esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A model for the prediction of resonance frequencies of distributed Helmholtz resonators is proposed. The model was initially derived based on high resolution computational simulations for twenty-five distributed Helmholtz resonators. End correction length was proposed to be correlated with two acoustic radiation effects, namely, acoustic radiation effect from the individual hole and acoustic radiation from the overall perforation area. The model was then acquired by making a convenient assumption that the end correction lengths due to these two acoustic radiation effects are additive. It is noteworthy that the assumption is not strictly validated analytically, as its purpose is to make the model easy to use in practice.  
The proposed resonant frequency model was successfully validated by comparison with experimental data for a further eighteen distributed Helmholtz resonators. The validation exercise demonstrated that the proposed model displays very good agreement with the experimental results for resonance frequencies for distributed Helmholtz resonators within a wide range of effective porosities (0.04-0.54), porosities (0.0123-0.135), and perforation aspect ratios (1:1-1:3). A new term, \"overall perforation area acoustic radiation effect\", is proposed to account for the acoustic radiations from the perforated area as a whole.  
This resulting model is easily applicable for engineers and is of significant value in the design of practical distributed Helmholtz resonators, such as those distributed Helmholtz resonators applied on gas turbine engines to control combustion instabilities.  
Conceptualization, J.W. and P.R.; methodology, J.W.; software, J.W. and Q.Q.; validation, J.W., P.R., and Q.Q.; formal analysis, J.W.; investigation, J.W.; resources, P.R., B.H., and Q.Q.; data curation, J.W. and B.H.; writing--original draft preparation, J.W.; writing--review and editing, J.W., Q.Q., and P.R.; visualization, J.W.; supervision, P.R.; project administration, P.R.  
This research received no external funding.  
The University of Hull and China Scholarship Council are greatly acknowledged for the financial support in making this research possible.  
The authors declare no conflict of interest.
# Myths and Realities about Genetically Modified Food: A Risk-Benefit Analysis
## 4 The Application of GMOs to Agriculture and Food Production
### Delayed Ripening  
The first genetically engineered crop product to be commercialized was the Flavr SavrTM tomato [22]. Tomato only remain solid and ripe for a limited time period. The tomato lifetime can even be shorter than the time it takes to reach the market because softening can harm fruit while it is transported, hence the interest shown in delaying the softening. Until then, green fruit are picked and ripened artificially by ethylene treatment, which confers tomato their ripe color but not the full array of flavors. In the 1980s, some research studies revealed that enzyme polygalacturonase (PG) might be related to tomato softening because it degrades cell-wall pectin. The Californian company Calgene proposed developing a more resistant tomato to rotting by adding an antisense gene that inhibits PG accumulation in ripening tomatoes. In 1987, Calgene researchers identified and cloned a tomato PG gene and produced a GM tomato by inserting PG antisense DNA constructions. They expected to produce fruit that remained firm for long time periods by permitting it to be transported to markets, even after ripening. Some generated tomato lines exhibit only 1% PG compared to conventional ones. Based on the results obtained with field trials, in May 1994, the US Food and Drug Administration (FDA) approved Flavr SavrTM tomato commercialization [90]. The only two differences observed between Flavr SavrTM tomatoes and conventional ones were that fruit cell-wall pectin degraded more slowly and that tomato paste viscosity was greater [90; 91]. Although demand was not low, its production ceased in 1997 due to high production and distribution costs [22].  
