esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Assessment of the Developed Algorithm Efficiency in Some Case Studies  
### Lake Ladoga  
Lake Ladoga is the greatest European lake located in north-western Russia. The lake has undergone severe eutrophication due to anthropogenic input of nutrients, first and foremost, phosphorus. As a result, its productivity significantly increased, with the summer-time phytoplankton community composition shifted from diatoms to blue-greens, and the trophic status became mesotrophic [25].  
The performance of the retrieval algorithm and the hydro-optical model for Lake Ladoga is illustrated on distributions of the _chl_, _sm_ and _doc_ concentrations in Lake Ladoga (Figure 4). These results are retrieved from c.a. 400 SeaWiFS images taken during the period 1998-2004 in July and averaged.  
Figure 4: Distributions of the _chl_ (**A**), _sm_ (**B**) and _doc_ (**C**) concentrations in Lake Ladoga in July as retrieved from SeaWiFS data and averaged over 1998-2004. The maps are given in the geographic projection.  
The retrieved concentrations of _chl_, _sm_ and _doc_ are in compliance with the historical data from Lake Ladoga [25; 26; 27]. In mid-summer, the concentration of _chl_ in Lake Ladoga, which is presently a mesotrophic water body, may vary within the range 1-25 \(\upmu\)g/L. The central and northern parts of Lake Ladoga are deep and remain rather cold even in July. As a result, the _chl_ concentrations are lower there than they are in shallow/well-warmed areas. This is especially so in the southern zone.  
The lake water is also rich in _doc_: in near shore regions _Cdoc_ can be as high as 10-13 mgC/L, and on average is about 8 mgC/L [25].  
Near-shore waters of Lake Ladoga also contain, although in moderate amounts, terrigenous _sm_ brought in with river discharge. The largest rivers discharging into the lake are located in the south-eastern area (the lower right-hand part of Fig. 4), but there is also an inflow of riverine waters along the western shore (left-hand side of Fig. 4).  
The central/deep regions of Lake Ladoga generally contain low amounts of _sm_ unless temporarily persistent winds and/or meandering currents transport suspended mineral matter offshore [27; 28]. All  
Figure 4: Cont.  
these features can be found in the CPA distributions given in Fig. 4, and this can be considered as a qualitative substantiation of the retrieved spatial distributions of _chl_, _sm_ and _doc_ in Lake Ladoga.  
Thus, the above indirect verification is clearly indicative that the algorithm restores correctly the spatial distribution of _chl_, _sm_ and _doc_ in accordance with the trophic status, bathymetry and hydrology of Lake Ladoga.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Mathematical Model of SMTrust  
There are certain properties that the routing metrics must hold to support the routing protocol requirements for RPL [73]. To guarantee that the routing protocol is appropriately operational, two properties are identified by [74] that need to be fulfilled through a routing metric, i.e., isotonicity and monotonicity. For a distributed Bellman-Ford algorithm-based proactive routing protocol, such as RPL, refs. [74; 76] have elaborated the theorems for isotonicity and monotonicity properties to fulfil the consistency, optimality, and loop freeness. The monotonic property ensures the convergence and loop-freeness of the routing algorithm, whereas isotonicity, affecting the path weights, ensures the optimality. In addition, ref. [76] proves that isotonicity and monotonicity are adequate to ensure that the routing protocol is loop-free, optimal, and consistent. Ref. [73] also supports that a composite metric in a routing protocol must hold the properties of isotonicity and monotonicity. Following the work of [73; 74; 76], and as adopted by [26], these properties for SMTrust are explained below to analyse and validate the proposed model.  
Let the routing metric algebra be defined by the quadruplet (\(P\), \(+\), \(fn\), \(\leq\)) called the path weight structure. \(P\) is the set of paths, \(fn\) is the function mapping a path to weight, \(\leq\) corresponds to costs order relationship that provides total order of weights, and \(+\) is the concatenation operator. For SMTrust, \(fn\) in path weight structure is path \(p\) trust value. Since SMTrust uses Bellman-Ford algorithm for path calculation, as in the standard RPL operation, and according to the Lemma and Theorems 7, 8, and 9 proved in [PERSON] et al.'s study [74] and [PERSON] [76], we can demonstrate that SMTrust holds the (left) isotonic and (left) monotonic properties.  
