esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cloud-Edge Collaborative Intelligent Security Encryption Strategy
### Granularity Upload and Download  
In terms of data sharing, in order for the data to be better utilized, the state encourages that enterprises open part of the data, but enterprises do not want their data to be seen by their competitors, so attribute encryption can satisfy this demand very well. Non-competing enterprises can choose whether they need to download this part of the data according to their own needs, and enterprises sharing this part of the data need them to be encrypted only once. Sensitive information in the traditional CP-ABE method may be leaked; the scheme proposed by [PERSON] et al. [33] applies a hidden strategy to make attribute encryption efficient and promising. [PERSON] et al. [34] propose a ciphertext strategy for shared decryption, where the authorized users can decrypt the message independently and the semi-authorized users can work collaboratively to decrypt the message, which is very efficient in terms of computational overhead and storage cost. [PERSON] et al. [35] propose an ECC-based CP-ABE technique for fine-grained access control to data or resources to reduce the overhead of decryption resources. By sharing industrial data with upstream and downstream companies, they can improve their products. Then, the data uploaded to the cloud need to be encrypted in order to reduce the encryption overhead; attribute encryption can be very good at the task, with a given time for encryption, to meet the decryption required. Through one encryption, the enterprise or organization that meets the required attributes for decryption and needs to decrypt their data can decrypt the data by themselves, while those that do not meet the attribute conditions, such as those of competitors, cannot be encrypted. At present, there are many scholars to carry out research on this, and there are many mature programs [36]. The flow chart is shown in Figure 3. The specific algorithm flow is as follows:  
1. The user enterprise shows its attributes and relevant evidence to the third-party trusted organization.
2. The third-party trusted organization audits the user's attributes and returns a blind token with a signature to the user.
3. When the user needs to obtain its attribute key, it submits the blind token to the Key Generation Center (KGC).
4. The KGC cannot obtain any information about the user's attributes. It can only confirm that the user does have the relevant attribute.
5. The Key Generation Center (KGC) first checks the legitimacy of the token and aborts if the signature is illegal; otherwise, it runs the key generation algorithm and outputs the blind key.
6. The user receives the blind key from the KGC and extracts the private key.
7. The user decrypts the data from the cloud and performs computation on the data.
8. The cloud computing center returns the computation results.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### AWS Well-Architected Framework
#### 3.5.3 Limitations  
1. While AWS Well-Architected has many advantages, it also has several restrictions and drawbacks that should be considered:
2. Adoption of the Well-Architected Framework may include a learning curve for teams who are unfamiliar with AWS or cloud architecture. To successfully comprehend and use the best, training and upskilling are required.
3. Overemphasis on AWS Services: The Well-Architected Framework strongly encourages the usage of AWS services, which may result in vendor lock-in. Organizations may grow unduly reliant on AWS-specific services, making migration to alternative cloud providers difficult if necessary.
4. Assessing Complexity: Performing Well-Architected reviews and evaluations can take a long time and a lot of resources. It necessitates data collection and analysis, architectural evaluations, and change implementation, which may be a big endeavor for organizations.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
###### Abstract  
Medical asset tracking systems track a medical device with a mobile node and determine its status as either in or out, because it can leave a monitoring area. Due to a failed node, this system may decide that a mobile asset is outside the area, even though it is within the area. In this paper, an efficient classification method is proposed to separate mobile nodes disconnected from a wireless sensor network between nodes with faults and a node that actually has left the monitoring region. The proposed scheme uses two trends extracted from the neighboring nodes of a disconnected mobile node. First is the trend in a series of the neighbor counts; the second is that of the ratios of the boundary nodes included in the neighbors. Based on such trends, the proposed method separates failed nodes from mobile nodes that are disconnected from a wireless sensor network without failures. The proposed method is evaluated using both real data generated from a medical asset tracking system and also using simulations with the network simulator (ns-2). The experimental results show that the proposed method correctly differentiates between failed nodes and nodes that are no longer in the monitoring region, including the cases that the conventional methods fail to detect.  
