esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FocMech-Flow Workflow  
The workflow of FocMech-Flow for focal mechanism inversion is shown in Figure 1. The main steps consist of four parts:  
1. Seismic Event Screening. Seismic events satisfying focal mechanism inversion are selected from the observation report. Seismic events recorded by at least 10 stations and with a maximum gap angle less than 70\({}^{\circ}\) are selected.
2. Data Preprocessing. According to the P-wave arrival time in the observation report picked by analysts, we take the P-wave arrival time as the center, cut a 4-s waveform window, and carry out de-mean, de-trend, and amplitude normalization processing.
3. Determination of the P-Wave First-Motion Polarity. The preprocessed vertical component waveform is centered on the P-wave arrival time; we cut a 1.28-s waveform window, detected by DiTingMotion, and the polarity of the P-wave is given.
4. Focal Mechanism Inversion. According to the earthquake location, station location, and regional velocity model, we calculate the takeoff angle, combined with the first-motion polarity, and invert the focal mechanism with the CHNYTX program.  
The first-motion polarity of the P-wave is detected automatically by DiTingMotion. DiTingMotion was trained by [45; 46] using the DiTing dataset [44] and the first-motion dataset from the Southern California Seismic Network [40]. DiTingMotion is a deep learning model based on a convolution neural network. The model consists of five convolution modules. The first two convolution modules contain two one-dimensional convolution layers and one maximum pooling layer, respectively. The last three convolution modules contain three one-dimensional convolution layers and one maximum pooling layer, respectively. In the last three convolution modules, each module outputs a set of polarity predictions and then determines the P-wave polarity as \"Up\" or \"Down\" by comparing the probability threshold. See Ref. [45] for details about the network structure, training process, and test results of DiTingMotion.  
The CHNYTX program [16] is used for focal mechanism inversion, which is based on the grid research method [6; 14]. By improving the calculation of the weight factor of the data points and the dynamic clustering of solution areas, the reliability of the focal mechanism is evaluated according to the number of clustering centers, the lowest weighted contradiction ratio, and the minimum root mean square (RMS) of the rotation angle.  
Figure 1: FocMech-Flow flow chart for focal mechanism inversion. A, B and C represent three levels of reliability of the focal mechanism solution. A is the best, B is the second, and C is the worst. P and T are the pressure axis and tension axis, respectively.
## 4 Experiment
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature** & **Proposed Method (Hybrid PLL-Based FOC)** & **Extended Kalman Filter (EKF)** & **Sliding Mode Observer (SMO)** & **Conventional Indirect FOC** \\ \hline \hline Rotor Flux Angle Estimation & PLL-based, pure integration-free & Kalman filter estimation & Observer-based estimation & Integration-based slip estimation \\ \hline Accuracy (Steady-state) & High (precise PLL alignment) & Moderate-High (depends on model accuracy) & Moderate (affected by cathetering) & Moderate (integration drift) \\ \hline Transient Performance & Good (hybrid switching mechanism) & Good (adaptive estimation) & Good (fast observer response) & Good (established method) \\ \hline Robustness to & Moderate (parameter sensitivity exists, can be mitigated by online identification) & Moderate (model-sensitive) & High (robust against uncertainties) & Moderate-Low (sensitive to rotor parameters) \\ \hline Computational Complexity & Moderate (PLL and simple switching logic) & High (complex computations) & Moderate-High (nonlinear observer) & Low (simple slip calculation) \\ \hline Implementation Cost & Moderate (standard DSP, no sensors required) & High (advanced DSP required) & Moderate-High (requires robust hardware) & Low (standard DSP sufficient) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed method with other common sensorless control strategies for induction motors.