According to the specification RFC 6550, RPL is a path addressing and source routing the protocol, meaning that the sender partially or fully specifies the route of the packet in the network. Hence, the node is enabled to communicate all the potential routes to the receiver. Since SMTrust follows the standard RPL operation, different definitions can be used for path weight structure by capturing different path characteristics [74]. Hence, in path weight structure (\(P\), \(+\), \(fn\), \(\leq\)) for SMTrust, \(fn\) is the function that is based on the maximum trust index value along the path \(p\). The order relationship \(\leq\) represents the minimizing trust relationship of paths. Meaning that for the trust index metric,'min' is the metric operator and'max' is the metric order relation. Thus, based on the proved theorems and lemma, provided that SMTrust relies on [PERSON]'s algorithm, it is obvious that SMTrust holds the (left) isotonic and (left) monotonic properties for the calculation of paths.
# Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement
## 6 Limitations  
In the experimental results, we can see that the model's accuracy is limited; this can be attributed to the following factors: (1) Limitations in annotated sample size--in few-shot settings, 5-way-5-shot and 5-way-10-shot annotated datasets were used to train models (excluding BC5 CDR); here, the limited nature of the training data means that it is difficult to cover the full diversity of named entity expressions and to account for the complexity of contextual environments. Additionally, the small number of annotated samples leads to unstable parameter updates, causing the optimization process to fall into local optima. (2) Limitations in model parameter size--the BERT-base model, comprising 12 attention layers and approximately 110 million parameters, was used for named entity recognition; the relatively smaller parameter size of this model led to a limitation in the achievable F1-score. (3) The granularity of the data--among the CoNLL03, OntoNotes 5.0, OntoNotes 4.0, and BC5 CDR datasets, the CoNLL03 dataset achieved the highest accuracy, which is primarily because it is a clean and general-purpose dataset; in OntoNotes 5.0 and OntoNotes 4.0, named entities were similarly divided into five categories (i.e., PER, LOC, ORG, MISC, and O); however, within the MISC category, the experimental data exhibit finer granularity and a larger number of subtypes, resulting in lower F1-scores on the OntoNotes datasets compared to on the CoNLL03 dataset; BC5 CDR, as a biomedical domain dataset, contains highly specialized terminology, and compared to the general-domain pretraining corpus used by BERT, its specialized nature leads to lower accuracy relative to general-domain datasets.
## 4 Discussion  
In reference [12], the computational complexity of the expectation maximization (EM)-based CE depends on the data tones. The complexity is calculated for interference cancellation, coefficient calculation and their proposed method. The complexity is higher for their proposed approach due to the greater number of virtual pilots.  
The computational complexity in reference [24] is very low due to the use of tensors for processing and data representation. Actually, the complexity of the CP decomposition-based method is given as \(\mathrm{O}(MTKL+MKL^{2}+L^{3})\). The number of paths \(L\) is too low so the complexity is modified as \(\mathrm{O}(MTK)\). The major computational task involves solving the three least squares problems at each iteration. The number of flops required to compute factor matrices for several iterations in the order of \(\mathrm{O}(MTKL+MKL^{2}+L^{3})\). When \(L\) is small, the dominant term has a computational complexity of order \(\mathrm{O}(MTK)\), which has a linear relationship with the size of the observed tensor \(Y\). If the joint compressed sensing is applied along with the CP decomposition, the complexity becomes large.  
Joint maximum likelihood channel and clipping amplitude estimation in reference [25] explores the potential of the receiver side compensation of the transmitter. Joint Channel and Clipping Amplitude Estimation (JCCAE) uses three algorithms for joint CE and Clipping Amplitude (CA) estimation. Since the three algorithms are iterative algorithms, the repetition of estimating the channel and CA results in high complexity.  
The four Bayesian interference methods [23], such as Sparse Bayesian Learning (SBL), Gaussian Mixture Model (GMM), Pattern Coupled Spare Bayesian Learning (PCSBL) and Block Spare Bayesian Learning (BSBL), are used for multi-user detection to control the estimated signals. The calculation of posterior distribution reduces the computational complexity of the transmitted signals. However, it is not devoted to developing interference users and the e-algorithms.  
The joint multi-user detection and CE methods are used in this paper with low computational complexity. Actually, many of the devices are in an active state so the user activity is low. Furthermore, there are a considerable number of zero elements in the transmitted signals due to the sparse property. This paper proposes a sparse \(k\)-NN algorithm to determine the active users and the optimal pilot patterns are selected for CE, which considers a small activity of users. The machine learning algorithm reduces the complexity to \(\mathrm{O}(n)\). The optimal pilot-based CE uses the CSO optimization algorithm with the smaller computational complexity of \(\mathrm{O}(n)\). Therefore, the overall system achieves the complexity of \(\mathrm{O}(n^{2})\). The proposed algorithm not only reduces the complexity but also reduces the MAI.
