esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 1 Introduction  
Speech/music classification algorithms are an important component of variable rate speech coding and coverage of the communication bandwidth, and provide effective means for enhancing the capacity of the bandwidth. In addition, a major concern in speech coding is optimizing speech input, and many types of input are being investigated, such as music. Speech/music classification algorithms are an essential part for providing high performance sound quality in speech coding. Recently, a number of adaptive multi-rate (AMR) voice codecs have been proposed to efficiently utilize the limited bandwidth resources available [1; 2; 3]. Precise determination of speech/music classification is quite necessary, as different bit rate allocations for the correct input/output formats affect the voice characteristic of these adaptive multi-rate voice codecs [4; 5].  
Recently, further improvements in speech/music classification problems have been achieved by adopting several machine learning techniques, such as the support vector machine (SVM) [6; 7], Gaussian mixture model (GMM) [8], and deep belief network (DBN) [9] for the selectable mode vocoder (SMV) codec. The enhanced voice services (EVS) speech/music classifier, which is known as the 3 rd-generation partnership project (3 GPP) standard speech codec for the voice-over-LTE (VoLTE) network, is also based on GMM, but its features were calculated either at a current frame or as a moving average between those in the current and the previous frames [10]. The speech/music classifier uses a binary classification, but the diversity of music is greater than that of speech, and it can be generally said that it is a multiclass classification method, according to each musical genre. The GMM is not suitable for solving multiclass classification problems due to scalability issues.  
In this paper, we propose a robust speech/music classifier based on long-short term memory (LSTM) [11; 12], which can solve the vanishing gradient problem [13] better than the RNNs. In the case of an audio signal, a high correlation exists between signal samples in consecutive frames in the sequence. LSTMs, a particular type of RNNs, were basically proposed as a scheme of extending NNs to sequential signals. The extension of recurrent connections allows LSTMs to utilize the prior frame and makes them more robust to manipulating sequential data compared to non-recurrent NNs. The proposed method employs an LSTM using a feature vector derived from EVS codec. To appraise the accuracy of the proposed algorithm, speech/music classification experiments are performed under a variety of simulated conditions.
## 3 Results and Discussion
### pH Measurement in Chicken Thighs  
The pH of the samples was also measured, but no significant differences (\(p>0.05\)) were noted for all samples regardless of the storage temperature, in comparison to the control samples. pH was initially 6.02 \(\pm\) 0.2 and increased during the first days of storage in all samples stored at 0-10 \({}^{\circ}\)C. Similar initial pH values have been reported for fresh chicken cuts [20,21]. At the end of the experiment, pH averaged 6.2 \(\pm\) 0.2 at 0 \({}^{\circ}\)C, 6.1 \(\pm\) 0.3 at 5 \({}^{\circ}\)C, and 6.0 \(\pm\) 0.3 at 10 \({}^{\circ}\)C. Perishable food products packed under vacuum or MAP, for example, poultry, exhibit an increase in pH values due to the growth of spoilage bacteria and the production of basic metabolic compounds [47,48]. In this case, the pH increase may be attributed to the growth of _Pseudomonas_ spp., which dominated the spoilage microflora of the chicken samples. These bacteria are known to increase the pH of food due to their metabolic products [54]. The increase in pH of chicken flesh during refrigerated storage may be also attributed to the formation of basic metabolic products of meat spoilage bacteria, for example, ammonia, trimethylamine, and other biogenic amines [20,55].
# Ripening of Hard Cheese Produced from Milk Concentrated by Reverse Osmosis
###### Abstract  
The application of reverse osmosis (RO) for preconcentration of milk (RO-milk) on farms can decrease the overall transportation costs of milk, increase the capacity of cheese production, and may be highly attractive from the cheese manufacturer's viewpoint. In this study, an attempt was made to produce a hard cheese from RO-milk with a concentration factor of 1.9 (RO-cheese). Proteolysis, volatile profiles, and sensory properties were evaluated throughout six months of RO-cheese ripening. Moderate primary proteolysis took place during RO-cheese ripening: about 70% of \(\alpha_{\text{sl}}\)-casein and 45% of \(\beta\)-casein were hydrolyzed by the end of cheese maturation. The total content of free amino acids (FAA) increased from 4.3 to 149.9 mmol kg\({}^{-1}\), with Lys, Pro, Glu, Leu, and \(\gamma\)-aminobutyric acid dominating in ripened cheese. In total, 42 volatile compounds were identified at different stages of maturation of RO-cheese; these compounds have previously been found in traditional Gouda-type and hard-type cheeseses of prolonged maturation. Fresh RO-cheese was characterized by a milky and buttery flavor, whereas sweetness, saltiness, and umami flavor increased during ripening. Current results prove the feasibility of RO-milk for the production of hard cheese with acceptable sensory characteristics and may encourage further research and implementation of RO technology in cheese manufacture.  
