esa-sceva/satcom-chunk-collection huggingface dataset:
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 2 Related Work
### Intent Detection and Slot Filling
In previous studies, slot filling has often been performed using generative models (such as the hidden Markov model) or discriminative models (such as the conditional random field (CRF) model) to estimate the conditional probabilities of slot labels in a sequence. However, with the emergence of deep-learning models, bidirectional recurrent neural network (RNN) models and long short-term memory (LSTM) models trained with contextually annotated sentences are now used for slot filling. In recent studies, CRF models have been combined with RNNs to train slot-filling models for unseen semantic labels and multi-domain tasks, enhancing their performance. [PERSON] et al. [49] presented an intent-aware neural ranking model, which used the \"Transformer\" architecture to perform language representation learning and to analyze user intent patterns in information-seeking conversations. Intent detection is usually viewed as a supervised classification problem, that is, mapping a sentence to some class within a finite set of classes. Slot filling, in contrast, is viewed as a token sequence labeling problem. Traditionally, intent detection and slot filling are performed separately or in a pipeline. Recently, some studies have investigated the use of joint models for simultaneously performing intent detection and slot filling [50], and have proven that these tasks are closely related to each other. Compared to the pipeline approach, joint models are less susceptible to error propagation between the intent detection and slot-filling models. In addition, they can be trained and tuned as a single model. However, joint models cannot be easily generalized to unseen data due to variations in natural language expressions for the same intent. Furthermore, domains and label sets can change over time in real-world applications. There is a lack of publicly available task-oriented datasets among the conversational corpora used for training. Moreover, the available datasets are limited to a few specific domains. These corpora may be divided into two types: the first type comprises user-system conversations, such as the Air Travel Information System [51] and WOZ2.0 [52]; the second type includes simulated human-system dialogues subsequently and manually converted into natural language, such as the machine-to-machine dataset [53].
## 2 Emission Sources at Seaports
It is therefore convenient to classify the port emission sources from the geographical location perspective into four categories: seaside, quayside, and yardside, landslide interface, and landside industry and support facility, as shown in Table 4. \begin{table}
\begin{tabular}{l l} \hline \hline
**Geographical Location** & **Emissions Sources** \\ \hline Seaside & Seagoing vessels; support vessels such as tugs, push-boats, tenders, fireboats, pilot boats, police boats; \\ \hline Quayside and yardside & Cargo handling equipment; quayside facility, storage facility; heavy-duty vehicles; light-duty vehicles; reef charging; \\ \hline Landside interface & Gate system; rail terminals; heavy-duty vehicles; light-duty vehicles; locomotives; cargo handling equipment; \\ \hline Landside industry and support facility & Offices; electrical grid; warehouses; cargo handling equipment; buildings; parking spaces; lighting; maintenance facility; \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Seaport emission source categorisation from geographical location perspective. \begin{table}
\begin{tabular}{l l} \hline \hline
**Scope** & **Emission Source Description** \\ \hline \multirow{2}{*}{Scope 1 Sources} & Fossil fuels used in port-owned vehicles (light-duty vehicles, support vessels) and employee vehicles for port business; \\  & Fossil fuels used in port-owned equipment such as cranes, folk lifts, diesel generators for electricity generation; \\  & Fossil natural gas used in port-owned buildings; \\ \hline \multirow{2}{*}{Scope 2 Sources} & Purchased electricity used by the port-owned buildings and equipment such as cranes, pumps, charging stations, reef \\  & containers, machinery in workshop and for building air conditioning, lighting, and other uses; \\  & Purchased steam used in port-owned buildings; \\ \hline \multirow{2}{*}{Scope 3 Sources} & Energy used by port tenants (logistics companies, industries), seagoing vessels, external heavy-duty trucks, rail \\  & locomotives, cargo handling equipment that is not controlled by port, waste transport and disposal, employee business \\  & travel by air, and port employees commuting to and from work. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Seaport emission source categorisation from emission scope perspective. The usefulness of the categorisation in Table 4 is that it gives a clear indication of where the emissions are generated; as a result, more focused solution measures can be identified to reduce the emissions. We will use this categorisation in the next section when presenting the emission reduction measures in structured matrices. It should be noted that different types of cargo handling equipment may be used at different port geographical locations, e.g., ship-to-shore crane at quayside, yard crane at yardside, rail-mounted gantry crane at rail terminals in the landslide interface, and fork lifts at the warehouses in the landslide support facilities.
