esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 6 Conclusions  
In this paper, we design and evaluate a multipath data-transmission-scheduling strategy based on path correlation applied in ICN, aiming to fully utilize network bandwidth resources and realize load balancing. We first introduce the principle of the multipath transmission mechanism, which combines the functions and characteristics of ICN and is able to utilize the infrastructure of IP network. Then, we estimate the path correlation according to the statistical correlation of RTT and propose a relay node selection strategy; In addition, we design a multipath data-scheduling strategy by combining the RTT and packet  
Figure 12: Average throughput under different scheduling granularities: (**a**) BT North America; (**b**) GEANT.  
Figure 13: Proportion of out-of-order chunks under different scheduling granularities: (**a**) BT North America; (**b**) GEANT.  
loss rate to ensure the load balance. Finally, we evaluate the proposed method through simulation experiments. The results show that the proposed data-scheduling strategy can maintain high bandwidth utilization while alleviating the packet out-of-order problem.  
Future work should focus on two aspects. Firstly, due to the differences between simulation and real implementation, future work will consider the deployment of our proposal in real network environments. Secondly, given that existing research still lacks a reasonable multipath congestion control mechanism, future work will mainly focus on this aspect to avoid congestion or failure caused by link overload.  
Conceptualization, Y.X., H.N. and X.Z.; methodology, Y.X., H.N. and X.Z.; software, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, H.N. and X.Z.; supervision, X.Z.; project administration, X.Z.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Strategic Leadership Project of the Chinese Academy of Sciences: SEANET Technology Standardization Research System Development (Project No. XDC02070100).  
Not applicable.  
We would like to express our gratitude to [PERSON] and [PERSON] for their meaningful support of this work.  
The authors declare no conflict of interest.
## 3 Self-Diagnosis  
One of the aims of the currently conducted research on structural health monitoring (SHM) is the development of the systems, which would allow detecting damage based on the analysis of data obtained from SHM [33; 34]. Such a structural diagnosis is based on various smart technologies that include a series of external devices installed on the structure and is independent of the structural properties.  
In the case of smart structures, the diagnosis of damages should be possible thanks to the special internal properties of the structure, not the smart devices with which the structure is equipped. Tensegrity structures are a good example. The analyses presented in the previous section proved that tensegrities are capable of self-control due to the infinitesimal mechanisms, which are balanced with self-stress states. This unique feature may be used not only for structural control, but also in the case of damage detection systems.  
The failure of a random structural member causes the redistribution of nodal displacements and internal forces in cables and struts. In a single-module tensegrity structure, the failure of any member means the damage of the structure. In the case of multi-module structures, the failure of one element eliminates self-stress within the module(s) that contains it. It is not identical to the total damage of the structure. Therefore, identification of the damaged area can occur immediately if the member forces are controlled. Moreover, damage detection in tensegrity structures can be realized by measuring internal forces only in active cables and struts, as the failure of one random structural member causes the redistribution of pre-stressing forces within the whole structure. The number of active and controlled members can be limited. The possible, as well as most probable scenarios of failure can be simulated and included in the expert system for monitoring the structural health.  
The analysis presented in the next section shows how the failure of one structural element influences the work of a whole tensegrity structure.
