esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Passive Optical Network
### Lessons Learned
Besides, they can also serve cellular network BS (ONU-BS) or an IEEE 802.11n/ac/s WLAN mesh portal point (ONU-MPP). In general, microwave and fiber technologies will not relent to be relevant in the existing 4G as well as 5G and beyond transport networks. However, as they are not well integrated, there have been cost and operational challenges that demand immediate attention [416]. Furthermore, note that the transport network should stay abreast of the evolving radio architectures, especially in the 5G networks. Moreover, ubiquitous transport solutions capable of supporting both 4G and 5G are highly essential for service providers. A promising transport network should be a hybrid one in which enhanced Ethernet, microwave, and optical transport solutions are uniquely integrated. The hybrid solution should have the ability for seamless and flexible management of the x-haul (fronthaul, midhaul, and backhaul) network to meet the associated requirements of the 5G use cases such as eMBB, FWA, as well as massive and critical IoT [417]. Besides, it should be based on functions virtualization to appropriately meet the on-demand connection requirements between the network elements. Moreover, apart from the need for enhanced availability, scalability, and performance; it is highly imperative to offer the ultra-high-speed broadband capability for supporting eMBB services and ultra-low latency for attending to delay-sensitive services by employing flexible slicing [418; 419; 420]. Nonetheless, as aforementioned in Section 6, there are a number of stringent requirements concerning PHY layer jitter, latency, and bandwidth being imposed on the fronthaul network. This can be in part attributed to the RAN equipment being defined as a single entity from the functional perspective [48]. In Section 8, we discuss a disruptive approach regarding the RAN decomposition scheme being implemented to enhance the system performance.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 4 Discussion
### Limitations  
Despite its advantages, the proposed methodology has certain limitations. For instance, when utilizing the free version of GEE, one will encounter various constraints, such as storage and image size limitations. To circumvent potential errors in computation time and storage issues, it is necessary to resample all images to a 30 m spatial resolution before proceeding with the classification process. Additionally, it should be borne in mind that a single query to Earth Engine is restricted to a 10 MB size limit. This limit is typically exceeded only when including large additional data directly in the query, like a shapefile or a GeoJSON structure that has been embedded within the query. Another limitation is that free GEE accounts are only permitted to ingest up to 250 GB of space and 10,000 assets.  
On the 14 August 2023 acquisition, the PlanetScope data did not provide complete coverage of the study area, with particular limitations in the East Jakarta region, covering approximately 96%. This limitation is primarily attributed to cloud cover issues, which are especially prominent in tropical regions like Indonesia and pose a significant challenge for passive remote sensing.  
The PlanetScope data acquired on 14 August 2023 yield the best available dataset for the study area. However, it is worth noting that other datasets exhibit cloud cover exceeding 50%. This resulted in a discrepancy between the total area classification and the administrative boundary. Additionally, not all of North and East Jakarta's urban green spaces were classified. Furthermore, Kepulauan Seribu was excluded from the analysis because it lacks a substantial urban green space, and most of its green space is primarily designated for cemeteries. New artificial islands located in the northern part of Jakarta were also not included in the analysis.
## 7 Conclusions  
1. Based on the theory of overlying rock failure and instability, the evolution process of overlying rock stress and plastic zone in the advancing process of working face is analyzed by numerical simulation. The results show that the vertical stress and plastic failure range of the surrounding rock in front of the working face increase evidently with the increase in advancing distance of the working face. When the working face advances to the first square, the vertical stress peak and plastic failure height of the surrounding rock in front of working face reach the maximum.
2. The spatial model of stope considering the influence of horizontal stress is established, and the stress transfer characteristics of overlying strata in stope are analyzed by combining the key stratum theory. It is known that 0-30 m in front of the coal wall of the working face is the influence range of the advanced abutment pressure, and the influence of the mining dynamic pressure in this range is great. The inclined direction of the working face, 0-20 m away from the coal wall of the roadway, is the influence range of the solid coal abutment pressure.
3. The \"Migrate-Transfer-Control\" technical system of roadway was proposed and applied to field practice. The field monitoring results show that the displacement variation in the surrounding rock surface of the roadway is smaller when the distance from the working face is greater than 90 m, i.e., the displacement of the two sides is 10 mm, and the roof subsidence is 18 mm; the deformation rate of surrounding rock increases significantly within 50 m from the working face, i.e., the maximum deformation rates of roof and floor, left side, and right side are 22 mm/d, 12 mm/d, and 9 mm/d, respectively.  
