esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 3 Massive MIMO Performance Gains
### Array Gain
#### 3.2.2 Array Gain under Imperfect CSI  
Practically, with multi-cell massive MIMO system, the propagation channel matrix \(G\) must be estimated at the base station through sending uplink pilot sequences from users to their base station. Because of the limitation in channel coherence interval, the number of orthogonal pilot sequences is limited also, as the number of antennas in base station increases with unlimited number, the number of served users increases also in each cell, hence the pilot sequences must be reused in other cells [32]. The reuse method is the main problem that causes the pilot contamination which affects the system performance through the error in channel estimation where the BS uses this estimation to build the precoding matrix for sending data to its users in downlink channel and this error increases the CSI delay, which is between the uplink training sequences and the downlink data for its users [13]. Under imperfect CSI, the array gain is computed according to the average received power, using expression (31) and with MRT precoding method (9) and substitute expression (5) in expression (9), the final expression for received vector to \(K\) terminals at \(\ell\)th cell is given by \[Q_{\ell}=f_{MRT}\sum_{j=1}^{L}\sqrt{p_{dj}}G_{j\ell}^{T}\left[\sqrt{p_{p}}\sum_{ \ell^{\prime}=1}^{L}G_{j\ell^{\prime}}+V_{j1}\right]^{*}s_{j}+n_{d\ell}. \tag{37}\]  
The average received power under imperfect CSI is defined as  
\[P_{re}^{Massive\,MIMO}=E\left[E\left[||\,Q_{\ell}||^{2}\middle|\hat{G}\right]\right]. \tag{38}\]  
In massive MIMO, as \(M\) increases without limit, the effects of additive received noise and fast fading disappear, as well as intra-cell interference, and according to expressions (19) and (20) for channel orthogonality we have  
\[P_{re}^{Massive\,MIMO} =E\left[E\left[||\,Q_{\ell}||^{2}\middle|\hat{G}\right]\right]\] \[=M\,p_{p}tr\left(P_{d\ell}D_{\ell\ell}+\sum_{j\
eq\ell}^{L}P_{dj} D_{j\ell}\right)\,\]  
then,  
\[P_{re}^{Massive\,MIMO}=M\,p_{p}tr\left(P_{d\ell}D_{\ell\ell}\right)+M\,p_{p} \sum_{j\
eq\ell}^{L}P_{dj}D_{j\ell}, \tag{39}\]  
with imperfect CSI, the average received power for MIMO system is given as  
\[P_{re}^{MIMO}=tr(P_{d\ell}D_{diag}), \tag{40}\]  
where \(P_{d}\) is the diagonal matrix of the transmit power assigned to each data stream in MIMO and \(D_{diag}\) is the diagonal matrix for the Eigen value of the transmitted fading correlation matrix \(R_{TX}\) in MIMO system. Then the array gain can be given according to expression (30) with respect to massive MIMO under imperfect CSI follows  
\[G_{array}=\frac{M\,p_{p}tr\left(P_{d\ell}D_{\ell\ell}\right)+M\,p_{p}\sum_{j \
eq\ell}^{L}P_{dj}D_{j\ell}}{tr(P_{d}D_{diag})}. \tag{41}\]
## 2 Materials and Methods
### Statistical Method  
Several statistical indicators were used to represent the characteristics of snow cover in the study area and the accuracy of simulation results or meteorological data. These indicators are listed as follows:  
1. Snow year  
The snow year is considered to be the time from the beginning of snow accumulation in a year to the next year before the snow starts to accumulate. According to the characteristics of snow cover in the Irtysh River Basin, we regard 1 September to 30 August of the following year as a snow year.  