Given the essential role that ethylene plays in ripening climacteric fruit, many researchers have paid attention to the genetic manipulation of those genes encoding the enzymes involved in ethylene biosynthesis. For instance, melons have short shelf lives because fruit ripening is caused by ethylene production. By inserting an antisense gene that inhibits the expression of ACC-oxidase (an enzyme involved in ethylene biosynthesis), a GM melon has been developed with flesh firmness enhancement and has an extended 30-day shelf life compared to the less than 12 days of conventional fruit [92]. Another example of GM crops for delayed ripening is banana. In developing countries, bananas are a staple food for millions of people, mainly because they are an important source of carbohydrates and nutrients. Delayed fruit ripening may be accomplished by reducing ethylene synthesis. Two genes, MaMADS1 and MaMADS2, have been functionally related to banana ripening. When repressing either gene (via antisense or RNA interference (RNAi)), a transgenic banana plant is created. It exhibits specific ripening delay and extended shelf-life phenotypes, including delayed color development and softening [93]. Delayed ripening allows the fruit to be picked from plants later, and this more natural ripening, which enhances fruit organoleptic properties and nutritional values.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 3 Methods
### Mixer-C of FSR  
We further propose a simpler mixing method tailored for far-field ASR, called Mixer-C. Mixer-C interpolates speech samples from the different channels with the same labels to augment the channel information. In far-field speech recognition, there exist multi-channel data collected by multiple microphones. The multi-channel information is instrumental in enabling models to capture not only the spectral characteristics of the target and interference signals but also their spatial attributes, as recorded by the different microphones. This added dimension is highly advantageous for improving the effectiveness of far-field automatic speech recognition (ASR). The multi-channel speech dataset can be denoted as \(D=\{D_{1},D_{2},\ldots,D_{C}\}\), where \(C\) signifies the numbers of channels. Each speech sample \(X_{i}^{c}\) from the \(c\)-th channel is associated with the same label \(Y_{i}\) as its counterparts in all other channels, denoted as \(X_{i}^{-c}\), where \(-c\) signifies all channels except for \(c\), i.e., \(-c=\{1,\ldots,c-1,c+1,\ldots,C\}\). As is shown in Figure 2, Mixer-C includes two steps: first, the channel selector selects the paired speech data \(X_{i}^{m}\) and \(X_{i}^{n}\) from different channels with the same label from the audio samples, and then mixes the selected speech data in the \(k\)-th layer of the neural network, which can be formulated as  
\[\hat{X}=\lambda g_{k}(X_{i}^{m})+(1-\lambda)g_{k}(X_{i}^{n}),m\
eq n \tag{8}\]  
where \(X_{i}^{m}\in D_{m}\) and \(X_{i}^{n}\in D_{n}\) correspond to speech samples from different channels that share the same label \(Y_{i}\). Due to the two samples sharing the same label, Mixer-C does not require mixing the loss. Mixer-C can enhance the model's understanding of information from different channels. Compared to Mixer, this approach does not require any additional computation of the loss function, thus improving computational efficiency.  
Figure 2: The pipeline of Mixer/Mixer-C. In Mixer, there are two stages for mixing: in the first stage, \(g_{k}(\hat{X})\) is obtained by mixing any two feature representations \(g_{k}(X_{i})\) and \(g_{k}(X_{j})\) extracted from audio samples; in the second stage, the loss function is formulated as a weighted combination of two losses that were computed by the corresponding label of mixed audio samples. In Mixer-C, \(g_{k}(\hat{X})\) is obtained by mixing two feature representations of audio samples belonging to different channels but sharing the same label.
# Floor Heave Mechanism and Anti-Slide Piles Control Technology in Deep and Large-Span Chamber
## 2 Discussion on the Floor Heave Mechanism in Deep Chambers  
### Plastic Flow Characteristics of the Floor in Deep Chambers  
The underground chamber is a composite structure composed of a roof, floor, and two sides. Before excavation, the underground rock mass is in a triaxial stress equilibrium state. Research suggested that after excavation, due to the redistribution of internal stress in the rock mass, the weight of overlying rock mass is transferred to the two sides of the roadway [25]. In weak rock roadways, the floor rock is mostly shale, sandstone, and other weak rocks with low strength. When the load transferred to the floor by the two sides reaches or exceeds its yield strength, the compression mold effect occurs, which results in the overall shear failure of floor rock mass, and the floor rock mass will enter the plastic flow state. Supposing the floor rock mass is an ideal rigid plastic material that follows the Mohr-Coulomb yielding criterion, and the effect of rock gravity is ignored, we built a slip line field of large-span chamber floor in yield state, which resembled a Hill slip line field according to deformation characteristics of floors in a large amount of deep large-span chambers [26]. The slip line field is shown in Figure 1, where we consider the load transferred from the roof and sides to the floor \(q_{0}\) and the supporting load (such as loads from anti-floating anchors) of the chamber floor \(p_{s}\). It should be noted that a Prandtl slip line field should be used for a small-span tunnel or chamber.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 2 Motivation and Background
### Background on 5G AKA and Cryptographic Primitives
#### 2.2.2 Pre-Quantum (X25519 and secp256r1)  
X25519 and secp256r1 (NIST P-256) are both elliptic curve cryptography (ECC) algorithms used for key exchange but they differ in terms of the elliptic curve that they use, the underlying mathematics, and certain characteristics.  
