esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Open Issues
In other words, the DOI parameter can be changed to approximate a specific type of hardware (e.g., MicaZ) or environment (e.g., indoors). Our latest research proposes using the Cox process to model the irregularity of the communication range [40]. In short, the transmission model, such as the disk, is inappropriate to model the WSN nodes. The probabilistic approach can be introduced to get more accurate transmission models. (2) Sink Node:  
Most research studies the connectivity issues assuming that all nodes in the network are equally important, without considering the role of sink or base station nodes, which are the data centers that all sensors data take as the final destination. To some extent, if the sink node losses connection with others, the rest of the nodes in the network could be useless for applications. Therefore, a network model that considers the role of sink node is necessary. (3) Higher Dimension Model:  
\(2-dimensions\) is a commonly-used model for WSNs. However, WSNs can apply to \(3-dimensions\), such as underwater, atmospheric, high buildings with many floors, mountains or space communications. Those models, especially referring to the transmission model, are necessary to check their performances in \(3-dimensions\). Very few researches consider resilient WSNs issues in \(3-dimensions\). The conclusion in [81] can be applied from \(2-dimensions\) to \(3-dimensions\) when \(k<3\). The transmission range must be at least 1.7889-times the sensing range in order to maintain connectivity among nodes for the truncated octahedral deployment, while the transmission range is between 1.4142- and 1.7889-times the sensing range for the hexagonal prism placement strategy or the rhombic dodecahedron placement [131]. The work in [132] proposes how to construct the full coverage and \(k-connectivity\) network in \(3-dimensions\), where \(k\leq 4\). Nevertheless, more proposals on higher dimensions are still imperative. A more complete survey on the topology control based on a \(3-dimension\) model for WSNs can be found in [133]. (4) Mobility:  
WSNs may be a highly dynamical network where the nodes move frequently, for example the WSNs can be applied to vehicle monitoring or wearable sensor devices. It is interesting to study the network connectivity in a dynamical network. It is very likely that those problems, which are intractable in static networks, could not be easily solved in dynamical networks either. Despite connectivity being more difficult to model and maintain in a mobile environment, the network resilience may benefit from nodes' mobility. For example, carefully controlling the node trajectory may enhance connectivity or WSNs can recover from failures more quickly. (5) Distributed Algorithms:  
Unfortunately, as shown in Table 1, many topology control approaches in graphs are centralized and have unaffordable complexity for WSNs. The WSNs are large distributed systems, so heuristic algorithms have to be distributed, as well. Those non-distributed algorithms harm the scalability and require higher maintenance cost, so implementing the algorithms in a distributed manner needs to be further studied.
## 2 Background
### Drawbacks of Certificate-Based Asymmetric Authentication  
Here are some drawbacks of this authentication model.  
* Centralized points of failure are produced when certificate issuance and management are left to Certificate Authorities (CAs). A CA's certificates are all at risk if the CA itself is compromised.  
* Digital certificate acquisition and management can be expensive and complicated, particularly for smaller businesses or individuals. The process of renewing certificates contributes to continuous maintenance.
* It can be difficult to revoke compromised certificates and make sure the network recognizes the revocation. There may be additional latency when checking Online Certificate Status Protocols (OCSPs) or Certificate Revocation Lists (CRLs).
* Identity data found in certificates may be exposed during the authentication procedure. Users' ability to control when and how their identity information is shared is restricted.
