esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Dynamic Joint Resource Allocation Algorithm Based on SFR
## 2 System Model and Problem Formulation
### System Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Definition** & **Symbol** \\ \hline Transmit power on edge RB in cell \(i\) & \(p_{i}^{o}\) \\ Transmit power on center RB in cell \(i\) & \(p_{i}^{in}\) \\ Channel gain for the edge-user located in cell \(i\) from its serving BS & \(\delta_{i,i}^{o}\) \\ Channel gain for the edge-user located in cell \(i\) from its adjacent BS \(m\) & \(\delta_{i,m}^{o}\) \\ Channel gain for the center-user located in cell \(i\) from its serving BS & \(\delta_{i,i}^{in}\) \\ Channel gain for the center-user located in cell \(i\) from its adjacent BS \(m\) & \(\delta_{i,m}^{in}\) \\ Number of cell-edge RBs in cell \(i\) & \(N_{i}^{major}\) \\ Number of cell-interior RBs in cell \(i\) & \(N_{i}^{minor}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbols definition. \[R_{i}^{in\,\min}=\sum_{k\in K_{i}^{in}}\gamma_{j,k} \tag{5}\]  
Let \(k\) be the number of users in cell \(i\), \(K_{i}^{o}\) and \(K_{i}^{o}\) be the set of cell edge users and inter users, respectively. \(\gamma_{i,k}\) denotes the minimum throughput requirement for the user \(k\).
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 3 Building Blocks
### Phase Shifters
It is worth mentioning that EO phase shifters relying on the Pockels effect require a lower driving power. As an example, monolithically integrated lithium niobate (LiNbO\({}_{3}\)) EO phase shifters with CMOS-compatible voltages enable excellent EO modulations [105], which can be readily used for power-efficient large-scale OPAs. A fine-tuning system with simplified control can be achieved with a cascaded optical phase shifter architecture, which also enables compact packaging with a small  
Figure 6: (a) Cross section of a TO phase shifter. (b) A p-i-n junction-based EO phase shifter for OPAs. Reprinted with permission from [43] © The Optical Society. (c) Simplified schematic of a cascaded-shifters-based OPA. Reprinted with permission from [99] © The Optical Society. (d) A compact spiral phase shifter. Reprinted with permission from [100] © The Optical Society. footprint [17; 77; 78; 99; 106], as shown in Figure 6c[99]. However, the grouped phase shifters hinder flexible steering. In general, without individual control, cascaded phase shifter structures may be more susceptible to thermal crosstalk as pointed out in [82]. Apart from the ability to achieve full 2D steering, an OPA with independent phase control enables phase calibration to mitigate fabrication errors, crosstalk, thermal and electrical drifts [45]. Significant flexibilities of OPAs with individually controlled phase shifter are achieved at the cost of increased chip complexities and sizes [45]. To alleviate demands, a folded row-column architecture was used to significantly reduce required phase shifter drivers and control circuits [45]. As presented in Figure 6d, ultra-compact ring resonator structures with a radius of a few microns can be used as compact phase shifters by varying the round trips experienced by light [100].
## 3 Packet Detection Algorithms
### Energy-Based Algorithms
#### 3.1.2 Double Sliding Window Packet Detection  
The double sliding window packet detection algorithm is also based on received energy, but it uses the ratio of signal power in two consecutive sliding windows to detect packets [14], as shown in Figure 7. Two sliding windows, \(A\) and \(B\), are used to calculate the energy inside them by Equations (3) and (4). The lengths of two sliding windows are \(M\) and \(L\), respectively. \(m_{n}\) is set as a ratio of the values calculated in two windows by Equation (5). Thus, when the packet is absent, the response is flat since the two windows contain almost the same amount of noise energy. When the packet comes into window \(A\), the energy level in \(A\) keeps increasing until \(A\) is totally covered by the packet. Later, window \(B\) starts to collect signal, so \(m_{n}\) decreases and comes back to flat when \(B\) is covered completely. Therefore, \(m_{n}\) is large only when the packet is just coming. The sample index n of the peak point in the triangle-shaped response marks the beginning of the packet. The lengths of two windows can be different but can often be set the same for the convenience of threshold setting, which will be discussed in Section 4.1.1.  
