esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 6 Experiment
### Linear ICA  
Linear Independent Component Analysis (ICA) [26] is a statistical method designed to separate a multivariate signal into independent non-Gaussian signals. It is particularly useful in scenarios where multiple signals overlap, and the objective is to discern the original signals from their mixtures. Building on this, we further explored the efficacy of PT KSD GAN within the context of the linear ICA model. This model serves as a benchmark in the evaluation of algorithms tailored for training unnormalized models.  
The generative process of true samples is relatively simple. The latent variables \(s_{i},i=1,\ldots,n\) are independent and identically distributed, and they are sampled from the standard Laplace distribution, that is, \(\mathbf{s}\sim Laplace(0,1)\). Then, take a linear transformation:  
\[x=Ws \tag{21}\]  
Figure 3: Results of PT KSD GAN on MNIST, CIFAR-10.  
The model parameter \(W\) is an \(N\times N\) matrix, and the determinant is not zero. Under Formula (21), we can deduce \(x\)'s log density:  
\[\log p(x;W)=\log p_{s}(W^{-1}x)-\log|W| \tag{22}\]  
\(p_{s}(\cdot)\) is the probability density function of latent variables \(\mathbf{s}\). Thus, (22) can be used as non-normalized likelihood to evaluate generative models. There are two hyperparameters: the bandwidth parameter \(\sigma\) and the regularizer \(\alpha\). The bandwidth parameter \(\sigma\) of the RBF kernel function is self-adapting according to the input sample number and sample feature dimension. Sample dimensions \(N\) range from 2 to 50. The value of regularized term coefficients is 0.8. We set the learning rate of the Adam Optimizer as 0.001 and the two hyperparameters of the Adam Optimizer as \(\beta_{1}=0.3\), \(\beta_{2}=0.9\). The number of iterations is 100,000, and the batch size is set to 1000. See Table 1 for the results of linear ICA with PT KSD GAN.  
Table 1 shows the log likelihoods of various algorithms: score matching [26], maximum likelihood [38], Learned Stein Discrepancy (LSD) [39], and Noise-Contrastive Estimation (NCE) [40]. For low-dimensional samples, we find that the power of PT KSD GAN is optimal. As the dimensions increase, the performance of PT KSD GAN is slightly inferior to ML and LSD. The reason is that the manual selection of kernel functions makes it impossible to select the optimal kernel function for the current dataset. On the whole, our approach can achieve the same performance as maximum likelihood.
## 2 Theoretical Background
### Objective Functions
#### 2.1.6 Damage Parameterization  
In this work, single beam elements have been considered for structural representation, and uncertain physical properties were considered as updating parameters. No mass reduction has been assumed as is acceptable in most real-life damage detection studies. The damage index \(d\) can be represented in terms of the bending stiffness of an individual element. Therefore, relative variation of the bending stiffness of the damaged element \(El_{d}\) compared to the initial estimated value \(El_{r}\) (reference model) according to damage mechanics [44] is given as:  
\[d=\ 1-\frac{EI_{d}}{EI_{r}} \tag{14}\]  
This definition is advantageous as not only can it estimate the damage severity, but also its location at the element level. Although the damage index usually takes values between zero and one, this formulation can be used between the values of \(-1\) and \(1\), thus acting more like a correction factor to the stiffness of the elements. Therefore, the formulation can be effectively used for model updating of a reference model, as well as for the damage severity estimation.
## 4 Discussion  
At present, the problems of phosphogypsum resource waste and special engineering characteristics of red clay soil are more prominent. Mixing phosphogypsum with red clay soil by adding lime for road base filler solves the problem of solid waste phosphogypsum piling and improves the bad engineering characteristics of red clay soil, which has important scientific significance and engineering application value. This paper systematically analyzes the road performance of phosphogypsum-stabilized red clay and analyzes its mechanism from the microscopic perspective to provide technical support for the application of phosphogypsum-stabilized red clay in roadbed engineering. However, there are still some problems:  
1. Environmental impact of lime-phosphogypsum-stabilized red clay: phosphogypsum contains harmful elements; the main chemical composition is sulfur, phosphorus, and fluorine. According to the phosphogypsum sample test results, sulfur, phosphorus, and fluorine content are in line with the standard, but these elements in the process of mixing with lime and red clay produce physicochemical reactions, the results of the reaction are beyond the standard;, whether the production of leachate is environmentally hazardous is worth devoting sufficient attention to.