# A Ku-Band Bi-Directional Transmit and Receive IC in 0.13 \(\upmu\)m CMOS Technology
## 4 Conclusions  
This paper presents a Ku-band CMOS transmit and receive IC in 0.13 \(\upmu\)m CMOS technology, which is the key part of the phased array antenna for mobile satellite communication beamforming systems. It is proposed to enable the bi-directional amplifier without switches to compensate for the gain, phase shifting for beam steering, and attenuating for weighting technique to reduce the side lobe level. The maximum phase shift range of 360\({}^{\circ}\) with a phase resolution of 5.625\({}^{\circ}\) and the maximum attenuation range of 31.5 dB with an attenuation resolution of 0.5 dB is achieved. The measured gain is 2-5 dB and the input and output return losses are >10 dB at 12-16 GHz. The saturated output power is \(-\)2 dBm at 14 GHz. The chip size is 2.5 \(\times\) 1.5 mm\({}^{2}\) including bonding pads. The total power consumption is 216 mW. The proposed Ku-band CMOS transmit and receive IC will enable the low SWAP-C Ku-band satellite communication and radar system.  
Conceptualization, [PERSON] and J.-[PERSON]; methodology, [PERSON] and J.-[PERSON]; investigation, [PERSON] and J.-[PERSON]; writing--original draft preparation, [PERSON] and J.-G.K.; writing--review and editing, [PERSON] and J.-G.K.; supervision, [PERSON]; project administration, [PERSON] and J.-G.K.; funding acquisition, J.-G.K. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data can be obtained from the authors on request.  
The authors declare no conflict of interest.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## Appendix A  
Software packages used for reading the spatial data, processing it, and assessing aquaculture ponds with an object-based method (segmentation): Google Earth Engine (JS API), python 3.9.6, pandas 1.3.2, geopandas 0.9.0, geofeather 0.3, seaborn 0.11.2, matplotlib 3.4.3, numpy 1.21.2, QGIS 3.18, GDAL 3.0.4.
## 4 Case Study
### Numerical Simulation  
Simulations were performed with an LDEM model, which has been successfully used by the authors' research group for analyzing quasi-brittle materials, as illustrated in refs. [24; 41; 44]. The boundary conditions appear in Figure 5a, whereas the model's relevant material properties appear in Table 1. The values for \(E\), \(\sigma_{p}\), \(\rho\), and \(G_{f}\) were adjusted to fit the experimental results presented in Figure 6. The adopted values for \(\rho\) and \(\sigma_{p}\) were compatible with those given in ref. [59], while the one for \(G_{f}\) was within the range specified in refs. [60; 61], \(700\,\mathrm{N}\,\mathrm{m}^{-1}\) to \(1500\,\mathrm{N}\,\mathrm{m}^{-1}\). As for \(E\), the adopted value was lower than the range found in ref. [59] (\(E=1.25\,\mathrm{GPa}\)). However, references [60; 61] show that basalt's Young's modulus is a greatly variable quantity.  
As discussed in Section 2, the material's characteristic length \(d_{eq}\) was determined through the Carpinteri number \(s\)[43], assuming \(\sigma_{p}=7.1\,\mathrm{MPa}\), yielding  
\[s=\frac{\left(\frac{G_{c}E}{d}\right)^{0.5}}{\sigma_{p}}=\frac{\left(\frac{100 0\times 1.25\times 10^{9}}{0.025}\right)^{0.5}}{7.1\times 10^{6}}=0.894, \tag{11}\]  
thus, using Equation (6), \(d_{eq}=s^{2}d=\left(0.894\right)^{2}\times 0.025=0.02\,\mathrm{m}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \# mod. & \(L\) & \(E\) & \(\
u\) & \(d_{eq}\) \\ \hline \(28\times 28\times 10\) & \(2.50\,\mathrm{mm}\) & \(1.25\,\mathrm{GPa}\) & \(0.25\) & \(0.02\,\mathrm{m}\) \\ \hline \(\rho\) & \(\mu(G_{f})\) & \(L_{cor}\) & \(CV(G_{f})\) & \(\beta\) \\ \hline \(2456\,\mathrm{kg}\,\mathrm{m}^{-3}\) & \(1000\,\mathrm{N}\,\mathrm{m}^{-1}\) & \(20\,\mathrm{mm}\) & \(160\%\) & \(3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The LDEM parameters adopted for the basalt prismatic specimen with a prefissure.  