wireless sensor network; mobile node; medical asset; node classification; failure detection Article Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
Footnote †: _Sensors_**2016**, 16, 240; doi:10.3390/s16020240
## 1 Introduction
Although the track and the vertical dynamics may have a significant influence on the system vibration (as mentioned above), the model used in this study neglects the track and considers only the lateral friction for two reasons: firstly, the inclusion of the track is not necessary to capture the essence of the falling friction mechanism and, secondly, representing the behavior of the track in modal terms (so that time integration based on spatial discretization of the vibration fields can be performed) is inaccurate or, at least, would lead to unnecessary complications, such as the necessity of a pseudo-modal approach [19; 26]. These complications are beyond the scope of this article. A consequence of this assumption is also that the effect of wheel-rail roughness on the self-sustained vibrations is neglected. Two types of initial conditions are studied, the modal-derived and the physically-derived ones, and for the latter a formulation allowing to relate the physical quantities \((U_{0},V_{0})\) to the modal quantities \((q_{0,i},q_{0,i})\) is described. The article is structured in two parts. First, the curve squeal model used is detailed, with its corresponding governing equations of motion, friction model and main parameters; the equations of the stability analysis of the system, as well as the derivation of the initial conditions constituting the test cases of this study, are then presented. Secondly, the results are discussed, providing an analysis of the limit cycles obtained in the time domain and in the frequency domain and paying special attention to the mode competition phenomena.
## 2 Materials and Methods
### Statistical Analysis  
Data are presented as means \(\pm\) standard deviation. The normality of the distribution of the data was checked by the Shapiro-Wilk test. A two-way (time \(\times\) condition) repeated measures ANOVA was performed to investigate the impact of time (pre - post) and condition (EC and CC) on the dependent variables. The sphericity assumption was checked by [PERSON]'s test of sphericity, and the degrees of freedom were corrected using the Greenhouse-Geisser correction when necessary. [PERSON]'s post hoc comparisons were performed when applicable, to examine interaction effects as well as the main effects of time and condition on the dependent variables. The magnitude between pre and post intervention effects was assessed according to [PERSON]'s d effect size (ES) [28]. The criteria to interpret the magnitude of the ES were as follows: trivial (d \(<\) 0.20), small (d = 0.20),moderate (d = 0.60), and large (d = 1.20). All statistical analyses were performed in SPSS v27 (IBM Corp., Armonk, NY, USA), and statistical significance was set at an alpha level of 0.05.
## 4 Results
#### Analysis  
The system exhibited strong performance in both test scenarios, as evidenced by Figure 8. The estimated path of the algorithm aligned closely with the true 70 \(\times\) 70 square path that the camera followed. Furthermore, the errors shown in Table 1 are minimal, even when the manual handling of the structure that held the camera is accounted for. Additionally, the performance of the robot in low-texture environments was proven with the tests using the tile pattern, which showed only slightly higher errors compared to the tests using the high-texture Aruco pattern.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 3 Technical Background
### Decision Feedback Equalization  
The equalizer for slow frequency selective channels can be selected to work on the principle of FFE; however, its performance degrades for channels having high spectral width. In contrast, DFE helps to solve wide spectral width problem in transmitted channels. A typical DFE consists of FFE with additional filters, as shown in Figure 6, where it can be seen that extra filters are used for further corrections using the past and current output. DFE takes the output of FFE as an input and further improves the data by using a feedback equalizer. The DFE outcomes with linear feedback filter [32] can be expressed as  
\[Y[t]=\sum_{k=0}^{N}[C_{k}[t]r(tZ-kn)-\sum_{j=1}^{N}[b_{j}[t]d(t-j)]], \tag{7}\]  
where LMS coefficient updates are determined as  
\[C_{k}[t]=C_{k}[n-1]+r(nZ-k\tau)e[t]. \tag{8}\]  
Figure 5: Feed forward equalization.  
\[b_{j}[t]=b_{j}[n-1]+d(n-j)e[t]. \tag{9}\]  
Hence, due to the above-mentioned features of FFE and DFE, we use DFE and FFE methods in our proposed work to mitigate CD and PMD.  
Analytical modeling of FFE and DFE is presented in this section with the aim to understand their operation in mitigating the channel effects. The next section explains the simulation results based on the network model and theoretical approach.
## 6 Case Studies  
### Test System Description  
An EGH-IES composed of a 33-bus electricity network [26], an 11-node gas network [7] and a 13-node heating network [13] operating in the \"electricity/gas following heat\" mode is used as a test system to verify the proposed PEF analysis method. The configuration of the test system is shown in Figure 5. Although the case studies below are limited to the \"electricity/gas following heat\" mode, other operation modes can be studied in a similar way.  
