esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 2 Background
### Deep Learning for Time Series Analysis
\begin{array}{l}f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f})\quad\text{//Forget gate}\\ i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i})\quad\text{//Input gate}\\ \hat{c}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c})\quad\text{//Candidate values}\\ c_{t}=f_{t}\odot c_{t-1}+i_{i}\odot\hat{c}_{t}\quad\text{//Cell state update}\\ o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\quad\text{//Output gate}\\ h_{t}=o_{t}\odot\tanh(c_{t})\quad\text{//Hidden state}\end{array} \]  
where:  
* \(\sigma\) is the sigmoid activation function;
* \(W\)-terms are weight matrices;
* \(U\)-terms are the weights of the recurrent connections between gates and the previous hidden state;* \(x\)-terms are current inputs;
* \(b\)-terms are biases;
* \(\odot\) denotes element-wise multiplication (Hadamard product). This architecture is particularly suitable for GNSS multipath detection because:  
* It can capture long-term dependencies in signal patterns;
* The gating mechanism helps identify relevant temporal features;
* It can process variable-length sequences of multipath measurements;
* The cell state provides a memory mechanism for tracking signal evolution. Understanding these fundamentals of multipath effects and LSTM cells is essential for appreciating how Layer-wise Relevance Propagation can be adapted to interpret deep learning models in GNSS applications, which we discuss in detail in Section 3. Given these capabilities of deep learning for time series analysis, we next describe how we adapted and optimized the network architecture specifically for GNSS multipath detection.
# Comparison of Volatile Profiles of Meads and Related Uniflorl Honeys: Traceability Markers
## 4 Conclusions  
The comparison of HS-SPME and DHLLE volatile profiles allowed us to determine differences between composition of honey (containing more monoterenes and aliphatic hydrocarbons), boiled (containing more aliphatic alcohols and acids) and not boiled meads (containing more aliphatic esters) and to observe variety-specific marker compounds in the honey-derived products. The chemical profiles obtained using different preparation techniques provided partially distinct data; thus, the research demonstrates that HS-SPME and DHLLE may be used as complementary techniques. Both methods may be useful to determine the origin of honey used to prepare mead, as well as to verify the mode of its preparation involving heat treatment (boiled) or lack of (not boiled) in terms of quality control based on the selected marker compounds characteristic for botanical origin (e.g., 4-isopropylcyclohex-1,3-diene-1-carboxylic acid and 4-(1-hydroxy-2-propanyl)cyclohex-1,3-diene-1-carboxylic acid for linden; valeric acid, \(\gamma\)-valerolactone, \(p\)-hydroxybenzoic acid for buckwheat; 4-hydroxybenzeneacetic acid, homovanillic acid and _trans_-coniferyl alcohol for honeydew; and methyl syringae for canola) or processing (e.g., benzaldehyde for honey; octanoic acid for boiled mead; 2-phenylethanol, ethyl caprate, ethyl hexadecanoate, ethyl octadecanoate and 3-methylbutan-1-ol for not boiled mead).  
Conceptualization, P.M.K. and I.J.; Methodology, P.M.K., I.J. and S.C.; Validation, P.M.K. and I.J.; Investigation, P.M.K. and I.J.; Writing--Original Draft Preparation, P.M.K.; Writing--Review and Editing, I.J., S.C.; Funding Acquisition, P.M.K. and I.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported by the Polish National Science Center funding granted under the decision DEC-2014/15/N/NZ9/04058 and the institutional financing of research activities at Faculty of Chemistry and Technology, University of Split.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the Miody Polskie Sp. z o.o. for providing the samples of varietal honey.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** The samples are not available from the authors.