## 3 Results and Discussion
### Experimental Results
#### 3.1.2 Frequency Domain Analysis
In fact, the results for \(|R_{1}|\) do not differ substantially from those obtained for the T-junction and shown in Figure 5a, neither in the low frequency values nor in the high frequency trend. On the contrary, the high frequency behavior seen in \(|R_{2}|\) and \(|R_{3}|\) is a clear indication of the change produced in the dynamic characteristics of the junction when the excitation port is changed, an effect that could be guessed from the time domain results of Figure 4 but now is fully confirmed. Actually, a well-defined trend of picks and troughs can be observed in both cases, with similar shape but a clear frequency shift, which provides a sort of acoustic signature of the dynamic behavior of the junction. The fact that such a behavior is not apparent in the spectrum of \(|R_{3}|\) for the T-junction shown in Figure 5b indicates that such dynamic issues are suppressed by the symmetric nature of the excitation through a perpendicular branch. Figure 6: Experimental results for the Y-junction in the frequency domain. (**a**) Excitation at port 1; (**b**) excitation at port 2; (**c**) excitation at port 3. Ports are denoted as in Figure 1. \(R_{i}\); reflection coefficient as seen from port \(i\); \(T_{ij}\): transmission coefficient between ports \(i\) and \(j\).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.4 Scheduling  
Scheduling is the process of making decision about resource allocation between a variable number of tasks [3]. Prioritizing data transmission and reception is a major issue in deploying telemedicine projects. Scheduling allows for a quick response in emergencies by ensuring preference to the most urgent needs of the patients in the network.
## 3 Overall System
### Visual Recognition System
#### 3.1.2 Robust Depth Data Augmentation System  
The objective of this system is to reduce or eliminate the instability and errors caused by factors such as changes in ambient light and noise interference in depth images, thereby enhancing the clarity and accuracy of the depth data. This, in turn, supports high-precision visual recognition tasks in complex environments.  
Due to the sensitivity of TOF cameras to ambient lighting, depth images often contain holes or missing values. To address this, an interpolation algorithm based on bilateral filtering is employed to fill these gaps. This method considers both spatial distance and depth value differences when computing weights, enabling a weighted-average interpolation that better preserves image structure. Unlike traditional interpolation methods that rely solely on spatial proximity, bilateral filtering incorporates depth disparities, resulting in more accurate weight calculations and improved edge preservation. This approach effectively maintains edges and fine details, preventing blurring across object boundaries in the depth image. The working principle of the bilateral filter used in this study is illustrated in Figure 4.  
The specific calculation method is shown in Equation (3), where the weight function is composed of the product of spatial Gaussian weights and depth difference Gaussian weights.  
\[w(i,j)=\exp\left(-\frac{\parallel\mathbf{x}_{i}-\mathbf{x}_{j}\parallel^{2}}{2 \sigma_{s}^{2}}\right)\times\exp\left(-\frac{(D_{i}-D_{j})^{2}}{  
have similar depth values contribute more to the interpolation result, effectively preserving the edges and fine details of the image.  
We can validate the effectiveness of the image preprocessing system by aligning the RGB image with the depth image and then inputting them into the depth range filter, followed by interpolation using the bilateral filter. Additionally, the preprocessed RGB-D image needs to be binarized. This step is intended to facilitate subsequent image processing tasks.  
Figure 4: Bilateral filtering-based interpolation for depth image hole filling.
# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
###### Abstract  
Accurate and efficient condition monitoring is the key to enhance the reliability and security of wind turbines. In recent years, an intelligent anomaly detection method based on deep learning networks has been receiving increasing attention. Since accurately labeled data are usually difficult to obtain in real industries, this paper proposes a novel Deep Small-World Neural Network (DSWNN) on the basis of unsupervised learning to detect the early failures of wind turbines. During network construction, a regular auto-encoder network with multiple restricted Boltzmann machines is first constructed and pre-trained by using unlabeled data of wind turbines. After that, the trained network is transformed into a DSWNN model by randomly add-edges method, where the network parameters are fine-tuned by using minimal amounts of labeled data. In order to guard against the changes and disturbances of wind speed and reduce false alarms, an adaptive threshold based on extreme value theory is presented as the criterion of anomaly judgment. The DSWNN model is excellent in depth mining data characteristics and accurate measurement error. Last, two failure cases of wind turbine anomaly detection are given to demonstrate its validity and accuracy of the proposed methodology contrasted with the deep belief network and deep neural network.  
wind turbine; supervisory control and data acquisition (SCADA) data; fault diagnosis; deep small-world neural network (DSWNN); adaptive threshold +
Footnote †: journal: Applied Sciences
## 3 Simulation Results and Sensitivity Analysis
### Influence of Buried Depth on the Pipe Under Traffic Load  
When analyzing the impact of the burial depth on the pipe, the backfill soil was considered to have good compaction. The change in the buried depth of the pipe was realized by controlling the thickness of Lay 06, and the thickness of the pavement structure and that of other backfilling layers were kept constant. The magnitude and speed of the traffic load were set to 0.7 MPa and 60 km/h, and the diameter and ring stiffness of the pipe were 600 mm and 4 kPa, respectively.  
