suitable deterministic function of its graph parent domain variables and \(E_{V}\). We term such a deterministic model a structural probabilistic model (SPM), in analogy with the structural causal model (SCM) of [19]; see Section 6.1 below. In an SPM, all stochasticity is confined to the error variables.  
An SPM can be represented graphically by introducing new nodes for the error variables, as illustrated in Figure 5 for the case of Figure 4, it being understood that the error variables are modelled as random, but all other parent-child relations are deterministic.  
It is easy to check that the conditional independencies represented between the domain variables, as revealed by the moralisation criterion, are identical, both in the original graph and in an SPM extension of it. So, when each graph is endowed with its own parent-child relationships (stochastic, in the case of the original graph, and deterministic, for the structural graph), both graphs describe the identical joint distribution for the domain variables. For purely probabilistic purposes, nothing is gained by introducing error variables to \"mop up\" the stochastic dependencies of a simple DAG model.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 2 Background
### Discovering Material Knowledge  
Material data fusion is not just the integration of multi-source heterogeneous data into the same database, but more importantly, the information contained in each data source is combined to obtain knowledge that can be used for material production and R&D (Research and Development). Knowledge discovery often requires methods such as knowledge reasoning and machine learning. Due to the characteristics of material data, discovering material knowledge from multi-source material data faces the following problems:  
* One important feature of material data is the small sample characteristic of material data. As we all know, the reason why machine learning has achieved some remarkable achievements in recent years is the development of big data in recent years, there is a large amount of data to improve the accuracy of machine learning. Therefore, applying small sample material data to machine learning to obtain high-quality material knowledge is a knotty issue.
* In order to get new material knowledge, machine learning and knowledge reasoning are needed to realize data mining between data, while traditional methods only use data mining methods. How to use the graph-based method to do the relationship reasoning between material entities to improve the efficiency and accuracy of reasoning is also a big challenge.
* Material data requires a lot of domain knowledge and rules for knowledge derivation. Combing the method of machine learning with the artificial power of experts is an open challenge.