esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Regression Analysis  
To effectively determine the timing for rehabilitation and to select appropriate rehabilitation strategies within the pavement management system, it is essential to consider the loss of pavement performance. Performing rehabilitation prematurely often proves inefficient, while postponing it may result in a decline in the operational functionality of the pavement [2; 40].  
Absolute pavement performance models are usually in the form of mathematical equations that show the dependable variable, i.e., the degradation of a particular pavement parameter related to time or load cycles. Utilizing accelerated pavement testing, alongside ongoing performance monitoring, is a reliable method for deriving these equations.  
Creating an absolute PPM from the collected data is achieved using regression analysis. First, each measurement surface needs to be evaluated for the parameter for which the PPM is to be made. This is shown in Figure 8. Once the parameter value is known, it composes an ordinate for the corresponding loading volume at the time of measurement.  
Absolute PPMs can therefore be derived from periodic measurements of existing pavements according to Equation (8). The parameter value may be relative to time, \(t/T\), or traffic load.  
\[p(x)=1-\left(\frac{t}{T}\right)^{B} \tag{8}\]  
Figure 8: Evaluation of scanned longitudinal unevenness.  
Figure 7: Recommended data collection of transversal and longitudinal unevenness.  
where \(p(x)\) is the parameter value where \(x=t\); \(T\) is life cycle duration in years; \(B\) is the coefficient in exponential functions which indicates the convex or concave shape of the curve; and \(T\) is the time ordinate.  
The downside of this simple solution is that the exponential function might not accurately represent the actual deterioration, and true degradation is forcibly approximated by regression analysis into the exponential shape. Absolute PPMs will use a polynomial function which better describes the deterioration process. The advantage is that this function describes initiation, progression, and failure in more detail. The difference between PPMs expressed as an exponential function and a polynomial function is shown in Figure 9.
## 5 Routing over the Hierarchical Virtual Topology
### Path Recovery after Node Failures  
When a node needs to find an alternative path because of the failure of a neighbor node, it is necessary to separately consider two cases: discovery of the minimum-weight path between two nodes that belong to the same first-tier module and recovery of a virtual long-distance link. Moreover, recovery of a virtual long-distance link can be considered for two different cases, as follows.  
1. Failure of the relaying node of a path assigned to an \(N\)-th-tier virtual long-distance link \((0\leq N)\).
2. Failure of the end node of an \(N\)-th-tier virtual long-distance link \((0\leq N)\).  
In Situation 2, there is no way of selecting an alternative end node for recovery of the virtual long-distance link. By assigning some redundant nodes as the end node of a virtual link, the virtual link can be recovered. In this paper, however, the node does not recover the virtual long-distance link in this situation, and the virtual long-distance link is lost, because we want to evaluate the adaptivity of the initial virtual topology. Therefore, we describe a method for the discovery of an alternative path in only Situation 1 for the recovery of a virtual long-distance link.  
The methods are based on finding a reverse path by a flooding control message. To detect the failure of a neighboring node, each node periodically broadcasts a Hello packet and constructs a table of neighbors. When a node detects the failure of a neighboring node via the absence of that node's Hello packet, it will flood a control message when it is necessary to find an alternative path. In some cases, nodes in the VWSN need to send many Hello packets. Meanwhile, there is a case that nodes detect the failure of a node only when the end-to-end communication failed. Although managing devices is a crucial viewpoint of network design, our scope is to show the adaptivity of the VWSN topology, and a method of resource management is out of scope of this paper.  
We note that the flooding range of control packets can be restricted in a first-tier module for the discovery of the alternative path between two nodes that belong to the same first-tier module. This reduces the number of control packets involved in forwarding. In contrast, in the recovery of a path crossing some modules, an alternative path may not be able to be found without wide-range flooding.  