Although X25519 is not NIST-standardized, it is widely used and considered secure and is defined in RFC 7748 [11]. Curve25519 is designed to be fast and secure. It is a Montgomery curve, which provides certain performance advantages in implementations. X25519 uses the elliptic curve Curve25519, defined over a prime field defined by the prime number \(2^{255}-19\). X25519 is often faster in computation, which makes it particularly efficient for key exchange in various protocols. Secp256r1, also known as NIST P-256, is an NIST-recommended elliptic curve [12]. Secp256r1 is a Weierstrass curve and is widely implemented in cryptographic libraries and security protocols, including TLS.
# Analysis of a Landslide on a Railway Track Using Laser Scanning and FEM Numerical Modelling
## 6 Conclusions  
The study shows that the laser scanning data describe the scenario of the landslide process advantageously. Firstly, the model from the LIDAR data was used to reconstruct the morphology of the land prior to the landslide. This model indicated the unfavourable location of the area where water accumulated due to prolonged rainfall. Secondly, the model from terrestrial laser scanning showed the area morphology after the landslide and clearly illustrated the location of the landslide with the main scrap and the resulting water flows. As a result, it was shown that laser scanning might be applied to monitor transport route embankments and determine the causes of potential landslides.  
The 3D numerical simulation confirmed the mechanism of the landslide process. The results significantly contributed to the identification of the causes underlying the landslide. The employed numerical models of the geological structure indicated the possibility of rainwater infiltration in the ground, which saturated the silty layer, thereby resulting in a rapid reduction in its shear strength.  
To conclude, the study showed that the unfavourable morphology, geological structure of the soil mass and prolonged rainfall activated the landslide on the analysed railway track.  
Figure 14: The structure of the geotechnical layers with identified aquatic layer IVa.  
**Author Contributions:** Conceptualization, E.P.; methodology, E.P., M.B. and D.S.; software, D.S.; validation, E.P., D.S. and M.B.; formal analysis, D.S.; investigation, E.P., D.S., J.S. and M.B.; resources, D.S. and M.B.; data curation, D.S.; writing--original draft preparation, E.P., D.S.; writing--review and editing, E.P., J.S. and M.B.; visualization, D.S. and J.S.; supervision, E.P. and M.B.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methods
### Polar Code
#### 2.2.3 Successive Cancellation (SC) Algorithm  
Decoding Polar Codes effectively often employs the Successive Cancellation (SC) algorithm, which leverages the recursive nature of the transformation matrix \(G\). The SC algorithm is designed to estimate the transmitted bits sequentially, using a series of probability calculations and decisions based on both the received data and previously decoded bits.  