# Octacalcium Phosphate Bone Substitute (Bontree(r)): From Basic Research to Clinical Case Study
## 4 Discussion
According to the results of in vitro and in vivo experiments, the Bontree(r) 1.0-granule has a high potential as a commercially applicable novel bone graft material, and it was selected for the evaluation of clinical significance. On the basis of the results of this basic research, we attempted to perform alveolar ridge or sinus augmentation using Bontree(r) 1.0-granule during dental implant surgery. Eight implants in three patients showed a 100% success rate after 1 year of functional loading. During the surgical procedure, clinical and systemic complications, such as pain, severe inflammation, or significant infection, were not observed. In Case 1, GBR was performed on the peri-implant dehiscence bony defects using Bontree(r) and an absorbable collagen barrier membrane, and clinically successful horizontal hard tissue gain was observed after 4 months. In Cases 2 and 3, vertical ridge or sinus augmentation with Bontree(r) was performed 6 months before implant placement, sufficient horizontal/vertical bone width and height were obtained clinically, and sufficient implant primary stability \(\geq\) 40 N was achieved. All ISQ values measured approximately 4 months after implant placement were >60, indicating successful secondary implant stability. In addition, no characteristic marginal bone loss around the implant was observed on radiographs for up to 1 year after loading. The clinically and radiographically successful bone regeneration with Bontree(r) can be attributed to the excellent new bone formation ability unique to OCP. In other words, OCP seems to provide a nucleus for promoting osteogenesis and creates numerous starting points for ossification, unlike HA and \(\beta\)-TCP [34]. In addition, [PERSON] et al. [14] reported that OCP-based bone grafts achieved comparable osseointegration to autologous bone grafts in a study of the mandible in a canine model. However, due to the limitations inherent to retrospective studies, the number of samples was too small, and we cannot confirm the clinical characteristics of Bontree(r) only on the basis of the three cases included in this study. In addition, the follow-up period of approximately 1 year may be too short to evaluate the success rate of the implants. Therefore, as a follow-up study, comparative studies with other bone substitute materials, long-term studies, and prospective studies using Bontree(r) in a much larger number of cases are needed.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 3 Audio Fingerprint Matching  
### Similarity Measurement  
In this paper, the similarity measurement used in audio fingerprint matching is the bit error rate:  
\[BER=\frac{\sum_{r=1}^{R}\sum_{s=1}^{S}F(r,s)\bigoplus F^{\prime}(r,s)}{R\times S} \tag{11}\]  
where \(\bigoplus\) is XOR operation, \(F(r,s)\) is the reference fingerprint in database and \(F^{\prime}(r,s)\) is the actual input which called the query fingerprint, \(s\) is bit index and \(r\) is frame index, \(R\) is the total number of frames in the matching segment, \(S\) is the number of the fingerprint dimensions.  
The threshold is set based on the experimental results. If the \(BER\) of the query is less than the reference which means they are very similar, the current reference should be as one of the candidate result, whereas there is a big difference between the query and the reference, the latter would not be selected as a retrieval result.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 3 Transformer
### Encoder-Decoder-Based Model  
An example of encoder-decoder architecture is the transformer model proposed in [24]. Its encoder and decoder blocks are stacked with multiple layers. As shown in Figure 3, the transformer encoder layer consists of a self-attention layer and a position-wise feed-forward layer. In addition to these two layers, decoder consists of a third cross-attention layer, which is responsible for attending to encoder output.  
Encoder-decoder models adopt bidirectional attention for the encoder, unidirectional attention for the decoder, and a cross-attention mechanism between them. Cross-attention in the decoder has access only to the fully processed encoder output and is responsible for connecting input tokens to target tokens. The encoder-decoder-based models are pretrained for seq2 seq tasks. They can also be pretrained on conditional generation tasks, where the output is generated in regard to the given input, for example in summarizing, question answering, and translation tasks. T5 [25] uses encoder-decoder architecture. As stated in T5, using encoder-decoder structure helped to achieve good performance regarding classification as well as for generative tasks.  
Although encoder-decoder models end up having twice as many parameters as their decoder-only or encoder-only counterparts, they still have similar computational cost. Compared to PrefixLM models where the parameters are shared, here, the input and target are independently processed and use separate sets of parameters. Unlike decoder-only language models that are trained to generate the input, encoder-decoder models output target tokens.  
The original transformer consisted of encoder-decoder blocks and was initially used for sequence-to-sequence tasks, such as NMT. However, it was discovered that, with the change in how the input is fed to the model, the single-stack (decoder or encoder) could also complete sequence-sequence model tasks. As a result, the subsequent models started containing either an encoder or decoder architecture. Below, we discuss these architectural variants of the original transformer model.
## 7 Limitations and Future Work  
The current work concentrates only on masses and calcifications in mammograms. It does not predict other abnormalities like asymmetries and architectural distortions. Furthermore, it is not able to reveal the regions in mammograms that play key roles in decision making, i.e., the interpretability of a decision.  