**Author Contributions:** Conceptualization, T.Q.; validation, Y.D.; formal analysis and investigation, B.D., Y.N., and T.Q.; data curation, Y.D., B.D., and X.H.; visualization, Y.D.; writing--original draft preparation, Y.D., Y.N. and P.M.; writing--review and editing, Y.D., B.D., and Y.Y.; supervision, T.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Scientific and Technological Key Project of \"Revealing the List and Taking Command\" in Heilongjiang Province (2021 ZXJ02A03, 2021 ZXJ02A04) and the Natural Science Foundation of Heilongjiang Province (LH2019E085).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 5 The Proposed Security Scheme  
Our proposed scheme secures the formation and monitoring operations of the Surveillance UAV Networks in the disaster areas. Consequently, the efficiency of the rescue and evacuation operation in the areas is improved, i.e., evacuating more people in less time as their exact locations can be detected, protecting the rescuers' lives as they know exactly what to expect in addition to more ability to use robots in the most danger sites, and finally gathering precise data to study the disaster and predict/resist it in the future. The proposed scheme is divided into two phases.
## 3 Methods
### Proposed No-Reference Depth Map Quality Evaluation Model
#### 3.1.1 Depth Map Edge Confidence Measure
Figure 4: Examples of various edge confidence maps for the _BMX_ sequence: (**a**) Hybrid recursive matching (HRM) [31] based depth map; (**b**) segment tree (ST) [32] based depth map; (**c**) rapid semi global matching (RSGM) [33] based depth map; (**d**) RSGM with joint bilateral filter (RSGM_FIL) [34; 35] based depth map. Figure 3: Effect of compression on depth maps of the _Musicians_ sequence: (**a**) Depth map compressed at QP=22 (high quality/low compression); (**b**) depth map compressed at QP=32 (medium quality/medium compression); (**c**) depth map compressed at QP=42 (low quality/high compression). The edge confidence measure operates on the principle that if a pixel is classified as an edge in the depth map, but not as an edge in the corresponding colour view, this pixel most likely indicates an error. The total number of depth-only edge pixels is divided by the total number of edge pixels in the corresponding colour view to provide a confidence rating, which points at the level of edge errors that exist in the depth map. This edge confidence calculation is performed for each frame of the available depth map dataset. This measurement is referred to as the depth edge confidence (DEC) measurement in this research and is utilised in developing the proposed no-reference quality evaluation model.
## 4 Experimental Analysis and Results
### Evaluation Metrics  
Performance is measured using standard metrics: Precision, Recall, F1-Score, and Exact Match Ratio (EMR). Precision quantifies the proportion of correct predictions among all predictions, while recall measures the proportion of true entities correctly identified. The F1-score, calculated as the harmonic mean of precision and recall, provides a balanced evaluation of a model's performance. Additionally, the Exact Match Ratio (EMR) measures the percentage of sentences where all entities are correctly identified without errors.  
\[Precision=\frac{TP}{TP+FP}\]  
A high precision value indicates that the model produces fewer false positives, which is particularly important in applications where the cost of false positives is significant.  
\[Recall=\frac{TP}{TP+FN}\]  
A high recall indicates that the model successfully captures the majority of positive instances, thereby reducing the occurrence of false negatives.  
\[F1\ Score=2\times\frac{Precision\times Recall}{Precision+Recall}\]  
This metric is especially useful when the class distribution is imbalanced, as it provides a more nuanced assessment than accuracy alone.  
\[EMR=\frac{Sentences\ with\ all\ correct\ entities}{Total\ Sentences}\]  
For example, if 500 out of 6000 sentences in ANERcorp are fully correct, EMR is \(\frac{500}{6000}\approx 8.33\%\).