Figure 4: (**a**) The dispositive used to fix the basalt specimen during the test. Detail A: irregularities in the specimen surface. Detail B: the excessive support flexibility. (**b**) The final configuration obtained during the test.  
The random field of material toughness is a Weibull distribution with a mean value \(\mu(G_{f})=1000\,\mathrm{N/m}\), a 160% variation coefficient, and a spatial correlation length \(L_{cor}=20\,\mathrm{mm}\) for the three orthogonal directions. The apparently high value for \(CV_{Gf}\) is justified by its link to the variation of each bar. Thus, for the whole 15 bars for each cubic module, the equivalent variability reduces by 2.5 times, leading to \(CV(G_{f})\) bar/2.5 = 64%. The link between the random field variability in the level of the cubic module and in the level of each bar is discussed in [41]. As a reliable estimate for the random field's correlation length still requires deeper studies regarding the rock's material properties, following [62], this value was assumed equal to the characteristic length \(d_{eq}\).
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Implementation Details
#### 3.2.2 Loss Function  
The model proposed in this research is an end-to-end pixel-wise classification framework, and its loss function employs a mixed weighted loss, referred to as combined loss. It combines the advantages of binary cross-entropy (BCEWithLogitsLoss) and dice loss, aiming to optimize both pixel-level classification accuracy and regional segmentation consistency. Specifically, by introducing adjustable weight parameters \(\alpha\) and \(\beta\), the contribution of the two losses is flexibly balanced. In addition, a smoothing term smooth is added to the dice loss calculation to prevent numerical jumping problems. In our experiment, we set \(\alpha=1.0\), \(\beta=0.7\), and \(\varepsilon=10^{-6}\). The specific formulas are as follows:  
1. BCE Loss: \[L_{\text{BCE}}=-\frac{1}{N}\sum_{i=1}^{N}\left[y_{i}\log(\hat{y}_{i})+(1-y_{i} )\log(1-\hat{y}_{i})\right]\] (4) where \(N\) is the total number of samples; \(y_{i}\) is the true label value; and \(\hat{y}_{i}\) is the probability value predicted by the model (the output after Sigmoid activation).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Formula** \\ \hline OA & \(\frac{TP+TN}{TP+TN+FP+FN}\) \\ P & \(\frac{TP}{TP+FP}\) \\ R & \(\frac{TP}{TP+FN}\) \\ F1-score & \(\frac{2*(Precision\times recall)}{Precision+recall}\) \\ IoU & \(\frac{TP}{TP+FP+FN}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model evaluation metrics.  
Figure 5: Partial images and their corresponding labels in the LoveDA dataset.  
2. Dice Loss: \[L_{Dice}=1-\frac{2\times\mathit{Inter}\sec\mathit{tion}+\mathit{smooth}}{\mathit{ Union}+\mathit{smooth}}\] (5) This can be expanded as \[L_{Dice}=1-\frac{2\sum{(p\cdot t)+\mathit{smooth}}}{\sum{p+\sum t+\mathit{ smooth}}}\] (6) Here, \(p\) is the probability value predicted by the model; \(t\) is the true mask; and \(\mathit{smooth}\) is the smoothing term used to prevent the denominator from being zero.
3. Combined Loss: \[L_{combined}=\alpha\cdot L_{\text{BCE}}+\beta\cdot L_{\text{Dice}}\] (7) where \(\alpha\) is the weight-controlling BCE loss, and \(\beta\) is the weight-controlling dice loss.
# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
###### Abstract  
This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (\(z\)-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R\({}^{2}\)) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.  
SKD11; rotary ultrasonic; laser preheating; machine learning; ensemble learning +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods  
### Voice Image Databases  
Among the most common corpora are NIST SRE, TIMIT, VoxCeleb and VoxCeleb2.  
NIST SRE is aimed at creating speaker identification systems based on telephone conversations. More than 6800 subjects participated in its formation. Recordings lasting from 10 to 60 s were made for each subject [26]. TIMIT includes 6300 audio recordings of phonemes pronounced by 630 speakers in English. Unfortunately, this number of pass phrase examples for each individual subject is insufficient for our study [27].  