Two photovoltaic plants (PV1 and PV2) with a capacity of 0.75 MW each and unity power factors are connected to bus 31 in the electricity network. Active power of PV1 and PV2 are modeled by Beta distributions with shape parameters \(\alpha_{1}=\beta_{1}=0.9\) and \(\alpha_{2}=\beta_{2}=0.95\). There are also two wind farms (WF1 and WF2) in the electricity network. They are connected to bus 32 with a capacity of 0.5 MW each and power factors of 0.85 (lagging). Wind speeds are modeled by Weibull distributions. Parameters of the wind farms are listed in Table 1.  
The three subnetworks are coupled by a CHP unit connected to bus 17, gas node 8 and heat node 13, a P2H connected to bus 31 and heat node 12 and a P2G connected to bus 32 and gas node 7. In the 11-node gas network, gas node 1 connected to a gas source is chosen as the gas slack node and gas nodes 2\(\sim\)11 are all load nodes. The gas users (User 1, User 2 and User 3) shown in Figure 5 require a special pressure level of 37 mbar and therefore three electricity-driven compressors are installed to  
Figure 4: Flowchart for solving the PEF problem of an EGH-IES based on MCS-LN.  
reach the required pressure level. In the 13-node heating network, heat node 13 connected to the CHP unit is the heat slack node and nodes 1-11 are all connected to heat loads.  
All loads are modeled by normal distributions. The original electricity/gas/heat loads in Refs. [7; 13; 26] are taken as the mean values, and the SDs of loads are set to be 5% of their mean values.
## 6 Limitations and Further Research  
As this study is based on a driving simulator, it has some limitations. The findings are highly dependent on the form of the simulator, the design of the experiment, and the differences among the participants. The electronic display screen and fixed driver seats in a driving simulator may not provide the same visual and physical feedback as that in a real vehicle, which may impact the driver's sense of control and immersion. We will strive to improve the fidelity and immersion of driving simulators to enhance the effectiveness of simulation-based training and research.  
In the actual traffic scenario, there are a lot of interaction behaviors between vehicles and pedestrians at unsignalized intersections [35; 36]. In further studies, we plan to design conflict scenarios between vehicles and pedestrians at unsignalized intersections to find more reasonable speed-calming measures.  
In addition, we used a low number of participants to demonstrate the feasibility of the methodology at this stage of the research. Although we have reached an acceptable conclusion, it is important to acknowledge that the results may not be as accurate or reliable as studies with larger sample sizes. Studies have shown that drivers of different ages and personality traits respond differently to traffic-calming measures [37]. In future studies, we aim to increase the sample size to improve the reliability and validity of the findings.  
Conceptualization, Y.Q.; methodology, Y.Q.; software, Y.Q.; validation, Y.Q.; formal analysis, Y.W.; investigation, Y.Q.; resources, Y.Q.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, M.G.; supervision, M.G.; project administration, Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors would like to acknowledge the Road Traffic Driving Simulation Laboratory of Kunming University of Science and Technology (Faculty of Traffic and Transportation Engineering) for granting permission to utilize their simulator in this study, thus facilitating its successful execution.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Perspectives and Limitations  
This scoping review, while extensive, is not without its limitations. First, the inherent nature of a scoping review limits the depth of analysis typically found in systematic reviews, particularly regarding risk of bias assessments and meta-analytic pooling of data. Thus, while we provide a broad overview of the landscape, the findings should be interpreted cautiously regarding their generalizability.  
Secondly, most included studies are in vitro, which, although valuable for initial material assessments, do not fully replicate the complex interactions and long-term conditions experienced in clinical environments. This restricts our ability to effectively extrapolate these findings to predict long-term clinical performance.  
Another significant limitation is the methodological heterogeneity among the included studies. Variations in study design, material handling, and outcome measurements across studies make it challenging to compare results directly or to draw definitive conclusions about the superiority of one material or technique over another.  
Additionally, our review was confined to articles published in English, which may have excluded relevant studies published in other languages, potentially introducing language bias. This limitation could omit significant research, especially from countries where CAD-CAM technologies are widely researched but published in non-English journals.  
Figure 9: The percentage distribution of investigations involving the biological response of the CAD-CAM RCB and a brief description of the only study found.  
