esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods and Algorithms
### Slice Decomposition  
After ensuring that the simplified representation contains the original slice completely, it is divided into convex sub-polygons. Convex polygons offer the advantage of allowing the continuous coverage of the polygon area with all infill strategies, especially raster- and zigzag-type infills, independently of their orientation. There are various approaches available for dividing a planar polygon into segments, where each leads to different results [31]. For this work, the approach presented by [PERSON] et al. [21] was chosen to achieve an optimal decomposition of the slice into a minimum number of convex partial polygons. The method can basically be divided into five steps which are executed in a loop until a final set of convex polygons covering the approximated slice has been generated:  
1. Identify external (outer contour) and internal (holes) polygons.
2. Sort the vertices of external polygons clockwise and the vertices of internal polygons counter-clockwise.
3. Calculate the angles between the incoming and outgoing edges of each polygon vertex \(P_{i}\).
4. Identify notches. A notch is defined as a vertex whose associated angle from the previous step is greater than \(0^{\circ}\). A polygon with at least one notch is not convex.
5. Eliminate notches.  
While the solution of Steps 1-4 is trivial, the main part of the work takes place in Step 5 of the loop. First, the identified notches are sorted by their calculated angles. The vertices \(P_{2}\), \(P_{10}\), \(P_{6}\) and \(P_{5}\) represent the identified and ordered notches for the first decomposition loop of the polygon shown in Figure 9. The notch with the largest angle is then selected as the so-called reference notch (here, \(P_{2}\)). Next, the two polygon edges bordering the reference notch are extended until they create the intersection points \(A_{i}\) and \(B_{i}\) (\(A_{2}\) and \(B_{2}\)) with another edge. Lastly, a distinction must be made among three different cases for creating a dividing line:  
* Notch-line (\(NL\)) decomposition, see \(\overline{P_{10}C_{10}}\);
* Notch-vertex (\(NV\)) decomposition, see \(\overline{P_{6}P_{12}}\); and
* Notch-notch (\(NN\)) decomposition, see \(\overline{P_{2}P_{5}}\).  
The decomposition algorithm results in connected convex sub-polygons dividing the approximated slice (cf. Figure 9). These convex polygons are then to be filled with standard infill strategies, whereby the infill strategy orientations are free to choose. The strategy itself and its orientation have an influence on the executability of the printing path by the robot. This should be exploited accordingly [6; 32].  
Figure 8: The result of the multidirectional slicing algorithm.
# Experimental Study on the Seismic Performance of Recycled Concrete Hollow Block Masonry Walls
###### Abstract  
This paper aims to manufacture recycled concrete hollow block (RCHB) which can be used for the masonry structure with seismic requirements. Five RCHB masonry walls were tested under cyclic loading to evaluate the effect of the axial compression stress, aspect ratio, and the materials of structural columns on the seismic performance. Based on the test results, the failure pattern, hysteresis curves, lateral drift, ductility, stiffness degradation, and the energy dissipation of the specimens were analyzed in detail. The results showed that with the increase of aspect ratios, the ductility of RCHB masonry walls increased, but the horizontal bearing capacity and energy dissipation of RCHB masonry walls decreased. With the increase of compressive stress, the bearing capacity and energy dissipation performance of RCHB masonry walls were improved, and the stiffness degraded slowly. The results also demonstrated that the RCHB masonry walls with structural columns, depending on whether the structural columns were prepared by ordinary concrete or recycled concrete, could increase the bearing capacity, ductility, and energy dissipation of specimens. The research confirmed that RCHB masonry walls could meet the seismic requirements through thoughtful design. Therefore, this study provided a new cleaner production for the utilization of construction waste resources.  
construction and demolition wastes; resource utilization; recycled concrete hollow block; masonry walls; seismic performance +
Footnote †: journal: Applied Sciences
# Toward Blockchain-Enabled Supply Chain Anti-Counterfeiting and Traceability
## 6 Discussion of Research Results
### Opportunities of Decentralizing Supply Chain Anti-Counterfeiting and Traceability
#### 6.2.1 Improved Data Integrity of Supply Chain  
With the advent of blockchain technology and other technologies such as distributed file storage, a multi-layered data storage and validation mechanism, involved with on-chain and off-chain data operations, on product records could be implemented in decentralized solutions of supply chain anti-counterfeiting and traceability.  