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 3 Research on Semi-Active Pipeline Vibration Control System
### Frequency Response Function and Transfer Function of Pipeline System  
For a single-degree-of-freedom piping system, the following equation can be obtained by Fourier transform at both ends of Equation (4):  
\[-\omega^{2}M\mathbf{X}(\omega)+j\omega\mathbf{C}\mathbf{X}(\omega)+K\mathbf{X}(\omega)=\mathbf{F} (\omega) \tag{12}\]The frequency response function of the pipeline system is as follows:  
\[H(\omega)=\frac{U(\omega)}{F(\omega)}=\frac{1}{-\omega^{2}M+j\omega C+K} \tag{13}\]  
The polar form of Equation (13) can be expressed as:  
\[\left\{\begin{array}{l}\left|H(\omega)\right|=\frac{1}{\sqrt{\left(K-\omega^{ 2}M\right)^{2}+\left(\omega C\right)^{2}}}\\ \phi=\arctan\frac{-\omega C}{K-\omega^{2}M}\end{array}\right. \tag{14}\]  
where \(\left|H(\omega)\right|\) is the amplitude-frequency characteristics of frequency response function, \(\phi\) is the phase-frequency characteristics of frequency response function.  
The transfer function of the piping system is the ratio of the Laplace transform of the system response to the Laplace transform of the system excitation. The Laplace transforms of excitation \(F(t)\) and response \(U(t)\) are defined as:  
\[F(s)=\int_{0}^{+\infty}F(t)e^{-st}dt \tag{15}\]  
\[U(s)=\int_{0}^{+\infty}U(t)e^{-st}dt \tag{16}\]  
where \(s=\sigma+j\omega\) is the Laplace transform variable.  
Under the zero initial condition, the following equation can be obtained by Laplace transformation on both sides of Equation (4).  
\[\left(Ms^{2}+Cs+K\right)U(s)=F(s) \tag{17}\]  
Therefore, the transfer function of the piping system can be express as:  
\[H(s)=\frac{U(s)}{F(s)}=\frac{1}{Ms^{2}+Cs+K} \tag{18}\]
## 5 Measurement Results  
An array of the five time-based sensor variants has been fabricated in a 130 nm CMOS technology with a 1.5 V supply voltage. The die photo, shown in Figure 12, reveals a total area of 2.5 mm \(\times\) 2.6 mm, accommodating 81 DMA-resistance cells, 108 DMA-capacitance cells, and 20 time-based sensors.  
The layout of a single time-based sensor (_R-C_ variant) occupies an area of 182 um \(\times\) 165 um.  
Measurement data have been collected from 11 chips, with four from one wafer and the remaining seven from another wafer.  
To ensure accuracy, each reported data point is the average of three measurements which helps suppress the impact of noise.
# The Observed Impacts of Wind Farms on Local Vegetation Growth in Northern China
## Materials and Methods
### Data Processing and Methods
The effects of the WFs on vegetation growth are quantified by using the average LAI difference between WFPs and NNWFPs (WFPs minus NNWFPs) after the building of the WFs (2012-2014) subtracted from that before building the WFs (2003-2005). The calculation of GPP and NPP anomalies differences follow a similar approach. In this research, the SCI (spatial coupling index) is used to assess the spatial coupling of the wind turbines pattern with LAI changes [14]. The differences of 4617 LAI in the study area are ranked in decreasing order, and the first 5% (231), 10% (462), and 15% (693) of pixels with the smallest LAI differences (the strongest decreasing signal) are selected. Then, the percentage of these 5%, 10%, and 15% pixels falling into the WFPs (484 pixels), NNWFPS (690 pixels), UWFPs (245 pixels),  
Figure 2: Definition of wind farm pixels (WFPs, 484 pixels in black), upwind wind farm pixels (UWFPs, 245 pixels in red), downwind wind farm pixels (DWFPs, 292 pixels in blue) and nearby non-wind farm pixels (NNWFPs, 690 pixels in green) at a spatial resolution of 0.01\({}^{\circ}\). and DWFPs (292 pixels) are calculated and referred to as SCI. For example, if there are 100 pixels in the top 10% pixels (462) with the smallest LAI differences falling into WFPs, the SCI of WFPs is 21.6% (100 pixels divided by 462 pixels). If the LAI changes are distributed randomly, the possibilities of the four groups (referred to as the threshold of SCI) would have been 10.5% (484 pixels divided by 4617 pixels) for WFPs, 14.9% (690 pixels divided by 4617 pixels) for NNWFPs, 5.3% (245 pixels divided by 4617 pixels) for UWFPs, and 6.3% (292 pixels divided by 4617 pixels) for DWFPs. If the SCI is larger than the threshold of SCI, it means that the VI changes have a good spatial coupling with the group.