As shown in Figure 9, the Von-Mises stress distribution laws of pipes with different buried depths were similar; the earth pressure played a decisive role in the Von-Mises stress value, and the stress slightly fluctuated when the traffic load was applied. The stress reached the maximum when the traffic load moved to the middle of the pipe. This result was consistent with the literature [57]. The increase in soil cover depth was important for reducing the traffic load [24], but it also caused the pipe to bear greater earth pressure. Therefore, the specification required the soil cover depth of a buried plastic pipe to be 0.5-1.0 m [36], which decreased the combined effect of traffic loads and earth pressure on the pipe.  
Figure 8: The Von-Mises stress of the pipe during the installation.  
Figure 7: Deformed shape of the pipe during the installation.
# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 2 Pilot Descriptions, Data and Processing Methods
### Analysis Indicators and Equations
Yearly import and export are analyzed according Equations (6) and (7), respectively:  
\[\forall t\ E_{\text{C}}^{i}(t)>E_{R}^{i}(t)\ \rightarrow\ \ E_{imp}^{i}=\sum_{y\_0}^{y\_end}(E_{\text{C}}^{i}(t)-E_{R}^{i}(t)) \tag{6}\]  
\[\forall t\ E_{\text{C}}^{i}(t)<E_{R}^{i}(t)\ \rightarrow\ \ E_{exp}^{i}=\sum_{y\_0}^{y\_end}(E_{\text{C}}^{i}(t)-E_{R}^{i}(t)) \tag{7}\]  
where \(y\_0\) and \(y\_end\) are the beginning and the end of the year, \(E_{imp}^{i}\) is the import, and \(E_{exp}^{i}\) is the export of the household \(i\) of the pilot in question. The Self-Consumption Ratio (SCR), as expressed in Equation (8), is the ratio of consumed renewable energy over the sum of all renewable electricity generated on site. Equation (9) formulates the Self-Sufficiency Ratio (SSR), which is the ratio of consumed renewable electricity generated on site over the sum of electricity consumption. The SCR and SSR are calculated according to Equations (8) and (9), respectively:  
\[SCR=\frac{E_{\text{C\_RE}}}{E_{RE\_tot}} \tag{8}\]  
\[SSR=\frac{E_{\text{C\_RE}}}{E_{\text{C\_tot}}} \tag{9}\]  
where \(E_{\text{C\_RE}}\) is the on-site renewable electricity consumption and \(E_{RE\_tot}\) is the total electricity generated by renewable sources and \(E_{\text{C\_tot}}\) is the total electricity consumption. For this paper, the time period is a year. The peak load is defined as the maximum electricity import of each household from the grid considering one year of data. Similarly, peak feed-in is the maximum export of each household to the grid. The degree of loads occurring simultaneously is crucial, as individual loads are aggregated on distribution lines and the transformer station. To evaluate the effectiveness of the installed energy systems, it is important to examine the peak load values in smart grid pilots in the Netherlands. In Table 2, we summarize the different residential connections in the Netherlands, regarding their power limits, availability for new connections, and applications. The limit values significantly vary depending on the type of household connection. Exceeding the concerned limit value could have consequences, such as an outage within the household. The connections can always be upgraded for the old and small households if PV or appliances were to be added.