2. Mean deviation (_MD_) and root mean squared error (_RMSE_)  
Mean deviation (_MD_) and root mean squared error (_RMSE_) are used to evaluate the accuracy of model simulation results or weather-driven data. The calculation formulas of MD and RMSE are as follows:  
\[MD=\frac{1}{n}\sum_{i=1}^{n}{(RD_{i}-O_{i})} \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(RD_{i}-O_{i})^{2}}} \tag{2}\]  
In the above formula, _RD\({}_{i}\)_ is the meteorological value recorded by meteorological forcing data or the snow parameter value simulated by the model at \(i\)th time, _O\({}_{i}\)_ is the observed meteorological element or snow parameter value at \(i\)th time, and \(n\) is the number of samples. The closer the \(MD\) and \(RMSE\) values are to 0, the higher the accuracy of meteorological forcing data or model simulation results are. If the \(MD\) value is greater than 0, it means meteorological forcing data or simulation results are overestimated, and if the \(MD\) value is less than 0, it means underestimated.  
The linear slope is used to indicate trends of snow depth, snow water equivalent, snow days, and other snow parameters. The Mann-Kendall (M-K) test is used to determine the significance of the trends. When the statistic \(p\) > 0.1, the change trend of the time series is not significant; otherwise, the change trend is significant. The calculation process of the M-K method can be found in the published literature [49].
## 2 O-RAN Architecture and Development
### O-RAN Alliance  
The O-RAN Alliance is a global industry consortium that was founded in 2018 to drive the development and adoption of open and intelligent Radio Access Networks (RAN). The alliance is made up of more than 200 member organizations, including mobile network operators, network equipment vendors, and software companies.  
The goal of the O-RAN Alliance is to create a more open, interoperable, and cost-effective RAN ecosystem that can accelerate the deployment of 5G networks and support new use cases and services. To achieve this goal, the O-RAN Alliance focuses on three main areas:  
* Standardization: the O-RAN Alliance works to develop and promote open standards for RAN interfaces and APIs, enabling multi-vendor interoperability and reducing network deployment costs;
* Software: the O-RAN Alliance develops and promotes open software for RANs, including software-defined radio (SDR), virtualized RAN (vRAN), and open APIs for software integration;
* Testing and integration: the O-RAN Alliance provides specifications, conformance testing, and integration guidelines to ensure that O-RAN solutions can be easily integrated into existing network environments and can interoperate with other vendors' solutions.  
Through its work in these areas, the O-RAN Alliance is helping to create a more open, flexible, and efficient RAN ecosystem that can meet the demands of 5G networks and support new use cases and services, such as industrial IoT, autonomous vehicles, and smart cities.  
Figure 1: The overall structure of this paper.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Drifter System Software  
The objective of the implemented software architecture (Figure 12) is to gather GPS location information (altitude, latitude, longitude, and speed) to be stored in a micro SD card in compliance with a JSON format for further analysis.  
Along with the data collection process, an interrupt routine monitors communications using XBee radio for data messages (frames) from RiverCore (RCFX). When a message is detected, the drifter responds with an RCFX frame. At the same time, sniffers also search for a message from the RiverDrone (RCDN) and the drifter responds with a frame containing: altitude, latitude, longitude, speed, RSSI.  
As previously mentioned, the drifter is a sensor that measures real-time speeds and river water levels in different sections and connects to Rivercore nodes through wireless technology. The EWIN platform has information from all RiverCore nodes that measure the water level and supplements it with the information provided by all drifters to generate more accurate information.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### OFDM Based UWAC Systems
#### 2.2.1 Cooperative-OFDM Systems  
The asynchronous time issues that occur between groups of nodes/relays limit OFDM-based UWAC FD-systems, increasing relative delay and fading and being the cause of ICI. As a result, several FD-OFDM-based underwater cooperative diversity systems have been developed in the literature to address the aforementioned issues. For instance, in amplify and forward (AF) cooperative underwater communication systems, a new effective OFDM is used to combat the issue of time delays [23], which encounter the asynchronous issue at the receiver [17, 18]. Short and manageable CP was used at the transmitter, and a low-complexity Viterbi decoder was used at the receiver [40].  
* **AUA-CCS**. A pairwise error probability (PRP) method is used in AUA-CCS to achieve maximum spatial diversity and time-invariant channels. The described protocol has been found robust to ISI, reduces asynchronous time issues, and boosts the spectral efficiency performance of time-varying channels [23]. However, despite having a fixed power amplifier factor, this protocol is not energy efficient [40, 56].