2. Lime-phosphogypsum-stabilized red clay water stability and swelling problem: lime-phosphogypsum-stabilized red clay water stability is very poor, basically within 24 h the mixture is completely loose and off, there is basically no strength. Swelling is very great, the expansion rate can reach 55%. Lime-phosphogypsum-stabilized red clay water stability and the swelling problem are the key problems of using it as roadbed filler; it is recommended to consider adding an admixture to solve the water stability and swelling problem.
3. Red clay is widely distributed and spatially variable, and phosphogypsum in different regions also varies, so it is still necessary to conduct a large number of experiments and studies for raw materials in different regions, from which we can draw experience and enrich the aspects of phosphogypsum stabilization of red clay.
4. Under the premise of meeting the requirements of roadbed specifications, compared with lime-fly ash-phosphogypsum mixtures, the strength of lime-phosphogypsum-stabilized red clay is reduced, the amount of phosphogypsum is increased, and the construction cost is greatly reduced. The strength characteristics and deformation characteristics of red clay have been improved. At present, there are few studies on the mechanism of lime-phosphogypsum improving soil performance. This paper analyzes its mechanism from a microscopic point of view and provides theoretical support for the application of lime-phosphogypsum in stabilizing red clay in road engineering.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 3 Concurrent Wake-Up Message
### Performance Simulation  
To evaluate the advantage of concurrent wake-up transmissions of two nearby nodes, we calculated the Friis free-space transmission equation [26] for two different output powers. As the transmission range is additionally affected by multi-path propagation effects like reflection, scattering and diffraction, the simulation included multi-path fading effects to calculate the expected wake-up range [27]. Figure 14 visualizes received signal strength over distance from receiver to sender. During the simulation, the sender and receiver were placed 1.2 m above the ground. The black curve illustrates the expected signal strength for a sending power of 0 dBm and the blue curve for a sending power of 3 dBm. Antenna gain was assumed to be 1.5 dB in each case. The dashed line shows an exemplary sensitivity threshold of a wake-up receiver at \(-51\) dBm. The curves clearly show the advantages of additional sending power. The wake-up range increases from approximately 20 m to 25 m, and local minima are much less severe. The simulation results will be compared to a free-space measurement in Section 5.3.  
Figure 14: Simulated received signal strength over distance for a transmit power of 0 and 3 dBm.  
Figure 13: Simulated and expected amplitude achieved by concurrently-sending nodes.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 2 Methodology  
### Intercrystal Scattering Simulation Model  
In Section 3, a simplified steel plate model is employed as an example to analyze the scattering problem within the system using the developed Geant4 5.2 simulation platform. Figure 2 shows the setup of the studied system, in which a line array of scintillation detector modules moves relative to the detected object to obtain the scanned image. Each detector module consists of a 10 \(\times\) 10 \(\times\) 30 mm\({}^{3}\) LYSO crystal and a silicon photomultiplier (SiPM). The distance between the \({}^{60}\)Co source and the detector array is 6 m, which is necessary for the passage of large containers, and the detected object is placed in the middle. To investigate the effect of crosstalk at different settings, two different collimator widths (5 mm and 10 mm) were simulated with 10, 20, and 30 cm thick iron plates, respectively.
## 3 Semantic Service Description and Context-Based Orchestration
### Semantic Discovery and Context-Based Orchestration
In the last step (step 9), the resulting concrete OWL-S CompositeProcess is forwarded to the OWL-S Engine, which invokes the contained services following the respective control constructs in order to control the manufacturing process for the present product in the plant. The developed system for semantic discovery and context-based orchestration can not only be used to realize an ad-hoc orchestration tailored for the present product (or product variant), but also for the plug-and-play integration of new field devices and the adaption of the manufacturing process in response to unforeseen events (e.g., device failures, changed requirements with respect to resource consumption). The former use case can be handled by the registration of the newly integrated service by means of its semantic description. The latter one is addressed with the context-based rating and selection of services and with the possibility to perform a re-orchestration at runtime (if necessary).