# Forecast of Medical Costs in Health Companies Using Models Based on Advanced Analytics
## 1 Introduction
LSTMs are an extension of previous RNNs which are able to retain a memory in the long term and use it to learn patterns in longer sequences of source data. Before LSTMs, RNNs were _forgetful_. They could retain a memory, but only about the steps of the process in its immediate past. LSTMs, however, introduce loops that can generate long-lasting gradients [12; 13]. They can retain the long-term patterns they discover as they run along with their loops. The other technique we used was clustering: it could also be considered an exploratory data analysis (EDA) technique that helps discover hidden patterns or data structures. The clustering technique may also work as an independent tool to obtain information about data distribution [11]. A cluster is the collection of data objects that resemble each other within the same group (class or category), and which are different from the objects of the other clusters [13]. Clustering is an unsupervised learning technique where there are predefined classes and previous pieces of information that define how data must be grouped or labeled in separate classes. There are a variety of clustering algorithms, and the most popular ones include hierarchical clustering [14; 15], Gaussian mixture models [16; 17], and others within the Sklearn package [18]. In our case, we used k-means, an algorithm that consists of dividing the data points of x by a set of k clusters, where each data point is allocated to its closest cluster. This method is defined by the target function which tries to minimize the sum of all the squared distances within one cluster and for all clusters [19; 20]. This work shows the process of grouping and the classification of accredited health entities using k-means [21]. This allowed the accredited health sector institutions to be grouped into two large clusters. The first was defined as institutions in the process of financial consolidation; and the second cluster was defined as large health institutions. The business profiles of the institutions under study were thus defined. Specifically, we summarize our contribution as follows. First, we predict the medical cost of a healthcare organization using the described techniques and suggest an avenue of improvement in further work: namely that understanding how and why cost-drivers increase may provide information about the risk factors and the possible starting points for defining preventive measures and strategies. This paper is structured as follows. In Section 2, we show related works. In Section 3, we describe the methodology and information about the data, data-processing operations, and the methods we used to evaluate the problem. In Section 4, we first present the results obtained with the LSTM networks and continue presenting the results obtained from combining cluster segmentation with LSTM networks. We then proceed in Section 5 to discuss the results to finally summarize the conclusions and directions for future research.
## 3 Principals of Routing Protocols and Their Breakdowns
### Data-Based Routing Protocols
#### 3.4.3 EAVARP [67]  
An energy-aware and void-avoidable routing protocol (EAVARP) is suggested for underwater sensor networks. The layering and data-gathering phases are the two steps of EAVARP. Concentric shells are constructed around the sink node during the layering process, and sensor nodes are dispersed over multiple shells. Data packets are transmitted using an opportunistic directed forwarding approach based on an opportunistic directional forwarding strategy (ODFS) during the data collecting phase. The ODFS analyzes the residual energy and data transmission of sensor nodes in the same shell to minimize cyclic transmission, floods, and voids.
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
###### Abstract  
In recent years, Long-Term Evolution Vehicle-to-Everything (LTE-V2X) communication technology has received extensive attention. Timing synchronization is a crucial step in the receiving process, addressing Timing Offsets (TOs) resulting from random propagation delays, sampling frequency mismatches between the transmitter and receiver or a combination of both. However, the presence of high-speed relative movement between nodes and a low antenna height leads to a significant Doppler frequency offset, resulting in a low Signal-to-Noise Ratio (SNR) for received signals in LTE-V2X communication scenarios. This paper aims to investigate LTE-V2X technology with a specific focus on time synchronization. The research centers on the time synchronization method utilizing the Primary Sidelink Synchronization Signal (PSSS) and conducts a comprehensive analysis of existing algorithms, highlighting their respective advantages and disadvantages. On this basis, a robust timing synchronization algorithm for LTE-V2X communication scenarios is proposed. The algorithm comprises three key steps: coarse synchronization, frequency offset estimation and fine synchronization. Enhanced robustness is achieved through algorithm fusion, optimal decision threshold design and predefined frequency offset values. Furthermore, a hardware-in-the-loop simulation platform is established. The simulation results demonstrate a substantial performance improvement for the proposed algorithm compared to existing methods under adverse channel conditions characterized by high frequency offsets and low SNR.  
LTE-V2X; timing synchronization algorithm; PSSS; robustness; hardware-in-the-loop simulation +
Footnote †: journal:  
Article
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 7 Virtualization Technologies
### The Challenges and Solutions Related to Cold Start  
Functions in such systems are running in a lightweight virtualized environment leveraging its low startup times. FaaS functions are short living, after the first invocation function instances are kept running by a short period of time; if no reinvocation happens during this period of time, the framework evicts them. However, the invocation of an evicted function takes much more time as the environment, in which the function is running, should be initialized. This phenomenon is called the cold start. Cold start is one of the challenges of FaaS, which can be mitigated by using unikernels instead of container based solutions [75]. The authors of [76] on the other hand show solutions to decrease the startup time of container based solutions. The authors also made analysis on the performance impact of using several Python libraries and implemented a solution to spectacularly decrease the latency and enhance the throughput of their solution compared to a Docker-based setup.  
