esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 3 Proposed Method
### Training Details  
The TensorFlow (version 2.6.2) library with the Adam optimizer was used to create the proposed DU-TCG network in Python (version 3.6.13), and channel matrices were generated using MATLAB R2021a. The test and training data sets, \(\left\{(y_{m},H_{m}),x_{m}\right\}_{m=1}^{train\_data}\) and \(\left\{(y_{n},H_{n}),x_{n}\right\}_{m=1}^{test\_data}\), were created randomly based on Equation (2) with different noise levels. The transmitted symbols, \(x\), were selected from modulation schemes such as BPSK, 16-QAM, 64-QAM, and 256-QAM. Various channel models, including the Kronecker channel [28], Rayleigh fading channel, and TDL-A and TDL-E MIMO channels [29], employ different random generators for their channel matrices. The training process uses \(5\times 10^{4}\) samples with an SNR of 25 dB. In deep-unfolded MIMO detection, training with high SNR values is usually preferred to provide a better model tuning. For instance, refs. [28; 42] have shown that higher SNR values provide a clearer signal, which allows the model to learn more effectively. Additionally, refs. [43; 44] have stated that training at lower SNR levels can even degrade model performance. All trainable parameters were initially set to zero. Subsequently, \(5\times 10^{5}\) samples were used for training with SNR values ranging from 0 to 20 dB, incremented by 2 dB. This wider range of SNR values allows a comprehensive evaluation of the detector's performance. The learning rate was first set to \(10^{-3}\) and was fine-tuned by halving it after each epoch, so that the detection is more robust. The average loss's point of discontinuity determines the stopping criterion. Also, to ensure a fair comparison under identical conditions, the number of layers used in deep-unfolded methods was chosen as the same. The number of layers used for Rayleigh channel was selected as 5, while 15 layers were used in other channel models to adapt to challenging channel conditions. The training method is computationally efficient, taking about two hours on a normal Intel i7-7500U processor, because the model has just three trainable parameters and works well with a small number of layers. It is anticipated that the training time will rise in proportion to larger MIMO systems or models with more trainable parameters.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 2 Quality Check of Broadcast Orbits
### Comparison of Reference Systems  
The study focuses on GPS week 1950, i.e., from 21 May 2017 to 27 May 2017. For each day a set of Helmert parameters are obtained, which define the transformation from precise to broadcast reference systems, and consist of three translations (\(T_{x}\), \(T_{y}\), \(T_{z}\)), three rotations (\(R_{x}\), \(R_{y}\), \(R_{z}\)) and one scaling factor (\(k\)) according to Equation (1).  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\\ z^{\prime}\end{array}\right]=\left[\begin{array}{c}T_{x}\\ T_{y}\\ T_{z}\end{array}\right]+(1+k)\left[\begin{array}{ccc}1&R_{z}&-R_{y}\\ -R_{z}&1&R_{x}\\ R_{y}&-R_{x}&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ z\end{array}\right] \tag{1}\]  
The weekly average values of each Helmert parameter has been computed. With these mean values the broadcast reference systems relative to precise reference system have been represented. Figures 3-6 show the rotations magnified in order to be visible (milliarcsecs are treated as degrees). The figure also contains a table with the sets of mean Helmert parameters and their standard deviation, for each block of satellites. Translations are measured in meters, rotations in milliarcsec and the scaling factor is expressed in \(\mu\)m/km (1 \(\mu\)m = 10\({}^{-6}\) m).  
Precise orbits refer to CoM whereas broadcast orbits refer to APC. The relative distance between APC and CoM is the APO (Antenna Phase Offset). Among the three (x, y, z) APO components, the z component, which correspond to the boresight direction, is the prevalent one. Since APO is constant, the satellite orbits described by CoM and APC differ mainly by the constant offset given by the boresight component of APO, which is oriented in radial direction. So we assume that APO is absorbed by the scale factor of Helmert transformation.  