## 3 Results and Discussion
### Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy  
Attenuated Total Reflection-Fourier Transform Infrared spectroscopy (ATR-FTIR) was performed for the structural characterization of raw materials, as well as the filaments and the printed specimens. The FTIR spectrum of pure BaTiO\({}_{3}\) (Figure 7a) shows a strong absorption peak at 551 cm\({}^{-1}\) due to the vibration of Ti-O bond [51]. The spectrum of HAp shows an intense broad band at 950-1100 cm\({}^{-1}\), with the maximum at 1022 cm\({}^{-1}\). This is due to stretching vibrations (\(\
u_{1}\) at 960 cm\({}^{-1}\), \(\
u_{3}\) at 1022 cm\({}^{-1}\), and \(\
u_{3}\) at 1088) of the phosphate groups in HAp. The double band at 559-602 cm\({}^{-1}\) is also associated with bending vibrations (\(\
u_{4}\) at 559 cm\({}^{-1}\) and 602 cm\({}^{-1}\)) of the phosphate groups. The peak at 632 cm\({}^{-1}\) is assigned to libration mode of hydroxyl group [52; 53].  
Regarding the neat polymeric materials, PLA and PCL (Figure 7b) have the same functional group (carbonyl, C=O) in their structure. The FTIR spectrum of PCL shows the characteristic peak at 1724 cm\({}^{-1}\) due to carbonyl stretching vibration, while in the case of PLA the respective peak is located at 1751 cm\({}^{-1}\). The spectrum of PCL also shows a characteristic band at 1238 cm\({}^{-1}\) assigned to the asymmetric C-O-C stretching vibration. The peaks at 1165 and 1292 cm\({}^{-1}\) are assigned to C-O and C-C stretching vibrations in the amorphous and crystalline state, respectively [54].  
The FTIR spectrum of PLA, except the carbonyl peak, is characterized by peaks located at 868 cm\({}^{-1}\) due to C-C stretching vibrations. The absorption bands at 868 and 754 cm\({}^{-1}\) are characteristic for amorphous and crystalline states, respectively. The bands at 1268, 1182, 1130, and 1042 cm\({}^{-1}\) are assigned to C-O stretching vibrations. Peaks at 1362, 1454, and 1382 cm\({}^{-1}\) are assigned to C-H deformation including symmetric and asymmetric bending vibrations [55; 56; 57]. The assessment of the major peak of HAp at 1022 cm\({}^{-1}\) in composite materials is not possible due to the overlapping with bands assigned to polymeric components (Figure 7c,d). The presence of HAp in the composites filaments or printed objects is confirmed by the absorption peaks at 559 and 602 cm\({}^{-1}\); however, they are hardly observed. In the case of BT containing composites, an obscure broad peak at the edge of the spectrum can be observed. Comparing the spectra of composite materials with the neat or PLA/PCL samples, there are no significant shifts in absorption bands of the functional groups. This indicates that there is no chemical interaction between PLA and PCL or between fillers and polymeric matrices. It should be noted also that no evidence of thermal decomposition of PLA and PCL was observed  
Figure 6: XRD graphs of filaments (**a**), 3D printed scaffolds (**b**), and pure HAp and BT powders.  
since as previously reported both polymers decompose at temperatures much higher than 210 \({}^{\circ}\)C, which is the temperature of the nozzle orifice in 3D printer.