The on-chain and off-chain storage and validation could then be in place to ensure data integrity and prevent the decentralized solutions from any attempted attacks. The attacks could be cloning attacks on NFC tags, modification attacks in case product identifiers and signatures stored in NFC tags are inconsistent with its counterparts stored in the backend databases or on-chain storage with deployed smart contracts, or reapplication attacks in case both read count and write count are inconsistent to its counterparts stored off-chain and on-chain respectively.  
With the decentralization enabled by the blockchain network, data integrity of processed product records is further improved which could further coupled with a concept of digital-asset tokenization representing every single product processed on the decentralized solutions. Any state change on specific product record could only be initiated with  
Figure 8: Comparison of decentralized and centralized architecture.  
invoking methods on an open smart contract deployed to the network, with its transaction now needing to be validated with consensus reached, with other nodes held and run by different participating entities of supply chain industry, on the network. The immutability of transaction states related to any state transition of specific product record operations would mean that any state change processed on the network could be referred and queried based on individual transaction hashes and block numbers, which could further be confirmed on blockchain explorers connected to blockchain nodes running on the specific blockchain network.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Computed Tomography (CT)
#### 4.2.2 Challenges and Future Directions  
One of the challenges in applying DL techniques to CT analysis is the limited availability of annotated datasets. Moreover, CT images contain high levels of noise, which can affect the performance of DL models. Therefore, developing DL models that are robust to noise is an important future direction. Moreover, developing transferable models that can handle variations in imaging protocols and patient populations is also an important future direction.
# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 2 Related Work
Different ranges of mobility speed were used--4.3 km/h, 4.3-11.4 km/h, and 11.4-32 km/h--to find out the influence of mobility, and how ADR mechanisms would help to overcome the possible effects of movement. The study concluded that with low-mobility scenarios, the ADR strategy of increasing the SF to increase the coverage area is still effective and yields good results; however, its benefits decrease as the mobility speed increases. The authors of [17] proposed a Gaussian-filter-based ADR and an exponential-moving-average-based ADR to improve the performance of a LoRaWAN network. Similar to [15], the authors used simulation tools to prove that the movement of end devices results in invalid configuration commands for setting the spreading factor and the transmission power for an end device, which are the parameters for the ADR mechanism; therefore, sent traffic may get lost. The NS-3 simulator was used to discover the impact of mobility on the end device convergence period. To model end-device mobility, the authors used a random walk mobility model with a speed varying between 0.5 and 1.5 m/s in a network consisting of one gateway. The usability of LoRaWAN in the field of vehicular networks was investigated by [18]. Their experimental study involved fixing a transmitter on a motorcycle moving at various speeds, from 20 to 90 km/h. The experiments were carried out on an elliptical track with dimensions of 135 m \(\times\) 70 m. The gateway was located in the center of the ellipse. The study reported robustness of LoRaWAN when data were transmitted in motion, and observed limited signal degradation at high speeds.
## 3 Results  
### DALEC-Crop Analysis and Calibration  
Based on an initial per-treatment calibration, the posterior distributions of the retrieved DALEC-Crop parameters were broadly similar across the range of N applications (see Supplementary Material, Figure S1). A notable exception to this parameter consistency, however, was shown for the product of the LNA and PNUE parameters used in the leaf N- and temperature-limited rate of photosynthesis equation (3). The LNA-PNUE product shows a distinct distribution for each of the treatments, consistent with the variation in applied N fertilizer.  
The DALEC-Crop cross-treatment and multi-year calibrations for each N treatment had a high agreement with all the LAI observations--the mean R\({}^{2}\) was 0.78 and mean NRMSE was 26% (Figure 4 and Table 3). Most of the observations were within the model uncertainty. The later LAI observations in the first season (i.e., 26 June and 10 July 2017) were generally not as well captured when compared to the earlier measurements, this was particularly the case for the higher treatments (i.e., >100 kg N ha\({}^{-1}\)). For all treatments, the performance of the calibration for the simulation of LAI in the first season (mean R\({}^{2}\) = 0.73 and NRMSE = 27%) was slightly lower than that of the second season (mean R\({}^{2}\) = 0.83 and NRMSE = 25%).  