For GPS and Galileo FOC (Full Operational Capability) satellites, the reference systems adopted by broadcast orbits are quite similar to those provided by precise orbits: the translation of the origin is less than 0.10 m and rotations are less than 2 milliarcsec. For Galileo IOV (In Orbit Validation), GLONASS and BeiDou higher differences are apparent. The Galileo IOV broadcast reference frame is offset to ITRF by at most 0.23 \(\pm\) 0.04 m in Y (Figure 5).  
The GLONASS M broadcast reference frame is offset to ITRF by at most 0.27 \(\pm\) 0.05 m in Y and the maximum rotation is 4 \(\pm\) 2 milliarcsec of arc about Y. The GLONASS K broadcast reference frame is offset to ITRF by at most 1.06 \(\pm\) 0.18 m in Y and the maximum rotation is 19 \(\pm\) 2 milliarcsec about X (Figure 4).  
The BeiDou IGSO broadcast reference frame is offset to ITRF by at most 0.31 \(\pm\) 0.08 m in Y and the maximum rotation is 3.6 \(\pm\) 2.0 milliarcsec about Z (Figure 6). For BeiDou MEO satellites we find Helmert parameters of the same order of magnitude of those found for IGSO satellites, but with a  
Figure 2: KSW reference system: radial (R), along-track (S) and cross-track (W).
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 2 Related Works  
Most of the recent research on LoRa network focuses on its outdoor performance and coverage. Significantly, LoRa proved it can deliver more than half of the packets from 5-10 km range in urban  
Figure 1: Google map illustrating the location of the target mangrove forest site in Sabak Bernam, Malaysia.  
applications and in maritime applications where 15-30 km distances observed [4]. A few studies expressed concern regarding image transmission over the LoRa network since images demand a high amount of bits and consume more energy in the transmission operation. One of these significant related works is a paper by [PERSON] [5] who built an \"off-the-shelves low-cost components\" image sensor platform which showed promising results of transmitting images over LoRa, although only a 1.8 km range achieved this without packet loss. Another paper by [PERSON] et al. [6] gives a theoretical analysis for the new wireless visual sensor protocol using LoRa modulation but this was not tested outdoors unlike our paper.  
To the best of our knowledge, there is no other study testing LoRa physical layer (PHY) capabilities in images transmission for a mangrove forest area thus far. This is because its long range capability affects the bandwidth which is considered a big barrier in transmitting images and multimedia data [7]. Although, most of the current implemented networks are short range networks, they have a wide bandwidth and high data rate (e.g., Zigbee, LTE, etc.), and they have a serious problem with a limited link budget [8]. Moreover, LoRa and other LPWANs are cheaper in installation and do not require repeaters for distance beside LPWAN are cheaper to install and do not require repeaters for distance. Also, LPWANs require less power consumption (see Figure 2).  
In the following section, details about LoRa networks and comparisons with other LPWAN networks are discussed.
# Free Vibration Analysis of Arches with Interval-Uncertain Parameters
###### Abstract  
The dynamic characteristics of a structure serve as a crucial foundation for structural assessment, fault diagnosis, and ensuring structural safety. Therefore, it is imperative to investigate the impacts of uncertain parameters on the dynamic performance of structures. The dynamic characteristics of arches with uncertain parameters are analyzed in this paper. The uncertain parameters are regarded as non-probabilistic uncertainties and represented as interval variables. A model of an arch considering interval-uncertain parameters is built, and kinematic equations are established. The natural frequencies are obtained using the differential quadrature (DQ) method, and the relationships between natural frequency, radius, and central angle are also analyzed. On this basis, the Chebyshev polynomial surrogate (CPS) model is employed to solve the uncertain dynamic problem, and the natural frequencies are seen to be the objective functions of the CPS model. The accuracy verification of the model is achieved by comparison with the Monte Carlo simulation (MCS). Simulations are carried out considering different uncertainties, and the results show that the bounds of natural frequencies are influenced not only by the types of uncertain parameters, but also by their combinations.
