esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2012**, _12_, 2118-2135; doi:10.3390/s120202118  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
Experimental Assessment of Different Receiver Structures for Underwater Acoustic Communications over Multipath Channels  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Electronics and Telecommunications, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway; E-Mails: [PERSON]RESS] (J.M.H.);  
[PERSON]ESS] (H.D.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]S];  
Tel.: +47-4622-0956; Fax: +47-7359-1412.  
_Received: 20 December 2011; in revised form: 1 February 2012 / Accepted: 3 February 2012 / Published: 14 February 2012_  
**Abstract:** Underwater communication channels are often complicated, and in particular multipath propagation may cause intersymbol interference (ISI). This paper addresses how to remove ISI, and evaluates the performance of three different receiver structures and their implementations. Using real data collected in a high-frequency (10-14 kHz) field experiment, the receiver structures are evaluated by off-line data processing. The three structures are multichannel decision feedback equalizer (DFE), passive time reversal receiver (passive-phase conjugation (PPC) with a single channel DFE), and the joint PPC with multichannel DFE. In sparse channels, dominant arrivals represent the channel information, and the matching pursuit (MP) algorithm which exploits the channel sparseness has been investigated for PPC processing. In the assessment, it is found that: (1) it is advantageous to obtain spatial gain using the adaptive multichannel combining scheme; and (2) the MP algorithm improves the performance of communications using PPC processing.  
**Keywords:** underwater acoustic communication; time reversal; passive-phase conjugation; matching pursuit; decision feedback equalizer
## 5 Numerical Results and Discussion
### The Influence of the Maximum Iteration Number on Detection Performance  
In a phase noncoherent channel with different iteration numbers, the BER, SER, and PER performance of the exact LLR in (8) are shown in Figure 5, where \(\rho=1\), \(N=1\). It can be seen from Figure 5 that when the maximum number of iterations is increased from 1 to 25,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detailed Description** \\ \hline Channel condition & phase noncoherent or Rayleigh fading \\ Power of the complex AWGN & \(1/SNR\) \\ Timing synchronization & Perfect \\ LDPC code & PEGReg (1008, 504) \\ Code rate & 0.5 \\ Degree distribution & (3, 6) \\ Data modulation & MPSK \\ Symbols & 16-ary orthogonal \\ Payload length of PPDU (bits) & 504 \\ Spreading factor & 16 \\ Chip rate (Mchip/s) & 1 \\ Symbol rate (ks/s) & 62.5 \\ Binary data rate (kb/s) & 250 \\ Carrier frequency (MHz) & 786 \\ fading phase \(\theta\) (rads) & Uniform distribution in \((-\pi,\pi)\) \\ PN length & 16 \\ fading amplitude \(\rho\) & Rayleigh distribution \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in simulations.  
Figure 4: H matrix of (1008, 504) LDPC code.  
the BER, SER, and PER performance can be improved under fading channels. In particular, as shown in Figure 5a, when BER = \(1\times 10^{-5}\), as the maximum number of iterations increases from one to three, the SNR gain is approximately 2 dB; when the maximum number of iterations is increased from three to five, the SNR gain is approximately 0.6 dB; when the maximum number of iterations is increased from five to eight, the SNR gain is approximately 0.3 dB; when the number of iterations is increased from eight to ten, the SNR gain is about 0.1 dB. Furthermore, after eight iterations, the performance can meet the requirement of the receiver. Additionally, the improvement is so small when the iteration ranges from 10 to 25 that we set the maximum number of iterations to be 10.  
Figure 5: _Cont_.
## 3 Proposed FFA-BiGRU Method
### Multi-Level Spatial Feature Learning
#### 3.1.2 Group Architecture Block: Deep Spatial Feature Extractor  
For the FFA module in Figure 1, the log Mel-spectrogram is first fed into a convolution layer to extract the shallow information of music; then, the output features are sent into three group architecture blocks to extract multi-level deep spatial features. Each group architecture block is a global residual block that contains 19 basic blocks with the same structure, as shown in Figure 1. The stacking of multiple basic blocks increases the depth and enhances the expressiveness of the network to learn high-level emotion-related information. A long shortcut connection is introduced between the first and the last basic blocks to avoid losing useful information. Each of the three group architecture modules can learn spatial features of different levels. Since each group architecture block consists of a stack of multiple basic blocks, we provide a detailed introduction of the basic block in the following.  
