esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussions  
The system presented in Figure 1 is simulated in MATLAB. The transmitter, channel and receiver are designed as separate blocks. The channel is designed to generated pass-band noise according to the scheme in Figure 1. The level of the noise is controlled by specifying the value of the noise energy \(\mathrm{E_{N}}\). The fading is generated using a fading generator with the empirical density function which corresponds to the theoretical Rayleigh density function.  
The simulation was conducted according to the following procedure. The sequence of message bits is generated at the input of the receiver. The spreading sequence is assigned to each bit or the symbol, depending on the systems structure. Then the spreading sequence modulated the carrier using OQPSK modulation, which is defined by the Standard for WSNs [1]. The modulated signal is affected by the noise and fading, which were generated inside the channel. The received signal is demodulated in the block of receiver. At the output of the receiver, the symbols were detected and then transferred into the corresponding bits.  
The detected bits are compared with the message bits generated at the input of the system and BER rate is calculated as the ratio of the number of errors detected and the number of bits transmitted. The Chebyshev's inequality method for accurate estimation of each BER value was used. According to this method it was needed to transmit at least \(10^{7}\) bits to achieve 99% confidence in BER estimates [14]. This BER value estimation was done for each signal to noise ratio that is defined as abscissa value in all Figures presented in this Section.  
Two sets of simulators are designed. The first set included simulators with one correlator receiver. In this case the bits were directly related to the spreading sequence as defined by the Standard for WSNs [1]. In this set of simulators, the channel was simulated using either the additive white Gaussian noise channel (AWGN) noise generator or the AWGN generator with fading. In addition, the system was simulated by including or excluding the interleaver and deinterleaver blocks. The second set of simulators included 16 correlators at the receiver side. In this case, the message random bits were transferred into symbols and the spreading sequences are assigned to the symbols according to the Standard specifications [1]. The results of simulation, obtained from both sets of simulators, are presented in this Section.
## 3 Methods
### Network Structure  
As illustrated in Figure 1, the proposed residual network for fusing multi-scale features while preserving structure comprises three key components: a downscale module, a depth feature extraction module, and an upscale module. Notably, both the downscale module and upscale module are constructed using the Multi-Scale Residual Block (MSRB), which plays a crucial role in extracting and integrating features at different scales.  
Figure 1: The architecture of the proposed network.  
Since it is difficult to extract effective facial features directly from LR face images, a preprocessing step is employed to upsample the LR face image \(I_{LR}\) using Bicubic interpolation, bringing it to the same dimension as the HR image. Shallow features are then extracted from the upsampled image using a 3 \(\times\) 3 convolutional layer, as depicted in Equation (1). Here, \(H_{sp}\) denotes the feature extraction process, \(H_{up}\) represents the upscale operation, and the resulting shallow features \(F_{0}\) are subsequently fed as input into the downscale module.  
\[F_{0}=H_{sp}\big{(}H_{up}(I_{LR})\big{)} \tag{1}\]  
In the downscale module, the face image undergoes gradual encoding and downsampling after the extraction of shallow features. This process involves applying three consecutive MSRBs to obtain the feature map \(F_{1}\), which shares the same spatial dimensions as \(I_{LR}\). To mitigate the issue of gradient attenuation with increasing network depth, the skip connections are incorporated within each Feature Enhancement Module (FEM) to fuse features from different scales. This fusion mechanism enables the feature map to retain a more comprehensive representation of facial information. The process can be described as follows:  
\[F_{1}=H_{down-i}(F_{0}) \tag{2}\]  
The downscaled face image is passed through the depth feature extraction module, as indicated in Equation (3). To extract multi-scale features from \(F_{1}\), both a MSRB and a Pyramid Attention (PA) module [26] are utilized, resulting in the generation of the deep facial feature \(F_{2}\).  
\[F_{2}=H_{deep}(F_{1}) \tag{3}\]  
Finally, the deep facial feature \(F_{2}\) is passed into the upscale module to match the spatial dimension of HR. It is then fused with the output feature \(F_{i}\) from the \(i\)th MSRB in the downscale module using an additional feature enhancement module (FEM). This fusion process generates the final output feature \(F_{out}\), which is subsequently subjected to a 3 \(\times\) 3 convolutional layer to adjust the channel dimension to 3. The resulting SR image \(I_{SR}\) is then obtained. The operation process is as follows:  
\[I_{SR}=H_{sp}\big{(}H_{up-i}(F_{2})+F_{i}\big{)} \tag{4}\]
## 3 Communication Technologies for Improved Energy Efficiency and Safety in Railways
### Some Examples of Remote Sensor Use for Improving Transport Energy Efficiency and Safety  
Railway transportation energy efficiency, safety, and security may be significantly improved through the implementation of ITS [148]. Within the ITS concept, distributed NB sensor networks may play crucial roles because they can be used for remote data collection and the interactive signalling of trains with high decision autonomy [34].  
