esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Volatile Components in HAB
#### 2.3.2 Terpenes

Table 2: A total of 82 terpene compounds in Chinese herbaceous aroma-type Baijiu. Linalool, geraniol, and \(\beta\)-citronellol are common terpen in Baijiu and present floral aroma properties. The odor perception thresholds of these compounds are 13.1 ug/L, 120 ug/L, and 300 ug/L [36], respectively. Geosmin is known for its beet, earth aroma as an off-flavor compound, whose odor threshold is 0.1 ug/L in 46%vol ethanol aqueous solution [37]. \(\beta\)-Ionone is believed to be responsible for the characteristic violet and floral aroma, whose concentration in Baijiu is generally 0.3-2.2 ug/L, and the odor threshold in 46%vol ethanol aqueous solution is 1.3 ug/L [36]. In addition, a series of terpen with low thresholds were also found in this study for the first time (as shown in Figure 5); for example, 1,8-cineole presents a fresh odor with an odor threshold of 0.26 ug/L, and \(\beta\)-cyclodextrin has an odor threshold of 5 ug/L [38].
# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 3 Method
### Attention-Based ID-CNNs-CRF Model
#### 3.3.3 Linear Chain CRF  
Though the deep learning-based NER method can automatically extract high-level abstract features for each token for category judgment, the rules of transfer among labels tend to be ignored. It has been proven that the correlations among adjacent labels can be very beneficial in sequence tagging ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017). The linear chain CRF combines the advantages of the maximum entropy model and the hidden Markov model. It considers the transfer relationships among the labels, enabling the acquisition of the optimal label sequence in the sequence labeling.  
We considered \(E\) to be the matrix of the score output by our model. The \(i\)-th column is the vector \(o_{i}\) obtained by Equation (8). The element \(E_{i,y_{i}}\) of the matrix is the score of the tag \(y_{i}\) of the \(i\)-th token in the sentence. We introduced a tagging transition matrix \(T\). The element \(T_{y_{i-1},y_{i}}\) of the matrix is the score of transition from tag \(y_{i-1}\) to \(y_{i}\). This transition matrix will be trained as the parameter of our network.  
Therefore, for a sentence \(S=\{w_{1},w_{2},w_{3},...,w_{n}\}\), the score of its prediction sequence \(y=\{y_{1},y_{2},y_{3},...,y_{n}\}\) can be expressed as Equation (9):  
\[s(S,y)=\sum_{i=1}^{n}E_{i,y_{i}}+\sum_{i=1}^{n-1}T_{y_{i-1},y_{i}} \tag{9}\]Under the condition of the given sentence \(S\), the probability of sequence label \(y\) is as follows:  
\[p(y|S)=\frac{e^{s(S,y)}}{\sum_{g\in Y_{X}}e^{s(S,g)}} \tag{10}\]  
During the training process, the likelihood function of the marker sequence is:  
\[log(p(y|S))=s(S,y)-log(\sum_{g\in Y_{X}}e^{s(S,g)}) \tag{11}\]  
where \(Y_{X}\) represents all possible sets of markers, and a valid output sequence can be obtained by the likelihood function. The set of sequences with the highest overall probability is output by Equation (12) when predicting the optimal label:  
\[y^{*}=\operatorname*{arg\,max}_{\hat{y}\in Y_{X}}s(X,\hat{y}) \tag{12}\]  
The loss function was minimized by backpropagation during training, and the Viterbi algorithm was applied to find the tag sequence with maximum probability during testing.
## 2 Experiment 1: The Refined Model
### Refined Model Setup  
We used three parameters, i.e., mean, ceiling and floor, to describe a speaker's F0 range. The ceiling and floor were defined, respectively, as the 95 th and 5 th percentiles of the logF0 values over all speech data of a given speaker. The raw F0s were extracted using Praat toolkits [51].  