The Successive Cancellation algorithm operates by considering the likelihood ratios (LRs) for each bit. The LR for a bit \(u_{i}\) is defined as the ratio of the probabilities of \(u_{i}\) being 0 to \(u_{i}\) being 1, given the received vector \(y\) and the estimates of the previous bits \(\widehat{u_{1}}^{i-1}\). This can be mathematically expressed as  
\[LR_{i}=\frac{P(u_{i}=0|y,\widehat{u_{1}}^{i-1})}{P(u_{i}=1|y,\widehat{u_{1}}^{ i-1})} \tag{4}\]  
The bit \(\widehat{u_{i}}\) is then decoded based on this likelihood ratio:  
\[\widehat{u_{i}}=\begin{cases}0,if\ LR_{i}\geq 1\\ 1,if\ LR_{i}<1\end{cases} \tag{5}\]  
Recursive Calculation of LRs:  
The recursive structure of the matrix \(G\) allows the LRs to be computed efficiently. Specifically, for a code of length \(N=2^{n}\), the decoding operation can be split recursively into two halves. The LRs for the first half of the bits \(u_{1}^{\frac{N}{2}}\) and the second half \(u_{\frac{N}{2}+1}^{N}\) are computed separately, using the previously decoded bits as follows: For the first half,  
\[LR_{1:N/2}\Big{(}y_{1}^{N/2},\widehat{u_{1}}^{i-1}\Big{)}=f\Big{(}y,\widehat{ u_{N/2+1}}^{N}\Big{)} \tag{6}\]  
For the second half,  
\[LR_{n/2+1:N}\Big{(}y_{N/2+1}^{N},\widehat{u_{1}}^{N/2}\Big{)}=g\Big{(}y, \widehat{u_{1}}^{i-1},\widehat{u_{N/2+1}}^{i-1}\Big{)} \tag{7}\]  
The functions \(f\) and \(g\) are defined based on the polar transform and take into account both the received vector \(y\) and the estimated bits so far. The SC algorithm systematically decodes each bit and uses the estimate to refine subsequent likelihood ratio calculations, moving from the least reliable to the most reliable bits according to the polarizing properties of the code construction.  
This recursive and sequential approach allows the SC decoder to efficiently utilize both channel outputs and prior decisions, making it highly effective for the decoding of Polar Codes, especially in scenarios where computational simplicity and performance are critical.  
This detailed explanation provides a clear view of how the SC algorithm functions, highlighting its mathematical basis and recursive nature, which are crucial for understanding its application in decoding Polar Codes.
## 5 Optimization Models Studied for the OPP Model  
PMUs are placed at substations in which transmission lines, generators, and loads are adjoined [29; 31; 60]. Specific reasons, such as the PMU's high cost and communication facilities not existing in some substations, increase the entire cost of installation [7; 38; 41; 51; 55; 60].  
Thereby, the power utility industries desire a reasonable number of channels per a PMU device to determine the entire cost of the investment. In addition to that consideration, an optimal PMU placement is essential in the research domain [37; 38; 41; 42; 51].  
The initial step is to identify if the solution achieved by the optimization algorithm is feasible or not. In the case of producing the sequence of points, we first derive a feasible estimate point in which the objective value is less than the older one on the previous trial point [13; 14; 15; 16; 17; 18; 19; 20; 21].  
Such a task between a feasible and an infeasible point is crucial to achieve optimality. What we desire is to deliver the optimum point over the feasible region constituted by the objective, the constraint function, and the decision bounds [21; 22; 23; 24; 25]. Therefore, the aim is to identify the feasibility and optimality of the response space solutions. Although there exist a few works in this domain [56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67], there is no possible combination of the PMU number and the appropriate locations.  
This work proposes an OPP-based model considering the limited number of channels per PMU device using extended ILP models initially presented in [56; 57; 58] and [60] where a set cover problem is introduced [28; 29; 30; 31]. The suggested optimization models have been solved by BBA, heuristic algorithms, and nonlinear algorithms.  
Heuristic algorithms work together with BBA to find a solution of the same quantity and quality in a minimal running time [25; 26]. Furthermore, transforming the binary-integer programming into a nonlinear polynomial model shows the robustness required to find a proper number of PMUs and their placement sites for a fixed number of channels [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. The numerical optimization challenge is to run mathematical as well as heuristic algorithms, including BBA, SQP, IPMs, GPSA, GA, and BPSO, to discover the best combination between the proper PMU number and the upper bound of channel capacity without increasing the cost of installation [56; 57; 58; 59; 60].