## 3 Results and Discussion
### Low-Carbon Operation Evaluation
Notably, only 11 STPs experienced an increase in carbon emission intensity after correction. In terms of low-carbon operation evaluation, 14 (19.4%) STPs achieved scores above 85 points, meeting the Class 1 standard for low-carbon operation. 16 (22.2%) STPs were classified as Class 2, and 42 (58.3%) STPs were classified as Class 3. However, 18 STPs scored below 60 points, categorized as high-carbon-emitting STPs, with 7 STPs scoring only 50 points. The design capacities of these high-carbon-emitting STPs were primarily 0.2 \(\times\) 10\({}^{4}\)-1 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d (3 STPs) and 1.0 \(\times\) 10\({}^{4}\)-4 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d (3 STPs), suggesting that smaller-scale STPs possess greater carbon reduction potential.
# Dual Quaternion Embeddings for Link Prediction
## 4 Our DualQuatE Model
### Connection to TransE and RotatE  
**Compared with RotatE**: RotatE embedded entity embeddings \(\mathbf{h}\), \(\mathbf{t}\) and relation embeddings \(\mathbf{r}\) into the complex space. RotatE utilized score function \(-||\mathbf{h}\circ\mathbf{r}-\mathbf{t}||\) to calculate the probability of each triple, where \(r_{i}\) is unit complex \(\cos\theta+\mathbf{i}\sin\theta\). DualQuatE can be transformed to RotatE by fixing rotation plane and removing translation variables. For instance, we can construct relation embeddings by Formula (10) in _xoy_ plane, where \(\mathbf{u}=\mathbf{k}\) and \(\mathbf{n}=\mathbf{0}\) (i.e., \(\mathbf{r}=\cos\frac{\theta}{2}+\sin\frac{\theta}{2}\mathbf{i}\)) and embed entities with corresponding forms: \(\mathbf{h}\) or \(\mathbf{t}=a\mathbf{i}+b\mathbf{j}\).  
**Compared with TransE**: TransE modeled relation as translation that embedded entity embeddings \(\mathbf{h}\), \(\mathbf{t}\) and relation embeddings \(\mathbf{r}\) to vector space. To express TransE, we can set \(\theta=0\) (i.e., \(\mathbf{m}=\mathbf{0}\)) in relation embeddings to ignore the rotation. In other words, the relation embeddings in DualQuatE can be expressed as \(\mathbf{r}=1+\frac{\xi}{2}\mathbf{n}\).
## 5 Conclusions  
In Section 4.1 it was shown that the amount of solutions required to approximate a Pareto front increases exponentially with a growing number of conflicting objectives. As illustrated in Figure 9, the amount of evaluations increases considerably for more than five objectives. This limits the applicability of the Pareto approach for real-world applications, which frequently require time-consuming evaluations especially when based on simulation.  
We have introduced the cascaded weighted sum (CWS), which can be described roughly as a combination of the weighted sum and the \(\varepsilon\)-constrained method. The major drawback of the pure weighted sum, the inaccessibility of parts of the Pareto front in non-convex cases, can be reduced to some extent by the CWS, see Section 3 and Figure 7. As the pure weighted sum, the CWS is not an _a priori_ method. The major advantage of the CWS is its ability to concentrate solutions on the region of interest with less computational effort than Pareto optimization. In addition, this effort difference increases immensely with an increasing number of objectives, in particular for more than five. The region of interest can be a result of previous experience or knowledge, of a first Pareto-based optimization, or a combination thereof.  