reverse osmosis; concentrated milk; hard cheese; cheese ripening; volatile compounds +
Footnote †: journal: Foods
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 3 Study Area and Data Collection
The possible relationship that can be captured between category, for example, is how sheeting type A can degrade to a rate closer to B while degrading at double the rate of C. In addition, one-hot encoding will expand our feature space to the number of different categories in each of our features, which can slow the training process and consumes more memory. Entity embedding is an approach that maps each category in a feature to a vector in the Euclidean space [31]. This mapping can be fixed or be learned during the model training process. Using this approach, the embedding will be able to learn some relationship between each category by their distances in the embedding space. Embedding is represented as \(n\times m\) weight matrix, where \(n\) is the number of unique categories and \(m\) is the embedding dimension. For this method, we experimented with two implementations. For the first approach, we gave each categorical feature its own embedding space. For the second approach, we created one embedding space for all the categorical features. By sharing the embedding space, we hoped that we would be able to capture the relationship between the different features such as how each sheeting type has different retro-reflection performances for each observation angle. For the feature age, we applied min-max normalization, where we divided each age by 10. While for the retro-reflection values we applied standard scaling according to the following equation:  
\[x=\frac{x-mean}{standarddeviation} \tag{1}\]  
Additionally, we also implemented linear and polynomial regression models to compare with the neural network system since most of the previous studies used these models to predict traffic sign retro-reflection degradation. In linear regression, the target value--retro-reflectivity--is assumed to be a linear combination of the predictors. Mathematically:  
\[\hat{y}=w_{0}+w_{1}x_{1}+\ldots+w_{d}x_{d} \tag{2}\]  
where, \(\hat{y}\) is the predicted retro-reflectivity, \(x=(x_{1},x_{2},\ldots,x_{d})\) are the predictors such as age and sheet color, \(w=(w_{1},w_{2},\ldots,w_{d})\) are the coefficients, and \(w_{0}\) is the intercept, also known as bias. The goal is to find the coefficients \(w_{i}\)'s and the bias \(b\) that minimizes the residual sum of squares between the actual and the predicted retro-reflectivity values for the samples in the training set. We trained our linear regression model using stochastic gradient descent in order to find the optimal weights and bias. As for the polynomial regression model, we generate a expanded feature set \(\hat{x}\) from \(x\) that contains all the polynomial combinations of the features in \(x\) with degree 2, in addition to the original features, which is then fed to the regression model.
# Introducing Cement-Enhanced Clay-Sand Columns under Footings Placed on Expansive Soils
## 4 Results and Discussion
### The Compressibility Characteristics  
The compressibility characteristics for clay-sand mixtures enhanced with cement is studied for cement additives of 1%, 2%, 4% and 8%.  
Figure 6a,b indicate the variations of compression index (C\({}_{\mathrm{c}}\)) (Figure 6a1-a4) and rebound index (C\({}_{\mathrm{r}}\)) (Figure 6b1-b4) under curing times for variable cement additives.  
It is clear that, as the cement content increased, the compression index (C\({}_{\mathrm{c}}\)) declined noticeably. Samples that had been cured for 28 days had C\({}_{\mathrm{c}}\) decreases by 29% to 64%. As the cement concentration increased, the rebound index (C\({}_{\mathrm{r}}\)) dropped. Samples cured for 28 days indicated a reduction in C\({}_{\mathrm{r}}\) by 19% to 51%.  
The potential for settling can be estimated using the compressibility factors when evaluating a shaft for superstructure load transformation. It is important to remember that this compressibility is predicated based on inflexible lateral confinement, which is not an accurate representation of the actual field conditions. Avoid using cement-enhanced columns and make sure they do not buckle when used as self-supporting structures.  