* **A-DTWR**. The A-DTWR FD-OFDM based system is proposed in [19] to combat the issues of low spectral efficiency and wide delays in time-varying frequency-selective channels in OFDM based FD-UWAC cooperative communication systems. Two users can exchange data using two relays in this system, and simultaneous transmission is carried out in FD fashion between the users. However, through each relay, the propagation delay and time errors (two forms of time errors) can be observed at the receiver [19, 57]. The first error occurs when the user's signals are misaligned on a single relay during the MAC phase, and the second error occurs when the system's two relays send the same copy to the destination at different times to a single user. To address these issues, a new full-duplex analog network coding (FD-ANC) system is being developed at the receiver section, which employs proper signaling and the Viterbi algorithm on each sub-carrier block. For the extraction of delay diversity from the two identical copies sent from the source to the destination, the Viterbi algorithm is used.
## 3 The Proposed Method
### LSTM Sequence Modeling and Attention Model  
#### 3.2.1 LSTM Sequence Modeling  
Since the video is a sequence of frames, we establish a model \(p(y|X_{t},h_{t-1})\) with RNNs. After extracting the features in videos, we input all \(X_{t}\) in LSTMs. The LSTMs have been proven to get great performance in many tasks, thus, we utilize LSTMs to process the features. In this subsection, we briefly describe the LSTMs unit which is the basic part of our model. LSTM relies on a fantastic structure of gates (\(i_{t},f_{t},o_{t},g_{t}\)) to control the flow of information to the hidden neurons and has the ability to preserve sequence information over time and capture long-term dependencies. Moreover, recent advances in computer vision also suggest that LSTM has potential to model videos for action recognition [25]. We follow the LSTM implementation in [45], which is given as follows:  
\[\begin{split} i_{t}&=\sigma(W_{ix}x_{n,t}+W_{im}h_{ t-1}+b_{i})\\ f_{t}&=\sigma(W_{fx}x_{n,t}+W_{fm}h_{t-1}+b_{f}) \\ o_{t}&=\sigma(W_{ox}x_{n,t}+W_{om}h_{t-1}+b_{o}) \\ g_{t}&=\tanh(W_{gx}x_{n,t}+W_{gm}h_{t-1}+b_{g}) \\ c_{t}&=f_{t}\odot c_{t-1}+i_{t}\odot g_{t}\\ h_{t}&=o_{t}\odot\tanh(c_{t})\end{split} \tag{2}\]  
where \(i_{t}\) is the input gate, \(f_{t}\) is the forget gate, \(o_{t}\) is the output gate, and \(g_{t}\) is calculated, as shown in Equation (2). \(c_{t}\) is the memory cell state at \(t\), \(h_{t}\) is the hidden state at \(t\), and \(x_{n,t}\) stands for input features at \(t\). \(\sigma\) means the sigmoid function and \(\odot\) denotes the element-wise multiplication. The weight matrices are denoted by \(W_{ij}\) and biases \(b_{j}\), which are the trainable parameters. The cores of the LSTM unit are a memory cell \(c_{t}\) and three gates \(i_{t}\), \(o_{t}\), and \(f_{t}\). Memory cell \(c_{t}\) encodes the information of previous memory cell \(c_{t-1}\) and current input by employing these three gates, which control the flow of information in LSTM units. For convenience, we denote \(h_{t},c_{t}=LSTM(x_{t},h_{t-1},c_{t-1})\) as the computation function for updating the LSTM internal state.  
In [42], they conduct a hierarchical structure with two LSTM layers, which is similar to our method. However, they change the structure of LSTM to fit different input. Compared with [42], we take the traditional LSTM as our loop-connected processor, and attention-again model is used to solve the problem of input state.  
To converge faster, we use the following initialization strategy (see [PERSON] et al. [44]) for the cell state and the hidden state of the LSTM:  
\[\begin{split} c_{0}&=f_{init,c}(\frac{1}{T}\sum_{t= 1}^{T}(\frac{1}{K^{2}}\sum_{i=1}^{K^{2}}X_{i,t}))\\ h_{0}&=f_{init,h}(\frac{1}{T}\sum_{t=1}^{T}(\frac{1 }{K^{2}}\sum_{i=1}^{K^{2}}X_{i,t}))\end{split} \tag{3}\]  
where \(f_{init,c}\) and \(f_{init,h}\) are two multilayer perceptrons and \(T\) is the number of time-steps in the model. These values are used to calculate the first location softmax \(a_{t,i}^{1}\) (see Section 3.2.2) which determines the initial input \(x_{n,1}\).