## 2 Materials and Methods
Metal coatings were finally applied on top of the mesa (n-type T2 SL cap layer) and on the back of the n-type GaSb substrate, in Ti/Au (40/150 nm) and Pd/AuGeNi (5/200 nm), respectively. Next, the samples were wire-bonded onto a pin leadless chip carrier (LCC) and placed in the LN2-cooled JANIS cryostat ready to perform electrical and electro-optical measurements. Dark current density-voltage (J-V) measurements (under a 0-degree field of view) were performed using a KEITHLEY 6517A Electrometer to apply the bias voltage and measure the current density delivered by the device. Capacitance-voltage (C-V) characterizations were carried out using a Boonton 72B coupled with a programmable source meter (Keithley 2400) and a programmable multimeter (Keithley 2000). The measurements were performed at fixed AC voltage (15 mV) and frequency (1 MHz). Photoresponse measurements were performed in front-side illumination using a VERTEX 70v FTIR spectrometer. The quantum efficiency (QE) was measured using a black-body source cavity CI Systems SR-200 with temperatures ranging from 973 K to 1273 K and a narrow bandpass filter [3 \(\upmu\)m; 3.5 \(\upmu\)m]. The QE value was then extracted from the slope of the photocurrent versus the incident flux [34]. Absorption, PL, TRPL, QE and dark current density measurements were performed as functions of temperature, and the results are discussed in the next section.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection
### Candidate Waveforms  
The candidate waveforms can be divided into two main categories, the multi-carrier waveforms and the single-carrier waveforms. Multi-carrier waveforms include OFDM and its multiple access form (OFDMA) and new versions, filter bank multi-carrier (FBMC) and generalized frequency division multiplexing (GFDM). Single carrier waveforms include traditional single-carrier (SC) and SC-FDP and its multiple access form (SC-FDMA). Since OFDM and SC-FDP and their multiple access forms are used in 4G and also they are the strong candidates for 5G, in the following we will introduce the system model of massive MIMO systems using these waveforms in details. More information on other waveforms such as FBMC, GFDM, and traditional SC can be found in [28; 29; 30].
# Influence Maximization with Priority in Online Social Networks
###### Abstract  
The Influence Maximization (IM) problem, which finds a set of \(k\) nodes (called _seedset_) in a social network to initiate the influence spread so that the number of influenced nodes after propagation process is maximized, is an important problem in information propagation and social network analysis. However, previous studies ignored the constraint of priority that led to inefficient seed collections. In some real situations, companies or organizations often prioritize influencing potential users during their influence diffusion campaigns. With a new approach to these existing works, we propose a new problem called _Influence Maximization with Priority_ (IMP) which finds out a set seed of \(k\) nodes in a social network to be able to influence the largest number of nodes subject to the influence spread to a specific set of nodes \(U\) (called _priority set_) at least a given threshold \(T\) in this paper. We show that the problem is NP-hard under well-known IC model. To find the solution, we propose two efficient algorithms, called _Integrated Greedy_ (IG) and _Integrated Greedy Sampling_ (IGS) with provable theoretical guarantees. IG provides a \(\left(1-(1-\frac{1}{k})^{t}\right)\)-approximation solution with \(t\) is an outcome of algorithm and \(t\geq 1\). The worst-case approximation ratio is obtained when \(t=1\) and it is equal to \(1/k\). In addition, IGS is an efficient randomized approximation algorithm based on sampling method that provides a \(\left(1-(1-\frac{1}{k})^{t}-\epsilon\right)\)-approximation solution with probability at least \(1-\delta\) with \(\epsilon>0,\delta\in(0,1)\) as input parameters of the problem. We conduct extensive experiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms in IM problem. The results indicate that our algorithm provides better solutions interns of influence on the priority sets when approximately give twice to ten times higher than threshold T while running time, memory usage and the influence spread also give considerable results compared to the others.  
Keywords:social networks; influence maximization with priority; optimization; approximation algorithm +
Footnote †: journal:
## 4 Deriving mission requirements for ocean meso-scale applications
### Ocean mesoscale mapping capabilities  
[PERSON] and [PERSON] [13] have quantified the mesoscale mapping capability when combining various existing or future altimeter missions in terms of SLA and zonal (U) and meridional (V) velocity. Their main results are as follows:  
* There is a large improvement in sea level mapping when two satellites are included. For example, compared to T/P alone, the combination of T/P and ERS has a mean mapping error reduced by a factor of 4 and a standard deviation reduced by a factor of 5.