Previous studies have shown that for multiple related tasks the Multi-Task Learning (MTL) algorithm works better than separate single tasks. The multi-task learning structure contains one or more shared hidden layers that were connected to all tasks [52]. Thus, the commonalities across these tasks can be captured by the shared hidden layers, while the differences among the tasks can be captured by other task-specific layers. Moreover, training multiple related tasks jointly in one model is more efficient than training them in isolation. The MTL method has been commonly used in speech technologies [53; 54]. In this refined model, we adopted the MTL-LSTM, of which the structure is shown in Figure 2, to estimate the three parameters (i.e., mean, ceiling, and floor) for F0 range jointly. There were one shared LSTM and one separate LSTM layers (each layer consisting of 64 units), after which one Dense layer (using linear activation) was added.  
The model was implemented with the KERAS platform [55]. Because [14] showed that the use of the FBANKs + F0 features attained the best estimate of F0 range, in this experiment we again used 40-dimensional FBANKs and 3-dimensional F0 parameters as the input features, which were all extracted using the KALDI toolkits [56]. Following [14], the net depth of MLT-LSTM was set at 30 time steps, corresponding to the c  
Figure 2: The network structure of the refined model.  
300 ms. The model was fit using the batch size of 32, epoch of 50. Thirty percent of the data were split for validation. The optimizer was _rmsprop_. For the error criterion, we used the mean absolute percentage error (MAPE) defined as \(MAPE=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{y-\hat{y}}{y}\right|\times 100\), where \(N\) is the total number of speech samples, \(y\) is the actual value, and \(\hat{y}\) is the estimated value.
## 1 Introduction
The main contributions of this paper are as follows. * A question decomposition model based on pre-training model and dependency analysis is created, which can break down a complex question into a set of several simple questions for subsequent model processing. This solves the issue that complex questions have multi-topic entities and multi-hop relations, which make it difficult for neural networks to learn directly. * A constraint representation method that includes temporal, spatial, and numerical operations is established to address the issue that the question constraints can conflict with downstream reasoning models, and an extraction model for separating the constraint information in the question is designed to increase the accuracy rate of the question reasoning. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Question Type** & **Subtype** & **Example** \\ \hline Simple & 1 entity, 1 relation & Who is the author of the Lord of the Rings? \\  & 1 entity, multi-relations & Where is the birthplace of the author of the Lord of the Rings? \\ \hline Complex & multi-entities, multi-relations & Which rivers flow through both China and India? \\  & & Which book was first co-authored by [PERSON] and [PERSON]? \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example questions for different types. * A question reasoning model based on beam search is intended to address the problem that the optimal path is challenging to find quickly due to the size of the knowledge graph, which can improve the model's accuracy while narrowing the search space. The rest of this paper is organized as follows: Section 2 presents the related work on KGQA tasks. We first describe the general structure of the model in Section 3 then introduce the three sub-models for question decomposition, constraint extraction, and question reasoning. The experimental Section 4 presents numerous experimental findings and ablation studies are conducted to support the model's validity. The conclusions and accompanying prospects are presented in Section 5.
# Effects of Random Values for Particle Swarm Optimization Algorithm
## 2 Standard and Modified Particle Swarm Optimization Algorithms  
### Standard Particle Swarm Optimization Algorithm  
In standard PSO algorithm, each particle represents a potential solution to the task within the search space. In the D-dimensional space, the position vector and velocity vector of the \(i\)th particle can be expressed as \(x_{i}=(x_{i1},x_{i2},\cdots,x_{iD})\) and \(v_{i}=(v_{i1},v_{i2},\cdots,v_{iD})\), respectively. After the random initialization of particles, the velocity and position of the \(i\)th particle are updated as follow,  
\[v_{i}(t+1)=wv_{i}(t)+c_{1}r_{1}(p_{i}-x_{i}(t))+c_{2}r_{2}\big{(}p_{g}-x_{i}(t )\big{)} \tag{1}\]  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{2}\]  
where, \(w\) is the inertia weight and can be used to control the influence of previous velocity on the new one; the parameters \(c_{1}\) and \(c_{2}\) are two constants which determine the weights of \(p_{i}\) and \(p_{g}\); \(p_{i}\) represents the best previous position of the \(i\)th individual and \(p_{g}\) denotes the best previous position of all particles in current generation; \(r_{1}\) and \(r_{2}\) represent two separately generated random values which uniformly distribute in the range of [0, 1]. The pseudocode of standard particle swarm optimization is shown in Figure 1.
