esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Compressive Strength  
The cubic compressive strength of all the mixtures were shown in Figure 4. It can be clearly observed that the incorporation of FA and BFS had an adverse effect on the compressive strength at 28 d, which was caused by the fact that the hydration speed of FA and BFS was slower than cement [32]. In harden concretes, it is the C-S-H providing the mechanical properties [17]. C-S-H is the reaction product of SiO\({}_{2}\) and Ca(OH)\({}_{2}\). The formation of C-S-H was still in progress and the strength was still developing at 28 d for concrete incorporated FA and/or BFS [32]. As the content of FA or BFS increased, the compressive strength decreased. Compared to BFS, concrete with the same content of FA showed a lower compressive strength, which was caused by the different chemical components in FA and BFS. In Table 1, compared to FA, BFS contained a larger content of calcium oxide (CaO), which provided a more suitable alkaline environment for the pozzolanic reaction, so FA showed more adverse effect on the 28 d strength than BFS. However, Figure 4a shows that A6 (10%FA-20%BFS) presented a slightly larger strength compared to A5 (30% BFS). The increase is about 1.3%. Compared to the standard deviation of A5, the increase can be negligible. On the other hand, some uncontrollable test errors may also lead to this result. At 60 d, there existed a slight increase in the compressive strength when FA and/or BFS were added. Linear regression (Figure 4b) was conducted to reveal the relationship between the cementitious components and the compressive strength, which is shown as Equation (3). In Equation (3), it can also be observed that FA and BFS play a negative effect at 28 d, while FA and BFS could slightly increase the compressive strength at 60 d. In contrast with BFS, the larger coefficients of FA indicate that FA plays a more significant role in affecting the compressive strength.  
\[\begin{array}{llll}28\;\mathrm{d}&S=8.35-3.78\cdot M_{FA}-0.32\cdot M_{BFS}& \quad(R^{2}=0.837)\\ 60\;\mathrm{d}&S=8.58+0.84\cdot M_{FA}+0.56\cdot M_{BFS}&\quad(R^{2}=0.888)\\ \end{array} \tag{3}\]  
where \(S\) is the compressive strength; \(M_{FA}\) is the fraction of FA; \(M_{BFS}\) is the fraction of BFS.  
For ordinary Portland cement concrete (OPC), strength at 28 d is a commonly used parameter to evaluate the concrete mechanical properties. Many standards and guidelines [17; 33; 34] are established based on this parameter. Research from ACI Committee 209 shows that strength of OPC at 28 d is about 85% of its final strength at moist-curing condition. For pervious concrete, because of its high porosity, the long-term strength development is different from OPC. For concrete incorporating mineral additives (FA, BFS, etc.), the strength development closely relates to the properties of the mineral additives, such as components, particle morphology, etc. The effect of FA and/or BFS on the long-term strength of pervious concrete is out of the scope of this study and will be explored in the future study.  
Figure 4: Compressive strength.
## 7 Conclusions  
In this study, we analyzed the combined multi-GNSS for CP time and frequency transfer using an RVCE approach, and the corresponding observation model was considered. The RVCE approach and data processing procedure were also detailed. In order to evaluate the performance of the RVCE, four time transfer links with external time and frequency references were computed with and without RVCE. Because that receiver clock estimation is easily affect by the inappropriate weights of multi-GNSSs and outliers from observations, which is closely related to the performance of precise time and frequency transfer. The RVCE approach used in this study is able to determine the observed weight for each GNSS and resist the influence of outliers and shows certain superiority to the raw solution. The smoothed residuals of this method are superior to those of the traditional multi-GNSS CP technique, and the corresponding RMS values were slightly improved for the four time links using RVCE, the range is 5.48% for ONS1-BRUX, 0.75% for NTS1-KAT1, 2.13% for NTS1-CEDU and 5.26% for BRUX-NTS1, respectively. With respect to the frequency stability of time links, the improvements over the short-term within 10,000 s are obvious. The average range is 13.48% for ONS1-BRUX link, 5.97% for NTS1-KAT1 link, 32.34% for NTS1-CEDU link, 7.76% for BRUX-NTS1 link. Therefore, combining multi-GNSS for CP time and frequency transfers using an RVCE approach can improve the validity of the solution within a certain precision range for different time links, particularly for observations that include outliers.  