# High-Speed Photodetectors for Microwave Photonics
## 5 Conclusions  
Figure 17 summarizes the RF output power versus frequency of PDs for high-power analog applications that have been reported in the literature. The green data points represent normal-incidence PDs onnative InP substrate. The RF output power reaches over 30 dBm at 10 GHz to 7 dBm at 100 GHz. Uniform light illumination, good thermal dissipation due to the flip-chip bonding on materials of high thermal conductivity, and advanced heterostructure designs yielded such high RF output power levels. The yellow data points represent the RF output power from waveguide PDs on native InP substrate. The output powers are lower than those of surface normal incident PDs due to the non-uniform light coupling from the waveguide into the absorber, which can cause localized space charge effects. The state-of-the-art performance of waveguide PDs on Si based on III-V materials is represented by the blue data points. Their RF output power reaches 17 dBm at 10 GHz and 2 dBm at 70 GHz. These waveguide PDs are integrated on Si by direct bonding. As a result, good thermal dissipation to the Si substrate can be expected, and output power levels similar to those of waveguide PDs on native InP are achieved. The high-power performance of monolithic Ge-on-Si PDs is represented by the red data points in Figure 17. To date, monolithic Ge-on-Si PD arrays have achieved high RF output power up to 40 GHz.  
Figure 16: (**a**) Phase-modulated link gain measurement setup. (**b**) Measured and calculated RF gain spectra of the phase-modulated link at 2 and 7 mA photocurrent for each PD.  
**Author Contributions:** [PERSON] and [PERSON] equally contributed to the writing of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 4 Edge Server Selection Method
### Resource Contention among Mobile Users  
According to Definition 5, the aggregate workload on one edge server cannot exceed the edge server's total capacity. Therefore, if the resources of an edge server have been exhausted, the follow-up users will not select the edge server to invoke resources over a period of time. Next, we analyze the edge server resource contention among multiple users in mobile edge computing.  
Although a mobile service's response time will not change when it is installed on different servers, the total waiting time of service invocation will still be changed when the user connects to different servers. The reason is that the data transmission rate is not the same between the user and different edge servers. In addition, the capacity of each edge server, service migration, and other factors will also affect the total waiting time, thereby reducing the user experience. Therefore, it is a crucial matter to decide how to select edge servers for different users.  
We assume that a random decision solution is generated for each user selecting servers, and each user can select edge servers to invoke services depending on the random decision solution. The decision solution is guaranteed to be available only when there is only one user in the mobile path or a user does not start to invoke services before the previous user finishes invoking services. However, in the real world, there are always many mobile users in the path. Due to the limited capacity of the edge server, if excessive mobile users select one edge server at the same moment, there must be some users that cannot get the response from the edge server. In this case, users' experience may be degraded, and thus, the random decision solution is not feasible.  
Therefore, we design the renewal algorithm to make the random decision solution feasible. The renewal algorithm can make the workload on each edge server less than its capacity when the user selects edge servers according to the random decision solution. The algorithm begins with initialization (Line 1), which gets a new decision solution by copying the original decision solution. Then, according to the original decision solution \(p_{1}\), all mobile users select the predetermined edge servers to invoke services (Lines 2-5). If a user selects the edge server \(s\) and it cannot have the service installed to fulfill the request from user \(u\) with exhausted capacity (Line 6), another server \(s^{\prime}\) that has sufficient remaining capacity will be randomly selected for the user (Line 7). Thereby, the information for selecting \(s^{\prime}\) for the user instead of \(s\) will be updated into \(p_{2}\) (Line 8). Finally, the feasible decision solution \(p_{2}\) will returned (Line 13).
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 6 Evaluation and Results
### Experiments' Parameters  
We chose 100 services with replicas to force the algorithm to perform comprehensive searches and selection. The replication rate of each \(WS_{i}\) varied between 20 and 50 over different clouds. Table 1 describes the QoS and its features, allowing each recognition module to define its value analysis strategy based on minimum or maximum data intervals. Each QoS parameter was set with a \(P_{1}\) to \(P_{9}\) identifier. For instance, for \(P_{1}\) the analysis of its quality values was performed using minimums, which enabled each QoS parameter to be evaluated according to its character.  
The experiments ran five scenarios, each recognizing WSs based on a value of \(\Delta WSC\), \(\Delta KS\), or \(\Delta PS\). The first scenario evaluated key signal recognition, and the second scenario evaluated partial signal recognition. Besides that, the third scenario combined key and partial recognition. The fourth scenario developed a case study of a real-time composition environment, and finally, the last scenario simulated the WSs ranking.  