## 2 Literature Review
### Anomaly Identification  
In terms of abnormal identification, a traditional approach is to use knowledge-based rule inference methods. [PERSON] et al. [30] and [PERSON] et al. [31] both employed knowledge rules to represent the reasoning process of several key anomalies, implementing expert systems for anomaly identification. The other approach for anomaly identification is based on parameter correlations, as research has revealed that the correlation among parameters can represent features of the system's operational state [32]. This correlation remains similar within the same scenario but differs across different scenarios. [PERSON] et al. [33] proposed a two-stage anomaly detection method. In the first stage, they decomposed [PERSON]'s T-squared statistic to identify the variables with the highest contribution to anomalies. In the second stage, they calculated the correlation between this variable and other multivariatevariables to determine the root cause of the anomaly, enabling the identification of anomaly types and analysis of their underlying causes.  
Therefore, correlation among multidimensional parameters varies in different scenarios, and anomaly identification can be achieved based on these correlations. Compared to knowledge-based rule inference methods, anomaly identification based on parameter correlation allows for the direct learning of abnormal patterns from data without needing prior knowledge or rule construction.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 2 SAS Theoretical Background  
The SAS technique is sensitive to the presence of inhomogeneities in materials, such as precipitates in the matrix, proteins in solutions, or cavities in metals. In all cases, the incident radiation, mainly neutrons and X-rays, is scattered as a function of the change in the density of the quantity that controls the interaction with the target material, namely the scattering length density and the electron density for neutrons and X-rays, respectively. In a SAS experiment, the coherent macroscopic scattering cross-section (SCS) \(\frac{d\Sigma}{d\Omega}\) is measured as a function of the scattering vector \(\vec{Q}\), whose module is defined as  
\[Q=\frac{4\pi}{\lambda}sin(\theta) \tag{1}\]  
where \(2\theta\) is the full scattering angle and \(\lambda\) is the wavelength of the incident radiation.  
In a general way, the SCS can be written [27] as  
\[\frac{d\Sigma}{d\Omega}(\vec{Q})=\frac{1}{V_{T}}\left|\int_{V_{T}}e^{i\vec{Q }\vec{r}}\rho(\vec{r})d^{3}\vec{r}\right|^{2} \tag{2}\]  
where \(V_{T}\) is the investigated volume, \(\vec{r}\) is the position vector, and \(\rho\) is the contrast, i.e., the difference of the scattering length density (SLD), the electron density (ED), and the refractive index density (RID), for neutrons, X-rays, and light, respectively. From now on, I will refer to the neutron case for clarity. However, it is trivial to move the following picture to the X-ray or light cases.  
Under the following conditions usually found in experiments:  
* The inhomogeneities, as well as the sample matrix, or solvent, are homogeneous: the so-called two-phase system.
* The scattering is isotropic, and the SCS depends on the modulus of the scattering vector \(Q\).
* The system is diluted, i.e., the concentration of the inhomogeneities is so low that the coherence between neutrons scattered by different inhomogeneities is negligible Equation (2) can be simplified, taking the following form: \[\frac{d\Sigma}{d\Omega}(Q)=\frac{1}{V_{T}}(\Delta\rho)^{2}\sum_{i=1}^{N_{T}} V_{i}^{2}|F_{i}(Q)|^{2}\] (3) where \(V_{i}\) is the volume of an inhomogeneity, \(\Delta\rho\) is the difference of the SLD between the inhomogeneities and the matrix, or solvent, and \(F_{i}(Q)\) is the form factor of the \(i\)-th inhomogeneity averaged over all the possible orientations. The sum in Equation (3) runs over all \(N_{T}\) inhomogeneities. A short overview of the main scattering formulas is here reported for clarity.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 3 Proposed Protocol Design  
This paper proposes a novel lightweight mechanism based on LoRaWAN to enable multi-hop communication named Listen-to-Talk-based Multi-hop LoRaWAN (LTM-LoRaWAN). The main goal is to allow out-of-range devices to deliver their traffic to the gateway while keeping the typical LoRaWAN functionalities as simple as possible. This is achieved by utilizing other in-range devices to forward the messages toward the gateway. The proposed system avoids any use of complex routing mechanisms or routing topology construction, nor does it use special entities or assign specific tasks to specific devices. This is to keep the functionality of the proposed one as similar as possible to standard LoRaWAN. The network model under investigation contains a network server, a gateway, and hundreds of LoRaWAN devices. No specific rules or functionalities are assigned to any device before the network deployment. All the end devices are standard devices. However, the proposed system entitles the devices to opt for forwarding their traffic based on their needs. Moreover, the proposed system introduces a lightweight mechanism within the end device's functionalities to allow them to participate in traffic forwarding. Figure 2 displays a simple representation of the considered LoRaWAN network containing out-of-range end devices in addition to standard LoRaWAN end devices.  