Additionally, we need to consider a method that can discover a route between two \(N\)-th-tier modules when all of the virtual links between them have been lost. The details of the method for this are shown in Section 5.3.4.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 2 Proposed Model
### Ad Hoc On-Demand Distance Vector  
In AODV, the use of destination sequence numbers prevents the problem of counting to infinity. As a result, the AODV loop has been removed. Route Errors (RERs) are used to warn the network of a route's connection failure, whereas Route Replies (RREPs) are used to complete the routes. Three different types of messages can be used to define AODV [26]. To begin the route-finding process, Route Requests (RREQs) are utilized. To complete the process, RREPs are employed.  
It is necessary for each node to keep two different counters, i.e., one for the number of nodes in the sequence and the other for broadcast ID. As shown in Figures 1 and 2, when the source node broadcasts an RREQ packet to its neighbors to start the path discovery, it maintains a record of the consequent information. This information is required to instrument both the reverse-path setup and the forward-path configuration that is used in conjunction with the transmission protocol (RREP). An RREQ has two sequence numbers, one of which is the source sequence number, and one that is the last known destination sequence number [5].  
Figure 1: Forward-path formation.
## 1 Introduction
Finally, a nonlinear robust control algorithm is proposed consisting of three parts: a nonlinear robust take-off controller for HAUQs, an adaptive control law, and a disturbance observer. The simulation results show the effectiveness of the proposed algorithm. The organization of this paper is as follows. Section 2 presents the kinematic equations with the uncertainties of the hybrid aerial underwater quadrotor in different coordinate systems. The design of the position controller and attitude controller as well as their stability analysis is discussed in Section 3. Finally, the controllers designed in this paper are tested via simulation in Section 4.
## 3 Proposed Methodology
### Bert Model Structure
#### 3.2.2 Bert Encoding Layer  
The Bert encoding layer, a crucial component of the text classification model, performs a complex transformation of the input text into comprehensive vector representations that encapsulate rich contextual information. This section will provide a more detailed examination of its functionalities:  
* Contextual Embeddings: In contrast to traditional word embeddings, which assign fixed representations to individual words irrespective of context, the Bert encoding layer generates contextual embeddings. These embeddings capture the nuanced meanings of words within the context of the entire input sequence, thereby enabling the model to comprehend subtle semantic nuances and disambiguate polysemous words.
* Transformer Architecture: The Bert encoding layer employs the Transformer architecture, a self-attention mechanism that enables the capture of long-range dependencies and contextual relationships within the input text. Through the use of multi-head self-attention mechanisms and position-wise feedforward networks, [PERSON] is capable of effectively processing input sequences, thereby capturing both local and global contextual information.
* Pre-trained Language Representations: [PERSON] is typically pre-trained on large-scale text corpora using unsupervised learning objectives, such as masked language modeling and next sentence prediction. During pre-training, [PERSON] learns to encode text into rich, contextualized representations by optimizing for various language understanding tasks. These pre-trained representations serve as a foundation for downstream tasks, including text classification, enabling the model to leverage extensive linguistic knowledge acquired during pre-training.
* Fine-tuning for Specific Tasks: While [PERSON]'s pre-trained representations capture general linguistic patterns, the encoding layer can be fine-tuned on task-specific data through supervised learning. This process involves updating the parameters of the Bert model using labeled data from the target task, such as sentiment analysis. This adaptation of the model's representations enhances performance on specific classification tasks.
* High-dimensional Vector Representations: The Bert encoding layer outputs high-dimensional vector representations for each token in the input sequence. These representations typically have hundreds of dimensions, capturing a wealth of information about the semantics, syntax, and context of the input text. The richness of these representations enables downstream layers of the model to make informed decisions during classification tasks.  
The Bert encoding layer serves as a robust and scalable foundation for contextualized text representations. It employs transformer-based architectures and pre-trained language models to transform input text into high-dimensional vector representations. Bert's capacity to capture rich contextual information enables Bert-based models to achieve state-of-the-art performance across a diverse range of NLP tasks, including text classification, sentiment analysis, and named entity recognition.