# A State of the Art Review of Systems of Linear Inequalities and Related Observability Problems
## 6 Examples of Applications
### Application to Traffic Problems  
Some applications to traffic problems are the following:  
1. The problem of optimizing the use of scanning resources for the estimation of route flows in traffic networks is dealt with in [40], where the authors analyze three problems: (a) how to minimize the number of cameras required for estimating a given subset of route flows, (b) finding the links to be scanned when \(n\) cameras are available, and (c) solving the same problems when errors are present. Finally, the Nguyen Dupuis, a small-sized and well-known network example, and the case of the city of Ciudad Real in Spain, are used to illustrate the methodology.
2. The problem of observability of subsets \(V_{1}\) of flows in terms of another subset \(V_{2}\), when simultaneously different types of flows, links, nodes, routes, origin-destination (OD), plate scanned, etc., are considered, is discussed in [7]. There are two problems considered: (a) which flows in \(V_{1}\) can be calculated in terms of the observed flows \(V_{2}\), and (b) find the subset of flows \(V_{2}\) that needs to be observed to obtain the flows in \(V_{1}\). In addition, important theorems providing necessary and sufficient conditions for solving these two problems are given and they are illustrated by its application to three simple examples.
3. The observability problem in traffic networks and the optimal location of scanning and counting devices, the treatment of plate scanning information, and the associated flow amount of information measured is dealt with. In addition, the problem of the optimal location of counters and plate scanning cameras is analyzed and illustrated with some illustrative and real examples.
## 5 Conclusions  
The results of this study demonstrated high accuracy and reliable data recording of RTK rovers to measure social networks in sheep. From the positional data, it was possible to identify sheep that were more likely to move first, but no single individuals were consistently identified. The accuracy of the RTK rovers, however, provide the capability to identify leader animals across different scenarios such as movement towards new feed areas or a water supply, or when transitioning between behavioural states such as resting and grazing. Social networks of the group were generated with high accuracy using an intermittent sampling frequency, opening up the potential for battery saving and deployment in longer-term studies on livestock in the field. However, this was only a single group of sheep in an initial test of the RTK rovers and thus many further opportunities exist to apply the devices to more groups across different contexts to further understand social networks in sheep. Future work could use this device to investigate (1) the interaction between livestock and their environment, (2) how sheep with different personality traits behave in a social network, and (3) how regrouping animals might influence leadership and association patterns among individuals.
## 3 Experimental Results
### Quantitative Results  
It is difficult to verify the diversity of output images using several images. Therefore, we conducted quantitative experiments using the Frechet inception distance (FID) [22]. The FID score can be used to measure the quality and diversity of images. The FID score is calculated using Equation (11):  
\[\text{FID}=\|\mu_{x}-\mu_{y}\|_{2}^{2}+Tr\bigg{(}\Sigma_{x}+\Sigma_{y}-2\Big{(} \Sigma_{x}\Sigma_{y}\Big{)}^{\frac{1}{2}}\bigg{)}, \tag{11}\]  
where \(x\) and \(y\) denote the image sets \(x\) and \(y\). In our experiments, \(x\) and \(y\) consist of real images and fake images, respectively. Because the FID score considers the mean (\(\mu\)) and variance (\(\Sigma\)) of the images, it can represent the visual quality and diversity of the images. If the two sets of images have a similar probability distribution, the FID score is low (Equation (11)). Therefore, lower FID scores are better  
Figure 5: Qualitative results for facial image generation. The rows from top to bottom present the results of BEGAN, BEGAN-CS, and the proposed model, respectively. Each column represents five epochs; i.e., (**a**) epoch 5, (**b**) epoch 10, and (**c**) epoch 15. (**d**) An enlargement of the red box in (c).  
when comparing GAN models. We measured the FID score based on 5000 real samples and 5000 fake samples in epoch 15 for each model. The results are summarized in Table 1.  
Our model produces the best results. As a result, our model can be seen as superior in terms of image quality and diversity.