The importance of FaaS can be seen by the fact that besides several cloud service providers has their FaaS solutions, e.g., AWS Lambda, Google Functions, Azure Functions, the open-source community has also embraced this technology.  
Several FaaS initiatives can be found on GitHub. A comprehensive overview of open-source FaaS systems is described by [77]. The authors of [78] examine the performance of OpenFaaS function runtimes in case of echo, compute intensive, and IO bound functions for Python, Node.js and Go. Furthermore the authors give a brief introduction to OpenFaaS and the examined language runtimes as well.
## 3 System Design
### System Scenarios
#### 3.2.4 Downloading a File on CEA System  
Figure 7 illustrates how users can download a file on the CEA system. Users use a transaction ID to retrieve transaction data from CEA Network. The transaction data contain a hash of the file and other transaction information. The users use that hash to download a file from the IPFS private network.
## 3 Results and Discussion
### Adsorption Orthogonal Experiment  
Following the chosen orthogonal design presented in Table 4, the experiments were systematically organized and successively assessed through AAS. The experimental results are detailed in Table 7.  
Through analysis and comparison using the range method, it was concluded that the three factors had the same influence on lithium adsorption efficiency. According to the orthogonal experimental results, the lithium adsorption efficiency in 16 experimental groups had little change, indicating that the three factors had little influence on lithium adsorption efficiency. In experiments 6 to 10, the lithium adsorption efficiency reached 100%. This was because the adsorption rate reached equilibrium during a certain time range, and as time increased, the concentration of Li\({}^{+}\) in the solution started to decrease, and the adsorption rate tended to slow down, but it had little effect on the final adsorption capacity [42], and the function of KOH was to remove impurities and may have had no effect on the ion sieve that had completed adsorption. In order to save time and cost, the optimum level was finally determined as A\({}_{2}\)B\({}_{2}\)C\({}_{1}\) (an adsorption time of 30 min, KOH pH of 8, and a KOH washing time of 10 min).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experimental**} & \multicolumn{2}{c}{**Factors**} & \multicolumn{2}{c}{**Lithium Adsorption**} \\ \cline{2-5}  & **A/min** & **B (pH)** & **C/min** & **Efficiency/\%** \\ \hline
1 & 10 & 7.5 & 10 & 99.04 \\
2 & 10 & 8.0 & 20 & 99.90 \\
3 & 10 & 8.5 & 30 & 99.72 \\
4 & 10 & 9.0 & 40 & 99.84 \\
5 & 30 & 7.5 & 20 & 99.96 \\
6 & 30 & 8.0 & 10 & 100 \\
7 & 30 & 8.5 & 40 & 100 \\
8 & 30 & 9.0 & 30 & 100 \\
9 & 60 & 7.5 & 30 & 100 \\
10 & 60 & 8.0 & 40 & 100 \\
11 & 60 & 8.5 & 10 & 99.90 \\
12 & 60 & 9.0 & 20 & 99.84 \\
13 & 80 & 7.5 & 40 & 99.78 \\
14 & 80 & 8.0 & 30 & 99.41 \\
15 & 80 & 8.5 & 20 & 99.84 \\
16 & 80 & 9.0 & 10 & 99.84 \\ \hline \hline \multicolumn{5}{c}{Total lithium adsorption efficiency} \\ \(\mathrm{Y_{1}}\) & 3.99 & 3.99 & 3.99 & \\ \(\mathrm{Y_{2}}\) & 4.00 & 3.99 & 4.00 & \\ \(\mathrm{Y_{3}}\) & 4.00 & 4.00 & 3.99 & \\ \(\mathrm{Y_{4}}\) & 3.99 & 4.00 & 4.00 & \\ \hline \hline \multicolumn{5}{c}{Average lithium adsorption efficiency} \\ \(\mathrm{Y_{1}}\)/4 & 1.00 & 1.00 & 1.00 \\ \(\mathrm{Y_{2}}\)/4 & 1.00 & 1.00 & 1.00 \\ \(\mathrm{Y_{3}}\)/4 & 1.00 & 1.00 & 1.00 \\ \(\mathrm{Y_{4}}\)/4 & 1.00 & 1.00 & 1.00 \\ \hline Range & 0 & 0 & 0 \\ Priority order & \multicolumn{2}{c}{A = B = C} \\ Optimum conditions & \multicolumn{2}{c}{30 min, PH = 8.0, 10 min} \\ \hline \hline \end{tabular} Note: Each sequence mentioned above was subjected to two parallel experiments for comparison to ensure the reliability of the results. The cited results were the average values.  