## 3 Design of the System with Base Generation (Proposed Study)
### Power Load of the Target Area (Customer)  
The target area (customer) in this study is an industrial complex consisting of two factories and a rural village (residential area). Rural villages are mostly households, and the two factories that make up the industrial complex are manufacturers of agricultural machinery and blowers, respectively. Rural villages and industrial complexes are located in the southern area in Korea; however, they are apart from each other, and the power systems of the two areas are not interconnected. The approximate location of the target area is near \(34^{\circ}59^{\prime}07.5\"\) N and \(126^{\circ}40^{\prime}55.0\"\) E. However, in this study, it is assumed that two areas (customers) are connected for the design analysis of the proposed system. Because the range of load fluctuations during the day in rural villages (blue line in Figure 3) is relatively small, when the base loads are excluded, there are practically few parts for an optimization design. Therefore, the optimization design was performed after increasing the fluctuations by adding the load (red line in Figure 3) of the industrial complex area.  
Figure 2: Flow chart of the system design method.  
The monthly power consumption for the two target areas is shown in Table 1 and plotted in Figure 4 to identify the customer's power consumption. The power consumption data were collected for one year from June 2019 to May 2020. During this period, industrial complexes used 376,821 kWh and rural villages used 425,034 kWh. The total power consumption was 801,855 kWh and the average daily power consumption was 2197 kWh.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Period Load** & **Residential Area** & **Industrial Complexes** & **Total** \\
**(kWh)** & **(kWh)** & **(kWh)** & **(kWh)** \\ \hline June 2019 & 28,495 & 32,370 & 60,865 \\ \hline July 2019 & 35,531 & 33,268 & 68,799 \\ \hline August 2019 & 39,920 & 27,648 & 67,568 \\ \hline September 2019 & 33,889 & 28,589 & 62,478 \\ \hline October 2019 & 35,505 & 41,842 & 77,347 \\ \hline November 2019 & 38,139 & 26,063 & 64,202 \\ \hline December 2019 & 40,964 & 33,718 & 74,682 \\ \hline January 2020 & 40,086 & 35,456 & 75,542 \\ \hline February 2020 & 35,819 & 38,096 & 73,915 \\ \hline March 2020 & 36,460 & 30,136 & 66,596 \\ \hline April 2020 & 32,823 & 26,165 & 58,988 \\ \hline May 2020 & 27,403 & 23,472 & 50,875 \\ \hline Total & 425,034 & 376,821 & 801,855 \\ \hline Monthly Average & 35,420 & 31,402 & 66,821 \\ \hline Daily Average & 1164 & 1032 & 2197 \\ \hline \end{tabular}
\end{table}
Table 1: Monthly power consumption of the target areas.  
Figure 3: Hourly power load of the residential and industrial complexes in a year (cumulative amount).
## 3 Challenges in Big Data for Machine Learning
### Ethical and Societal Implications  
The adoption of ML in big data applications introduces complex ethical challenges, particularly concerning justice, responsibility, and bias. Justice-related concerns arise when models trained on biased datasets reinforce or amplify existing inequities, such as in hiring or credit scoring, where under-represented groups often face systemic disadvantages. Addressing these issues requires fairness-aware algorithms, bias mitigation techniques in preprocessing, and continuous monitoring to ensure equitable performance [250; 251; 252].  
Responsibility and accountability become critical in high-stakes domains like healthcare and finance, where decisions have significant implications for individuals and society. The opacity of many ML models complicates efforts to assign accountability for harmful outcomes. Clear governance frameworks, explainable AI (XAI) tools, and mechanisms for auditing model decisions are essential to maintain trust and transparency [253].  
Bias in ML often stems from data imbalances, such as under-representation of certain demographics, leading to poorer performance for marginalized groups. For instance, facial recognition systems have been shown to exhibit higher error rates for minorities.  
Techniques like reweighting, fairness constraints, and algorithmic debiasing are necessary to reduce discriminatory outcomes and ensure inclusivity [254; 255].  
Beyond these concerns, ML has broader societal implications, such as the displacement of workers due to automation and the misuse of AI in surveillance or disinformation campaigns. Proactively addressing these issues through collaborative policy making, ethical design principles, and robust regulatory frameworks will ensure that ML systems advance innovation while upholding societal values [256; 257].