\[a_{n}=\sum_{k=0}^{M-1}r_{n-k}r_{n-k}^{*} \tag{3}\]  
\[b_{n}=\sum_{k=1}^{L}r_{n+k}r_{n+k}^{*} \tag{4}\]  
\[m_{n}=a_{n}/b_{n} \tag{5}\]  
Simulation. Figure 8 depicts the simulated response of this algorithm. The lengths of both windows (\(M\),\(L\)) are set to 16. There is an obvious triangular peak around the  
Figure 6: The response of received signal energy detection algorithm.  
Figure 7: Double sliding window algorithm.  
index of 101, which is the real start position of the packet. The other points are very low, which shows that \(m_{n}\) does not depend on the received power. This algorithm is more noise-resistant than the first one, but has twice the amount of computation. Both the above algorithms need no additional knowledge of packets; however, if the receivers know the packet structure, the following algorithms can be considered.
## 7 Conclusions  
In summary, this paper introduces the DNC method, which is based on both the intrinsic characteristics of nodes and the information from their neighboring nodes. DNC computes a node's importance score as the sum of its degree and the local clustering coefficient of its first-order neighbors, so its time complexity is very low. Comparative experiments involving eight different centrality metrics on various real networks were conducted. The experimental results highlight DNC's strong performance concerning accuracy, monotonicity, and time efficiency. Nevertheless, despite these strengths, DNC exhibits certain limitations. It focuses solely on local network information and could be extended to consider global network attributes to enhance its performance. Additionally, DNC has the potential for extension to weighted and directed networks. The insights gained from this research will play a pivotal role in advancing the development of efficient techniques for identifying and protecting critical nodes within network systems.  
Conceptualization, N.Z. and S.Y.; methodology, S.Y.; software, J.W.; validation, S.Y., H.W. and X.Z.; formal analysis, N.Z.; investigation, J.W.; resources, T.L.; data curation, H.W.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y.; visualization, N.Z.;  
Figure 11: Rank distribution plots of DNC and the baseline methods on WS networks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Network** & **DNC** & **CI** & **KS** & **CC** & **KSGC** & **LGM** & **LGC** & **SC** & **IGC** \\ \hline BA & 0.9810 & 0.9983 & 0.3183 & 0.9972 & 0.9944 & 0.9895 & 1.0000 & 0.9762 & 1.0000 \\ WS & 0.9999 & 0.9977 & 0.0438 & 0.9956 & 0.9873 & 0.9775 & 1.0000 & 0.9641 & 1.0000 \\ ER & 0.9691 & 0.9980 & 0.0579 & 0.9973 & 0.9937 & 0.9893 & 1.0000 & 0.9751 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Monotonicity indices \(M\) of DNC and the baseline methods on BA, WS, and ER networks.  
supervision, J.W.; project administration, T.L.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Key Research and Development Program of Yunnan Province (202102 AA100021); the National Natural Science Foundation of China (62066048 and 62366057); the demonstration project of comprehensive government management and large-scale industrial application of the major special project of CHESO: 89-Y50G31-9001-22/23; and the Science Foundation of Yunnan Province (202101 AT070167) and supported by a grant from the Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province (2022 ZHNY10).  
Not applicable.  
The data presented in this study are available from the corresponding author upon request. The data are not publicly available due to the following reasons. The information contained in the data is proprietary to the funding organization that supported this research, and public sharing is restricted to protect their intellectual property and competitive interests.  
The authors declare no conflicts of interest.
# Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
## 2 Materials and Methods
### Models' Implementation and Training
#### 2.3.2 DL-Based Building Detection  
The third building detection model was based on a DL architecture. The FCN-based semantic segmentation [50] classifies the building or non-building class for each pixel directly within the image inputs. The FCN gives a pixel-wise output (label map) without needing a hand-engineered feature vector to feed the classifier for building extraction of remotely sensed data [7; 73; 74; 75; 42].  
