esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Based on the analysis of the thermal discharge impact of HYNPP on the surrounding marine environment, the following conclusions can be drawn:  
Significant Thermal Impact on the Receiving Water Body: The discharge of warm water from HYNPP has a significant impact on the temperature of the receiving water body, particularly in the vicinity of the discharge outlet (Zone A). This impact is evidenced by the substantial increase in SST in Zone A compared to the other zones.  
Seasonal Variation in Thermal Impact: The thermal impact of HYNPP's discharge exhibits significant seasonal variability. Winter, characterized by higher discharge temperatures and stronger vertical mixing, leads to a more concentrated thermal plume with higher temperature rises. In contrast, summer, with lower discharge temperatures and weaker vertical mixing, results in a more dispersed thermal plume with lower temperature rises.  
Spatial Distribution of Thermal Impact: The spatial extent of the thermal impact decreases with increasing distance from the discharge outlet. Zone A, located closest to the outlet, experiences the most significant warming, while Zones B and C, situated farther away, exhibit progressively lower temperature increases.  
Compliance with Sea Water Quality Standard: While the thermal impact of HYNPP's discharge is significant, it generally complies with the applicable sea water quality standards. However, it is crucial to continue monitoring the thermal impact, especially in Zone A, to ensure long-term environmental sustainability.  
Conceptualization, X.W. ([PERSON]); Data curation, Q.M.; Formal analysis, L.W.; Methodology, [PERSON] ([PERSON]); Software, [PERSON] ([PERSON]); Visualization, L.W.; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 41906156).  
Not applicable.  
Landsat imagery was obtained from United States Geological Survey (USGS) and is available [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 25 November 2024) with permission from USGS. Tide data were obtained from National Marine Information Centre, Ministry of Natural Resources, China. Public access is available through website ([[https://psmsl.org/data/obtaining/stations/723.php](https://psmsl.org/data/obtaining/stations/723.php)]([https://psmsl.org/data/obtaining/stations/723.php](https://psmsl.org/data/obtaining/stations/723.php)), accessed on 15 November 2024).  
**Acknowledgments:** We acknowledge the U.S. Geological Survey for providing freely available Landsat-8/9 data. We thank the National Marine Information Centre, Ministry of Natural Resources, China, for providing historical tide data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
Because of the limited availability of images at a very high spatial resolution (suitable for detecting geologic hazards) acquired periodically over the same area, long-term careful monitoring of post-earthquake disaster development and vegetation recovery through images are rarely conducted. In our research, satellite imagery from before the earthquake and four epochs of high-resolution airborne optical images after the Wenchuan earthquake were utilized to monitor and assess the development of secondary geologic hazards (refers to the geologic hazards triggered by the Wenchuan earthquake or aftershocks in this paper) by focusing on 15 representative large-scale geologic hazard sites in the affected areas. The condition of the vegetation recovery was also analyzed in the context of the landslides and debris flows, and variation rate was displayed to illustrate their development at different stages after the Wenchuan earthquake. Besides, geological and topographic factors were also analyzed to discover the hazard development and recovery trajectories. The results provide scientific data of the vegetation recovery and hazard development, which can be used for prevention and reconstruction practices in the Wenchuan earthquake area.
# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 4 Quantifying Utility and Privacy in HTC
### HTC Model Architectures
The training hyperparameters are taken from [PERSON] [39]: filter sizes of 3, 4, and 5 for the three convolutional blocks with 100 filters each and a dropout probability of \(p_{do}=0.5\). **Transformer Networks. ** Transformer layers are ANN layers that are especially suited for processing longer texts due to a mechanism called _self-attention_. Due to self-attention, a single Transformer layer can relate all tokens of a text to each other [18]. In contrast, CNNs require multiple convolutional layers to relate the information between two arbitrary tokens in a text. The transformer classifier we use in this paper is the BERT model as formulated by [PERSON] et al. [40]. BERT comprises twelve transformer layers, each consisting of two sub-layers. To lower the computational effort that is needed for training BERT, we follow related work and initialize the BERT layers with pre-trained weights [40; 41]. During training, we employ the Adam optimizer and a dropout probability of \(p_{do}=0.1\). The BERT HTC architecture is illustrated in Figure 3a. In our methodology, we therefore do not consider recurrent neural networks (RNNs) such as long short-term memory (LSTM). The reason is that they can achieve comparable results to CNNs [39], while the training process can be more difficult due to tuning the hyperparameters to avoid vanishing gradients. In addition, training RNNs is less efficient due to the lack of parallelization capabilities.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP Third Generation Partnership Project  
5G Fifth generation  
AM Acknowledged mode  
AMD Acknowledged mode data  
E2E End-to-end  
eCNS Evolved core network solution  
EPC Evolved packet core  
FTP File transfer protocol  
HSS Home subscriber server  
KPI Key performance indicator  
KQI Key quality indicator  
LMT Local management terminal  
LTB The-larger-the-better  
LTE Long term evolution  
MME Mobility management entity  
MMS Maximum segment size  
MOS Mean opinion score  
NTB The-nominal-the-best  
OA Orthogonal array  
OAM Operation, administration, and maintenance  
P-GW Packet data network gateway  
PCRF Policy and charging rules function  
PDCP Packet data convergence protocol  
PDU Protocol data units  
PSNR Peak signal to noise ratio  
QoE Quality of experience  
RAN Radio access network  
RLC Radio link control  
RRC Radio resource control  
S-GW Serving gateway  
SDU Service data unit  
SNR Signal-to-noise ratio  
SON Self-organizing networks  
SSIM Structural similarity  
STB The-smaller-the-better  
TCP Transmission control protocol  
TM Taguchi's method TTI Transmission time interval UDP User data protocol UE User equipment UM Unacknowledged mode UMD Unacknowledged mode UMDS Universal mobile telecommunications system
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
## 1 Introduction
When dealing with accelerometer data, time dependency is a feature that we do not want to lose, as it can help distinguish between two activities which would otherwise be quite similar. Hidden Markov models solve the time-dependency issue by representing and learning the data through the exploitation of their sequential characteristics [12]. They have been found to outperform both K-means and Gaussian mixture models when used for the classification of activities recorded in laboratory settings [9]. However, when recording data in free-living conditions, we are presented with a much more complex set of activities, which hinder HMM performance because HMM models the duration of the states implicitly as a geometric distribution, which is unlikely to be particularly informative for modelling the activity duration [13]. State duration is another critical feature for human activity classification when dealing with activities that have similar acceleration profiles but differ in duration, and it significantly impacts the prediction accuracy of the model [13]. Consequently, we introduce hidden semi-Markov models (HSMMs), which solve the issues mentioned above by modelling the classification problem as an HMM but include an explicit distribution for the state duration, which is related to the number of observations emitted by the state. This is also the main difference between HSMMs and HMMs, as the latter only have one observation being emitted from each state, and the state duration is implicitly modelled through self-transitions. An HSMM was previously used to segment accelerometer data by [PERSON] et al. [14], who found a small correlation between HSMM-inferred states and states found with the cut-off method. However, this may be because the study did not perform model selection by testing different feature spaces and other parameters such as duration and observation priors hyperparameters. Therefore, the potential of the HSMM's inference remains unexplored. Moreover, they did not assess the usefulness of inferring physical behaviours through an HSMM in epidemiological studies, and thus the feasibility of HSMMs in epidemiological research remains unclear. In this work, we address the problem of the unsupervised classification of human activity by using accelerometer data. The methodology that we used is based on HSMM. We assess the ability of the HSMM to make a correct inference, by first tuning the HSMM parameters to optimise the inference performance. Then, we investigate the ability of the HSMM approach to be useful for epidemiological studies, with the examples of association between anthropometric data and physical behaviours. In particular, we assess the association between body mass index (BMI) and waist circumference (WC) and sedentary behaviour, moderate-intensity activity and light-intensity activity [15, 16].