## 4 Results
### Data Comparison
Figure 24 shows a Bland-Altman plot that affirms a notable alignment between the two signals, with most of the points nestled within the limits of agreement. Additionally, it is paramount to consider the inherent biases of the sensors, which can introduce measurement discrepancies. External noise, including environmental factors or electronic interference, can also influence the readings, affecting the system's accuracy. Figure 23: Differences between the two pressure time series. Figure 22: OSWS and reference device plot in an outdoor scenario for atmospheric pressure. Furthermore, concerning PM 2.5 measurements, Figure 25 captures the simultaneous readings from the OSWS and the reference device in an outdoor scenario. Figure 26 details the disparities between the two PM 2.5 time series, shedding light on periods of convergence and divergence. Finally, Figure 27 employs a Bland-Altman plot to visualize the agreement of PM 2.5 values measured outdoors between the OSWS and the reference device, reinforcing the consistency and reliability of the OSWS measurements. Figure 24: Bland–Altman plot for pressure measured outdoors. Figure 25: OSWS and reference device plot in an outdoor scenario for PM 2.5. These numbers provide essential information and insights about the data gathered. Regarding the Spearman correlation, all the data collected from the sensors are considered to have a robust positive correlation since all the Spearman correlation values are above 0.8 [40; 42]. In the case of the RMSE parameter, all the measurements were below five units, and the MAPE was below 10%. It is essential to mention that the air temperature and relative humidity may be affected by the buildings near the OSWS and the house where the device was placed [14].
## 3 Purification Routes Applied to Lignocellulosic Acid Hydrolysates
### Liquid/Liquid Extraction  
Several organic solvents (e.g., chloroform, n-hexane, and ethyl acetate) have been tested for the removal of fermentation inhibitory compounds from lignocellulosic acid hydrolyses using various hydrolysate:solvent ratios (2:1, 1:1, 1:2, 1:3, (_v_/_v_)) [73]. Increasing the hydrolysate:solvent ratio improved the efficiency of the removal of the fermentation inhibitors until 1:2 (_v_/_v_), and a further increase did not produce noticeable changes. Overall, ethyl acetate was the most efficient solvent with removal rates of 50% for phenolic compounds, 94% for furfural, and 40% for HMF. The sugar loss (8%) was less important than with alkalinization/overlining or evaporation processes.  
In another study, four extractions with a lignocellulosic acid hydrolysate:ethyl acetate ratio of 1:1 (_v_/_v_) were efficient to remove all the phenolic monomers and the furfural, and also led to the same removal level of acetic acid as evaporation [74]. Based on the yield of ethanol in the following fermentation stage, liquid:liquid extraction was far more efficient (3-fold factor) than evaporation. However, the level of acetic acid left in the solvent extracted acid hydrolysate was still too high in this study, as it led to a longer fermentation duration than with an acid hydrolysate free of acetic acid [74]. Besides, the high consumption of solvent and the necessity to recycle it are the main limiting factors for the economic efficiency of the liquid/liquid extraction process.
## 3 Experiments and Results
### Tensile Test for Strain Measurement  
DIC strain measurement is currently the most important noncontact strain measurement method. The system presented in this study is also applicable to strain measurement. A tensile test was performed to verify the capability of the proposed system for strain measurement along with a commercial 3D-DIC system, as shown in Figure 11. A steel sheet was stretched until fracture occurred. Stretching speed is set to 2 mm/min. The proposed single camera and commercial 3D-DIC systems (Q-400 provided by Dantec Dynamics) were used to obtain the images and calculate the strain. The two systems were triggered by the same trigger source for image acquisition, and the acquisition frequency is 1 frame/s. Figure 12 shows the strain cloud before fracture, as calculated by the two systems. The left and right parts were obtained by the proposed system and the Q-400, respectively. Figure 13 displays the engineering strain curves measured by the two systems. The strain data represented by the curves are the mean values of the strain on the short straight line in Figure 12. Figure 13 presents two strain curves that are consistent. The curve calculated by the proposed system is smooth because the DIC calculation under three perspectives has the advantage of error reduction.  
Figure 11: The setup for the strain test.