The state-of-the-art classification convolutional model VGG-16 [76; 77] structure was repurposed for the segmentation task employing the method fully described in [50]: adapting the original architecture into an FCN and transferring its learned representations by fine-tuning to the desired task. The repurposed architecture combines the semantic global information of deep, coarse layers to learn the feature hierarchy and the local knowledge of shallow, fine layers with 32-, 16-, and 8-pixel strides to improve the segmentation accuracy and enable pixel-wise predictions.  
We fit the three models on a common portion of the dataset (the training set) by feature extraction and classifier training for the shallow ML-based models and fine-tuning through transfer learning for the DL model and then evaluated their discriminatory ability on the untouched data left (the test set).
## 5 Conclusions  
In this study, we analyzed spatiotemporal variations in WUE and its response to droughts over China from 1982 to 2015. The ecosystem resilience to drought was then estimated using a dimensionless index at each grid cell. The multiyear-averaged WUE was 1.55 g C kg\({}^{-1}\) H\({}_{2}\)O in China with relatively higher values in Northeast China and for deciduous broadleaf forests. The annual WUE increased (significantly increased) over 77.1% (56.2%) of Chinese territory. The annual WUE showed negative response to SPEI in forest regions in the northeast of Inner Mongolia, Northeast China and some regions of southern China as well as in deciduous broadleaf forests, deciduous needle leaf forests and shrub lands biomasses. An apparent lagging effect of drought on annual WUE was found in east parts in Northeast China, Inner Mongolia, west and east parts in North China and central parts in Tibetan Plateau. The terrestrial ecosystems showed resilience to drought in approximately 36.0% of Chinese territory mainly including several sub-regions (e.g., Central China, Northeast China, Southwest China) and vegetation types (e.g., deciduous needle-leaf forests, deciduous broadleaf forests, evergreen broadleaf forests and evergreen needle leaf forests). The results obtained in this study can improve our understanding on how ecosystem WUE responds to drought stresses and can inform local governments to take appropriate measures and strategies for sustainable water resources and ecosystem management in China.  
**Author Contributions:** [PERSON] and [PERSON] designed and constructed this study framework; [PERSON] undertook data analysis and drafted the manuscript; [PERSON], [PERSON], [PERSON] and [PERSON]. ([PERSON]). contributed to the manuscript; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. ([PERSON]). contributed significantly to our results section and helped to process the dataset.  
**Funding:** This study was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23100401 and XDA23040504), the National Key Research and Development Program of China (2016 YFC0401401 and 2016 YFA0602402) and the National Science Foundation of China (41601035).  
**Acknowledgments:** Thanks to the National Earth System Science Data Sharing Infrastructure, National Science and Technology Infrastructure of China. ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn))) for offering the GPP dataset. The authors also thank [PERSON] for producing ET datasets.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Development and Evolution of the Ground Fissure
### Development and Evolution Law of the Ground Fissure  
#### 3.2.1 The Ground Fissure Evolution Process  
The development positions and occurrences of the ground fissure were monitored during the mining process of the working surface (Figure 4). As the working face progressed to 200 m (Figure 4a), the initial ground crack (L1) emerged on the surface, located 95 m from the goat's center. As the working face advanced to 240 m (Figure 4b), additional ground cracks (L4, L5, L6, and L7) appeared in the middle of the goat, and the step cracks (L2, L3) formed at the cut point. The maximum width and drop measured were 6.3 cm and 8.7 cm, respectively. Due to the working face mining early propulsion slow, the surrounding rock fully discharged pressure, and the rock and surface fissure fully developed in front of the 20 m advance crack (L8). During this early phase of slow advancement, the surrounding rock fully released pressure, allowing fissures in both the rock and surface to fully develop.  
When the working face reached 280 m (Figure 4c), the overall fissure pattern advanced in a \"C\", with new cracks (L11) appearing 28 m ahead, and the cutting crack (L9) extending to the mining boundary. At this stage, the working face progressed rapidly at 14 m per day, leading to less surrounding rock damage due to the inability of the rock to fully release pressure and delaying the transmission of overburden load. At a 300-m advancement of the working face (Figure 4d), new cracks (L14, L15) emerged parallel to the working face, while additional diagonal cracks (L17, L18) extended beyond its boundary. The new crack (L16) was 335 m away from the cutting point. The results show that as mining progresses, ground fissures developed in a \"C\" shape along the middle of the goat, with boundary fissures extending outward once formed.  