The first and second scenarios used three data sets, with 10, 50, and 100 WSs having with two, four, and nine signals, respectively. A set of two signals used \(P_{1}\) and \(P_{2}\), four signals used \(P_{1}\) to \(P_{4}\), and nine signals used \(P_{1}\) to \(P_{9}\) QoS.  
The third scenario used 50 WSs, and each test combines the two types of recognition. We defined a series of tests to combine \(P_{1}\) to \(P_{9}\) QoS. The system started its recognition using relations of 2 to 7, 4 to 5, and 1 to 8 partial and key signals, respectively. Other tests inverted these relations so that the system recognized using key signals first. The fourth scenario used three WSs of an ACM.  
Finally, the fifth scenario evaluated Ar_WSDS concerning other approaches. Tests were performed on a set of nine services to be selected, and all of them had replicas. The test was performed using the following initial conditions for Ar_WSDS:  
* Reliability, availability, and response time were the only signals activated;
* \(\Delta WSC=90\%\) was defined such that the best WS must be chosen; and
* Key signal recognition used WS values that were very close to their boundary values, which enabled the system to select services with high quality.  
We will now show the results of each scenario.  
Figure 8: A WS recognition case study.
# Suppression Effect of Waterborne Polymer on Soil Used for Backfilling at Construction Site
## 3 Results
### Wet Dust Suppression Performance of Polymers
#### 3.2.3 Dust Suppression Efficiency of Waterborne Polymers  
Construction sites are typical fugitive emission sources, with complex compositions of soil for backfilling, large randomness of the dusting process, and strong individual characteristics of the site space, which altogether hamper the calculation of the emission factor. Nankai University established a PM\({}_{10}\) emission factor \(EF\) (g/m\({}^{2}\)h) model at a construction site by taking the dust area rate \(D\) (%), wind speed \(u\) (m/s), moisture content \(M\) (%) of the soil for backfilling, silt loading \(S\) (g/m\({}^{2}\)), and vehicle activity \(N\) (number of vehicles) as key factors [31]:  
\[EF=0.02534 Du^{1.983}M^{-1.993}S^{0.745}N^{0.684}\ R^{2}=0.971 \tag{6}\]  
The particle size coefficient of TSP was 1.0, PM\({}_{10}\) was 0.49, and PM\({}_{2.5}\) was 0.1. The Ministry of Environmental Protection adopted this model because it embodied both the powder nature and the dust emission mechanism [5], and it was used for evaluating the wet dust suppression performance in this paper.  
During the collection of backfill powder with \(D\) = 30%, the \(S\) was determined to be 89 g/m\({}^{2}\). When \(N\) was 5 and \(u\) was 7 m/s, the PM\({}_{10}\) emission factor \(EF_{\text{b}}\) of the watered samples was 0.76 g/m\({}^{2}\)h, and that of the suppressed samples was 0.16 g/m\({}^{2}\)h, as calculated using Equation (6). As defined by Equation (7), the dust suppression efficiency (\(\eta\)) of waterborne polymers on the backfill powder PM\({}_{10}\) was 78.95%.  
\[\eta=\left(1-\frac{EF_{S}}{EF_{b}}\right)\times 100\% \tag{7}\]
## 3 Results
### Photovoltaic Configuration
In this way the signal does not lose its characteristics such as speed or power, undergoing as few manipulations as possible. Another advantage of this approach is the fact that this small device is very light, and since it does not need other associated devices to operate, it can be used in situations where small dimensions and low weight are essential, such as in space applications. Figure 3: (**a**) Photocurrent response of the PD to a 35-fs pulsed laser at different beam energy, tuned to photon wavelength, \(\lambda\) = 800 nm. (**b**) Photocurrent difference, \(I_{\text{ph}}\), between maximum and minimum in PD response peaks vs the laser light pulse energy for \(\lambda\) = 800 nm; in red, the curve obtained by fitting the experimental data with a power function is shown, highlighting a linear behavior; (**c**) \(I_{\text{ph}}\) as a function of photon wavelength measured with an incident fs laser light pulse energy, E = 100 nJ and (**d**) rise time acquired as a function of photon wavelength of an incident 35 fs laser pulse at energy E = 100 nJ.