# Experimental Study on Mechanical Behavior of Shear Connectors of Square Concrete Filled Steel Tube
## 3 Test Results and Theoretical Analysis
### Load-Displacement Relationships and Parametrical Study
#### 3.2.6 Number of Steel Plate Layer and Interlayer Spacing  
The load-displacement curves of different number of layers and their interlayer spacing are shown in Figure 7f. In the figure, Specimen P8-8-0-120 is a single-layer steel plate, other specimens are double-layer steel plates. The interlayer spacings between the steel plates, \(s\), are 100, 150, and 200 mm. The heights of the steel plates, \(h\), are 35 mm for all the specimens. Hence, the \(s/h\) ratios for these three specimens are 2.86, 4.29 and 5.71. As shown in the Figure 7f, the bearing capacities of the double-layer steel plate specimens are higher than that of the single-layer steel plate specimen. Further, there is a correlation between the increase in the bearing capacity and the interlayer spacing (or \(s/h\) ratio). The ultimate bearing capacities of Specimens P8-8-100-120, P8-8-150-120, P8-8-200-120 are 1.13, 1.36, 1.98 times higher than that of the single-layer steel plate specimen. It can be seen that the bigger the interlayer spacing of steel plates, the higher the bearing capacity. When the interlayer spacing is 100 mm, the bearing capacity of the double-layer steel plate specimen is only slightly higher than that of the single-layer steel plate specimen. This result is similar to that of reference [21], which is for circular concrete-filled steel tubular specimens. This is because the two steel plates affect each other. Hence, the plates are unable to exert the maximum capacity of each layer. When the interlayer spacing is 200 mm, the bearing capacity of the double-layer steel plate specimen is 1.98 times higher than that of the single-layer steel plate specimen. This is an indication that the interaction between the two steel plates is minimal under this interlayer spacing (or \(s/h\) ratio). This result is consistent with that of the study [22], which is for a circular concrete filled steel tube. Therefore, in order to ensure each steel plate can exert its maximum effect, the design spacing to the height ratio of the steel plate should be smaller than 5.7.  
Figure 8 shows the relationship between the enhancement coefficient \(\beta\) and the \(s/h\) ratio of the steel plate. In the figure, the black dots are the test data; the solid line is the fitting curve. According to the test, the equation for the fitting curve is \(\beta=0.2969s/h+0.2145\), however, this equation is complicated. The simplified equation \(\beta=0.30s/h+0.22\) has been developed, which will be more applicable for the design. Equation (3) can be used to calculate the enhancement coefficient \(\beta\) of double-layer steel plates.  
\[\left\{\begin{array}{cc}\beta=1.0&s/h<2.6\\ \beta=0.3s/h+0.22&2.6\leq s/h\leq 5.9\\ \beta=2.0&s/h>5.9\end{array}\right. \tag{3}\]
# Experimental Research on Bond Behavior between GFR Bars and Stirrups-Confined Concrete
## 4 Parameterization Analysis
### Influence of Bar Diameter  
As has been indicated in prior literature [6; 9; 11; 12; 13], the larger the bar diameter, the smaller its bond strength. Two widely recognized viewpoints can explain this phenomenon. The former reasoning stating that bars with a larger diameter require longer embedment length in order to develop the same normal bond stress; larger embedment lengths reduce the average bond strength, as confirmed by the references [6; 13]. As the latter reasoning states, [PERSON]'s effect [9; 13] can lead to a slight reduction in bar diameter as a result of longitudinal stress. This bar reduction increases with bar diameter, which can lead to reduced frictional/mechanical locking stresses. This conclusion has been confirmed by experiments that were conducted previously; this tendency is not affected by the failure mode, concrete strength, the confined effect of stirrups, or the surface characteristics of GFRP bars (Figures 5-8). Additionally, with an increase of GFRP bar diameter, there was an increased likelihood of concrete splitting in specimens (Table 3).  
Figure 10: Typical failure modes of glass fibre-reinforced polymer (GFRP) bars exhibited by specimens with pullout failure: (**a**) R1, (**b**) R2, (**c**) R3, and (**d**) R4.