Basic Block: The basic subnet in group architecture is the basic block, and its detailed realization is given in Figure 1. It is a local residual learning structure consisting of two convolutional layers, followed by a ReLU layer and a channel-spatial attention module, respectively. Local residual learning can improve training stabilization and allow the less important information to be bypassed through multiple local residual connections while the main network focuses on effective information. In the basic block, two convolutional layers (each containing 64 filters with a filter size of 3 \(\times\) 3) are used to extract local spatial features, and the channel-spatial attention mechanism allows the network to focus on important information from the channel and spatial levels.
## 4 Results and Discussion
### Results of the Modified Model  
The elliptic aspect ratios of the blade leading and trailing edge of the reference model are two. The most efficient points of the elliptic aspect ratios of the blade leading and trailing edge derived through the GA were calculated as 5.5 and 16, respectively. To verify the predicted point, numerical analysis was conducted with the calculated elliptic aspect ratios of the blade edges. Table 4 specifies the comparison results, and the relative errors are less than 1%; thus we can conclude that the prediction is verified. At this time, the power and head decrease by about 8% and 11%, respectively, compared to the reference model, and the efficiency increases by 2.16%.
# Sports Information Needs in Chinese Online Q&A Community: Topic Mining Based on BERT
## 5 Limitations  
There are three limitations of the study. First, we only studied one platform, Zhihu, which is the largest online Q&A community in China. It does not wholly represent Chinese netizens' sports information needs. Other online communities, such as Baidu Know, need further research to reflect their sports information needs more accurately. Second, in terms of the methodology, we chose the more mature BERT model, combining K-means and TF-IDF for topic analysis. With the development of NLP technology, more and more short text models suitable for texts of social networks have been created, and more accurate topic mining models could be used in future studies. Finally, the presentation of questions is just one side of the demand for sports information.
# Online EEG Seizure Detection and Localization
## 1 Introduction
While imaging modalities, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), have higher spatial resolution compared to electroencephalography (EEG), the EEG signal has a higher temporal resolution, which is essential for the online detection of seizures. Furthermore, EEG monitoring can be mobile and it is more cost-effective. Hence, we chose to focus on EEG for seizure detection. Neurologists generally use the time domain EEG waveform for seizure detection. However, the spectral characteristics of the EEG signal [4; 5; 6] can also be used to characterize the behavior of the EEG waveform before and during a seizure. We use measures of similarity between the spectral characteristics of EEG signals from different regions as a proxy for the similarity of neuronal activity in the regions. These measures can be used for the detection of hypersynchronization. Viewed as a network, the normal functional connectivity between neurons is analogous to small world behavior, with there being more local connections than distant ones. However, this behavior disintegrates during seizure activity, and similarities can be observed in neighboring leads of the EEG. We consider each lead of the EEG to be a node in a fully-connected network with edge weights or node-to-node distances that depend on the behavior of the underlying neuronal network. The relationship between these nodes is similar to the relationship of the small world network comprised of the neurons. In order to detect when the small world behavior disintegrates, we propose a distance measure between EEG leads which associates a smaller spectral difference between the EEG signals at these leads to a smaller distance between the nodes representing the EEG leads. In this way, the brain as a network can be monitored and its behavior can be analyzed. A novel distance measure between electrodes is proposed to observe the similarity in the frequency domain. This measure is used for localizing the origin of a seizure. Since the power of an EEG signal increases--sometimes dramatically--during most seizure activities, this behavior, observed and measured in both time and frequency domains, is one of the most popular features used for the detection of abnormal brain activity in the literature [23]. We also use a measure of the increase in the EEG signal power to indicate the possible presence of seizure activity. However, a seizure is not the only reason for increased power in the EEG signal. Therefore, if this condition is fulfilled, we look for evidence of hypersynchronization using the distance metric described in the next section.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 5 Evaluation
### Transmission Overhead When Exchanging Information between Cloud and Device  
We evaluated the benefit of using KubeEdge to manage NDN devices by comparing it with two other disaster management systems that do not use Edge computing platforms: the normal publish/subscribe NDN management system [38] and the state-of-the-art NDN-DM system [39]. In our system, KubeEdge uses Mosquito as the publish/subscribe framework for information exchange between cloud and devices. Meanwhile, the NDN publish/subscribe communication framework and NDN push-based mechanism are used in the other two systems respectively. We created two NDN device management systems that followed these works' design to make a comparison with our system. As mentioned in previous parts, responders can upload an event, a requirement, or his location to the cloud, and the commander on the cloud can update responder missions. We considered one event/requirement/location/mission as a status. We monitored the number of transmitted packets between cloud and device when the amount of exchange status increases. The result is shown in Figure 17.  