## 4 Model Implementation and Simulation  
In order to evaluate the models presented in the previous sections, we implemented the two- and three-state HMM in the DESERT Underwater framework [15], an open-source underwater network simulation and experimentation tool publicly available in [23]. Notably, the DESERT Underwater legacy physical module, called UWPhysical, models the path-loss with the Urick and Thorp formulas, and computes the signal to noise ratio using the model presented in [2]. Although this model is largely used by researchers, it does not well address the variability of the acoustic channel. Therefore, we implemented two new physical layers from scratch, one called UWHMMPhysical that uses the two-state HMM described in Section 3.3, and one named UWHMMPhysicalExtended that uses the three-state HMM presented in Section 3. In both physical layers we included the statistics of each link using the so-called link-stats objects, and let the physical layer compute the probability that a packet is correctly received at a specific moment, hence providing a per-link channel variability. The link-stats objects are independent of each other: in the case of near nodes that share the same channel, the same link-stats object can be used to model the channel variability in the same way: in the case of the sea experiment considered in these simulations, the links between the nodes are considered independent, hence a different link-stats object is used to model the channel variability between every pair of nodes.  
The most relevant difference between the two- and three-state HMM is the way the transition probabilities are computed. As explained in Section 3.3, the two-state HMM can be computed via a closed formula, while for the three-state HMM the transition probability can only be computed by means of matrix exponentiation. This implies that the exponentiation has to be performed efficiently, so that even with a big exponent \(n\), the complexity is limited and not growing without bounds. Given that \(n\) monotonically increases during the simulation, it is not necessary to compute \(P^{n}\) starting from the initial  
Figure 7: Examples of transition matrices for the two-state HMM: transition matrix P of the links from node 4 to node 2 observed in topology 2 (**a**), from node 3 to node 2 observed in topology 2 (**b**), and from node 1 to node 3 observed in topology 1 (**c**).  
transition matrix \(P^{0}\), as this would cause a degradation in performance. Specifically, we save the aforementioned matrix each time we compute it, so that we can operate conveniently on the last available \(P^{k}\) and compute \(P^{n}\) with a number of exponentiations equal to \(n-k\), which is strictly less than \(n\). As a result, the computation time of a simulation using the three-state model is not much longer than the same simulation relying on the legacy physical model or on the two-state HMM.
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 3 Results
### RT-PPP Performance in NEP
#### 3.2.1 Performance of Real-Time CLK93 Products
Regarding the RMS, the Galileo clock still has the smallest RMS value at 0.254 ns. However, the RMS value of the GLONASS clock is the largest, indicating an inferior consistency in the GLONASS clock biases. \begin{table}
\begin{tabular}{l c c c c} \hline  & **X (m)** & **Y (m)** & **Z (m)** & **3D (m)** \\ \hline GPS & 0.039 & 0.038 & 0.032 & 0.063 \\ GLONASS & 0.052 & 0.051 & 0.055 & 0.092 \\ Galileo & 0.034 & 0.031 & 0.033 & 0.056 \\ BDS & 0.087 & 0.082 & 0.076 & 0.142 \\ \hline \end{tabular}
\end{table} 
Table 3: Eleven–day mean RMS values of CLK93 products’ orbits. Figure 7: RMS values of CLK93 products’ orbits with reference to GFZ’s final products.
## 2 Related Studies
### Application of Social Network Analysis (SNA) in Online Health Communities  
Social network research in online health communities has become a focal point in academic fields. Through in-depth analyses of user interactions, support networks, and knowledge flow across multiple dimensions, researchers have revealed the complex social network structures and dynamic characteristics of digital social platforms.  