In future studies, we aim to expand the scope of the algorithm by including abnormalities like asymmetries and architectural distortions and explore various fusion techniques for integrating CC and MLO views and different channel attention mechanisms, including SKNet, to enhance its performance in a multi-label classification system.  
Additionally, we plan to implement spatial attention mechanisms for revealing the regions in mammogram images that play crucial roles in decision making and improving accuracy by utilizing complete contextual information. Future work will also investigate multi-label classification algorithms for more effective breast cancer diagnosis and risk assessment, particularly those centered on problem transformation and adaptation techniques.  
The benchmark datasets, which we used to develop and evaluate the proposed system, are annotated according to the fourth edition of BI-RADS. However, the fifth edition of BI-RADS is available now, and there is a need to annotate the datasets according to this edition and evaluate the system's performance.
## 2 Features of the Study Area
### Definition of Land Use and Selection of Design Criteria  
The residential area covers a surface of 3,002,622.27 m\({}^{2}\) and is subdivided into the following land uses: rooftops of residential buildings, commercial establishments, and services (823,054.09 m\({}^{2}\), equivalent to 27.41% of the total area), green spaces (261,800.22 m\({}^{2}\), 8.72%), roads and sidewalks (1,256,923.15 m\({}^{2}\), 41.86%), and bare soil areas (660,844.81 m\({}^{2}\), 22.01%) (Figure 1). Among these areas, rooftops were selected as an alternative to intercept rainfall and propose strategies to mitigate water scarcity in the population, primarily because a water treatment system is not required for domestic use [17]. There are different types of dwellings (which vary from a single family to multifamily buildings), distinguished by the number of users, rooftop area, and the water supply needed to meet daily consumption. Additionally, there are various commercial and service areas; those representing higher water demand (i.e., a larger number of users, larger rooftop area required, among others) were selected as models.
## 2 Materials and Methods  
### Study Area  
The Beijing-Tianjin-Hebei region (Figure 1) is located in the northern part of the North China Plain, with geographical coordinates of 113\({}^{\circ}\)04\({}^{\prime}\)-119\({}^{\circ}\)53\({}^{\prime}\) E and 36\({}^{\circ}\)01\({}^{\prime}\)-42\({}^{\circ}\)37\({}^{\prime}\) N. It includes two municipalities directly under the central government (Beijing and Tianjin) and 11 prefecture-level cities in Hebei Province (Shijiazhuang, Baoding, Tangshan, Langfang, Handan, Xingtai, Cangzhou, Qinhuangdao, Hengshui, Zhangjiakou, and Chengde), with a total area of approximately 216,000 square kilometers, accounting for 2.3% of the national territory. The Beijing-Tianjin-Hebei region is predominantly plains, with mountains and plateaus. The Yanshan and Taihang mountain ranges surround the northwest, the southeast is the North China Plain, and the east borders the Bohai Sea. It has a temperate monsoon climate with four distinct seasons, annual average precipitation of 400-800 mm concentrated in summer, and is prone to droughs and floods.  
The Beijing-Tianjin-Hebei region has a total population of approximately 110 million (2020), with a GDP accounting for about 10% of the national total, making it the economic core area of northern China. The Beijing-Tianjin-Hebei coordinated development strategy was elevated to a national strategy in 2014, with the core objective of relieving Beijing of non-capital functions and promoting industrial, transportation, and ecological integration among the three regions. Beijing serves as the national political, cultural, international exchange, and technological innovation center, focusing on \"high-precision and advanced\" industries, with the tertiary industry accounting for over 80%. Tianjin is the northern shipping center, characterized by advanced manufacturing and port economy, primarily developing high-end manufacturing and R&D. Hebei is a major industrial and agricultural province with a high proportion of traditional industries such as steel and building materials, undertaking manufacturing industries from Beijing and Tianjin while upgrading traditional industries.  
The BTH region was selected for this study due to its significance as a representative case of 21 st-century mega-regional urbanization. Covering 216,000 km\({}^{2}\), BTH exemplifies the scale and complexity of contemporary urban agglomerations that will define global urbanization patterns through 2050. The region's increase in impervious surface area over our 35-year study period represents one of the most dramatic urban transformations globally, making it an ideal laboratory for testing our ISC methodology and vulnerability assessment framework. Furthermore, the BTH polycentric development pattern, diverse environmental gradients (from megacity cores to pristine mountains), and unique policy context (coordinated regional development strategy) provide insights directly applicable to other rapidly urbanizing mega-regions worldwide.