## 5 Model Parameter Identification
### Least Squares Setup  
The following relationships between the parameters and measurable data was used with the NNLS algorithm [28]:  
\[OCV(SOC)=\alpha_{0}+\alpha_{1}SOC+\alpha_{2}SOC^{2}+\alpha_{3}SOC^{3}+\alpha_ {4}SOC^{4}+\alpha_{5}SOC^{5} \tag{49}\]  
\[U_{pa}=I_{L}R_{pa}\bigg{(}1-e^{-\frac{t}{R_{pa}c_{pa}}}\bigg{)} \tag{50}\]  
\[U_{pc}=I_{L}R_{pc}\bigg{(}1-e^{-\frac{t}{R_{pc}c_{pc}}}\bigg{)} \tag{51}\]  
\[U_{L}=OCV(SOC)-I_{L}R_{0}-I_{L}R_{pa}\bigg{(}1-e^{-\frac{t}{R_{pa}c_{pa}}} \bigg{)}-I_{L}R_{pc}\bigg{(}1-e^{-\frac{t}{R_{pc}c_{pc}}}\bigg{)} \tag{52}\]  
\[\theta=\bigg{[}\alpha_{0},~{}\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4}, \alpha_{5},~{}R_{0},~{}R_{pa},\frac{1}{R_{pa}C_{pa}},~{}R_{pc},\frac{1}{R_{pc} C_{pc}}\bigg{]} \tag{53}\]  
where \(t\) represents the time vector and \(OCV(SOC)\) is the \(OCV\) curve approximated to a 5 th order polynomial. \(\theta\) represents the parameter vector. Moreover, the capacitance values are estimated using a fraction to account for their higher magnitude [28; 29; 30].  
Finally, MATLAB(r)s \(Isqcurvefit\) function was used with the data from cycle 5. Cycle 5's voltage curve can be used to obtain the value for \(\alpha_{0}\), which is when the battery has an SOC value of 0%. Furthermore, \(Isqcurvefit\) requires values for the boundaries and initial conditions. The selected values are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Unit** & **Lower Bound** & **Upper Bound** & **Guess** \\ \hline \(R_{0}\) & \(\Omega\) & 0.003 & 0.500 & 0.020 \\ \hline \(R_{pa}\) & \(\Omega\) & 0.03 & 0.500 & 0.100 \\ \hline \(C_{pa}\) & \(1/(\Omega\)F) & 0.00025 & 0.002 & 0.001 \\ \hline \(R_{pc}\) & \(\Omega\) & 0.03 & 0.500 & 0.100 \\ \hline \(C_{pc}\) & \(1/(\Omega\)F) & 0.02 & 0.100 & 0.010 \\ \hline \end{tabular}
\end{table}
Table 1: NLLS Boundaries and Initial Guess.
## 3 Methodology  
When the VMD algorithm is used to preprocess the micro-vibration signal, the VMD algorithm is first applied to the modal decomposition of the acceleration data. According to the improved VMD algorithm mentioned in Reference [20], the measured micro-vibration acceleration signal is decomposed into several orders of finite bandwidth eigenmode function component. Then, the frequency of each order of intrinsic mode function components is extracted by power spectral density to determine the frequency range of each order of intrinsic mode function components and eliminate the noisy modes. Figure 4 shows the measured micro-vibration acceleration signal, which is decomposed into several order intrinsic mode function components by the variational mode decomposition algorithm. Figure 5 shows the spectral diagram of each order of intrinsic mode function signals.  
Figure 3: Micro-vibration original signal disturbed by noise.  
Figure 2: Micro \(-\)vibration measurement system.  
As can be seen from Figure 5, the essence of the VMD algorithm is to decompose the acceleration signal into several intrinsic mode function components of different frequency bands. Each order of intrinsic mode function components obtained by the VMD decomposition algorithm have noise in other frequency bands with small amplitude. In this paper, we proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition (AGMD). The steps of the algorithm are as follows.
## 8 Evaluation of the MRLG Protocol  
In this section, we assess the improvements introduced by MRLG compared with CTP (see Section 2). To evaluate the performance of the MRLG protocol under the IEEE 802.15.4 technology, we conducted  
Figure 4: For different mobility patterns when varying the sink speed: (**a**) mean position estimation error, (**b**) routing overhead.  
a series of simulation experiments using the network simulator ns-2 [37]. The methodology adopted to conduct the tests was the following: each obtained value is the average of ten independent experiments, providing in most cases a confidence interval smaller than 5% for a confidence level of 95%. Each of the test scenarios adopts a grid topology for the sensor nodes and has a mobile sink, which starts at a random position and then moves throughout the simulation area according to the random waypoint mobility model [38]. The sensors deployed have four neighbors, except those at the borders. The different simulation parameters are those defined in Table 2, unless stated otherwise. The default inter-message period for MRLG RREQ packets was set to 1 second, while special announcements were generated once every 10 min.  