In summary, when the working surface continues to advance, cracks develop from the bottom to the surface, and the distribution of surface cracks reflects the deformation and destruction characteristics of the rock layer. According to the fracture trend expansion of these fissures, with the working face mining, the ground fissure develops periodically, with an average spacing of about 14 m and a cycle pressure step distance of 15.8 m. However, the small width and step drop of surface fissures, along with good crack closure, indicate that the covered rock does not exhibit a complete full-thickness fracture.  
Figure 3: Arrangement of the surface observation lines.
# Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
## 4 Results
### Computational Efficiency  
In terms of solution efficiency, both formulas provide a greatly reduced computation time compared with the common data interpolation method, the proposed zero/pole formula of [39] and the FDGM (fractal derivative generalised Maxwell) model [9]. Table 5 compares solution times of [PERSON]'s isothermal friction model for a single friction point using a resolution of 200 frequency segments, and several complex modulus implementations. The commonly used data interpolation method was used as a benchmark and had a mean computation time of 0.318 s per friction point. The FDGM model (that utilises a zero/pole formulation) had a computational time of 1.371 s, which is an increase of 332% with respect to the interpolation method. The proposed LBEM model had a reduced computation time of 0.184 s, a reduction of 42% with respect to the interpolation method and 87% with respect to the zero/pole model. On the other hand, the polynomial method resulted in a 93% or 98% more efficient solution compared to interpolation and the zero/pole techniques, respectively, and is also 12% less computationally demanding than the LBEM method. The reduced efficiency of the LBEM approach in comparison to the polynomial method is related to the use of computationally expensive mathematical functions, such as exponentials and the hyperbolic secant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Interpolation** & **FDGM** & **LBEM** & **Poly** \\ \hline Computation time (sec/\(\upmu\) point) & 0.318 & 1.371 & 0.184 & 0.022 \\ \% w.r.t interpolation & \(\sim\) & +332\% & \(-\)42\% & \(-\)93\% \\ \% w.r.t zero/pole & \(-\)77\% & \(\sim\) & \(-\)87\% & \(-\)98\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computation times of the models.
# OPC-UA Agent for Legacy Programmable Logic Controllers +
## 2 Background  
### OPC Unified Architecture  
OPC-UA defines an information model and a set of abstract services and describes the mapping to implementation technologies, such as binary coding over TCP/IP or XML over HTTP [15; 16]. It is therefore platform-independent and supports different stack implementations to interoperate with each other in different programming and runtime environments. The client-side OPC-UA stack provides a programming interface, and the client application requests service by using the interface. The stack then generates and transmits a request message based on the service definition [17]. When the server-side OPC-UA stack receives the message, it replies with a response message containing data provided by the server application.  
An information model is defined by _nodes_ and _references_[3; 16; 18]. Each node has a set of _attributes_. A _node class_ describes the node type and specifies the node attributes. Several base node classes are defined in the standard: _objects_ are used to represent real-world entities, and _variables_ and _methods_ typically belong to objects and represent values and services, respectively. A reference describes the relationship between nodes. In OPC-UA, the node hierarchy is called the _address space_ and is application-specific.  
The OPC-UA service defines several functions for exchanging data between distributed devices, including service discovery, session establishment, node management, synchronous data access, and asynchronous data update [16]. In OPC-UA, variables are usually modeled as attributes, and _read_ services are primarily used to access the attributes of nodes synchronously. This service allows the client to specify a list of nodes and attributes and perform bulk reads with a request. It is generally the preferred service for reading variables. Another sophisticated method of data collection is to use a _subscription_ service to receive the asynchronous updates of variables. A client creates a subscription with the desired publishing interval and then creates _monitored items_ within the subscription by specifying node and attribute IDs, sampling intervals, and filters. The sampling interval defines how often the server checks for changes to variables and the filter specifies the conditions in which to put the sampled value into the data queue. The values in the queue create notifications in the next publishing cycle. When the server receives a publish request from the client, the notification then generates a response that delivers the updates.