[PERSON], [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data, and wrote the paper under the supervision of [PERSON]; [PERSON], [PERSON] and [PERSON] contributed to discussions and revisions.  
The work was supported by the National Key Research and Development Plan of China (grant no.: 2016 YFB0501804), the National Natural Science Foundation of China (grant no.: 41504006, 41674034) and the Chinese Academy of Sciences (CAS) programs of \"Pioneer Hundred Talents\" (grant no: Y620 YC1701) and \"Light of the West\" (grant no.: Y712 YR4701). Further support was granted through the National Time Service Centre (NTSC) programs \"Young Creative Talents\" (grant no.: Y824 SC1S06) and \"The Frontier Science Research Project\" (grant no.: QYZDB-SSW-DQC028).  
The authors would like thank the International GNSS Service (IGS) and the Centre for Orbit Determination in Europe (CODE) for providing the GNSS data. Thanks also go to the reviewers for their valuable comments which have improved this paper considerably.  
The authors declare no conflict of interest.
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 3 Model
### Trust Model  
#### 3.3.1 Binary Conversion  
User \(u\)'s preference for item \(i\) is represented using the binary variable \(\dot{r}_{u,i}\). The binary value is defined as:  
\[\dot{r}_{u,j}=\left\{\begin{array}{ll}1\,s\,>\beta\\ 0\,s\,\leq\beta\end{array}\right. \tag{4}\]  
where \(s\) is the preference, and \(\beta\) is the threshold value. In cases in which the user preference is defined as the number of page views/site visits/interactions, \(\beta\) is 0. Furthermore, the binary value \(\dot{r}_{u,i}\) is one if the user has interacted with the item; otherwise, its value is 0. By using this criterion, we can generate the new binary preference matrix even if the data are explicit. If the data include explicit ratings, a suitable value for \(\beta\) is the midpoint of the rating scale. One notable advantage of this strategy is that this representation can take various types of implicit data such as the number of views, site visits, and time spent on the site. The binary conversion steps defined in Equation (4) are not necessary if the data is already in the binary format (e.g., liked or not liked), as these may add unnecessary computation to the algorithm. Although Equation (4) supports the generation of a binary preference for different types of implicit data, it takes only a single input. However, in the real-world setup, it is common to use as many available implicit feedbacks as possible. The additional implicit data can be incorporated into Equation (4) using a weighting mechanism as follows:  
\[F=\,\sum\limits_{0}^{m}s_{m}w_{m} \tag{5}\]  
\[TF=\,\frac{F}{1+F} \tag{6}\]  
where \(F\) is the weighted sum of the total feedbacks, \(s\) is the implicit feedback of the \(m\)th type, \(w\) is the respective weight, and \(TF\) is the resulting combined feedback. We use Equation (6) to ensure that the total weighted feedback lies in the range between 0 and 1. This formulation accommodates both the explicit rating and multiple forms of implicit rating. To binarize the data, we then change Equation (4) as follows:  
\[\dot{r}_{u,i}=\,\left\{\begin{array}{ll}1\,TF>\beta\\ 0\,TF\leq\beta\end{array}\right. \tag{7}\]  
where \(\beta\) is the threshold and can be set to a preferred value, which is usually 0.5. In contrast to Equation (4), this formulation handles multiple inputs; therefore, the output binary value represents a richer form of preference.