Figure 16: The comparison of mobility handover duration.  
Figure 17: Number of transmitted packets to synchronize status between cloud and devices comparison between our system versus NDN Publish/Subscribe framework and NDN-DM.  
The result shows that the number of transmitted packets when using NDN Publish/Subscribe increases proportional with the number of statuses, while they are equal when using KubeEdge and NDN push mechanism in NDN-DM. Specifically, the number of exchange packets between cloud and devices when using the NDN Publish/Subscribe framework is 4 times higher than our system and NDN-DM. The reason is that the NDN Publish/Subscribe needs to exchange four interest/data packets between them to synchronize one status [38], while the cloud/device only needs to push one packet containing the status to the Mosquito framework in KubeEdge. The same reason is applied for NDN-DM as only one packet is needed to push a status to the destination. With lower transmission overhead when exchange status between cloud and devices, our system can avoid congestion and packet losses in high network traffic conditions in disaster scenarios. Our system is as efficient as NDN-DM in terms of reducing information transmission overhead. The advantage of it over NDN-DM is presented in the next part.
## 7 Positioning of the Edge Cloud Model _wrt._ Existing Solutions  
In this section we would like to compare the proposed model with existing alternatives. Since the Edge Cloud has evolved from CDN and Cloud computing, the proposed model is positioned with respect to such solutions. We start with a high level architectural comparison of different models, which is shown in Figure 6. Note that in this figure, as expected, \"edge\" is in close proximity to the end user while \"core\" is in a location distant from the end user (which is not necessarily in the core of the Internet as shown in Figure 1). In traditional Cloud computing, both computing and storage facilities are deployed inside the Internet core [5]. In CDNS, some of the storages are also deployed in the edge along with redirection. In the Cloud-based CDNs [17], a utility-based distribution is implemented at multiple core locations. The proposed model, integrating the virtues of CDN and Cloud computing, brings the computing facilities closer to the user at the edge and implements storages at the edge and core (e.g., may access third-party content from a remote Edge Cloud). We take Amazon EC2 [15], Akamai [10] and MetaCDN [17] as benchmarks of Cloud computing, CDN and Cloud-based CDN models, respectively. Table 1 compares the proposed model with these existing content/service delivery models. Cloud computing could be used for the computing of remote server to support virtual client. Although both CDN and proposed model deploys infrastructure (e.g., storage, redirection, computing, _etc._) at the edge, only the proposed model requires InP involvement. Consequently, the InP are in the value chain only in case of the proposed model. Cloud computing and CDN require large infrastructures and hence immense investments [5, 17]. However, our proposed  
Figure 6: High-level architecture of different content/service delivery models. model is a smaller scale cloud while MetaCDN essentially piggybacks on top of existing infrastructures. Among these services, CDNs are expensive and ask for a long-term contract. In Cloud computing, the latency will be high if the Cloud is deployed far away from users and higher bandwidth is needed for data-intensive computing [14]. For other models, latency is low when the content is delivered from local cache or a storage from close proximity. The required bandwidth is also low for simple content, but high for rich content (e.g., audio/video streaming).
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### SiM-RPO & CoSiM-RPO  
Reference [44] proposes two routing schemes, SiM-RPO and CoSiM-RPO, in order to improve energy efficiency by employing single-hop and two-hop data forwarding. The network area is divided into four equal parts. Each part has its own mobile sink, which traverses its assigned area on a triangular traversal path (Figure 10).  