# Smart Healthcare Network Management: A Comprehensive Review
## 2 Literature Review
### IoT Network Management Protocols
Another major challenge is efficiently managing this large volume of data throughout its lifecycle. [PERSON] et al. [62] take a data-centric approach, outlining key data management techniques designed to ensure the consistency, interoperability, granularity, and reusability of IoT-generated data in smart cities. Additionally, they identify security and privacy strategies while discussing the networking and computing technologies that support smart city implementations [63]. The Open Mobile Alliance (OMA) SpecWorks [64] developed a device management protocol called LightweightM2M (LwM2M). The protocol is designed for sensor networks and supports a machine-to-machine (M2M) environment. It has also been developed to manage lightweight and low-power devices on various networks, which is necessary to realize the potential of IoT. The LwM2M protocol features an architectural design based on REST. It defines an extensible resource and data model and builds on the CoAP standard, making it suitable for the remote management of M2M devices and related service enablement. The Message Queuing Telemetry Transport (MQTT) protocol, widely used in IoT systems, is vulnerable to various cyberattacks, including replay and Man-in-the-Middle (MITM) attacks. A novel cyber range system was proposed in [65] to analyze vulnerabilities and implement countermeasures. This system provides an experimental environment for real-world attack scenarios, supporting new research to enhance MQTT protocol security and ensure robust communication in IoHT systems. To address network congestion and reliability issues in IoHT, an adaptive data communication model was introduced in [66]. The model ensures efficient and reliable data transmission in high-traffic scenarios by dynamically switching between MQTT-SN and CoAP protocols based on network conditions. A hybrid network management system combining the NETCONF protocol and decision tree algorithms was designed to improve configuration efficiency and fault diagnosis in IoHT networks [67]. This system achieves a fault diagnosis accuracy of 96%, outperforming traditional methods and demonstrating the potential of AI in enhancing network management scalability and adaptability. A comparative analysis of the Lightweight Machine to Machine (LwM2M) and Extensible Messaging and Presence Protocol (XMPP) highlights their suitability for IoHT applications in [68]. While LwM2M offers efficient device management and low power consumption, XMPP supports robust messaging in constrained systems. The study recommends protocol selection based on specific IoHT application needs.
## Materials and Methods
### Statistical Analysis  
The position data determined using the four satellite receivers in the different tests were subjected to analysis of variance (ANOVA) and Tukey's test in order to evaluate the statistical significance of the tests at 95% confidence level [25]. Principal Component Analysis (PCA) was applied in order to reduce the variable data setting and extract composite quality indicators. Each distance was considered as the dependent variable of the measured experimental parameters. The obtained principal components were considered as significant if their Eigen values were \(>\)1. All the statistical analyses were carried out using Statgraphics Centurion (Statpoint Inc., The Plains, VA, USA, 2005).
## 3 Literature Reviews
### Identity-Based Cryptography  
For efficient communication, identity-based cryptography [20, 21] will be adapted for our secure routing protocol design. Identity-based cryptography is a type of public-key cryptography in which a publicly known string representing an individual or organization is used to derive a public key. In a WSN setting, a node'sid, which is publicly known, is used to derive its public key. In non-interactive key agreement scheme, any two nodes use other node's id and derive a mutual secret without even communicating. This mutual secret, unknown to any other nodes, can be used to generate a cryptographic key for secure communication between the nodes under consideration. Though it still suffers from the high-computation cost of pairing, it is promising because there has good research outcomes for sensor network applications [22].
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 2 Background and Related Research
### Nanogrid System Design
Island mode is when the nanogrid is operating independent of any outside energy sources, and loads are solely supported by DER and ESS with the responsibility to maintain set frequency and voltage parameters [2]. Re-connection mode is the transition period where the nanogrid is reconnected to the microgrid. Before synchronization can occur, the frequency, voltage, and phase angle between the two must be within acceptable parameters in order for the nanogrid and microgrid to resume unified operations [2].
## 4 Modulation Techniques for Underwater VLC  
Modulation techniques for underwater VLC have been extensively studied, forming a foundational aspect of UVLC research. This section advances the discussion by examining adaptive, hybrid, and spatially optimized techniques tailored to dynamic underwater environments.