The proposed protocol involves two main phases to carry out delivering faraway device traffic to the gateway. The first phase is related to out-of-range LoRaWAN devices. It covers the process according to which a device can identify itself as being out of the range of the gateway. The second phase is related to the process of transmitting and forwarding out-of-coverage devices' traffic. The latter phase contains the proposed strategies that are embedded in the procedure of both out-of-range devices and other in-range devices.
## 6 Results
### Performance Metrics  
* Packet Delivery Ratio (PDR): This is a vital metric in WSNs that quantifies the proportion of successfully delivered packets to the total packets transmitted. It serves as a key indicator of the network's reliability and effectiveness in ensuring the accurate  
Figure 4: The simulated network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Simulator & NS3 \\ Nodes & 100 \\ Simulation Time & 50 S \\ Attack Nodes & 5, 10, 15, 20, and 25 \\ Normal Nodes & 95, 90, 85, 80, and 75 \\ Initial Energy & 200 J \\ Consumed Energy for Transmitted Packet & 0.00174 J \\ Routing Protocol & ECP-LEACH \\ Simulation Area & 1000 m \(\times\) 1000 m \\ Mobility & Random Rectangle Position \\ Mobility Speed & 20 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The simulation parameters.  
transfer of data from source to destination. The equation for the PDR is shown in Equation (1): \[\textit{Packet Delivery Ratio}=\frac{\textit{Number of Packets Received}}{\textit{Total Number of Packets}}*100\] (1)
* Energy Consumption: This is a critical parameter, particularly in resource-constrained environments. This metric gauges the energy expended by network devices during communication processes. The efficient management of power consumption is essential for extending the operational lifespan of battery-powered devices and reducing the overall environmental impact associated with energy usage.
* Throughput: This is a fundamental measure of network performance representing the volume of data successfully transmitted over the network within a specified timeframe. Higher throughput values indicate improved network capacity and efficiency in handling data transfer, thereby contributing to enhanced overall performance. The equation for throughput is shown in Equation (2): \[\textit{Throughput}=\sum_{n=1}^{Total\textit{Packets}}\frac{\textit{Packet Size}_{n}*8}{(\textit{Packet Receive Time}_{n}-\textit{Packet Send Time}_{n})*1024}\] (2)
* End-to-End Delay: This is a temporal metric that evaluates the total time taken for a packet to travel from its source to its destination. This metric encompasses both transmission and processing delays and is particularly relevant for applications with stringent latency requirements, such as real-time communication or multimedia streaming. Minimizing the end-to-end delay is crucial for ensuring timely and seamless data delivery. The equation for end-to-end delay is shown in Equation (3): \[\textit{End to End Delay}=\sum_{n=1}^{Total\textit{Packets}}{(\textit{Packet Receive Time}_{n}-\textit{Packet Send Time}_{n})}\] (3)
* Accuracy: This is a metric used to evaluate the performance of a classification algorithm. It is defined as the ratio of correctly predicted instances to the total instances in a dataset. In the context of machine learning, accuracy is often expressed as a percentage. The formula for accuracy is shown in Equation (4): \[\textit{Accuracy}=\frac{\textit{Number of Correct Predictions}}{\textit{Total Number of Predictions}}*100\] (4)
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 4 Comparative Experiment on Deep Learning Model Performance
### Main Training Method and Conditions  
Primary training is executed using the previously determined epoch and NMS IOU threshold values. The training dataset provided in Table 3 is employed. Both cases, ORG and TRANS, contain the same number of images and vehicle objects. The ORG dataset exists in the OI coordinate system, and the TRANS case is generated by geometrically transforming ORG using IPT into the TI coordinate system. The performance of OI and TI is compared in every distance section, illustrated in Figure 15. To evaluate OD performance for each distance section, we set the ROI at 200 m (Figure 15), dividing it into four sections of 50 m intervals for AP value comparison. Vehicle objects from ORG and TRANS, the test dataset in Table 3, are allocated to corresponding sections on OI and TI based on the bounding box center point. Details of the test dataset allocation are shown in Table 5. AP value comparison is solely performed on the test dataset, adhering to the prescribed distance sections. Notably, objects in ORG and TRANS images vary in size and shape due to IPT. Thus, vehicle objects are annotated for OD for each case. Reference coordinate systems differ for both cases, causing variations in allocated sections, despite having the same object. Consequently, the number of images and vehicle objects in the prescribed sections may differ, as seen in Table 5.  