In one such example, presented in [149], the problems of adhesion coefficient and head wind velocity affecting the energy expenditure and transportation safety were analysed. It was postulated that these influential factors, which can be estimated from measured atmospheric variables (temperature, humidity, wind speed, and wind direction) can be subsequently used for the prediction of worsened conditions on the railway track and for the predictive scheduling of trains depending on the estimation of the journey time duration using a suitable mathematical model [36]. Thus, the deployment of NB remote sensor networks could significantly increase the transportation safety and security indices. Figure 7 illustrates the concept developed in [149], wherein head wind and track adhesion (estimated based on atmospheric variable measurements) have been used to estimate the feasibility of the driving (freight hauling) mission over a demanding mountainous railway route (see also [36]). A suitable knowledge base utilising these structured information sets from the remote sensors, and the simplified model of the freight train are used to determine whether the driving mission is feasible or not under the existing weather conditions. It was indicated in [149] that this kind of approach may be useful for dispatching services to amend the freight train timetables in real time. Moreover, track conditions can be used  
Figure 6: Principle of operation of LTE NB-IoT remote sensor node [36].  
to optimise the energy (fuel) expenditures based on a suitable computer model supplied with the real-time track condition information, thus supporting the transportation sector's \"greening\" and sustainability [36].  
Another example of possible NB-IoT sensor use in conjunction with AI for increased traffic safety was presented in [150]. A traffic entity management algorithm based on fuzzy logic was designed to reduce traffic congestion in the case of a partial restriction of one of the two routes of the transport corridor, which is a typical problem in railways (Figure 8). Under the assumptions of available traffic entity (vehicle) queue lengths (e.g., by using NB-IoT remote sensor nodes for vehicle registration and tracking) the proposed fuzzy logic-based traffic entity scheduling algorithm was compared with conventional traffic scheduling, and it demonstrated the ability to effectively minimise vehicle queue lengths under stochastic conditions of incoming traffic flows [150].  
Figure 7: Principal representation of train supervision system with information flow [149].
## 4 QoS Provisioning Techniques for Future FiWi Networks
### Aggregation Techniques
Since ONU-HT APs use two-level aggregation techniques for optical traffic destined to HT STAs, we set the A-MSDU frame to 4095 octets due to the maximum MPDU length limitation for an A-MPDU data frame (see Fig. 3).
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 3 An Energy Consumption Optimization for ICN
### Existence Proof and Solution of Optimal Stopping Rule  
**Proposition 1:**_Equation (10) has an optimal stopping rule._  
**Proof:** According to [19], the optimal stopping rule exists when the following two conditions are satisfied:  
\[\begin{array}{ll}\text{\textcircled{1}}&E\big{\{}\text{sup}_{N}Y_{N}\big{\}}< \infty\\ \text{\textcircled{2}}&\text{limsup}_{N\to\infty}Y_{N}\leq Y_{\infty}\end{array} \tag{14}\]  
According to (11), the value of the reward function \(Y_{N}\) changed at the \(N\)th node is known as \(Y_{N}\) = \(X_{N}-NmP_{c}\), for any \(N\) = 1, 2, 3,..., _/ {sup_N }_\(Y_{N}\)_\(<\infty\), and because \(N\), \(m\) and \(P_{c}\) are positive values. Therefore, \(E[sup_{N}\)\(Y_{N}]\)\(<\infty\) is set up, and condition \(\textcircled{1}\) is satisfied. When \(N\)\(\to\)\(\infty\), \(NmP_{c}\)\(\to\)\(\infty\), otherwise \(-NmP_{c}\)\(\to\)\(\infty\), then \(Y_{N}\)\(\to\)\(-\infty\). Therefore, it is clear that \(Y_{\infty}\)\(\to\)\(-\infty\), _lim_\(sup_{N\to\infty}Y_{N}\)\(\leq\)\(-\infty\)\(=Y_{\infty}\), condition \(\textcircled{2}\) is satisfied. \(\square\)  