## 3 Results and Discussion
### Study on the Micro-Particle Distribution Characteristics
Figure 9 was even clearer, showing the influence of flow rate on the particle/droplet chord length distribution. Agglomeration among particles/droplets was easier under low flow rate during hydrate slurry dissociation (in Figure 9a, CLD curve moved to the right side), while high flow rate could restrain agglomeration effectively (in Figure 9b, CLD curve moved to the left side). Yet decreasing ranges of the mean and square weighting chord lengths became smaller in the higher water-cut system. That was caused by higher water-cut facilitating the collision and agglomeration among particles/droplets, leading to easier hydrate plugs in pipelines. Also, in Figure 11, at 5 MPa system pressure and 1.0 m/s flow rate conditions, a chord length distribution curve of 10% water-cut moved to the left, while that of 25% water-cut moved to the right side during the decomposition. It indicated that particle/droplet agglomeration occurred in a 25% water-cut system rather than a 10% water-cut system. In sum, the particle agglomerating trend and the plug probability went up with the water-cut in hydrate slurry decomposition. (3). Influence of AA Dosage  
Various concentrations of AA were used in hydrate slurry decomposition experiments to investigate the impact of AA dosage on particle/droplet pattern and distributing characteristics. The experimental data were presented in Figures 12 and 13. It can be seen from Figure 12 that the dosage of AA had remarkable influence on the average chord length and the square weighting chord length of hydrate particles during the hydrate slurry decomposition. Hydrate particles easily start to polymerize without the adding of AA in the initial stage of hydrate decomposition process. It was evident that AA had greatly prohibited these micro-particle/droplet  
Figure 11: Influence of water-cut on chord length distribution during hydrate dissociation (**a**,**b**). (**a**) Influence of water-cut on chord length distribution (10% water-cut); (**b**) Influence of water-cut on chord length distribution (25% water-cut). Figure 10: Influence of water-cut on chord length during the hydrate dissociation (**a**,**b**). (**a**) Influence of water-cut on mean chord length (5 MPa, 1.0 m/s); (**b**) influence of water-cut on square-weighted chord length (5 MPa, 1.0 m/s). agglomerations during decomposition, resulting in relatively stable mean and square weighting chord length curves in Figure 12. Meanwhile, comparison in Figure 13 demonstrated that the chord length distribution curve went rightwards obviously without AA because of agglomeration, while curves changed little with AA before and after decomposition. In general, AA was so efficient in anti-agglomeration that could be widely applied to assure the hydrate slurry transportation.
## 3 Results and Discussion
### Characterization of Mexican Plum Powder (Pulp and Peel)  
Table 1 shows the results obtained for the characterization of the dried Mexican plum powder (pulp and peel) (\"Cuernavaquena\" ecotype) based on dry weight (d\({}_{\text{w}}\)) and fresh weight (f\({}_{\text{w}}\)) in order to compare with other studies which based their results on fresh weight.  
The value of the PC was similar to the content for Mexican plum reported by [PERSON] et al. [33] (2.04 mg GAE/g\({}_{\text{fw}}\)) and lower than the content for Mexican plum ecotype \"Cuernavaquena\" reported by [PERSON] et al. [34] (2.39 mg GAE/g\({}_{\text{fw}}\)). The value of TA, shown in Table 1, was lower than the results obtained in _S. purpurea_ by [PERSON] et al. [35] (18 \(\upmu\)g cya-3-glu/g\({}_{\text{fw}}\)) and higher than that reported by [PERSON] et al. [33] (3 \(\upmu\)g cya-3-glu/g\({}_{\text{fw}}\)). Regarding the AA, DHA and total vitamin C content, most of the vitamin C content reported by other authors is based only on the ascorbic acid content; therefore, only the values for AA shown in Table 1 were compared with other reports. AA obtained in the present work was lower than the values reported by [PERSON] et al. [36] (270-360 \(\upmu\)g AA/g\({}_{\text{fw}}\)) and [PERSON] et al. [37] (296 \(\upmu\)g AA/g\({}_{\text{fw}}\)) in _S. mombin_ and _S. purpurea_, respectively. Regarding AC by ABTS and DPPH content, shown in Table 1, these were lower than the values for ecotype \"Cuernavaquena\" reported by [PERSON] et al. [34] (20.59 \(\upmu\)mol TE/g\({}_{\text{fw}}\) for ABTS and 14.72 \(\upmu\)mol TE/g\({}_{\text{fw}}\) for DPPH). These differences in the content of bioactive compounds and antioxidant capacity may be due to the moisture content and the sample pre-treatment used in this work (freezing and dried at 40 \({}^{\circ}\)C for 24 h), which may  
Figure 2: Phenolic compounds accumulated percentage obtained in each extraction.  
degrade the bioactive compounds, in contrast to the previous authors who analyzed the fresh fruit. In addition, these differences can be due to the fruit characteristics (different ripeness state, harvest season, ecotype, among others) [4,34] and extraction conditions (temperature, time, solid-solvent ratio and type of solvent and concentration) [38].