## 5 Results  
In this section, a comparison between the LQ and the RL-based controllers applied to a vehicle platoon composed of one leader and two followers is proposed. Firstly, a discussing about the string stability is provided, then the simulation of two driving situations, to assess the performance of the two controllers, is shown.  
* Standard driving cycle: the leader vehicle tracks the FTP75 driving cycle (saturated to a minimum speed value of 2 m/s to avoid backward movement of the platoon), and the followers try to keep the reference inter-vehicle distance defined by a linear spacing policy and a reference platoon velocity.
* Cut-in scenario: a new vehicle (not belonging to the platoon) invades the lane occupied by the platoon, thus altering the equilibrium of the controlled system. The controller's ability to react when an unexpected obstacle breaks the platoon's equilibrium can therefore be verified.  
The followers are controlled by a centralised platooning controller (either LQC- or RL-based) to keep an inter-vehicular distance that depends on the actual vehicle speed (according to a constant-time headway spacing policy). The vehicles forming the platoon are heavy-duty battery electric vehicles with a single-speed transmission and a total mass of 13 tons. The truck data and the parameters of the controllers are available in Table 3; the road slope is assumed to be null (\(p=0\)). The LQ and RL-based controllers are tested in the same framework, consisting of the driving scenario and the nonlinear vehicle platoon model described in Section 2.  
Figure 11: Comparison between the WLTP and FTP75 for the RL assessment.
## 2 Background
### Overview of Eco-Materials for Sustainable Building
#### 2.2.1 Rammed Earth  
The term \"rammed earth\" refers to the historical construction technique or methods in which the materials (earth) are rammed in layers, being the soil its main component [45; 46]. The rammed earth is based on a mixture of gravel, sand, silt, and clay, which are wetted to reach optimum moisture and then compacted by layers inside formworks to achieve a homogeneous and continuous wall structure [47]. Some examples of historic rammed earth buildings are the Alhambra in Granada (Spain) and the Potala Palace in Tibet (China).  
Rammed earth structures can be constructed using natural soil (unstabilized rammed earth) without additives [48; 49], whose compressive strength is relatively low (1.0 MPa to 2.5 MPa) [49], resulting in thick-walled structures. To overcome this limitation, stabilizing materials such as cement or lime are added to rammed earth mixes (stabilized rammed earth) [50; 51], and sometimes waterproofing agents are added to reduce erosion from rain [49]. The use of rammed earth reduces energy consumption during construction because of the availability of raw materials and the simplicity of preparation [52].  
The rammed earth construction has seen a number of codes and standards that have been approved by some countries, but, due to the different types of soils and climate differences around the world, it is difficult to create codes or standards that are internationally appropriate for all countries [53]. Australia was one of the first countries to produce a national design and construction code for rammed earth. The first edition, named _Bulletin 5_, was developed in 1952 by the Commonwealth Experimental Building Station [54]. Germany was also one of the first countries to publish rammed earth codes and standards, between 1947 and 1956. In 1999, the _Lelmbau Regeln_ (German rules for earthen architecture) [55] was developed, a national document that includes general requirements for earthen structures and rammed earth, suitable soil types, appropriate tests, construction methods, design procedures, etc. New Zealand published three codes for unfired earthen building materials (including rammed earth) in 1998: the first code for earth walls of 6.5 m height or less, the second code for earth walls up to 3.3 m height for seismic zones, and the last code specialized in soil and cement mixtures [56]. In Spain, the Ministry of Transport and Public Works published, in 1992, guidelines for the design and construction of earthen structures, focusing mainly on rammed earth [57]. The New Mexico Building Code of 1991 contains methods of construction, testing, and curing for rammed earth [58]. Finally, Zimbabwe made a significant step in promoting rammed earth construction by publishing a standard code of practice in 2001 [59]. Figure 1[60] shows an example of rammed earth construction (left panel), along with a scheme of the construction process for a rammed earth walled structure (right panel).