Figure 14: AP value for trained deep learning models across NMS IOU thresholds: (**a**) case ORG; (**b**) case TRANS.  
Figure 15: Schematic diagram for division of distance sections in an image.  
For the primary training hyperparameters, the epochs and NMS IOU threshold determined during pre-training are employed. The remaining training parameters remain consistent with those detailed in Table 4. In this instance, case ORG has 100 epochs, case TRANS has 50 epochs, and both cases adopt an NMS IOU threshold of 0.6. Other training conditions align with those established during preliminary training.
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 3 MIR SC Generation in ChG Fibers  
MIR SC generation in ChG fibers has attracted wide attention since the first reported SC sources from an As-Se fiber in 2005 [97]. The MIR spectral region is particularly important because most molecules display fundamental vibration absorptions in this region. ChG glasses that exhibit excellent properties in this region have significant advantages over silica, tellurite and fluoride glasses, and are the unique media that can extend the SC spectrum to MIR or even to the far-infrared (FIR) region (longer than 10 \(\upmu\)m).  
SC sources from ChG fibers with a large bandwidth, high brightness, and high degree of coherence have recently become of great interest due to their potential applications in many fields. To date, the main research institutions engaged in MIR SC generation in ChG fibers are the Technical University of Denmark, the University of Laval, the Toyota Technological Institute, Ningbo University, and Mcgill University. Similar to Section 2.2, MIR SC generation in ChG fibers is reviewed in this chapter from the three aspects of fiber structure: SC generation in step-index fibers, MOFs, and tapered fibers.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 4 LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES
### LSHADE-SPACMA Hybridization Framework  
In order to enhance LSHADE-SPA's performance, a hybridization framework between the recent version of CMA-ES and LSHADE-SPA is applied [26]. Each individual \(D\) in the population produces an individual \(u\) offspring using either LSHADE or CMA-ES according to the probability variable (_FCP_) class. _FCP_ values are chosen randomly from memory slots that are set to \(M_{FCP}\). One memory slot is modified at the end of each generation based on the output of each algorithm. As a result, populations were gradually assigned to a better performance algorithm. Updates were produced using individuals that successfully created new individuals. The memory slot of the \(M_{FCP}\) is then updated based on the following formula:  
\[M_{FCP,G+1}=(1-C)M_{FCP,G}+C\ \Delta_{AlG1} \tag{24}\]  
where \(C\) is the learning rate, and \(\Delta_{AlG1}\) is the improvement rate for each algorithm calculated according to Equation (25):  
\[\Delta_{AlG1}=min\Big{(}prob_{max},max(prob_{min},\frac{\omega_{AlG1}}{\omega_ {AlG1}+\omega_{AlG2}}\Big{)} \tag{25}\]  
where \(prob_{min}\) and \(prob_{max}\) are the minimum and maximum probabilities set to each algorithm, respectively. Thus, to execute both algorithms together, _FCP_ values must be maintained in the range of 0.2 to 0.8. The variable \(\omega_{AlG1}\) is the summation of differences between the old and new fitness values for each individual belonging to the algorithm in Equation (26):  
\[\omega_{AlG1}=\sum_{i=1}^{n}f(u_{old})-f(u), \tag{26}\]  
where \(f\) is the fitness function. \(u_{old}\) and \(u\) are the old and the offspring individuals, respectively. \(n\) is the number of individuals belonging to algorithm \(AlG1\).