## 5 Tracking the Read Threshold
For instance, in Figure 5 the number of 1-to-0 errors is about 4-times the number of 0-to-1 errors. In this case, we can choose the value if \(A\) such that \(A=1\) indicates four times more 1-to-0 errors then 0-to-1 errors. To average the ratio over different life-cycle states, we choose the ratio that corresponds to the value \(\Delta V_{th}=0\) for the mutual information \(I\left(A;V_{opt}\right)\). The lower part of Figure 13 shows the adjusted readout voltage with this approach. Now the adaptation is closer to the optimal voltage \(V_{opt}\). Figures 12 and 13 show results for one block in a single life-cycle state. Figure 14 demonstrates the tracking accuracy averaged over different life-cycle states. In this box plot, the median of the error probability at \(r_{7}\) is represented as the central marker and a box represents the interquartile range, i.e., the interval for 50% of all measurements when ordered from lowest-to-highest error probability. The whiskers represent the most extreme data points without outliers. The box plots consider the simple \(\pm 1\) voltage adaptation, the enhanced adaptation, and the worst-case static readout voltages. For every block in the data set, the worst-case readout voltage was selected within the range of optimal readout voltages. Hence, these values correspond to an optimal readout voltage of one page within a block, which was selected as static readout voltage for the entire block. The results in Figure 14 show that a static readout voltage can significantly increase the error probability compared with the optimal voltage. Figure 13: Threshold voltage adaptation of a TLC-Block on reference \(r_{7}\) (55 h bake, 3000 P/E). Again, we consider the LDPC code used in Section 4 for the discussion of the ECC performance. We discuss results for the FER using hard-input decoding. For a BER of 0.006, the hard-input decoding results in a FER of about 0.01. Therefore, we assume that a soft-read operation is required for all BER values greater or equal 0.006. For the worst-case static readout voltage, a significant amount of pages exceed the BER of 0.006. When using the tracking approach the maximum BER is about 0.004, which results in a FER of less than \(10^{-9}\) with hard-input decoding. Consequently, the tracking improves the latency and the power consumption by reducing the profitability of soft-read and soft-input decoding procedures. The \(\pm 1\) adaptation and the enhanced adaptation obtain a similar performance which is close to the optimal read voltage. Consequently, the bias compensation is not critical with respect to the achievable error rate. However, the bias compensation may be required for soft-input decoding [15; 22]. Many soft-input decoding algorithms assume quantization intervals that maximize the mutual information between the input and output of flash memory, which requires an accurate voltage adaptation.
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 3 Methodology
### Graph Construction
#### 3.4.2 Weights Assignment for Linking Edges  
Before we solve the graph with KSP algorithm, we need to assign the weight value for each linking edge according to our previous theoretical analysis, which is the most key point of the graph construction. As shown in Figure 2, given a depth of 1, a player occupying the location \(i\) at time \(t\) can arrive at one of the nine neighboring locations at time \(t+1\). In the original KSP tracking approach, they are assigned the same weights, only depending on the player occupancy probability of node \(m_{i}^{t}\) regardless of any appearance features. In our proposed method stated above and according to Formula (11), the weights of edges would depend on not only the player occupied probability, but also the similarity between the node pairs to be connected. The more similar the appearance between them, the smaller the weights imposed. According to this, \(m_{i}^{t}\) labeled as blue in Figure 2 will definitely move to the blue node instead of yellow node. How to measure the similarity between two nodes is described in Section 3.5.  
Therefore, once we built a directed acyclic and node-disjoint graph, we could then implement [PERSON]'s algorithm to find \(k\) paths between the \(v_{\text{source}}\) and \(v_{\text{sink}}\) such that the total cost of the paths is minimal. This issue is well studied in the network optimization literature [46].