\end{table}
Table 7: Result of orthogonal experiments for adsorption.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 3 Proposed Method
### Implementation and Data  
The proposed network architecture has a structure similar to DRGAN [16]. The content encoder is composed of an input convolutional layer, a down sampler and four residual blocks. The noise encoder consists of an input convolutional layer, a down sampler and an adaptive average pooling layer with a \(1\times 1\) convolutional layer. The blur encoder has four strided convolution layers and one fully connected layer. For the generator, the architectures are symmetric to the content encoder, but vary for generating images of different domains. We use skip-connections between \(E^{c}\) and \(G^{c}\), with SPADE [23] and adaptive instance normalization [24], to fuse features from different levels. Then, the discriminator applies a series of convolutional and pooling layers to give a binary judgement.  
This model was implemented in PyTorch with a Ubuntu 20.04 operation system and an NVIDIA Quadro RTX 8000 GPU. During training, the Adam optimizer was used, and the learning rate was set to 0.0002 for 80 epochs. According to information in [16; 21], the hyper-parameters in our framework were experimentally set to the following values: \(\lambda_{adv}=1\), \(\lambda_{cycle}=10\), \(\lambda_{recon}=10\), \(\lambda_{noise}=1\), and \(\lambda_{KL}=0.01\).  
We acquired two datasets for this study. One consisted of 30 low-quality OCT images and 30 clean OCT images from three different pork larynxes. The second dataset contained the same number of images captured from two ex vivo rabbit eyes. These custom datasets were captured using a commercial OCT device (TEL320C1, Thorlabs, Inc., Newton, NJ, USA). The pixel size was 0.40 \(\times\) 2.47 \(\upmu\)m (width \(\times\) height), and the size of each image was set to 10,000 \(\times\) 1024 pixels. Therefore, the FOV was 4.00 \(\times\) 2.53 mm.  
The clean images in our datasets were obtained using the Speckle Averaging function of the ThorImageOCT software (version 5.2), which uses 4 successive A-scans to compute the mean and variance values used in the averaging process.  
A test set was formed by randomly selecting 20 images with noise and blur and 20 corresponding clean images from each dataset. The remaining 40 images were randomly combined into pairs to form a training set. Since tissue information is concentrated in the middle part of the OCT images, all images were center-cropped to a pixel size of 900 \(\times\) 450 to improve training efficiency.  
To obtain noise features for the noise loss function, a window with pixel size of 256 \(\times\) 256 was used to extract noisy patches from the background of low-quality images. This window was applied to the images in the training set using a stride of 8 pixels. This process extracted a total of 19,360 patches from low-quality images for the input **X**, and 19,360 patches from clean images for the input **Y**.
# Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression
## 1 Introduction
In practical conditions, however, the distribution of wind power at different times or conditions is inconsistent [18], and this problem will decrease the accuracy of the predicted CI. Reference [28] established a probabilistic WTPC by moving the power curves fitted by B-Spline. Although it does not need to assume a prior distribution, only power average information is used during the modelling process. Quantile regression (QR) provides an effective regression analysis method without considering the distribution pattern of random variables [29]. Reference [30] used a multi-core parallel quantile regression neural network (QRNN) for probabilistic wind power forecasting. However, there are few QR based WTPC models in recent published literatures. In addition, power outliers will decrease the fitting accuracy of WTPC. Reference [15] filtered the power outliers by setting thresholds based on GP, but it cannot effectively eliminate the stacked outliers caused by wind curtailment. According to [31; 32], stacked outliers can be filtered by clustering algorithms, such as fuzzy c-means and DBSCAN. In some cases, however, these methods lead to a high proportion of normal data being eliminated. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). The major contributions are summarized as follows. (1) Typical LF based methods have good performance in WTPC modelling, but only deterministic fitting results can be obtained. Considering the structure of LF and QR, we combine the asymmetric absolute value function from the QR cost function [29] with the LF model parameters, so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles. (2) When the wind turbine is operating normally, the distribution of power at a given wind speed is approximately symmetric about the mean [33]. According to that, we propose a novel outlier filtering method that utilizes the symmetrical relationship of power distribution. It can effectively filter both sparse outliers and stacked outliers, and adjust the number of iterations according to the number of power outliers. To further evaluate the performance of the proposed method, both deterministic and probabilistic evaluation metrics are applied to evaluate the performance of the proposed WTPC model. The results show that the QRLF based power curve model is able to provide both accurate deterministic fitting results and an appropriate predicted CI. The rest of this paper is organized as follows. Section 2 presents the mathematical principle of QRLF. Section 3 introduces the WTPC modelling process based on QRLF. The case study is shown in Section 4. Conclusions are drawn in Section 5.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 3 System Background
### Performance Goals  
To achieve efficient and secure group handover authentication for PMIPv6, the design of security goals for SL_GAS should include the following:  
* Traceability and anonymity: 6L\({}_{\text{MN}}\) should update its anonymous identification during inter-domain handover, which can only be known by AAA.
* Perfect Forward Secrecy (PFS): the adversary must be unable to extract any valid information from the previous ciphertexts when the current key is compromised.
* Mutual authentication and key agreement (AKE): AAA verifies the group's identity during the initial authentication phase and informs LMA and 6L\({}_{\text{MAGs}}\) of the results. The group must simultaneously authenticate the identities of 6L\({}_{\text{MAGs}}\), LMA, and AAA to increase security. It is essential to confirm the legitimacy of both the group and the target 6L\({}_{\text{MAGs}}\) before the group proceeds with its operations. Establishing a secure session key between the group leader, 6L\({}_{\text{MNGL}}\), and the 6L\({}_{\text{MAGs}}\) is also necessary to ensure the confidentiality of the ensuing data transmission during the initial authentication and handover authentication.
* Protocol attack resistance: For the designed scheme to be secure and effective, it must be resilient to attacks, as explained in the treat model section, including replay, impersonation, MITM, DoS, Sybil attacks, etc.
* Performance optimization: SL_GAS must consider computation overhead, signal cost, and transmission overheads to reduce the authentication delay. Thus, the performance of SL_GAS for initial authentication and handover authentication must be superior to that of existing schemes.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 3 System Model
### Computation Model
This then assigns bandwidth \(B_{ij}\) to relay \(j\); the task offloading rate then can be written as:  
\[\mathcal{R}_{ij}(U_{i})=B_{ij}\log_{2}\left(1+\frac{h_{C}^{j}p_{j}^{T}}{\sigma ^{2}}\right),i\in\mathcal{N},j\in\mathcal{N},i\
eq j, \tag{11}\]  
where \(\sigma^{2}\) represents the power of the additive white Gaussian noise (AWGN) at relay \(j\). All of the channels are assumed to be orthogonal and ignore the interference caused by other relays. \(h_{C}^{j}\) is the channel state information from relay \(j\) to the C-MEC and \(p_{j}^{T}\) is the transmission power of relay \(j\), respectively. As proposed in Reference [21], only white Gaussian noise is considered in this paper. Then the time of task offloading can be expressed as:  
\[T_{ij}^{off}(U_{i})=\sum_{j\in\mathcal{N}}a_{ij}\left(\frac{\sqrt{\left|\left| \mathbf{w}_{j}-\mathbf{w}_{c}\right|\right|^{2}}}{\mathcal{R}_{ij}(U_{i})}\right),i \in\mathcal{N},j\in\mathcal{N},i\
eq j \tag{12}\]  
The energy consumption of the task offloading can be calculated as:  
\[E_{ij}(U_{i})=p_{i}^{T}T_{ij}^{off}(U_{i}),i\in\mathcal{N},j\in\mathcal{N},i\
eq j \tag{13}\]  
Once the C-MEC receives the offloading task from relay \(j\), it will execute it and transmit the results back to relay \(j\) promptly.