## 2 Calculation Method for Surface Settlement  
### Overview  
When an extra-large cross-section tunnel passes beneath airport runways, railway embankments, and other critical structures in sensitive environments, the settlement control standards are extremely stringent, typically requiring millimeter-level micro-settlement. Micro-settlement control for such tunnels typically involves advanced pipe-roof support and sequential excavation methods.  
Surface settlements caused by tunnel construction under pipe-roof protection can be classified into three main components: surface settlement due to tunnel excavation (u1), surface settlement caused by pipe-roof installation (u2), and surface settlement resulting from groundwater level drawdown (u3). Assuming the soil is an average soil and without considering the rheological effects between the three types of settlement. The total surface settlement can be calculated using Equation (1).  
\[u=u_{1}+u_{2}+u_{3} \tag{1}\]  
The surface settlement caused by tunnel excavation comprises transverse and longitudinal components. The transverse settlement arises from flexural deformation caused by insufficient primary support stiffness, with the maximum typically occurring near the tunnel centerline. The longitudinal settlement results from bending deformation due to inadequate pipe-roof stiffness, with the maximum generally located near the tunnel face. The surface settlement resulting from pipe-roof installation consists of ground loss during the installation of individual pipes and additional settlement due to the superimposed effects of group pipe construction. Changes in groundwater levels alter the effective stress within the soil, thereby causing over-consolidation settlement in the strata. The extent of this settlement varies significantly across different strata.
## 3 Case Study
### Simulation Results
#### 3.7.3 Evaluation of Abnormal Time Series Prediction Performance  
There was an abrupt variation in the displacement during the period ranging from 27 April 2010 to 11 July 2010 for G2-HY, as shown in Figure 13. This abnormal phenomenon is usually caused by environmental factors or material properties. The prediction performances of the nine models are evaluated using the measurement indices, as shown in Tables 4 and 5. The proposed model significantly outperforms the other models on all the indicators, regardless of the proportion of data used for training (60% or 30%). For instance, the MAE value of the hybrid model is reduced by approximately 67.9% during the testing phase compared with that of the CNN model. The improvements in the MSE and R\({}^{2}\) are as high as 52.3% and 8.1% compared with the CNN-GRU-SSA model when in the testing phase, respectively. Combined with the boxplot, as shown in Figure 11a,b, extreme outliers exist in all the other models for the two monitoring points except in the case of the proposed model, while only slight outliers exist in the proposed model with a relatively concentrated residual distribution. Thus, the mutation resistance and robustness of the proposed model are proven.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{_MSE_} & \multicolumn{2}{c}{_RMSE_} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-9}  & **G2-HY** & **G3-HY** & **G2-HY** & **G3-HY** & **G2-HY** & **G3-HY** & **G2-HY** & **G3-HY** \\ \hline Hybrid model & 0.8340 & 0.1926 & 1.1624 & 0.0562 & 1.0782 & 0.2370 & 0.9842 & 0.9055 \\ MLP & 3.2137 & 0.3015 & 15.5721 & 0.1383 & 3.9461 & 0.3720 & 0.7377 & 0.6385 \\ CNN & 2.5986 & 0.2872 & 10.5526 & 0.1278 & 3.2484 & 0.3575 & 0.7958 & 0.6483 \\ LSTM & 2.7929 & 0.2520 & 7.0410 & 0.0953 & 2.8101 & 0.3087 & 0.8177 & 0.6845 \\ GRU & 1.9728 & 0.2536 & 6.6281 & 0.0964 & 2.5745 & 0.3105 & 0.8519 & 0.7199 \\ LSTM–SSA & 2.3448 & 0.2367 & 5.8182 & 0.0829 & 2.4030 & 0.2880 & 0.8736 & 0.7744 \\ GRU–SSA & 1.7885 & 0.2379 & 5.3936 & 0.0834 & 2.3224 & 0.2888 & 0.8980 & 0.7747 \\ CNN–LSTM–SSA & 1.8223 & 0.2345 & 5.5960 & 0.0806 & 2.3656 & 0.2839 & 0.8994 & 0.7930 \\ CNN–GRU–SSA & 1.7745 & 0.2378 & 5.1085 & 0.0847 & 2.2602 & 0.2910 & 0.9051 & 0.8073 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of different models for an arch dam with 30% of the data used for training.  