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.7 Cryptographic Keys  
To provide privacy and security in a BC, public-key cryptography is required. However, the limited resources IoT devices fail to meet the computational requirements of contemporary safe cryptographic methods. When used on IoT devices, asymmetric cryptography based on RSA is particularly sluggish and energy-intensive. As a result, not only should the computing load and memory be considered when selecting a cryptographic system, but also the amount of energy spent [68]. Most IoT devices cannot use the current RSA key sizes. Since the failure of 768 bit and 1024 bit RSA implementations in 2010, a 2048-bit key is a minimum size considered safe. Although it is conceivable, using a 2048 bit certificate with an ephemeral key exchange technique imposes significant overhead and computation requirements that are impossible to meet on the restricted hardware [4; 63].  
Elliptic Curve Cryptography (ECC) (where cryptographic keys can be generated more efficiently, smaller, and quicker using Elliptic Curve Theory-based PKI), on the other hand, is a significantly lighter substitute to RSA. ECC has previously been demonstrated to beat the power consumption and speed of RSA when used on resource-limited systems [4]. Hash functions are particularly important in BC since they are used to sign transactions. As a result, hash functions must be safe in IoT applications (i.e., they must not produce collisions), be quick, and spend as little energy as feasible [4]. SHA-256d, SHA-256, and Scrypt (an algorithm that makes big attacks on the hardware more expensive by requiring substantial memory) are the most common BC hash algorithms (used by Litecoin, Gridcoin, and Dogecoin) [88]. SHA-256's performance has been tested on a variety of IoT devices, including wearables. Researchers that looked at the footprint and energy need of SHA-256 in ASICs concluded that using Advanced Encryption Standard (AES) is more efficient. Other researchers recommended utilizing ciphers such as Simon because of the power restrictions, but more research and practical evaluations of real-world BIoT applications are still required [4; 88].
###### Abstract  
The problem of fault propagation which exists in the deeply integrated GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) system makes it difficult to identify faults. Once a fault occurs, system performance will be degraded due to the inability to identify and isolate the fault accurately. After analyzing the causes of fault propagation and the difficulty of fault identification, maintaining correct navigation solution is found to be the key to prevent fault propagation from occurring. In order to solve the problem, a novel robust algorithm based on convolutional neural network (CNN) is proposed. The optimal expansion factor of the robust algorithm is obtained adaptively by utilizing CNN, thus the adverse effect of fault on navigation solution can be reduced as much as possible. At last, the fault identification ability is verified by two types of experiments: artificial fault injection and outdoor occlusion. Experiment results show that the proposed robust algorithm which can successfully suppress the fault propagation is an effective solution. The accuracy of fault identification is increased by more than 20% compared with that before improvement, and the robustness of deep GNSS/INS integration is also improved.  
deep GNSS/INS integration; fault identification; convolutional neural network; vector tracking loop +
Footnote †: journal: Sensors
## 3 APD Breadboard Design and Implementation  
In this section, we report the development of the APD breadboard. The objective was to develop a sensor breadboard setup that allowed the testing of a highly sensitive sensor for LiDAR applications.  
In a time-of-flight (TOF) LiDAR, a light pulse is emitted and a fraction of it is detected, then a timing trigger is given to the electronics to start the time counting. The light travels and is backscattered on a target surface. For long distances, a very small fraction of light is received by the LiDAR receiver aperture, and time counting is stopped. In this way, it is possible to compute a distance. The capacity to detect lower-power light pulses enables LiDAR operation at longer distances [6; 13; 14].  
The sensor used in the tests, Hamamatsu G14858-0020 AB, is a high-speed and high-gain Indium Gallium Arsenide (InGaAs) semiconductor APD with an active area of 200 \(\upmu\)m in diameter, working in the near infrared region. To detect the optical signals, the avalanche sensor must be operated in a reverse bias condition [25; 26]. Usually, InGaAs sensors have usable gains ranging from 10 to 40. According to the manufacturer's datasheet, the considered APD has gains up to 40, and its performance is characterized by gain = 10. In this work, we have tested the APD to very high gains of around \(\sim\)261. This was achieved by implementing quenching circuitry and decreasing the operation temperature of the APD.  
To characterize the performance of the sensor in the avalanche zone and near Geiger mode regions, the APD was tested at a wide temperature range and excited with different optical powers. This was carried out with the support of a small, custom-made vacuum chamber and a cascade-performance thermoelectric cooler (TEC) system.