When evaluating DALEC-Crop under the constraints of LAI observations at the validation plots, the multi-year model calibration had a high accuracy when estimating yields at these plots for both seasons--the mean NRMSE was 11% (Figure 5). The model calibration for the first season was slightly more accurate than that of the second season (mean NRMSE = 11%).  
Figure 4: Time-series validation plots comparing DALEC-Crop LAI estimates for the cross-treatment and multi-year parameter calibrations compared to observations (black error bars) for the 2016–2017 (top row) and 2017–2018 (bottom row) winter wheat growing seasons. Plots shown for the 0 kg N ha\({}^{-1}\) (T-0; left column), 100 kg N ha\({}^{-1}\) (T-100; middle column) and 200 kg N ha\({}^{-1}\) (T-200; right column) fertilizer treatments. Standard deviation of the LAI values is shown for the observations and modelled estimates (green shading).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Multi-Year Calibration**} \\ \cline{2-5}  & \multicolumn{2}{c}{**2016–2017**} & \multicolumn{2}{c}{**2017–2018**} \\ \hline \multicolumn{1}{c}{**N Application**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**NRMSE (\%)**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**NRMSE (\%)**} \\ \multicolumn{1}{c}{**(kg N ha\({}^{-1}\))**} & & & \\ \hline
0 & 0.86 & 38 & 0.81 & 27 \\
50 & 0.8 & 23 & 0.99 & 24 \\
100 & 0.74 & 23 & 0.72 & 20 \\
150 & 0.65 & 29 & 0.96 & 23 \\
200 & 0.62 & 23 & 0.69 & 29 \\ \hline Mean & 0.73 & 27 & 0.83 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary validation statistics comparing DALEC-Crop LAI estimates for each nitrogen (N) fertilizer treatment derived from the multi-year single model calibration for the 2016–2017 and 2017–2018 growing seasons.
## 3 Base Model of Study and Hypotheses Under Analysis  
### Characteristics of the Room Model  
With the aim to quantify the divergence of DA and DAp under different scenarios and subsequently to the validation process, a simulation procedure is carried out. A virtual venue measuring 6.00 m wide, 8.00 m in length, and 3.0 m high, corresponding with the typical dimensions for a Spanish classroom, was defined according to regional standards [39] and to a characterization of existing educational buildings [40] to analyze both dynamic metrics. A window of variable size (window-to-wall ratio (WWR) of 30%, 45%, and 60%) is located in one of the facades. The window glazing has an optical transmittance of 0.75. The inner surfaces of the studied model act as diffuse reflectors, following the Lambertian distribution, where the luminous intensity of the reflected light is proportional to the cosine of the angle between the observer's line of sight and the surface normal. Two average reflectance sets are addressed in the calculation process, considering bright surfaces with high reflectance values and dark surfaces corresponding to low reflectance values. The parameters related with the calculation model are described in Figure 3.  
The dynamic metrics are quantified on the central axis of the room. As seen in Figure 4, the studied points are located on this axis of the grid (Y = 4.0 m) with a spacing of 0.40 m from each other and at 0.60 m above the floor, based on the usual position of the work plane in a classroom.  
Figure 3: Characteristics of the room model.  
The window facing is also decisive in the dynamic metrics quantification. Two orientations were considered for carrying out the simulations for quantifying the divergence between DA and DAp. According to the northern locations described above, a North orientation provides the worst case scenario for using the natural light, while windows facing South usually allow the maximum use of daylight [44].  