## 3 Networks and Protocols of Communication
### Cellular Technologies
#### 3.2.2 Narrow Band  
In 2016, the international consortium 3 GPP developed a new standard of communication called Narrow Band IoT (NB-IoT) which operates on a frequency channel of a width of 200 kHz. The NB-IoT is a wireless communication standard from the LPWAN family, also developed for M2M applications, which enables sensors and other electronic devices to communicate with each other [33]. Compared to other LPWAN technologies, NB-IoT provides electronic systems with communications at a low cost. It enables the exchange of data in hard-to-reach locations and under conditions that may be challenging for other networks, such as underground sites or locations outside of communities. It uses enhanced mechanisms of energy saving to improve battery life and to support small data transfer. To facilitate the use of NB-IoT networks, network procedures are designed to simplify access complexity [34].  
The NB-IoT architecture uses 6 different layer protocols, where they are offered several access controls and system-related features, which improve security, as well as the use of the packet data convergence protocol layer - Packet Data Convergence Protocol (PDCP) and the Middle Access Layer - MAC). The Radio Link Control layer (RLC) is responsible for mobility and also provides security through LTE networks [35].
## 4 The Proposed IDS Framework at Flow-Level
### The Exploration Module
```
1:procedureDeep Q-Learning
2: Conduct preprocessing on dataset
3: Pretrain a stacked autoencoder
4: Train the CGAN
5: Initialize \(Q\) function for agent, set target \(Q\) function \(\overline{Q}=Q\)
6: Initialize the agent replay buffer and the sample agent replay buffer for training the agent and the sample agent, respectively
7: Copy parameters to \(Q\) and \(\overline{Q}\) from the encoder
8: Set target Q-function \(\widehat{Q}=Q\), \(\widehat{\overline{Q}}=\overline{Q}\)
9:for each episode do
10: Randomly choose \(s_{0}\) from the dataset
11:for each sample within the episode, \(t\in[0,N]\)do
12: Given state \(s_{t}\), take action \(a_{t}\) based on \(Q\)
13: Given state \(s_{t}\), take action \(\widehat{Q}_{t}\) based on \(\overline{Q}\) (\(\varepsilon-greedy\))
14: Compare \(a_{t}\) with the true label, obtain the reward \(r_{t}\) and \(\overline{r}_{t}\)
15: Derive the next state: choose \(s_{t+1}\) for which the true label is \(a_{t}\) from dataset or CGAN, with a probability of 1-\(\varepsilon\) and \(\sigma\), respectively
16: Store \(s_{t},a_{t},r_{t},s_{t+1}\) into the agent replay buffer
17: Store \(s_{t},\bar{a}_{t},r_{t},s_{t+1}\) into the sample agent replay buffer
18: Sample a batch \(s_{t},a_{t},r_{t},s_{t+1}\)from the agent replay buffer
19: Sample a batch \(s_{t},\bar{a}_{t},r_{t},s_{t+1}\)from the sample agent replay buffer
20: Target \(y=r_{t}+\gamma max\widehat{Q}(s_{t+1},a)\)
21: Update \(Q\) through back propagation to make \(Q(s_{t},a_{t})\) close to \(y\)
22: Target \(\overline{y}=\overline{r}_{t}+\gamma max\widehat{Q}(s_{t+1},a_{t})\)
23: Update \(\overline{Q}\) through back propagation to make \(\overline{Q}(s_{t},\overline{a}_{t})\) close to \(\overline{y}\)
24: For each \(C\) steps reset \(\widehat{Q}=Q,\widehat{\overline{Q}}=\overline{Q}\)
25:endfor
26:endfor
27:endprocedure
```  
**Algorithm 3** Deep Q-Learning Framework for Detection at Packet-level
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 3 Health Risks Associated with Radon Exposure
The relationship between radon exposure and other cancers, such as leukaemia, has not always been shown, despite evidence from animal and human epidemiological research that there is an elevated risk of lung cancer in the general population. Their review sought to clarify the relationship between home radon exposure and the prevalence of childhood leukaemia by synthesising the data from ecological and case-control studies conducted in populations exposed to radon, taking into account dose estimation and evidence of radon-induced genotoxicity. The analysis of 12 ecological studies revealed a positive correlation between heightened radon levels and a higher incidence of childhood leukaemia in 11 studies, with 8 of these correlations being significant. Several case-control studies