VoxCeleb and VoxCeleb2 are focused on text-independent speaker recognition, which makes them unsuitable for our experiment, since our task requires a text-dependent procedure. These datasets contain recordings of interviews and public speeches, where speakers pronounce arbitrary, rather than fixed, pass phrases.  
RedDots includes recordings of 100 speakers who spoke 22 similar phrases/sentences during a weekly session, resulting in a data corpus of 124,800 audio files. This is the only suitable corpus, but the authors did not take into account the speaker's state when forming the database.  
We created our own dataset, AIC-spkr-130, focused on voice password authentication with biometric template protection. Voice passwords were short phrases that consist of one, two, or three (but short) words (e.g., \"access control\" and \"allow access\"). Each subject chose a password from a pre-prepared dictionary. Subjects' passwords are not unique (i.e., one key phrase could be used by several subjects). The proposed dataset includes recordings of 130 speakers aged 18 to 50 years in WAV format with a sampling frequency of 8 kHz and a sample size of 16 bits (mono). Later, we expanded the dataset by including recordings of the same speakers and passwords in an altered psychophysiological state such as (sleepy or alcoholic intoxication states).  
According to the guidelines of the Russian Ministry of Health, alcohol intoxication was modeled at three stages: the first is a blood alcohol content of 0.2-0.3%, which does not have a significant effect on the body; the second is 0.3-0.5%, where mild euphoria and decreased coordination begin; the third is 0.5-1%, which corresponds to mild intoxication with impaired perception and reasoning ability. The volume of alcohol that the subject had to consume was calculated using the Widmark Formula (1):  
\[c=A/m\times r, \tag{1}\]  
where \(c\) is the concentration of alcohol in the blood in %, \(A\) is the mass of the drink consumed in grams, \(m\) is the body mass in kilograms, and \(r\) is the Widmark coefficient.  
The sleepy state was achieved by taking sedatives of plant origin (motherwort, mint, or valerian), which reduced the heart rate by 3-5% compared to the norm. In each PPS, the subjects pronounced 8 passwords at least 60 times for each phrase, which provided a sufficient amount of data for analysis.
# A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List
## 1 Introduction  
Rapid advancements in information technology have led to the rapid development of multimedia technologies such as 4K ultra-high-definition videos, 360-degree immersive multimedia, and high dynamic range videos. Consequently, there has been a significant surge in data volume, placing immense pressure on data storage and transmission. Existing coding standards such as High-Efficiency Video Coding [1] have difficulty in meeting this requirement.  
To address these challenges, the Joint Video Coding Expert Group embarked on the development of the next-generation video standard and introduced a novel video coding standard known as Versatile Video Coding (VVC) [2]. The primary objective of VVC is to achieve a compression efficiency improvement of over 50% while maintaining comparable video quality to HEVC. VVC incorporates a new segmentation technique called Quadtree with Nested Multi-Type Tree (QTMT) [3]. In addition to the QT partition structure, VVC encompasses four other multi-type tree (MTT) partition structures: vertical binary tree (VBT), horizontal binary tree (HBT), vertical ternary tree (VTT), and horizontal ternary tree (HTT). By introducing QTMT, a coding unit's (CU) shape becomes more flexible and diverse, thereby increasing the number of CUs that necessitate recursive traversal during the rate-distortion optimization (RDO) process. Figure 1 illustrates an example of the partition structure obtained after recursive traversal, along with the corresponding tree structure. Flexible partition structures, while enhancing compression efficiency, also increase computational complexity. Under the condition of an all-intra configuration, the average complexity of VVC is 18 times higher than that of HEVC [4]. This has hindered the widespread adoption of VVC (Versatile Video Coding) and its application in real-time scenarios.  
To solve the problem of the high computational complexity in the intra coding of VVC, a fast partition method is proposed. The main contributions of the proposed method are as follows:  
1. The concept of an MPPPL is proposed, and the method for constructing the MPPPL is provided. In the decision-making process of the partition mode, the partition mode in the MPPPL is sequentially tested, and the optimal partition mode is decided in advance according to the calculation results.
2. According to the influence of the partition mode distribution in the MPPPL on the partition mode selection, the pixel mean value difference is used to shorten the MPPPL or to decide the optimal partition mode in advance.
3. Using the pixel content similarity, the calculation of reference line three is skipped in the intra prediction process, which reduces the computational complexity of the intra prediction.