Lastly, the rapid evolution of CAD-CAM material technology means that some newer materials may not have been included in this review. As the field advances, new materials with potentially improved properties and performance could offer different insights than those discussed here.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 5 Example Analysis  
### Case Overview  
This study takes the distribution center of an express company in Chongqing, China, as an example to analyze the distribution of 104 customer nodes. The carriage size is 4.8 \(\times\) 2 \(\times\) 2.2 m, the vehicle travel speed is 25 km/h, and the maximum load of the vehicle is 15 tons. Moreover, the rental and maintenance cost is CNY 100 per time, the penalty cost of violating the customer service time window during the transportation is 35 CNY/h, the transportation cost is 4 CNY/km, the vehicle arrives with 10 min of preparation, and each unloading of the pallet consumes 5 min.  
As shown in Figure 9, its logistics distribution nodes are distributed around the distribution center 0, its location coordinates, and customer demand time window as shown in Table 3. As logistics are usually distributed twice a day from each distribution  
Figure 8: The pseudo-code of the 3D loading logic.  
center to the same site, the demand for logistics nodes is calculated on a 0.5-day basis. In this study, the earliest time window for each node is 8 am, and the latest time window is different from each other. There is no specific standard for packaging in the courier industry, so this study uses seven common sizes of goods of varying sizes for study. As shown in Table 3, the demand for goods varies from one distribution point to another.  
As shown in Table 3, the goods' serial numbers are arranged according to volume requirements from largest to smallest, which facilitates the layer theory to produce higher loading rates. Goods 1-3 belong to medium-sized goods, 4 to small- and medium-sized goods, and 5-7 to small goods. Table 4 indicates the demand for all goods (pieces) at each logistics and distribution point.
# Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
###### Abstract  
Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.  
fog computing; deep learning; layer definition algorithm; smart agriculture +
Footnote †: journal: Applied Sciences
# Analysis of the Impact of the New Two-Lane Shield Tunnel Underpass on the Existing Tunnels
###### Abstract  
To address the issue of vertical settlement in existing tunnels beneath newly constructed two-lane tunnels, and to mitigate further impacts on tunnel operations, it is essential to investigate the effect of tunnel construction on the integrity of the existing tunnel structure. A calculation formula for the vertical displacement of the existing tunnel is derived by simplifying the calculation model and employing a two-stage analysis method. A three-dimensional numerical model of the double-line shield tunnel beneath the existing tunnel of Dalian Metro Line 4 is established using Midas GTS NX finite element software 2021(v1.1). The study focuses on the influence of the new tunnel's excavation on the existing tunnel, examining how various parameters in the shield construction process affect the settlement. Through comparative analysis of theoretical calculations, numerical simulations, and engineering monitoring data, the results indicate that the calculated displacement settlement trends align closely with the numerical simulation and are consistent with the field monitoring data. The findings provide valuable insights for the development of effective protection measures for existing tunnels during shield tunnel construction.  
Keywords:shield tunnel under-passing; existing tunnel; tunnel settlement; additional stress; numerical simulation
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.10. Dash7  
Dash7 [80; 81] is a proprietary open source, ultra-low power and long-range wireless communication protocol which was initially designed for military usage and has been adapted for use in commercial applications. Dash7 technology is based on the ISO/IEC 18000-7 open standard using an active RFID. This technology is currently managed by the Dash7 Alliance which offers interoperability among Dash7-based devices. Dash7 operates in the 433 MHz band, supports nominal and maximum data rates of 28 kb/s and 200 kb/s respectively. Dash7 is able to cover distances in the order of hundred meters to a few kilometers [82]. Dash7 networks are specifically suited for low power consumption applications where data transmission is sporadic and operated considerably slower such as telemetry systems [83]. Dash7 utilizes Bursty, Light, Asynchronous, Stealth, and Transitive (BLAST), _i.e._, Dash7 networking technology is especially appropriate to be used in bursty and light (packet sizes are maximized to 256 bytes) applications with asynchronous communication [81]. In addition, Dash7-based devices are inherently portable and upload-centric, thus, the devices are not required to be managed by fixed infrastructure such as base stations [84]. Dash7-based devices are being used today in a vast number of applications such as building automation, smart meters, hazardous material monitoring, manufacturing and warehouse optimization, inventory management and mobile payments [85].