Figure 13: _Cont._  
The five better models are selected here for comparison, as shown in Figure 13. All the prediction models can fit the changes in the abnormal phenomena when 60% of the data are used for training. When 30% of the data are used for training, the other models will be polluted training dataset; only the proposed hybrid model performs well. Therefore, even if the monitoring time series has abnormal data, the proposed hybrid model still has good generalization performance, indicating that it is a promising dam deformation prediction model.
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Producer Mobility  
Producer mobility is still a major challenge for ICN. To better exemplify the issues with producer mobility, let us consider the scenario previously addressed and illustrated in Figure 4, where MN, after receiving an Interest packet I1 from CN forwarded to MN via router A, sends back packet D1. MN then starts a handover to router B. In this situation and again depending on the topology, the CN will keep on sending Interest packets to get subsequent data chunks. Routers in between keep on looking up their FIBs and, as there is already an entry towards the respective Name Prefix, routers forward the CN Interest packets towards the respective Face (to router A). This process can result in significant latency. To circumvent this issue, there is the need to rely on additional mobility management solutions.  
Producer mobility is currently being handled via _anchor-based approaches and anchor-less_ approaches, as illustrated in Figure 5. Anchor-based mechanisms, of which the most relevant is KITE [2; 8], follow IP-based approaches and often recur to the use of a \"rendezvous\" (RV) functional entity to temporarily assist data transmission. KITE tries to exploit the forwarding states to keep track of nodes in movement. KITE considers that applications can send Interest packets to a routable and static anchor entity (an RV) to create the PIT entries as breadcrumbs. The RV is therefore a mediating entity, host-driven. Via this RV-based approach, Interest packets can reach a producer on the move.  
This approach requires additional structures in routers--a separate FIB or PIT--as well as additional state to be kept. Furthermore, in current approaches, the RV is considered to be static--mobility of the RV is not handled. Therefore, while such approaches may be relevant from a perspective of interoperability towards IP-based mobility solutions, the overhead introduced can be significant.  
Figure 4: Producer mobility example. The MN is a producer node on the move. Some data packets are sent before handing over. Once the MN attaches to the visited network, no data packets are sent, as the MN does not get Interest packets.  
In what concerns anchor-less solutions, producers push a notification once a move occurs. Such notification can be based on Interest packets or on Data packets, being currently the preferred choice to rely on a special Interest packet known as _Interest Update_. This is the case, for instance, of MAP-ME [7], or of MobiCCN [25]. _Interest Updates_ allow for arbitrary small data to be placed in Interest packets as a name component. Such packet is not registered in PITs, as no data are expected to be sent back. Time-to-completion can be reduced by relying on different strategies, such as occurs in MobiCCN, where a specific Name Prefix \"_greedy:_\" supports communication based on a greedy routing protocol. Conversely, a _make-before-break_ approach can be followed, as occurs in the _Publisher Mobility Support in Content-centric Networks (PMC)_ solution [26].
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
###### Abstract  
Automated driving requires correct perception of the surrounding environment in any driving condition. To achieve this result, not only are many more sensors than in current Advanced Driver Assistant Systems (ADAS) needed, but such sensors are also of different types, such as radars, ultrasonic sensors, LiDARs, and video cameras. Given the high number of sensors and the bandwidth requirements of some of them, high-bandwidth automotive-grade networks are required. Ethernet technology is a suitable candidate, as it offers a broad selection of automotive-grade Ethernet physical layers, with transmission speeds ranging from 10 Mbps to 10 Gbps. In addition, the Time-Sensitive Networking (TSN) family of standards offers several features for Ethernet-based networks that are suitable for automotive communications, such as high reliability, bounded delays, support for scheduled traffic, etc. In this context, this paper provides an overview of Ethernet-based in-car networking and discusses novel trends and future developments in automotive communications.  