Comparing specimens with 30% and 40% clay contents to those with 10% and 20% clay contents, there was a greater reduction in the compression index (C\({}_{\mathrm{c}}\)). The use of high clay content is not encouraged.  
Figure 5: Rigid column support.  
Figure 6: Compression and rebound indexes of mixtures vs. cement contents at all curing days: (**a1–a4**) compression index (C\({}_{c}\)), (**b1–b4**) rebound index (C\({}_{r}\)).
# OCRL1 Deficiency Affects the Intracellular Traffic of ApoER2 and Impairs Reelin-Induced Responses
###### Abstract  
Lowe Syndrome (LS) is a rare X-linked disorder characterized by renal dysfunction, cataracts, and several central nervous system (CNS) anomalies. The mechanisms underlying the neurological dysfunction in LS remain unclear, albeit they share some phenotypic characteristics similar to the deficiency or dysfunction of the Reelin signaling, a relevant pathway with roles in CNS development and neuronal functions. In this study, we investigated the role of OCRL1, an inositol polyphosphate 5-phosphatase encoded by the _OCRL_ gene, mutated in LS, focusing on its impact on endosomal trafficking and receptor recycling in human neuronal cells. Specifically, we tested the effects of OCRL1 deficiency in the trafficking and signaling of ApoER2/LRP8, a receptor for the ligand Reelin. We found that loss of OCRL1 impairs ApoER2 intracellular trafficking, leading to reduced receptor expression and decreased levels at the plasma membrane. Additionally, human neurons deficient in OCRL1 showed impairments in ApoER2/ Reelin-induced responses. Our findings highlight the critical role of OCRL1 in regulating ApoER2 endosomal recycling and its impact on the ApoER2/Reelin signaling pathway, providing insights into potential mechanisms underlying the neurological manifestations of LS.  
l owe syndrome; endosomal pathway; LRP8; Reelin; Golgi
## 2 Materials and Methods
### Experimental Design and Statistical Analysis  
MWI-assisted solvolysis trials were carried out using response surface methodology (RSM), considering designs of experiments with three replicates of the central points to define the best depolymerization conditions for the three organic solvents employed in this study. Organosolvent concentration (c, %), treatment time (t, min), reaction temperature (T, \({}^{\circ}\)C), and solid/liquid ratio (s/L, g biomass dry weight/cm\({}^{3}\) of organosolv solution) were the chosen factors to optimize the responses of the proposed models. Percentages of both cellulose (_cel_) and lignin (_lig_), obtained from direct characterization of post-treatment solids once the residual masses of solvolysis solids, were known, together with the amount of cellulose and lignin eliminated (\(Y_{c}\), % and \(Y_{l}\), %, respectively), calculated according to Equations (1) and (2).  
\[Y_{c} = \frac{m_{0}\ -\ m_{cel}}{m_{0}} \tag{1}\] \[Y_{l} = \frac{m_{l0}\ -\ m_{lig}}{m_{l0}} \tag{2}\]  
where \(m_{0}\) is the cellulose contained in OTP raw material, \(m_{cel}\) is the cellulose mass of post-treated solids, \(m_{l0}\) refers to the lignin content in the original OTP biomass, and \(m_{lig}\) concerns the lignin weight in solvolysis solids.  
The statistical software programs used to perform the analysis of the experimental results from hydrolytic experiments with the organic solvents were MODDE v 8.0 (Unimetrics AB, Princeton, NJ, USA) and Design-Expert v 13.0 (Stat-Ease Inc., Minneapolis, MN, USA). The relationship between the selected output variables (\(Y\)) and the encoded input parameters (\(x_{i}\) and \(x_{j}\)) correspond to second degree polynomials, expressed generically according to Equation (3). Analysis of variance (ANOVA) was the statistical method utilized to select model factors with significant effect and define the independent, linear, quadratic, and interaction coefficients (\(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\), respectively).  
\[Y = \beta_{0}+\beta_{i}\Sigma x_{i}+\beta_{ii}\Sigma x_{i}^{2}+\beta_{ij} \Sigma x_{i}x_{j} \tag{3}\]
## 2 Theoretical Foundation
### Machine Learning (ML) for Signal Detection  
ML algorithms excel at estimating transmitted symbols based on received signals, channel conditions, and noise statistics. The key advantages of ML in signal detection include the following: (1) learning from extensive datasets to adapt to dynamic communication environments; (2) automatic feature extraction for classification and regression tasks; and (3) supervised learning for mapping received signals to corresponding symbols, while unsupervised learning uncovers latent patterns without explicit labels.  