In user interaction research, scholars have constructed social network models, discovering the fragility and high dispersion of user interactions. For instance, [PERSON] et al.'s study on the Baidu Quit Smoking Bar demonstrated that users lack sustained interactions, with the network presenting a highly fragmented state. [PERSON] and [PERSON] (2016) analyzed the Douban Depression Group, and revealed the depth and breadth of user interactions by using clustering coefficients and reciprocity indicators to precisely characterize the structural features of social networks [19].  
Support networks and knowledge flow represent the core functions of online health communities. [PERSON] et al.'s (2021) research on online depression communities profoundly explored the transmission mechanisms of information support and emotional support, analyzing how user attributes influence support network formation [20]. Through centrality indicators and community detection, researchers not only identified key roles within networks but also systematically compared network characteristics across different platforms. [PERSON] and [PERSON]'s (2006) research on emergency practitioners' forums was particularly outstanding; they constructed information-seeking and sharing networks, evaluated knowledge dissemination efficiency, and accurately located influential key nodes [21]. Cross-platform comparative studies also provided important perspectives for understanding the diversity of digital social networks. [PERSON] and [PERSON]'s (2013) comparative analysis of forums, blogs, and notes [22], and [PERSON] and [PERSON]'s (2012) comparison of QuitNet forums and Facebook [23], both revealed differences in user interactions and information dissemination across platforms.  
In role identification, researchers adopted multi-dimensional analytical methods. Through centrality indicators, core-periphery analysis, and other techniques, scholars successfully identified key user roles in online communities and their influences on network dynamics. [PERSON] et al. (2010) divided users into five roles in melanoma discussion groups, conducting an in-depth analysis of interaction patterns across different roles within support networks [24]. [PERSON] et al. (2012) focused on key roles in knowledge sharing within diabetes forums, systematically revealing these users' critical contributions to network structure and knowledge dissemination [25].  
This research not only enriched our understanding of online health communities but also provided important theoretical and methodological references for subsequent social network research.
## 5 Optimization of LPG Distribution Routes Using Clark and Wright Heuristics  
### Vrp  
The vehicle routing problem (VRP) is a powerful tool for solving logistics problems, as it allows improving the sequence of visits to different locations, minimizing the distance or travel time, while including constraints such as vehicle capacity, traveled distance, or delivery time windows [46]. This type of optimization problems is of the combinatorial type and usually belongs to the NP-Hard class (Non-deterministic Polynomial-Hard), which means that it is not possible to solve them in polynomial time [47].  
Heuristics and metaheuristic methods have been proposed to solve VRP problems [48]. Metaheuristic algorithms are the combination of different heuristic techniques to perform the search domain exploration in a more efficient way; these include particle swarm optimization [49; 50], simulated annealing [51], genetic [52; 53], and neural networks [54].  
On the other hand, heuristics algorithms find an optimal solution from the total set of feasible solutions, but they do not guarantee that the selected solution is the most optimal. They use iterative techniques that gradually create a solution, where at each step, a new element is added, which is evaluated and discarded when it is not better than another. These include the [PERSON] [55] and [PERSON] and Wright [56; 57; 58] algorithms.  
The use of a heuristics in any NP-Complex problem is justified by searching for local solution spaces and trying to find optimal solutions in reasonable times and avoiding stagnation in the universe of solutions. In issues with very large search spaces, it is essential to use this type of technique, since this formulation discriminates unpromising search paths, which, if objectively posed, will always yield optimal solutions. It is worth mentioning that these models sacrifice the validation of the universe of solutions and the detection of the most optimal solution, in order to generate one in a reasonable time.  
By far the best-known heuristic approach to the VRP problem is the \"savings\" algorithm by [PERSON] and Wright. This algorithm involves a single depot, a homogeneous fleet of vehicles, and a set of customers who require delivery of goods from the single depot. The basic concept is to find a feasible set of vehicle routes that minimizes the total travel distance and the total number of vehicles used, taking into account some constraints such as vehicle capacity, delivery time windows, or distance traveled. This makes it ideal for solving the optimal route selection problem for home delivery of LPG by tanker trucks.  
For the reasons stated above, in this development, it is proposed to use the Clarke and Wright savings algorithm. The algorithm, in addition to providing a reasonably optimized solution for the VRP, is much simpler and less computationally intensive than the metaheuristic algorithms [59].