Nodes transmit their stored data to a mobile sink upon contact through one-hop communication. However, the authors argue that due to the harsh underwater environment, the data may be received in error. To cope with this problem, CoSiM-RPO is proposed. In CoSiM-RPO, the receiving mobile sink checks the received data for errors. If the errors are above a certain threshold value, the mobile sink  
Figure 10: Proposed Network Model [44].  
requests transmission of the same data from other nodes (relay nodes) that might have received the data when it was being transmitted by the sender to the sink. The relay node transmits the data to the sink node. Based on the newly received packet, the sink measures the error. If a certain threshold is satisfied, the transmission is considered successful; otherwise, the packets are dropped. CoSiM-RPO improves the chances of successful delivery. However, if a certain node does not have any other neighbor, the technique may not be effective. Moreover, the number of neighbors also has impact on the bit error rate of the received packet. Data is collected by mobile sinks directly from the sensor nodes. This eliminates the disadvantages of multi-hopping, such as voids and link level reliability issues, neighbor discovery overhead, etc. However, the nodes hold data until a mobile sink is in range. This makes the proposed protocol unsuitable for real-time applications, such as surveillance and target detection, if an ample number of mobile sinks are not employed. Moreover, the path determination for mobile sinks in the case of the mobile network becomes more complex.
## 3 Attitude Controller Design
### Controller and Estimator Design  
Define unknown parameters \(\hat{\vartheta}=-[K_{p},M_{q},N_{r}]^{\top}\), which are the diagonal of \(D\). The estimator error is  
\[z_{i}=\hat{\vartheta}_{i}-\hat{\vartheta}_{i}+\beta_{i}(\
u_{2i}) \tag{44}\]  
where \(\beta_{i}\) is a continuous function.  
For the convenience of controller design, we rewrite \(\mathcal{M}\) as  
\[\mathcal{M}=D\
u_{2}=\begin{bmatrix}D_{11}\
u_{21}\\ D_{22}\
u_{22}\\ D_{33}\
u_{23}\end{bmatrix}=\begin{bmatrix}\theta_{1}\rho_{1}(\
u_{21})\\ \theta_{2}\rho_{2}(\
u_{22})\\ \theta_{3}\rho_{3}(\
u_{23})\end{bmatrix} \tag{45}\]  
where \(\rho_{i}\) is a continuous function.  
The controller can be constructed as  
\[\tau_{m}=\begin{bmatrix}(\hat{\vartheta}_{1}+\beta_{1}(\
u_{21}))\rho_{1}(\
u _{21})\\ (\hat{\vartheta}_{2}+\beta_{2}(\
u_{22}))\rho_{2}(\
u_{22})\\ (\hat{\vartheta}_{3}+\beta_{3}(\
u_{23}))\rho_{3}(\
u_{23})\end{bmatrix}- \kappa-K_{2}I\hat{\
u}_{2} \tag{46}\]  
where \(K_{2}\) is a positive-defined matrix valued function.  