Table 2 summarizes the calculation parameters, defining the name model in accordance with the defined variables.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Window-to-Wall**} & **Reflectance** & **Reflectance** & **Reflectance** & **Illuminance** & \multirow{2}{*}{**Location**} & **Window** \\  & **Ratio (\%)** & **(\%)** & **Ceiling** & **(\%)** & **Flow** & **(\%)** & **Threshold (x)** & **Orientation** \\ \hline  
\_500 LN & 30 &
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 3 Evaluation Results and Discussion
### Evaluation Results  
#### 3.3.1 Parameter Setting  
Parameters of the proposed method are set as follows. The reference frequency in Equation (11) is set as \(f_{ref}=261.6256\) Hz (C4), which is commonly adopted in related works [14]. The parameter \(l\) is set as recommended in [17], i.e., \(l=4/3\) semitones. The selection of \(\zeta\) is elaborated in [12], and the same value, i.e., \(\zeta=0.15\), is set herein. [PERSON] pointed out that western music tends to have notes that are rarely shorter than 150 milliseconds in duration [23], so the minimum note length is 150 ms. The parameter \(\gamma\) is set according some primary experiments on Bach10. Strictly speaking, the performance on different datasets varies slightly with this parameter. However, it is still set as \(\gamma=0.4\) for all the following evaluations. As mentioned before, the main energy of the human voice concentrates at the first few harmonics, and the spectral slope of music instruments decays 3 dB  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Collection** & \multicolumn{1}{c}{**Description**} \\ \hline ISMIR2004 & There are 20 excerpts of polyphonic recordings with five types of melody sources, the synthesized voice (syn), saxophone (jazz), MIDI instruments (MIDI), classical opera (opera), and real-world pop (pop). These recordings last roughly 20 s. The sampling rate is 44.1 kHz, and ground truths are given at 5.8 ms intervals (with hop size 256 samples). \\ \hline MIR1 K & There are 1000 excerpts of Chinese karaoke excerpts recorded at 16 kHz sampling rate with 16-bit resolution. These excerpts were selected from 5000 Chinese pop songs sung by 19 male and female amateurs. These excerpts last from 4 s to 13 s, and total length of the collection is 133 min. The melodic pitches are also located at 10 ms intervals. \\ \hline Bach10 & There are 10 pieces of recordings downloaded from Internet. There are four parts (soprano, alto, tenor, and bass) of each piece recorded separately with a sampling rate 44.1 kHz. The time interval of ground truth is also 10 ms. The soprano is considered as the melody, and mixed with other parts with signal-to-accompaniment ratios of \(-5\) dB, 0 dB, 5 dB, and 10 dB. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation collections.  
to 12 dB per octave [16], so \(N_{h_{1}}\) and \(N_{h_{2}}\) are set as \(N_{h_{1}}=3\), \(N_{h_{2}}=10\), respectively. Some primary experimental results show that when \(N_{h_{1}}\) varies from 3 to 6, or \(N_{h_{1}}\) from 6 to 12, the performance of the proposed method is not severely affected.
###### Abstract  
The study aims to assess the impact of modified atmosphere packaging (MAP) on the oxidation status of five types of analogue meat products, crucial for extending shelf life and maintaining quality, and seeks to optimize packaging strategies to mitigate oxidation and provide possible solutions for enhancing the overall quality of analogue meat products. Gas ratios in MAP, as well as thiobarbituric acid reactive substances (TBARS), free fatty acids (FFA), total polyphenol content, and antioxidant capacity were assessed through four different assays (2,2-diphenyl-1-picryl-hydrazyl: DPPH, Azino-Bis (3-Ethylbenzothiazazoline-6-Sulfonic Acid): ABTS, Ferric Reducing Antioxidant Power: FRAP, Cupric reducing antioxidant capacity: CUPRAC) for analogue meat products (steak, noodles, filet, burger, and mince) on the last day of their shelf life. O\({}_{2}\) ratios in the MAP for all the products did not differ significantly (\(p>0.05\)), but CO\({}_{2}\) concentrations significantly differed (\(p>0.05\)) in the MAP of the evaluated products. The minced product exhibited higher oxidative stability with the lowest TBARS (3.20 mg MDA\(\cdot\)kg\({}^{-1}\)) and FFA (1.12% total fat as oleic acid), along with a high antioxidant capacity (DPPH: 32.26, ABTS: 4.49% inhibition, CUPRAC: 11.48 Trolox mmol/kg). The filet product was more susceptible to the oxidation process, as evidenced by the significantly (\(p>0.05\)) higher TBARS value (9.71 mg MDA\(\cdot\)kg\({}^{-1}\)), lower polyphenol content (1.01 mg gallic acid/g), and antioxidant capacity (FRAP: 4.75 mmol/g, CPRAC: 5.57 Trolox mmol/kg).  