When the maximum expected benefit \(W^{*}\) is obtained by the user group, the stop time \(N\) is the solution of the optimal stop problem (Formula (10)). According to the problem solving method in [30], we can see that when \(X_{N}\)\(<W^{*}\), the node needs to be replaced and the user continues to observe other nodes. When \(X_{N}\geq W^{*}\), the node should stop observing and caching content. Therefore, the stop rule is converted to:  
\[N^{*}=\min\Big{\{}N\geq 1:\Big{(}d_{u_{x}\to v}^{2}-d_{u_{x}\to v_{N}}^{2} \Big{)}\geq W^{*}\Big{\}} \tag{15}\]  
Here, the maximum expected benefit \(W^{*}\) satisfies the best equation:  
\[W^{*}=E[\max(d_{u_{x}\to v}^{2},W^{*})]-P_{c} \tag{16}\]  
According to the optimization formula of Theorem 3.1 in [30], the stopping rule \(W^{*}\) is solved by:  
\[\begin{array}{ll}W^{*}&=E[\max(d_{u_{x}\to v}^{2},W^{*})]-P_{c}\\ &=\ \int_{0}^{W^{*}}W^{*}\cdot dF(x)+\int_{W^{*}}^{L}x\cdot dF(x)-P_{c}\end{array} \tag{17}\]  
Here, \(L\) is the distance between the user node \(u_{x}\) and the network node \(v\). Let \(W^{*}=\int_{0}^{L}W^{*}\cdot dF(x)\). By combining it with Formula (16), we can get:  
\[\begin{array}{ll}\int_{0}^{L}W^{*}\cdot dF(x)=\int_{0}^{W^{*}}W^{*}\cdot dF( x)+\int_{W^{*}}^{L}x\cdot dF(x)-P_{c}\\ \Rightarrow\int_{0}^{W^{*}}W^{*}\cdot dF(x)+\int_{W^{*}}^{L}x\cdot dF(x)-\int_ {0}^{L}W^{*}\cdot dF(x)=P_{c}\\ \Rightarrow\int_{W^{*}}^{L}(x-W^{*})dF(x)=P_{c}\end{array} \tag{18}\]  
Where, \(F\) is the distribution of \(X_{N}\). In this paper, \(F\) is assumed to be a uniform distribution. For discrete random variable \(x_{N}\), we can have:  
\[E(x_{N}-W^{*})^{+}=P_{c}Z \tag{19}\]
# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 4 Proposed Method  
The model described in this paper comprises three main subtasks: candidate entity extraction, event type detection, and argument identification. First, the text embedding and text representation are obtained by the pre-trained language models RoBERTa-wwm and Transformer [29]. Then, we utilize two independent modules to extract the head and tail position information of candidate entities. The interaction between the head and tail position information is achieved through a multiplicative attention mechanism, resulting in a score matrix for predicting candidate entities. Subsequently, the candidate entity representation is fused with the sentence representation and the document representation is obtained for event type detection using Transformer. Finally, we decode the event and role information using a multi-granularity decoder for the recognition and prediction of argument elements. The structure of the proposed model is shown in Figure 7.  
The DEE task can be described as extracting one or more structured events, denoted as \(Y=\left\{y_{i}\right\}_{i=1}^{N_{k}}\), from an input document \(D=\left\{S_{i}\right\}_{i=1}^{N_{k}}\) consisting of \(N_{s}\) sentences, where \(N_{k}\) represents the number of events contained in the document. Each event extracted from the document contains event types and their associated roles. We denote the set of all event types as \(T\) and the set of all role types as \(Role\). The structured event information extracted from the document is denoted as \(y_{i}^{t}\), where \(t\in T\) represents the event type. The \(n\) role information corresponding to each event type and the arguments used to populate the role information are denoted as \(\left\{r_{i}^{1},r_{i}^{2},\ldots,r_{i}^{n}\right\}\in Role\) and \(\left\{\alpha_{i}^{1},\alpha_{i}^{2},\ldots,\alpha_{i}^{n}\right\}\), respectively.  
Figure 6: Statistics on the number of roles played by candidate entities.