We evaluate the performance when varying the number of sensors and the sink speed. The metrics used for the performance assessment are: the packet loss ratio, the latency and the routing load. To assess performance we compared the MRLG algorithm against a standard solution based on sink announcements, being CTP an example of such a protocol. With CTP, route discovery messages always propagate throughout the WSN to reach all nodes. The interval between consecutive route discovery messages was of 5 s since it offered the best trade-off between routing overhead and packet loss ratio when supporting sink mobility (for sink speeds up to 10 m/s).
###### Abstract
The existing unloading models do not take into account the effect of displacement and stress unloading at the inner edge of the frozen wall on the design thickness of the frozen wall. To reduce the displacement of frozen walls during excavations, a technique using short-sectional excavation and shaft lining construction is adopted [21]; it causes interaction between the frozen wall and the shaft, and the radial stress in the inner edge of the frozen wall is incompletely unloaded. With the increasing demand for underground resources, the depth that the shaft is sunk through the alluvium increases, and the excavation height-radius ratio for each section is generally reduced to 1/3-2/3. The characteristics of the inner edge of the frozen wall being radially incompletely unloaded become more obvious, and the deformation of the frozen wall is subject to greater constraints. Therefore, the design process should consider the attributes of being radially incompletely unloaded at the inner boundary of the frozen wall. In this paper, based on the construction technique using short-sectional excavation and shaft lining, a mechanical model was established for a frozen wall with its inner edge radially incompletely unloaded, comprehensively considering the in situ stress and the frozen wall-shaft lining interaction. Analytical solutions were derived. A parameter \(\alpha\) expressing the degree of being unloaded was introduced, and then a new method of designing and calculating values for an elastoplastic frozen wall thickness was developed based on \(\alpha\) and the solutions. The influences of the mechanical parameters of the shaft lining, frozen wall, and surrounding unfrozen alluvium on the design thickness of the frozen wall were also discussed. The range of parameter \(\alpha\) was estimated based on the frozen wall-shaft lining interaction forces from field data from a given project. A concrete application case is presented at the end of this paper.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 2 Materials and Methods
### IPSO-AR Method  
#### 2.2.1 Standard PSO Algorithm  
Particle swarm optimization is a computational method that optimizes a problem by iteratively attempting to improve a candidate solution in reference to a given fitness function. The SPSO algorithm was developed by introducing the concept of inertia weight into PSO yields [33; 34]. With an \(N\)-dimensional search space, the total population size is \(n\), assumed as \(x_{i}=(\begin{array}{cccc}x_{i1}&x_{i2}&\cdots&x_{iN}\end{array}),u_{i}=( \begin{array}{cccc}v_{i1}&v_{i2}&\cdots&v_{iN}\end{array})\), which can be denoted as the position vector and velocity vector of the particle \(i\). The current optimal position is denoted as \(pBest_{i}\), and the current optimal position of the population is denoted as \(gBest\). The SPSO algorithm can be described as  
\[\begin{array}{l}v_{in}^{(k+1)}=\omega v_{in}^{(k)}+c_{1}r_{1}(pBest_{in}^{(k )}-x_{in}^{(k)})+c_{2}r_{2}(gBest_{in}^{(k)}-x_{in}^{(k)})\\ x_{in}^{(k+1)}=x_{in}^{(k)}+v_{in}^{(k+1)}\end{array}, \tag{9}\]  
where \(i=1,\begin{array}{cccc}2,\end{array}\cdots\begin{array}{cccc}n\end{array}\); \(\omega\) denotes the inertia weight; \(c_{1}\) and \(c_{2}\) are two positive constants, called cognitive learning rate and social learning rate, respectively; and \(r_{1}\) and \(r_{2}\) are the random function in the range [0,1].  
In this study, different particle positions represent the corresponding integer ambiguity candidate. Their fitness function can be described as  
\[F=const-\log[f(\overline{Z})], \tag{10}\]  
where \(F\) denotes the fitness function value, and \(const\) denotes one constant. In this work, \(const\) is set as 100.