meat substitute; MAP; TBARS; DPPH
## 2 Materials and Methods
### Measurement Setup of Absorption Rate  
Influence of numerical aperture on formation phenomena of molten area was investigated through measuring the absorption rate \(A_{\text{ab}}\), which was calculated using Equation (1).  
\[A_{\text{ab}}=\frac{E_{\text{ab}}}{E_{\text{in}}}=\frac{E_{\text{in}}(1-R)^{2} -E_{\text{tr}}}{E_{\text{in}}(1-R)} \tag{1}\]  
where \(E_{\text{ab}}\) is the absorbed laser energy, \(E_{\text{in}}\) is the input laser energy, \(R\) is the reflectivity at the surface of glass, and \(E_{\text{tr}}\) is the transmitting laser power [19]. Measurement of the absorption rate \(A_{\text{ab}}\) would be helpful to understand the formation of molten area deeply and hence, discussion of the size of molten area. As shown in Figure 5a, the reflectivity \(R\), input laser energy \(E_{\text{in}}\) and transmitting laser power \(E_{\text{tr}}\) are the necessary factors to calculate the absorption rate \(A_{\text{ab}}\) using Equation (1). Since the reflectivity \(R\) and the input laser energy \(E_{\text{in}}\) are known, the transmitting laser power \(E_{\text{tr}}\) should be experimentally measured to obtain the absorption rate \(A_{\text{ab}}\). As can be seen from Figure 5b, the laser beam was focused inside the glass specimen fixed on a jig by the objective lenses of various  
\begin{table}
\begin{tabular}{c c} \hline \hline Wavelength \(\lambda\) (nm) & 1064 \\ Pulse repetition rate \(R_{\text{p}}\) (MHz) & 1.0 \\ Pulse duration \(\tau_{\text{p}}\) (ps) & 12.5 \\ Average power stability & \textless{}1\% r.m.s \\ Spatial mode & TEM\({}_{00}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of laser oscillator.  
Figure 4: Schematic illustration of optical system and specimen setup in high-speed observation.  
\begin{table}
\begin{tabular}{c c} \hline \hline Pixel & 312 \(\times\) 260 \\ Frame rate (fps) & 64,000 \\ Exposure time (\(\upmu\)s) & 4 \\ Color, Gradations & Monochrome, 10 bits \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of high-speed video camera.  
numerical apertures, and a photodiode type power meter was placed 2.0 mm below the glass specimen to measure the transmitting laser power \(E_{\text{tr}}\). At this point, reflection and scattering phenomena of laser beam were not considered, and the measurement and calculated results were recorded as the absorption rate \(A_{\text{ab}}\).
## 3 The Deployment Models  
To analyze encrypted traffic, including TLS-encrypted traffic, deployment models are widely used to deploy either, middleboxes for traffic interception, or traffic sensors for passive inspection in practice. We discuss three main deployment models for encrypted NTA: TLS interception, inspection using cryptographic functions, and passive inspection without decryption.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 2 Related Works
### Offuscated Gradient
#### 2.2.2 Stochastic Gradient  
A stochastic gradient introduces probabilistic elements into the model, thereby inducing uncertainty in the gradient computations of the attacker. By integrating these probabilistic aspects, the model produces diverse outputs and gradients for the same input,  
Figure 2: Examples of FGSM Attack.  
creating challenges for attackers to accurately estimate the gradient. This can be achieved using methods such as dropout and batch normalization. To enhance the generalization capability of the model and avoid overfitting, a dropout was employed, which randomly deactivates certain neurons. This added complexity further hinders an attacker's gradient estimation, making dropout a representative technique in the realm of stochastic gradients. However, an attack method known as expectation over transformation (EOT) [28] has been proven to be capable of counteracting the stochastic gradient, indicating that it may not be a steadfast defense mechanism.