## 4 Discussion
### The Spatial Autocorrelation of Land Use and Drivers Analysis in ZXEB  
The Moran's \(I\) for all global land-use types that presented spatial autocorrelation in 1990, 2000, and 2017 has been summarized in Figure 9. Figure 9 indicates that, before 2000, the Moran's \(I\) of cropland, woodland, and grassland increased. This was mainly a result of the three land-use conversions being relatively stable during this period; the TPs were larger than 0.95, generally, even when slightly increased (woodland) or decreased (cropland, grassland), the aggregation still increased. The Moran's \(I\) of water bodies decreased, mainly because of a water-use increase in the area during this period, which increased the aggregation of water bodies. Following 2000, the Moran's \(I\) showed an opposite trend; the Moran's \(I\) of cropland, woodland, and grassland decreased, and that for water bodies and built-up land increased. This was mainly a result of the rapid urbanization process, causing large areas of cropland, woodland, and grassland to change into built-up land.  
Figure 8: Urban expansion in eleven prefecture-level cities in the ZXEB (1990, 2000, and 2017). Note: Guangzhou and Foshan are connected.  
Figure 9b indicates that the Moran's \(I\) of built-up land, as well the urban land and rural settlements, increased first and then decreased, while Moran's \(I\) of infrastructure areas always decreased. This was mainly a result of the development of built-up land like \"make a Pisa\"; the gap between peri-urban and urban land decreased quickly [54]. The Moran's \(I\) indicated that all land-use types in the ZXEB area were related highly and, after 2000, the mode of land-use conversion process changed.
## 2 Proposed Fast Method for Recovering Reference-Wave Intensity
Hence, if the radius of the object is \(\omega_{0}\), as a rule of thumb we set the following condition:  
\[\pi\omega_{0}{}^{2}\ll l\times l, \tag{9}\]  
where \(l\times l\) is the sensing square area of the CCD. Second, we assume \(r\) to be the diameter of the smallest detail of interest in the object. The distance from the object to the CCD is \(d\), and \(\lambda\) is the light wavelength. We then stipulate that, according to far-field diffraction theory [2]:  
\[\frac{\lambda}{r}d\ll l. \tag{10}\]  
Under the above criteria, we see from the constructed complex hologram given by Equation (7) that the diffraction pattern of the object does not spread over all the hologram plane, and the complex zeroth-order term in the hologram, \(A\cdot(1+\mathrm{i})\), essentially becomes the background, albeit being complex when we inspect the area of the hologram that is far away from the diffraction pattern on the recording plane. So, if we take the real part of this background, i.e., \(\mathrm{Re}\,[A\cdot(1+\mathrm{i})]\), we have \(A\). Once \(A\) is found, we use Equation (8) to extract the object wave. Note that, however, \(A=R^{2}\) (\(1+\beta\)) is space-variant in general because \(\beta=1\,O\,|\,^{2}\!/\!R^{2}\), with \(\,|\,O\,|\,^{2}\) being space-variant. Hence, in actuality, we should take an area of the background of the hologram and perform average locally over the area to better estimate the value of the reference-wave intensity, \(R^{2}\). Note that if \(\beta\ll 1\), which is the case in the area of the complex hologram where the diffraction pattern of the sample is negligible, we have \(A=R^{2}(1+\beta)\sim\!R^{2}\), effectively finding the value of \(R^{2}\) using our scheme. In that case, we can use Equation (5) directly to find \(I_{0}\), and subsequently use Equation (4) to find \(O\) without the need for iteration to find \(R^{2}\), as with the technique proposed by [PERSON] and [PERSON]. Alternatively, once we have \(A\) from the above procedure, we put its value into Equation (8) to calculate \(O\). We show simulations and experimental results by employing Equation (8) in the following section.