In Section 4.2, optimization projects were divided into three types, the individual project type, projects treating some variants of the task, and the type of repeated optimization of the same task with more or less small variations. The unquestioned domain of Pareto optimization is the first type of optimization projects. For the second type and five or more objectives, a combination of both methods can be advantageous as was described in Section 4.3.2. For the third project type of repeated optimization of task variants and with no or only minor human interaction, the Pareto front is not required, as the regions of interest are already known from previous solutions or initial Pareto optimization. The concentration of the CWS on that region is beneficial, as the computational effort can be reduced significantly. This is of importance especially in those cases, where a fast solution is required or the amount of evaluations is limited due to long run times.  
Thus, we can conclude that both methods have their place and their field of application. Additionally, they can complement each other.
## 3 Results
### Multi-source data fusion. Due to data errors and other reasons, a single source of information early warning cannot fully reflect the actual situation on the site, resulting in a low rate of accuracy of early warning, and cannot provide accurate guidance for on-site construction. To the above problems, a feature-based information fusion model that learns new evidence correction parameters while reducing the effect of global uncertainty is proposed in this research. The predicted values of surface settlement, groundwater level, and pile horizontal displacement obtained from machine learning training are used as risk early-warning source, and the corresponding early-warning probability distributions can be obtained through the cloud model. Finally, the improved D-S evidence theory is used to fuse the multi-source data, which combines the characteristics of the three single information source, and the final early-warning result is given by fusing the judgements of each evidence. In order to illustrate the fusion process, monitoring point C1 was taken as the object of study. The fusion results from a single data source, the traditional D-S method, and the proposed method of this paper are compared as shown in Table 7, where the bolded values are the maximum probability early-warning levels. The monitoring point C1 contains three primary indicators (surface settlement, groundwater level, and pile horizontal displace
# Simplified Integrity Checking for an Expressive Class of Denial Constraints
## 1 Introduction
In this paper, we discuss the use of program transformation operators to the generation of simplified integrity constraints. We implement these operators in terms of rewrite rules based on resolution, subsumption, and replacement of specific patterns. In particular, one of the main contributions of this study is the broad applicability of these operators to a large class of integrity constraints that encompasses all the most common dependencies that are used in standard database design practice, including tuple-generating dependencies (TGDs) and equality-generating dependencies (EGDs), which we express in denial form with negated existential quantifiers, possibly nested. Another important contribution is the \"preventive\" perspective of the proposed technique. Traditional methods for simplified integrity checking work as follows: first, a pending update is executed on the database; then, the simplified check is tested against the updated database; and, if the test fails, the update needs to be retracted (typically, with a rollback of the enclosing transaction). Unlike all such methods, we advocate an approach that checks constraint violation _before_ the execution of the pending update. This means that, if we detect that the update will harm consistency, we will not even execute it, thereby saving a significant amount of unneeded work consisting of the execution and subsequent retraction of an update. The techniques described here can be immediately applied to standard database practice, since our logical notation can be easily translated into SQL. In particular, denials correspond to queries whose result must be empty and their maintenance could be enforced through native database constructs such as triggers [13], whose importance is gaining momentum even outside the scope of relational databases [14]. Our results build on the operators informally presented in [15]. To the best of our knowledge, there is no other approach to date that has considered such an expressive class of constraints for the problem of simplified integrity constraint checking. _Outline._ After reviewing related work in Section 2, we introduce some preliminary technical notions in Section 3 in order to set the notation and terminology commonly adopted in relational and deductive databases. We formally introduce the main operators used in the integrity constraint simplification process in Section 4. The core of this proposal is presented in Section 5, where we extend the scope of our operators to a very expressive class of integrity constraints that we call extended denials, for which we offer a comparison with other methods discussed in the pertinent literature along with practical considerations showing how to integrate the procedure into practical systems. Finally, we discuss the extent of our results and provide some concluding remarks in Section 6.