## 3 Experiment and Analysis
### Sensitivity Analysis of the Proposed Model's Parameters
#### 3.2.2 Sensitivity Analysis of the Outlier Proportion Parameter  
The impact of the outlier proportion parameter \(\alpha\) on the proposed model is analyzed using \(\varepsilon_{x}=0.220\) m, \(\varepsilon_{y}=0.110\) m, \(\lambda=0.250\) m, \(\delta=0.110\) m, and \(\tau=0.001\). As shown in Figure 20, the model achieves a recall of 80.7% when \(\alpha=0.0\). Recall increases rapidly, reaching 90.0% at \(\alpha=0.014\), and peaks at 91.5% for \(\alpha\) values between 0.023 and 0.062, with a median of 0.043. Beyond this range, recall slightly declines until \(\alpha=0.430\), where it stabilizes at 90.4%.  
Figure 19: Sensitivity of the proposed model to DENC’s directional proximity thresholds \(\varepsilon_{x}\) and \(\varepsilon_{y}\) using \(\lambda=0.250\) m, \(\delta=0.110\) m, \(\alpha=0.080\), and \(\tau=0.001\)—case study.
## 1 Introduction  
The detection of peaks in signals is an important step in many signal processing applications. Up to now, many different methods have been developed, including those based on traditional window-threshold techniques [1; 2; 3; 4], wavelet transform [5; 6; 7; 8; 9; 10; 11; 12; 13], Hilbert transform [14], combining Hilbert and wavelet transform [15], artificial neural networks [16; 17], techniques using templates [18; 19], morphology filtering [20; 21; 22], nonlinear filtering [23; 24; 25; 26], Kalman filtering [27], Gabor filtering [28], Gaussian second derivative filtering [29], linear prediction analysis [30], higher-order statistics [31], K-Means clustering [32], fuzzy C-Means clustering [33], Empirical Mode Decomposition [34], hidden Markov models [35], and techniques using entropy [36], momentum [37], histogram/cumulativedistribution function [38], intensity weighted variance [39], stochastic resonance [40], or a smoothed nonlinear energy operator [41].  
The problem with most of the peak detection algorithms available is that the more generally applicable the algorithm, the more free parameters (_i.e._, window length or threshold value) have to be chosen in order to be able to apply the algorithm to the signal. In contrast, algorithms with only a few parameters are in general restricted for use in specific applications such as the detection of R-peaks in electroencephalography (ECG) signals [1,7,8,10,12,14,15,16,17,20,21,22,30,31,32,33,34] or peaks in chromatography data [3,4]. Additionally the existence of noise in the analyzed signal is a challenge for many of the peak detection algorithm available.  
The unsatisfactory situation with regard to existing peak detection methods motivated the development of a simple and effective new peak detection algorithm for noisy periodic and quasi-periodic signals based on the insight that the general applicability of an algorithm could be preserved by (i) using a multiscale technique to detect all local maxima of the signal and (ii) finding the \"real peaks\" by automatically analyzing the results of the multiscale technique applied. In particular, the goal of our work was to develop a peak detection algorithm that has the properties of (i) not having any free parameters values that have to be chosen by the user prior to the analysis; (ii) being able to detect peaks in periodic and quasi-periodic signals; and (iii) having a peak detection efficiency that is fairly robust against high and low frequency noise.  
It should be noted that the goal of the present work was to develop a peak detection algorithm specifically to detect peaks in noisy _periodic_ and _quasi-periodic signals_. Thus, it was not the intention to develop a general peak detection method that is also applicable to _non-periodic signals_, such as signals normally obtained in the field of analytical chemistry, including mass spectra, optical spectra or gas chromatograms.
# A Back-to-Back Gap Waveguide-Based Packaging Structure for E-Band Radio Frequency Front-End
## 2 Unit Design of the Packaging Structure
### MS-GW Transition and Connecting Structure Design
illustrated in Figure 13, in this particular design, two metal nails with chamfers of side lengths _ln_1 and _ln_2 are utilized. The structure can be regarded as equivalent to a 90\({}^{\circ}\) bend waveguide facilitating the lossless transmission of electromagnetic energy. The simulated results are presented in Figure 14; the return loss is less than 30 dB, demonstrating excellent matching performance without causing any deterioration to the overall performance of the packaging structure.