## 5 Experimental Results
### Communication Performance  
One method to evaluate whether the symbols are well reproduced or not is by calculating the error vector \(\mathrm{e_{v}}\)[26], which is a metric of communication performance. It is defined as  
\[\mathrm{e_{v}^{[i]}=v_{rx}^{[i]}-v_{id}^{[i]}}, \tag{37}\]  
where \(v_{rx}^{[i]}\) is the ith symbol received and \(v_{id}^{[i]}\) is the ith sent symbol. These symbols correspond to an ideal sent symbol with a certain amplitude and phase. The error vector calculates the distance between the received and sent symbol. The bigger the difference between symbols, the longer the distance and the bigger the error vector. This test is normally performed over a long random sequence, in this case up to 256 symbols. For a sequence of m symbols, the Error Vector Magnitude (\(\mathrm{EVM_{rms}}\)) is obtained, which is the normalized root mean square of the error vector \(\mathrm{e_{v}}\) over a sequence of m symbols. It is calculated as  
\[\mathrm{EVM_{rms}[\%]=100\cdot\sqrt{\frac{\sum_{i=1}^{m}\left|v_{rx}^{[i]}-v_ {id}^{[i]}\right|^{2}}{\sum_{i=1}^{m}\left|v_{id}^{[i]}\right|^{2}}}}. \tag{38}\]  
The value of \(\mathrm{EVM_{rms}}\) is given as a percentage, measuring the error over a whole sequence. The lower the value, the better the communication performance. The prototype reached an error of 12% at a distance of 0.4 m.  
Other tests could have been performed, such as Bit Error Rate (BER) or the error according to the reception distance, but the performance of these tests depends on the  
Figure 20: Detail of some waveforms when amplitude and phase changes occur. Output voltage, \(\mathrm{v_{o}(t)}\), output current, \(\mathrm{i_{o}(t)}\) and received light, \(\mathrm{v_{rx}(t)}\).  
demodulator as well as the optical receiver, which must be designed for this specific application. The design of the demodulator and the optical receiver are outside the scope of this paper.
## 4 Case Study
### Monitoring and Measurement Results  
According to the monitoring and measurement, the vault settlement results before and after the design were compared to verify the feasibility of the FLSP design method. In Figure 15, the time-history curve of the vault settlement is given, in which the section of mileage stake number ZK279 + 840 adopts the original design of four \(\phi\)42 \(\times\) 4 millimeter FLSPs, and the ZK279 + 850 section adopts two \(\phi\)108 \(\times\) 3-millimeter FLSPs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Thickness** & \multicolumn{4}{c}{**Diameter**} \\ \hline  & **42 mm** & **50 mm** & **89 mm** & **108 mm** \\ \hline
2 mm & 891 & 1164 & 2801 & \\
3 mm & 968 & 1269 & 3074 & 4127 \\
4 mm & 1022 & 1343 & 3275 & 4404 \\
5 mm & \(\backslash\) & 1398 & 3433 & 4624 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lateral stiffness of each type of FLSP (unit: kN/m).  
Figure 14: Comparison of the settlement of active and passive load-bearing vaults.  
The vault settlement of the ZK279 + 840 section was 25.5 cm after the upper bench construction and increased to 41.2 cm before the construction of secondary lining, exceeding the reserved deformation of the tunnel. Through the optimized design of the FLSP, the vault settlement of the ZK279 + 840 section was 16.2 cm after the upper bench construction. It increased to 32.6 cm before the construction of the secondary lining, which was roughly the same as the reserved deformation.  
The numerical simulation results show that the vault settlement after the upper bench excavation on the ZK279 + 840 section was 18.7 cm, and that of the ZK279 + 850 section was 16.1 cm. The actual settlement of the ZK279 + 840 section was 25.5 cm, which was larger than the numerical simulation results. The actual settlement of the ZK279 + 850 section was 16.2 cm, which was roughly the same as the numerical simulation. On the one hand, the stress release rates of different sections are different; on the other hand, when the deformation of the loess tunnel is too large, the soil layer loses its bearing capacity, resulting in unpredictable loosening pressure.  
From the analysis of the monitoring and measurement results, it can be seen that through the optimized design, the FLSP can control the settlement of the vault within the specified range, showing the feasibility of this method of design optimization.