## 1 Introduction
The improvement of performance of a single Global Navigation Satellite System (GNSS) mainly depends on increasing the number of navigation satellites, optimizing the spatial geometric configuration and improving of the accuracy of the system's spatial signals, including the improvement of the accuracy of the precise orbit and satellite clock biases, whereas, the fusion of multiple GNSSs can significantly increase the number of observed satellites, optimize the spatial geometry and improve continuity and reliability of positioning [11]. However, in the past the data processing of multi-GNSS focused on the fusion of GPS and GLONASS [11; 12; 13; 14]. By processing the simulated data, the contribution of the BeiDou navigation satellite system to global PNT users has been analyzed in [15], which demonstrated that the introduction of the BeiDou system could benefit significantly the satellite visibility and the dilution of precision for global users. Research on the BeiDou positioning and combined BeiDou/GPS positioning will obviously benefit the application and popularization of the BeiDou system. With the orbit and satellite clock biases derived based on a small network of six stations distributed in Asia-Pacific area, a centimeter level PPP was realized by combined BeiDou and GPS data [16]. The impact of \"inter-satellite-type-bias\" (ISTB) was also analyzed when multiple GNSS receivers were used to determine the carrier's attitude [17]. In this contribution, we also adapted the PANDA software developed by Wuhan University to process the BeiDou/GPS observations of \"BETS\" in the modes of BeiDou static and kinematic PPP, GPS static and kinematic PPP and BeiDou/GPS static and kinematic PPP.
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 5 Ablation Experiments
### The Studies of ASPP, CRRASPP, and Deep Supervision  
Table 7 has already demonstrated the effectiveness of CRRASPP in the bottleneck layer. However, the ASPP [53] module is also capable of capturing multiscale feature information. To conclusively prove that CRRASPP outperforms the ASPP module, a comparative experiment was conducted between the two. Additionally, deep supervision has the ability to mask irrelevant noise. To further validate the necessity of deep supervision, experiments assessing its effectiveness were also performed.  
Table 9 demonstrates that the CRRASPP module significantly outperformed the ASPP module, particularly in the context of segmenting multiscale targets. This superiority is primarily attributed to CRRASPP's ability to capture both global semantic information and local details more effectively through adaptive dilation rates. Moreover, the integration of \(1\times 1\times 1\) convolutions with a stride of 1 in CRRASPP further enhances its capacity to capture long-range contextual information.  
Table 10 demonstrates that incorporating deep supervision into the MEASegNet network led to a notable enhancement in performance. By integrating deep supervision across multiple layers, the model was able to capture more nu  
Figure 12: HD95 results of the ablation experiments for different convolutional kernels in SLRFB.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Dice (\%)**} & \multicolumn{4}{c}{**HD95 (mm)**} \\ \cline{2-9}  & **WT** & **TC** & **ET** & **AVG** & **WT** & **TC** & **ET** & **AVG** \\ \hline SLRFB33 & 92.46 & 87.01 & 83.69 & 87.72 & 4.48 & 9.9 & 17.80 & 10.73 \\ SLRFB55 & 92.46 & 86.61 & 83.53 & 87.53 & 4.19 & 9.79 & 17.80 & 10.59 \\ SLRFB57 & 92.26 & 87.47 & 83.81 & 87.85 & 4.4 & 8.68 & 16.49 & 9.86 \\ SLRFB35 (our) & **92.50** & **87.49** & **84.16** & **88.05** & **4.18** & **7.96** & **14.40** & **8.85** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of ablation experiments for different convolutional kernels in SLRFB, with the best performance highlighted in bold.  
feature representations. This, in turn, bolstered its generalization ability when using unseen data while mitigating the influence of irrelevant noise.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 3 Method
### Overview  
This paper introduces an improved SLAM algorithm built upon ORB-SLAM2, which adds a semantic segmentation thread to the original threads for tracking, local mapping, and loop closing, with the purpose of identifying dynamic objects.  
As shown in Figure 1, the RGB and depth images were obtained from the RGB-D camera. The enhanced SLAM system sent the RGB image to the tracking thread for feature extraction and both the RGB and depth images to the semantic segmentation thread. In the semantic segmentation thread, YOLOv5x was first used for target detection to obtain semantic information, which was then combined with the depth image to generate masks for dynamic objects, thereby initially identifying the dynamic regions in the image. Generating masks using depth information allowed for the creation of masks, similar to traditional semantic segmentation results, without the need for complex neural networks. This approach effectively reduced network complexity and improves algorithm efficiency without compromising performance.  