on indoor radon exposure and childhood leukaemia were reviewed alongside ecological research, and the majority of studies found a modest connection, with only a few demonstrating significance. The report also notes that the radon dose estimate is a significant source of ambiguity in radon risk assessment. One of the variables influencing risk estimations in case-control research is the techniques used to measure radon exposure in children's homes. Through the use of genetic endpoints such as chromosomal aberration, micronucleiei formation, gene mutation, and deletions and insertions, the impacts of radon-induced genetic damage were examined both in vitro and in vivo. Applying a meta-analysis, it was shown that exposure to indoor radon increased the incidence of both acute lymphoblastic leukaemia (ALL) and generalised paediatric leukaemia. Understanding the long-term health effects of radon exposure is very important, and extensive research in this area is necessary for assessing the potential risks associated with prolonged radon exposure and developing effective strategies for prevention and mitigation. Longitudinal studies and comprehensive analyses of large-scale datasets are essential for assessing the cumulative impact of radon exposure, while considering factors such as geology, climate, and seasonality. The findings from such research endeavors will empower policymakers, public health authorities, and individuals to implement proactive measures and create healthier indoor environments.
## 1 Introduction
The main objective in this study is to gain a better understanding of the influences of \(\rm E_{\rm ig}\) and turbulence level on \(\tau_{\rm delay}\) for lean premixed turbulent combustion, which may be relevant to lean-burn SI engines with high thermal efficiency. To the first level approximation, this study measures \(\rm MIE_{\rm L}\) and \(\rm MIE_{\rm T}\) of a lean n-butane/air mixture with an effective Lewis number \(Le\approx 2.1>>1\) using pin-to-pin electrodes at a fixed spark gap of \(d_{\rm gap}=2\) mm in a large dual-chamber, fan-stirred cruciform burner capable of generating near-isotropic turbulence with negligible mean velocities. Here, MIE is the measured value of \(\rm E_{\rm ig}\) determined at 50% ignitability, the subscripts L and T represent laminar and turbulent conditions, and \(Le\) is defined as the ratio of thermal diffusivity and mass diffusivity with the mass diffusivity being that of the deficient reactant and the abundant inert. Near-isotropic turbulence was generated by a pair of counter-rotating fans and perforated plates in the central uniform region of the cruciform burner measured by high-speed PIV, detailed in Refs. [21; 22]. Furthermore, as will be shown later, \(\tau_{\rm delay}\) is determined at a minimum critical radius (\(R_{\rm min}\)) having a minimum flame speed (\(\rm dR/d\rm t\))\({}_{\rm min}\) during the early stage of flame kernel development. Hence, the effects of \(\rm E_{\rm ig}\) or measured \(\rm MIE_{\rm L}\) and \(\rm MIE_{\rm T}\), as well as the r.m.s turbulence fluctuation velocity (\(u^{\prime}\)) on \(\tau_{\rm delay}\), can be scrutinized. Moreover, two ignition transition (IT) modes at \(d_{\rm gap}=0.8\) mm and 2.0 mm, based on MIE as a function of \(u^{\prime}\) for the present lean n-C\({}_{4}\)H\({}_{10}\)/air mixture, similar to that recently reported in Ref. [23] using a lean primary reference fuel (PRF) with \(Le\approx 2.98>>1\), are also discussed. At small \(d_{\rm gap}=0.8\) mm, an irregular IT is observed, where \(\rm MIE_{\rm T}\) decreases first with increasing \(u^{\prime}\) (turbulence facilitated ignition, _TFI_) and then \(\rm MIE_{\rm T}\) increases drastically when \(u^{\prime}\) is greater than some critical value (\(u^{\prime}\)) [24]. The phenomenon of _TFI_ was first observed by [PERSON] et al. [25]. At large \(d_{\rm gap}=2.0\) mm, a regular IT is found by which the increasing slopes of \(\rm MIE_{\rm T}\) with \(u^{\prime}\) change from modestly to exponentially when \(u^{\prime}>u^{\prime}\). Finally, we discuss the relationship between the regular IT and the ignition kernel delay time, as well as their implication to the potential application in SI engines operated at turbulent lean-burn conditions.