As in case of conventional methods, the performance evaluation in ML-based approaches relies on metrics like SER and MSE, while the optimization algorithms and activation functions are crucial for enhancing model performance [3]. SER is used to detect the error in the signal detected vs. the actual signal. SER is related to the bit error rate (BER); however, signal detection implementation occurs at the symbol level. The BER can be computed if required through Equation (11):  
\[\text{BER}=\frac{1}{\log_{2}(M)}SER \tag{11}\]  
While the ultimate goal is to analyze MIMO signal detection, this study utilized a link-level simulation that does not account for frequency variations within the MIMO system.  
Deep learning is a subset of ML that offers advanced architectures like DNNs that are capable of learning complex communication system representations. DNNs have multiple hidden layers between input and output layers, enabling them to extract intricate relationships and patterns from large datasets of received signals and channel information. This allows DNNs to learn robust detection models that can handle non-linearities and complexities inherent in real-world wireless channels, potentially surpassing the performance limitations of conventional techniques like MMSE and ZF, especially in scenarios with high modulation orders or dense MIMO systems [4].  
Understanding the above classification helps researchers and engineers select appropriate methods based on the specific needs and constraints of wireless communication systems [5].
# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 4 Results and Discussion
### Simulation Setting  
In this section, the performance of EVA has been calculated using Network Simulator 2 (NS2) with the AquaSim package for underwater [39]. The random topology with various number of sensors have been employed (i.e., 25 - 400) with a high level are of 1250 m\({}^{3}\). The transmission range has been set equal to 250 m as it basically appropriates for such underwater networking applications and the initial energy is assigned to 100 J with 15 s packet generation time and size is 64 bytes. On the other hand, the hello packet interval is equal to 100 s the energy model has been employed using the same that employed in our published protocols. The energy consumption in terms of transmitting, receiving, and idle listening is set to (2 w, 0.75 w, and 8 mw) [11,14,17]. A Broadcast MAC has been employed as a Media Access Control (MAC) [40]. We want to highlight that the result has been averaged from 50 runs for the whole performance evaluation at the same time. This could help in extracting the results for all performance evaluations in the same situation and highlighting them as error bars in the result figures. Table 3 below shows the mentioned setting clearly.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results
### Perceptual Quality  
Compared to DCT based techniques, the proposed algorithm shows less perceptual quality. When we watermark the 512 \(\times\) 512 as shown in Figure 3, with a 32 \(\times\) 32 bit of watermark, proposed technique yields a PSNR value of 41.8 dB and similar DCT technique [3] shows a PSNR value of 50.8 dB. Even though the PSNR of the proposed algorithm is numerically less, however, the resultant perceptual quality for PSNR of 41.8 dB is high.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Existing FWHT Algorithms and Similar DCT Algorithm** & **Time for Embedding and Decoding (Seconds)** \\ \hline [PERSON] (2013) & 1.9 \\ [PERSON] et al. (2012) & 3 \\ [PERSON] et al. (2011) & 7.5 \\ [PERSON] et al. (2003) & 3 \\ Proposed Algorithm & 0.665 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Execution Time for Different Algorithms.