## 2 Related Work
### LLM in MEC Networks  
LLMs are characterized by their extensive parameter sets and significant computational demands. MEC facilitates data processing and computational tasks for LLM-based applications by performing operations close to end-users, thereby enhancing the efficiency and adaptability of AI services for mobile environments. The authors of [18] introduced an edge inference framework tailored for LLMs in wireless networks, utilizing distributed edge computing to mitigate challenges related to privacy, latency, and resource constraints. This framework employs a novel optimization algorithm, OT-GAH, to address the NP-hard problem of batch scheduling and resource allocation for real-time LLM inference on edge devices. Similarly, [PERSON] et al. [19] proposed an active inference-based method for offloading LLM tasks and managing resources in cloud-edge systems, overcoming limitations of traditional DRL approaches. Their method enhances data efficiency, adaptability to varying task loads, and sensitivity to latency, with simulations demonstrating superior performance compared to conventional DRL techniques. The EdgeShard framework, presentedin [20], leveraged cloud-edge collaboration (CEC) to optimize LLM inference by partitioning models into shards distributed across edge and cloud infrastructures. A dynamic programming algorithm minimizes inference latency and maximizes throughput, yielding significant performance gains in experimental evaluations. [PERSON] et al. [21] introduced PerLLM, a personalized inference scheduling framework that optimizes resource allocation and scheduling in edge-cloud environments to meet diverse service demands while reducing energy consumption, with experiments confirming its efficacy across multiple model deployments. In [22], the Edge-LLM framework is proposed for deploying LLMs in resource-constrained edge settings, incorporating adaptive quantization, an FM cache mechanism, and a value-density-first scheduling algorithm to enhance computational efficiency and reduce task timeouts. Additionally, [PERSON] et al. [23] presented Voltage, a distributed inference system that improves transformer model performance on resource-limited edge devices by distributing workloads across multiple devices, achieving notable gains in speed and efficiency compared to single-device deployments. However, high and variable transmission latency between edge devices and the central cloud infrastructure remains a critical challenge, significantly affecting the performance of LLM inference and finetuning processes.
## 3 Results
### Analysis of the Spatiotemporal Influencing Factors of RSEI in the Shendong Mining Area
#### 3.3.2 The Changing Trends of \(RSEI\) in Areas of Land-Use Type Conversion  
The spatial patterns of Remote Sensing Ecological Index (\(RSEI\)) change varied significantly across different land-use conversion areas. As shown in Figure 6, the area exhibiting a highly significant increase in \(RSEI\) was most pronounced in regions where unused land was converted to forest land, with a proportion of 82.30%. Areas showing a significant increase in \(RSEI\) were also considerable in regions with conversions from unused land to cultivated land (28.75%), cultivated land to grassland (26.78%), and cultivated land to forest land (22.71%). Conversely, the areas where \(RSEI\) decreased were more prevalent in regions with conversions from forest land and grassland to built-up land, with proportions of 7.31% and 5.95%, respectively.
## 5 Evaluation
### Experimental Results
#### 5.3.2 Frequency Gain  
Figure 5 shows the frequency gain distributions concerning the fraction of malicious users \(b\), privacy budget \(\epsilon\), and number of target keys \(r\) for the three types of poisoning attacks (M2 GA, RMA, and RKVA) using synthetic data (Gaussian distribution).  
Notably, M2 GA (Figure 5a) yielded the highest gains across all poisoning schemes. This outcome is unsurprising given that M2 GA assumes the most potent control over the output by malicious users, thus posing the most significant threat to LDP schemes.  
The emPrivKV results consistently exhibited the lowest gains for all poisoning attack types and parameter settings of \(b\), \(\epsilon\), and \(r\). As the proportion of malicious users \(b\) escalated, the gains for PrivKV increased accordingly (as shown in Figure 5a). In contrast, the gain of emPrivKV remained stable at 0.5. For instance, at \(b=0.25\), emPrivKV achieved 70.25% of the gain of PrivKV, highlighting its superior resilience against the most severe poisoning attack (M2 GA).  
Figure 6 shows the frequency gains for the MovieLens dataset. The gain distributions were similar to those obtained using the Gaussian synthetic data, except for the effect of  
Figure 4: \(MSE_{m}\) of mean with regard to \(\epsilon\). (**a**) Synthetic data. (**b**) MovieLens. (**c**) Clothing.  
Figure 5: Frequency gain of poisoning attacks (synthetic data). (**a**) M2 GA. (**b**) RMA. (**c**) RKVA.  
the fraction of malicious users parameter \(b\) (see Figure 6a,c). The gain did not depend on \(b\) for M2 GA (Figure 6a) and was unstable for RKVA (Figure 6c).  