## 3 Synthesized Transmitting Coil
### Optimization of the STC Parameters Using FoM
Thus, the optimal design of the STC can be found by determining the number of sections and total turns according to the winding process and instrument space, as well as a suitable combination of winding parameters. In order to clarify the design procedure, we can use a three-section STC as an example. Figure 4 shows the values of the FoM, \(w_{\rm r}\), and \(h_{\rm s}\) for different designs using the same simulation parameters shown in Figure 3, where the horizontal axis denotes the variation of the ratio of \(n_{2}\) to total turns. It can be observed from Figure 4 that decreasing the ratio of \(n_{2}\) to total turns increased \(w_{\rm r}\) and \(h_{\rm s}\), which decreased the main lobe width and increased the side lobe height. This is consistent with the results shown in Figure 3. In order to optimize the MF performance of the proposed STC, we chose to use the parameters corresponding to the maximum FoM that can be achieved, where FoM = 0.6294 and \(n_{2}/300\) = 0.613 as shown in Figure 4. As a result, the optimal design parameters of the three-section STC can be acquired, since \(n_{1}\) = \(n_{3}\) (\(n_{2}\) = 184, \(n_{1}\) = \(n_{3}\) = 58 herein). Similar to the three-section case, the case with more sections can also be extended. It should be noted that the maximum FoM will correspond to different section schemes and the relationship among the coil turns in each section. Figure 4: MF performance of the proposed STC with different winding parameters.
## 6 Fuzzy Based Adaptive Resource Allocation  
As shown in [16], fuzzy logic can be integrated with management tasks to optimize and customize their performance. This section presents the implementation of a fuzzy rule based system within the CNQF RB and _its use case scenario for adaptive class based resource allocation to support policy-driven admission control_. The ability to provide class based differentiated services (or service tiers) is one of the characteristics of QoS-enabled networks. Thus the mechanism for allocation of resources per service class has a critical impact on the availability of the services and efficient utilization of network resources. Because fuzzy logic allows approximate reasoning without requiring crisp and definite values (unlike traditional measurement-based methods), it can produce intelligent results for management purposes with minimal monitoring overhead. For this reason, fuzzy logic based approaches are desirable within evolving management systems as control and management tasks increase in complexity placing more demand on monitoring of resources, services and network elements.
# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 2 Related Work
### Cross-Project Defect Prediction  
Software defect prediction (SDP) plays an essential role in software engineering as it helps in identifying possible flaws and vulnerabilities in software systems [6]. A large amount of research has been done over time to improve software defect prediction methods'efficacy. To create reliable models that can spot flaws early in the software development lifecycle, researchers have looked into a number of techniques, such as machine learning, data mining, and statistical analysis. With regard to within-project defect prediction, the majority of the earlier SDP models relied on WPDP. The data used in WPDP come from the training and evaluation stages of the same project. Scholars have recently paid greater attention to the CPDP model [27; 30]. The goal of cross project defect prediction models is to leverage training data from other projects. Newly established project defects can be predicted using the prediction models. For cross project defect prediction, [PERSON] et al. [31] proposed a two-phase transfer learning model (TPTL) that builds two defect predictors based on the two selected projects independently using TCA+ and combines their prediction probabilities to improve performance. The two closest source projects are chosen using a source project estimator. [PERSON] et al. [32] addressed the issue by performing a large-scale empirical analysis of 40 unsupervised systems. Three types of feature and 27 application variations were included in the free-source dataset used in the experiment. Models in the occurrence-violation-value based clustering family significantly outperformed models in the hierarchy, density, grid, sequence, and hybrid-oriented clustering, according to the experiment's findings. A cluster-based feature selection technique was used by [PERSON] et al. [33] to choose the important characteristics from the reference data and enhance its quality. Their experiments on eight datasets demonstrated that their proposed technique outperformed WPDP, standard CPDP, and TCA+ in terms of _AUC_ and F-measure. [PERSON] et al. [34] presented GB-CPDP, a graph-based feature learning model for CPDP that uses Long Short-Term Memory (LSTM) networks to develop predictive models and Node2 Vec to convert CFGs and DDGs into numerical vectors.