In the tracking thread, feature point extraction and matching were performed using the GCNv2 network. Compared to ORB, the GCNv2 network can automatically learn multi-level high-dimensional feature representations, allowing it to capture more complex patterns and information. ORB has poor robustness to changes in lighting, rotation, and scale, especially in complex scenes. In contrast, GCNv2, through training on large-scale datasets and employing data augmentation techniques, is better equipped to handle these variations, thereby improving the stability of the model. Additionally, we designed a dynamic point removal strategy based on depth information. By calculating the standard deviation of the depth values of potential feature points, we determined whether these potential points were dynamic, thereby enabling the removal of dynamic points while retaining static feature points for pose estimation, backend optimization, loop detection, and mapping. This method improved the system's accuracy, robustness, and efficiency.
## 3 Results and Discussion  
### X-ray Diffraction Analysis  
The X-ray diffraction (XRD) patterns of the nonirradiated rGO and the irradiated Fe/N-rGO are presented in Figure 1a,b, respectively.  
In Figure 1a, one main peak was observed at 20 = 24.67\({}^{\circ}\), corresponding to the (002) plane of the rGO phase with the calculated d-spacing of 0.36 nm. Both peak positions and inter-planar distance confirmed the formation of rGO. rGO exhibited its characteristic peak  
Figure 1: (**a**) X-ray diffraction (XRD) patterns of reduced graphene oxide (rGO) (before irradiation) and (**b**) 10% Fe/N-rGO, 15% Fe/N-rGO and 20% Fe/N-rGO.  
in the range of 20\({}^{\circ}\) to 30\({}^{\circ}\). Generally, the XRD pattern of rGO displays a broad peak with a low intensity. The peak intensity corresponding to the (002) plane indicates that some oxygenated species are present in the sample. Graphite exhibits a peak at 20 of \(\sim\)26.5\({}^{\circ}\), corresponding to the (002) plane, whereas a GO peak appears at 20 of 11.49\({}^{\circ}\), corresponding to the (100) plane [12; 15; 29; 30; 31]. In Figure 1b, no sharp and narrow peak was observed. This amorphous XRD pattern confirmed the presence of Fe nanoparticles in the samples. The characteristic peaks of rGO in Figure 1a are not detected in Figure 1b possibly because of the existence of iron compounds that overwhelm the weak carbon peaks. Nevertheless, the broad peaks were likely attributed to the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) phase and the particle size was at a nanoscale.
# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 2 State of the Art Review
### Use of Voice Quality Index Instruments in the Medical Domain
For example, the AVQI was also used to determine the effectiveness of the NHS waterpipe as a superficial hydration therapy in healthy young women's voice production. The technique was determined to be useful by the authors, and the perceived phonatory effort decreased significantly at the last assessment point [39]. The authors of [40] attempted to assess the influence of type 1 diabetes mellitus on the voice in pediatric patients using voice quality analysis. The study findings revealed that the AVQI value was higher in the patient group, although not statistically significant. Acoustic measures, such as AVQI and the maximum phonation time (MPT), were used in the study [41] to predict the degree of lung involvement in COVID-19 patients. Each participant created a phonetically balanced sentence with a sustained vowel. In terms of AVQI and MPT, the results demonstrated substantial disparities between COVID-19 patients and healthy persons. [PERSON]'s study [42] sought to determine the efficacy of a 4-week breathing exercise intervention in patients with voice complaints. The total scores of AVQI and several of its subcomponents (shimmer and harmonic-to-noise ratio), as well as the GRBAS scale's grade, roughness, and strain, showed dramatically enhanced voice quality. However, neither the kind nor frequency of vocal symptoms, nor the perceived phonatory effort, changed as a result of the intervention.
## 3 Types of Channel Hole Defect  
Before designing test methodologies for screening channel hole defects, we attempted to categorize different channel hole defects based on two criteria: (1) the types of abnormal profiles in the channel hole, and (2) whether abnormal channel hole profiles simply increase bit errors (i.e., degrade the flash reliability) or cause malfunctions (i.e., flash chip failure).  
Figure 6 illustrates different abnormalities in the channel hole profile due to the non-ideal etching process during 3D NAND manufacturing. As described in Section 2, it is quite difficult (or mostly impossible) to form the channel hole as a right rectangular shape due to non-ideal effects (e.g., fluid dynamics of etchant) during the etching process. Even though various conditions in the etching process can be controlled carefully, the best result we can obtain is a reverse rhombus-shaped profile, denoted as _normal_ in Figure 6a. In our study, compared to the normal profile, we define three different abnormalities in the channel hole profile: Not-open, Bowing, and Bending. Furthermore, depending on the degree of abnormality in the channel hole profile, channel hole defects are divided into two categories: Soft defects and Hard defects. Based on this classification, we will take type-oriented optimized approaches to detect and screen out channel hole defects.