* The velocity field mapping is more demanding in terms of sampling. The U and V mean mapping errors are two to four times larger than the SLA mapping error. Only a combination of three satellites can provide a velocity field mapping error below 10% of the signal variance.  
[PERSON] et al. [14] have shown that sea level mapping errors were larger than the ones derived from [PERSON] and [PERSON] [13] formal error analysis by a factor of 1.5 to 2. This is mainly due to high frequency signals (periods below 20 days). In areas with large mesoscale variability, these signals represent 5 to 10% of the total sea level variance [15] and are associated with high wavenumbers. They account for 15 to 20 % of the total velocity variance. In shallow and high latitude regions, these high frequency signals account for up to 30-40% of the total sea level and velocity variance; there, part of these signals correspond to large scale barotropic motions.  
To better analyze the impact of the high frequency signals on the sea level and velocity mapping, [PERSON] and [PERSON] [16] systematically computed the mapping errors on the instantaneous fields and on 10-day averaged fields for various satellite configurations. Their main finding is the role of high frequency signals in the error budget. Since these signals cannot be resolved with any of the configurations they assumed (up to 4 Jason-1- like satellites in an interleaved orbit pattern), they strongly limit the mapping accuracy. As a result, the velocity mapping errors always remain larger than about 15-20% of the signal variance even for a four satellite configuration.
## 4 Mechanical Properties of Geopolymer Concrete
### Factors on Workability of GPC and PCC
GGBS/FA ratio has a remarkable role on the fresh and hardened GARC properties. More details are summarized regarding the discussed literature reviews in Table 2. \begin{table}
\begin{tabular}{c c c} \hline
**Author** & **Aim** & **Results** \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [30]} & Effect of superplasticizer on workability & The used superplasticizer was less effective in comparison with its behavior in OPC mixture. Based on workability results, PC had the effective role for Class C compared to Class F. Naphthalene had a similar effect for both fly ash types. \\  & & The mixture containing activated fly ash with \\  & Effect of different superplasticizers and activator combinations on workability & multi-compound activator (Na\({}_{2}\)SiO\({}_{3}\)/NaOH = 2.5) had higher \\  & & workability and strength compared to NaOH-activated fly ash. In activated fly ash by NaOH solution (8M), Naphthalene SP led to an increase of 136\% in workability with no drawback \\  & & effect on concrete strength. \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [31]} & The effect of ambient temperature & Workability was enhanced when alkaline solution was \\  & (20–23\({}^{\circ}\)C) on improvement of GPC & increased along with the reduction in compressive strength. \\  & workability and compressive strength. & SS/SH ratio of 2.5 indicated less workability comparable to \\  & & those with 1.5 and 2. \\ \hline \multirow{3}{*}{[PERSON] et al. [32]} & The effect of variation in cement & The slump values of cement were commonly enhanced while \\  & characteristics on SCC workability & adding FA up to certain extent and then remained constant. \\ \cline{1-1}  & and strength & The decrement in compressive strength was observed while \\ \cline{1-1}  & & increasing FA concentration regardless of \\ \cline{1-1} \end{tabular}
\end{table} 
Table 2: Effective factors on the improvement of GPC properties and workability.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Data Reduction  
It is clear that another way to save energy is to reduce the volume of data to be transmitted to the sink. Several methods can be used for data reduction: aggregation, compression and prediction.  
It is known that the data collected at the sensors contains spatial correlation, especially in some types of measurements when the sensors are close together. If these redundant data are sent to the BS, then it causes a waste of bandwidth and an increase in the energy consumed by the nodes.  
Given that the spatial correlation is stronger among data from nodes located close to each other, [PERSON] et al. [56] proposed the Low Energy Adaptive Clustering Hierarchy (LEACH) as organization of the network that consists of grouping neighboring nodes in clusters, one of them acting as Cluster Head (CH). The nodes within the cluster send data to the CH, eventually after local processing at the node. Then, correlated data received from these nodes can be processed for data reduction at the CH before being routed towards the BS. In Section 2.4.1, we address hierarchical routing and LEACH in more detail.