## 2 Materials and Methods
### Proposed Method to Improve Construction Schedule Robustness
The options differ in the resulting process time and cost. Let us assume that the option-related process duration \(t_{ij}\) is a random variable of known distribution type and parameters, and the option-related process cost \(c_{ij}\) is deterministic. Therefore, the expected value of the duration of process \(i\), if decided to be delivered using option \(j\in W_{i}\), is \(\Delta_{ij}\ =\ t_{i}-t_{ij}\), where \(t_{ij}\) is the expected value of the random variable \(\mathbf{t}_{ij}\). Let us put the options in the ascending order according to the values of \(\Delta_{ij}\) and number them accordingly (\(j\ =\ 1,\ 2,\ \ldots\)). Selecting actions of reacting to the process start delays consists of determining the best option and the best time of its deployment. The latter is defined by a lag, marked as \(\lambda\), between the baseline start of the process and the moment when acceleration measures begin. It is assumed that the first option of the time compressing actions (i.e., the option that offers the highest acceleration) is selected if \(s_{i}^{r}-s_{i}\geq\lambda\). If \(s_{i}^{r}-s_{i}\geq\Delta_{i,j+1}+\lambda\), then the next option is to be selected. To facilitate operative management, the lag \(\lambda\) is constant and equal for all processes. Its value is subject to optimization: \(\lambda\) is defined in a way that minimizes the sum of the cost of process start delays, \(C_{d}\), and the cost of the duration compression measures, \(C_{w}\):  
\[\text{min}C(\lambda):\ C(\lambda)\ =\ C_{d}+C_{w}\ =\ \sum_{i\in V_{d}}c_{i}^{d} \cdot E(\mathbf{s}_{i}-s_{i})+\sum_{i\in V}\sum_{j\in W_{i}}\big{(}c_{ij}-c_{i} \big{)}\cdot E\Big{(}\mathbf{x}_{ij}(\lambda)\Big{)}, \tag{1}\]  
where \(\mathbf{s}_{i}\) is the random variable representing the start of process \(i\in V_{d}\), and \(\mathbf{x}_{ij}(\lambda)\) is an auxiliary random variable; it equals 1 if option \(j\) is decided for the delivery of process \(i\), and 0 otherwise; the value depends on the value of \(\lambda\). The expected values of \(\mathbf{s}_{i}\) and \(\mathbf{x}_{ij}(\lambda)\) are estimated based on simulation experiments for different \(\lambda\).
# Study About the Performance of Ascon in Arduino Devices
###### Abstract
On the other hand, while previous studies have addressed the general efficiency of lightweight cryptographic algorithms in embedded systems, many of them either focus on theoretical performance metrics, simulations, or on high-end platforms with hardware acceleration. Other works explore system-level challenges such as DoS (denial of service)-resilient networked control systems or learning-based security controllers, which, although highly relevant, differ significantly in scope. In contrast, this study provides a practical, empirical performance evaluation of multiple Ascon variants across a diverse set of real-world Arduino devices, ranging from 8 to bit AVR microcontrollers to more powerful 32-bit architectures. The novelty of this work lies in its hands-on benchmarking of Ascon in resource-constrained, low-cost hardware environments, using actual execution metrics (e.g., cycles, memory usage, throughput), which are crucial for developers aiming to adopt Ascon in lightweight IoT deployments without hardware acceleration. This fills a gap in the current literature by providing concrete implementation data directly applicable to practitioners in embedded cryptography. More specifically, after this introduction, Section 2 provides a detailed description of the Ascon cipher suite, which includes an authenticated encryption algorithm and a hash function. The Ascon's characteristics as an authenticated encryption and some domains where Arduino-based systems with Ascon are applied, are commented in Section 3. Section 4 contains the essential concepts needed to understand the basis of the security of this type of algorithm and describes the most relevant attacks on Ascon to date. A study of Ascon performance on the four constrained devices of the Arduino family is presented in Section 5, namely Arduino DUE, Arduino Mega2560, Arduino Nano Every, and Arduino Nano ESP32 (Arduino S.r.l., Somerville, MA, USA). The performance of Ascon on these devices highlights its reliability as the emerging standard for lightweight cryptography. Finally, the conclusions of this work are presented in Section 6.
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 4 Discussion  
The proposed architecture has been implemented and tested for a small image patch of size \(256\times 256\). But the architecture can easily be extended to larger image size, as histopathology images are typically very large in size. The total processing time for a full resolution image, with a size \(20{,}000\times 20{,}000\), is expected to be in the order of hours in a regular CPU. With optimized implementation and FPGA boards with higher clock frequency, the overall processing time is expected to be in the order of minutes.  
The proposed architecture shows a modest 34.5% performance improvement compared to a regular CPU, which is mainly because of the lower clock frequency (100 MHz) of the FPGA board. The speed-up factor can be improved further by exploiting the data and task parallelism in each sub module. Empirically, it can be said that smaller modules implemented on FPGA give low performance (speed up) improvement over the software (on general purpose processor) implementation as the data parallelism achieved with FPGA can be overshadowed by operating clock frequency. For larger modules, having possible data parallelism can give significant performance improvement worth of going for special hardware accelerator. Our larger goal is to design a CAD system for histopathology for which the nuclei detection is one module. It is expected that the other modules in the CAD system will have larger speed up resulting in a high overall system performance.