The MovieLens data yielded greater gains than the synthetic data (by a factor of 2-5), primarily because of the different distributions of keys and the presence of numerous low-frequency keys (e.g., minor movie titles with minimal audiences). These low-frequency keys are more susceptible to low-resource poisoning attacks. Moreover, with an equivalent number of malicious users, the manipulated keys were already saturated in the MovieLens dataset, resulting in greater gains compared to the synthetic data scenario.
## 5 Field Test
### Monitoring Scheme  
In order to understand the variation of the grouting pressure during construction and its effect on the forces on the steel corrugated plate, the experimental field monitoring program included the concrete grouting pressure and the hoop strain of the steel corrugated panel. The grouting pressure is monitored using a vibrating wire double-membrane earth pressure gauge, the hoop strain is monitored with a vibrating wire surface strain gauge, and the corresponding data are collected by a frequency reading instrument. The location relationship of monitoring points is shown in Figure 15.  
Figure 14: Partial display of the test site: (**a**) the construction process, (**b**) the concrete mixer and pump, (**c**) the grouting device, and (**d**) the overall support effect.  
Figure 15: Schematic layout of monitoring sites.  
The earth pressure gauges are embedded in the pit walls of the second and third rings of soil. After fixing, lead the wire close to the pit wall and the surface of the superstructure to the ground to connect the acquisition instrument. The earth pressure gauges are respectively arranged at different heights of the four adjacent segments selected clockwise on the second ring and marked as P1-P4 in turn. Select a segment in the third ring, and arrange an earth pressure gauge on its surface, marked as P5.  
Vibrating wire surface strain gauges are arranged on the surface of the steel corrugated panel to monitor its hoop strain. The strain gauge base is fixed with strong waterproof adhesive and arranged at the three crests of the second ring steel corrugated sheet, which are marked as S1-S3 from top to bottom.  
The earth pressure gauge (P) has a range of 200 kPa and a linearity error of no more than 0.667% F-S. The surface strain gauge (S) has a range of 1200 \(\mu\epsilon\) and a linearity error of no more than 0.792% F-S. The test is monitored in real time, with initial readings taken on the sensor after the steel corrugated plate has been assembled. The YT-DSY frequency reader is used to continuously collect data after the concrete pouring has started.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 3 Circuit Implementation
### Frequency Synthesizer and I/Q LO Signal Generation  
The local oscillator (LO) block was used to generate quadrature signals for the mixer. In the GNSS receiver designed, it was important to achieve both low phase noise and a flexible frequency selection. The latter requirement requires the use of a fractional frequency synthesizer. To satisfy the specification and keep the power consumption low, an all-digital phase locked loop (ADPLL) was used. In ADPLL, the high frequency signal is generated in a digitally controlled oscillator (DCO) and the signal frequency is determined by a series of bits. To obtain high accuracy of the output signal frequency, it was necessary to implement a full custom designed MOM (Metal-Oxide-Metal) capacitor with a very small capacitance value.  
In ADPLL design there is a tradeoff between phase noise and power consumption. The DCO is the most critical block as it is usually responsible for around 70% of the dissipated power in the frequency synthesizer circuit. To generate quadrature signals, the DCO was designed to work on two times higher frequency and its output is connected to a quadrature divider. To minimize the dissipated power, a frequency divider was stacked on the Colpitts DCO [23]. The Colpitts oscillator architecture was chosen because it has less phase noise and can work with a lower supply voltage in comparison to cross-couple oscillators. On the other hand, a Colpitts generator requires a higher _negative-gm_ to satisfy the startup conditions when compared with a cross-coupled generator. This leads to increased power consumption. For this reason, the typical Colpitts architecture was modified by adding a cross-coupled transistor pair to relax the startup conditions and reduce power consumption (see Figure 19).  
Figure 18: Intermediate frequency noise characteristics.  
Two ADPLLs for the L1/E1 and L5/E5 bands were implemented in the GNSS SoC. The phase noise of each frequency synthesizer equals \(-\)121 dBc/Hz at 1 MHz from the carrier frequency, while 4.5 mA is consumed from a 1.5 V supply. The post-layout DCO simulation tuning characteristics and phase noise are presented in Figures 20-23.  