With the control input (46), the closed-loop system (43) can be written as  
\[\begin{bmatrix}\hat{\eta}_{2}^{\top}\\ I\hat{\
u}_{2}\end{bmatrix}=\begin{bmatrix}0_{3\times 3}&J_{2}(\eta_{2})I^{-1}\\ -I^{-1}J_{2}^{\top}(\eta_{2})&S(\
u_{2})-K_{2}\end{bmatrix}\begin{bmatrix}\frac{ \partial H^{\top}}{\partial\hat{\eta}_{2}}\\ \frac{\partial H^{\top}}{\partial\hat{\eta}_{2}}\end{bmatrix}-\begin{bmatrix}0 \\ \Delta\end{bmatrix} \tag{47}\]where  
\[\Delta_{i}=z_{i}\rho_{i}(\
u_{2i}) \tag{48}\]  
The estimator can be designed as  
\[\dot{\hat{\theta}}_{i}=-\frac{\partial\beta_{i}}{\partial(\
u_{2i})}[\dot{\
u}_{ 2i}+\Delta_{i}] \tag{49}\]  
We can see that \(\Delta_{i}\) can be obtained from (47), which is  
\[\begin{split} I\
u_{2}+\Delta&=I\
u_{2}+\left[-I^{ -1}J_{2}^{\top}\eta_{2}\quad S(\
u_{2})-K_{2}\right]\begin{bmatrix}\frac{ \partial H^{\top}}{\partial\eta_{2}}\\ \frac{\partial H^{\top}}{\partial I\partial_{2}}\end{bmatrix}-I\dot{\
u}_{2}\\ &=\left[-I^{-1}J_{2}^{\top}\eta_{2}\quad S(\
u_{2})-K_{2}\right] \begin{bmatrix}K_{1}\tilde{\eta}_{2}\\ I\dot{\
u}_{2}\end{bmatrix}+\dot{J}_{2}(\eta_{2})\dot{\eta}_{2d}+J_{2}(\eta_{2 })\dot{\eta}_{2d}\end{split} \tag{50}\]  
As a result, \([I_{i}\dot{\
u}_{2}+\Delta_{i}]\) in (49) is a function of \(\
u_{2},\eta_{2},\dot{\eta}_{2d},\dot{\eta}_{2d}\), and they are measurable or can be calculated from the given reference signal.  
The continuous function \(\beta_{i}\) can be selected as  
\[\beta_{i}(I_{i}\
u_{2})=\gamma_{i}\int_{0}^{\
u_{2i}}\rho_{i}(\zeta)d\zeta= \frac{1}{2}\gamma_{i}\
u_{2i}^{2} \tag{51}\]  
where \(\gamma_{i}>0\), which implies that  
\[\frac{\partial\beta_{i}}{\partial\
u_{2i}}=\gamma_{i}\rho_{i}(\
u_{2i}) \tag{52}\]  
The control scheme is illustrated in Figure 2.
###### Abstract  
Landslides are the nation's hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development.  
Keywords:landslide; multi-criteria decision analysis (MCDA); analytical hierarchy process (AHP); receiver operating characteristics; area under the curve (AUC) +
Footnote †: journal: Applied
## 2 Materials and Methods
### Results of Resilience Analysis  
Resilience analysis of a transportation system is generally performed using traffic simulation models [40; 41; 42; 43; 44]. In our previous study [25], the level of functionality of the twin-tube motorway tunnel, during the period in which the north tube was partially or completely closed due to a traffic accident, was calculated as the ratio between the average travel time before and after the traffic accident. We calculated the reduction in the functionality F* (%) for all the scenarios examined (the functionality level F* was assumed to be equal to 100% in absence of incidents). Then, we built the functionality curves vs. time (i.e., F* (%) \(-\) t) and for all the scenarios computed the resilience loss: R\({}_{\text{LOSS}}=\int_{\text{t}_{\text{b}}}^{\text{h}}[100-\text{F}^{*}(\text{t})] \text{dt}\), in which t\({}_{\text{o}}\) is the time instant when the traffic accident occurs in the north tube and t\({}_{\text{h}}\) is the time instant when the twin-tube tunnel is again fully functional. Smaller resilience losses were found with the partial closure of the north tube rather than the complete one. However, when an alternative route was activated for HGVs only, both in the event of the partial and of the complete closure of the north tube, minor reductions of resilience losses were found compared to those without considering HGV rerouting.  
In order to describe the resilience through a parameter, whose value ranges between 0 and 1, we computed the resilience index (\(\mathrm{R(t_{h})}\ =\ \frac{\int_{h}^{h}\mathrm{P}^{z}(t)\mathrm{dt}}{\mathrm{t_{h} }-\mathrm{t_{b}}}\) ) that is widely used in the literature for transportation networks. A low resilience index denotes a high resilience loss (i.e., a resilience index equal to one corresponds to a zero-resilience loss). The resilience index confirmed the abovementioned findings of the resilience loss in all the scenarios investigated.  
Given these findings, the intent of this paper was to assess the impacts on user safety related to each of the mentioned fire scenarios in order to help tunnel management agencies (TMAs) decide which arrangement is more appropriate to adopt in the case of a traffic accident in a tube of a twin-tube road tunnel.