automotive communications; Automotive Ethernet; Time-Sensitive Networking (TSN); IEEE 802.1Q
## 2 Methods
### Threshold Setting
#### 2.3.2.  
Normalized Tangent Angle Threshold Criterion  
[PERSON] et al. [13,14] investigated and summarized the deformation process of several landslides with creep characteristics, and proposed the concept of the normalized tangent angle, and also provided a threshold criterion for early warning in a targeted manner. In this paper, a local modification was made to further divide two parts between 45\({}^{\circ}\) and 80\({}^{\circ}\) with 60\({}^{\circ}\) as the dividing line, in order to match the needs of five levels of warning-level classification. The criterion has been modified as follows:  
45\({}^{\circ}\)\(\leq\)\(\alpha_{i}\)\(<\) 60\({}^{\circ}\), blue warning. The deformation speed is relatively small and requires attention to the subsequent deformation;  
60\({}^{\circ}\)\(\leq\)\(\alpha_{i}\)\(<\) 80\({}^{\circ}\), yellow warning. There are some signs of deformation acceleration that should be considered seriously;  
80\({}^{\circ}\)\(\leq\)\(\alpha_{i}\)\(<\) 85\({}^{\circ}\), orange warning. The deformation rate is large, so some necessary precautions are required; and  
\(\alpha_{i}\)\(\geq\) 85\({}^{\circ}\), red warning. The landslide may fail at any time.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 2 Related Work
### Semantic Segmentation  
The text detection method based on image semantic segmentation extracts the feature map of the input image first, then predicts the probability that each pixel in the input image is a text instance based on the feature map. Afterward, the final output is obtained by post-processing according to the geometric features of the text.  
To solve the problem of indistinguishable close text instances, based on the idea that the central regions of different text instances have distinguishable boundaries, PSENet [18] proposes a progressive scale expansion algorithm. Starting from a distinguishable text region with the smallest scale, the text region is gradually expanded to the surrounding pixel points in a breadth-first search, increasing the text region until the largest text region is found as the prediction result.  
DBNet [19] uses the differentiable binarization operation instead of the standard binarization operation with fixed thresholds, and it introduces the binarization operation into the segmentation network so that the threshold of binarization is no longer fixed but is obtained by adaptive learning of the network, which greatly simplifies the post-processing process and also improves the performance of text detection.  
FCENet [20] introduces the Fourier Contour Embedding (FCE) method to accurately fit complex shaped text regions, converting the contours of text instances from point sequences to Fourier feature vectors. The text instances are predicted in the Fourier domain, and then the text contour point sequence is reconstructed in the image space domain by the inverse Fourier transform to obtain the final prediction results. The method is more accurate in predicting results for highly curved text shapes, but it does not work well for non-curved text.  
To solve the problem of complex model design and time-consuming training, the method of FCOS was proposed in [21]. The approach uses a Bidirectional Feature Pyramid Network (BiFPN) as the backbone network to improve the model learning capability, increase the receptive field, and improve the feature representation of FCOS in text detection tasks.
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
## 4 In-Situ Measurement-Based Fatigue Reliability Analysis
### Derivation of Stress Spectra Using In-Situ Measurement
To this end, the reference points in the FE model were selected through their distance to the intersecting line between the deck plate and rib wall. Based on the stochastic traffic model and multi-scale FE model, the in-situ measurement data was transferred into stress spectra, using the procedures illustrated in Figure 13.  
The derivation is performed in a sampling-based way, in which the number of target samples is set as \(N_{target}\) (i.e., \(10^{6}\) in this study). Through sampling with the measurement-based traffic model, information of the mono-vehicle can be generated. The information is then input to the FE model to calculate the stress history induced by the sampled vehicle, which is further processed to stress ranges using rain-flow cycle counting [46]. After that, the derived stress range is archived in a database and the above procedure will continue until the target number of samples is achieved. Finally, the stress spectra can be established through statistics on the derived database of stress ranges.