Figure 19: Simplified schematic diagram of the designed digitally controlled oscillator (DCO).  
Figure 20: Tuning characteristics of the L1/E1 digitally controlled oscillator.
## 5 Simulations and Discussions
### Simulation of Direct Trust  
We mainly simulate the influence of the parameters in the direct trust evaluation model in this subsection. The processes of improvement and decrement of the direct trust evaluation function \(f_{dr}\) with three types of service clients (risk taking, cautious, and conservative) are simulated. Figure 2 shows the processes of improvement of the direct trust function \(f_{dr}\) denoted by Equation (7) and derivative function of \(f_{dr}\) with different values of parameter \(\alpha\), where the service client is a cautious type, such as \(T_{c}=M=0.5\). Because the value of \(T_{c}(t)\) is constant, which denotes environmental factors, \(C\) is as 1 in following simulations. Figure 3 shows the processes of a decreasing of direct trust function associated with Equations (5) and (6), where the service client is also a cautious type. Figure 4 compares the processes of improvement and decrement of the direct trust function of three types of service clients (risk taking, cautious, and conservative) denoted as \(T_{c}=H=0.8\), \(T_{c}=M=0.5\), and \(T_{c}=L=0.2\) when parameter \(\alpha\) is \(0.04\).  
Figure 3: The processes of decrement of direct trust function. (**a**) The processes of decrement of direct trust; (**b**) Derivative functions of direct trust.  
Figure 2: The processes of improvement of direct trust function. (**a**) The processes of improvement of direct trust; (**b**) Derivative functions of direct trust.  
From Figures 2 and 3, we see that parameter \(\alpha\) determines the rates of the processes of improvement and decrement of the direct trust function. If parameter \(\alpha\) is bigger, the process of improvement and decrement of the direct trust function is more rapid. The derivative functions of direct trust show that direct trust functions change rapidly in the beginning. Finally, values of derivative functions of the direct trust approach zero. From Figure 4, we see that values of the direct trust function of different types of clients (risk taking, cautious, and conservative) will approach their trust characteristics values, which are denoted as \(H,M\) and \(L\) in the processes of improvement of direct trust. On the other hand, values of the direct trust function of different types of clients will approach negatives of their trust characteristics values, which are denoted as \(-H,-M\) and \(-L\) in the processes of the decrement of direct trust. In the former case, risk-taking service clients develop high levels of trust. And in the latter case, risk-taking service clients develop high levels of distrust. Simulation results are consistent with real life.
###### Abstract  
Early screening is a key component of intensive intervention therapy and rehabilitation for children with autism spectrum disorder (ASD). Electroencephalogram (EEG) signals provide real-time, high-sensitivity monitoring of pathological activities in children with ASD. This study used a dataset of 52 samples with 19-channel sleep data and proposed a new approach to diagnosing ASD based on energy differences between the left and right hemispheres of the brain. The preprocessing stage included decimation, band-pass filtering to remove unwanted frequencies, artifact subspace reconstruction to eliminate artifacts, and amplitude normalization to preserve the relative relationships between the signal features, which were crucial for the subsequent analysis and classification. Five band-pass filters were applied to decomposing the EEG signals. For each decomposed band, the signals from each brain hemisphere were analyzed separately by calculating the peak sensor's envelope and obtaining the mean envelope for each hemisphere, resulting in two mean signals (right and left). Features were extracted using a sliding window approach applied to the mean signals of each hemisphere, with varying overlap ratios (12.5% to 87.5%, in 12.5% steps). The maximum, mean, and minimum energy values were used individually as features. Three types of SVM kernels--linear (L), the radial basis function (RBF), and quadratic--were employed for classification. The proposed method achieved the highest accuracy, sensitivity, and F1-score of 91.7%, 91.4%, and 91.6%, respectively, in the Theta band using SVM-L with the maximum energy features and the maximum overlap ratio.  
Keywords: autism spectrum disorder (ASD); EEG signal analysis; brain hemispheres; machine learning; sliding window technique; early diagnosis; neurodevelopmental disorders +
Footnote †: journal: Applied sciences