esa-sceva/satcom-chunk-collection huggingface dataset:
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 5 Experiments
### Performance Evaluation
#### 5.1.3 Recall (R)  
As shown in Equation (3), recall (also known as sensitivity) is the fraction of truly positive projected levels to overall positive actual levels. A low recall value suggests a high false-negative rate and vice versa.  
\[R=\frac{TP}{TP+FN}\times 100 \tag{3}\]
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
###### Abstract  
Aerial cameras are one of the main devices for obtaining ground images in the air. Since the industrial community sets higher requirements of aerial cameras' self-locating performance yearly using aerial cameras to locate ground targets has become a research hotspot in recent years. Based on the situation that no ground control point exists in target areas, the calculation principle of the aerial remote sensing image positioning algorithm has been analyzed by establishing different positioning models. Several error analysis models of the positioning algorithm based on the total differential method and the Monte Carlo method are established, and relevant factors that cause the positioning error are summarized. The last section proposes the optimization direction of aerial camera positioning algorithms in the future, which are verified by related simulation experiments. This paper provides a certain degree of guidelines in this area for researchers, who can quickly understand the current development and optimization direction of target geo-location algorithms of aerial remote sensing imagery.  
Keywords: aerial camera; remote sensing image processing; target positioning; error analysis +
Footnote †: journal: Applied Sciences
## 2 Related Work  
### Adversarial Attacks  
Since the beginning of the adversarial machine learning (AML) research field [12; 13], many attempts have been made to create taxonomies of attacks [10; 14]. On the basic plane, adversarial machine learning attacks can be categorized broadly into several types based on various criteria such as the attacker's knowledge, the attacker's capabilities, and the attack's target.  
The most commonly used types include the following. (1) White-Box vs. Black-Box Attacks: In a white-box attack, the adversary has complete knowledge of the model, including its architecture and parameters. This allows for the creation of sophisticated and highly effective adversarial examples. In contrast, a black-box attack assumes limited knowledge of the model, with the adversary potentially only having access to input-output pairs. The transferability of adversarial examples is often exploited in black-box attacks [4]. (2) Evasion vs. Poisoning Attacks: Evasion attacks occur at the test phase where adversarial examples are crafted to mislead the model's prediction. This is often achieved by adding carefully designed noise to the input [13]. In poisoning attacks, the training phase is targeted where the adversary manipulates the training data to compromise the learning process itself, leading to incorrect models being learned [15].  
(3) Targeted vs. Non-Targeted Attacks: In a targeted attack, the goal of the adversary is to cause a specific misclassification, e.g., making a model classify a stop sign as a speed limit sign. Non-targeted attacks aim to cause any misclassification, without a specific target in mind [5]. (4) Exploratory vs. Causative Attacks: This classification overlaps somehow with the Evasion vs. Poisoning dichotomy, but it was historically the first classification type. In exploratory attacks, the adversary exploits a model's weaknesses after being trained. In contrast, causative attacks involve influencing the learning process to introduce specific vulnerabilities that can be exploited later [16].  
Each type of attack requires different detection and defense strategies, emphasizing the importance of understanding the specific adversarial landscape when building robust machine learning models. In this work, we focus on detecting exploratory (evasion) attacks in black-box scenarios, targeting models processing tabular data [17; 18; 19].
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Classifier  
This section focuses on the CatBoost classifier, an open-source machine learning library developed by the Russian search giant Yandex in 2017. CatBoost is specifically designed to effectively handle categorical features in decision tree-based based learners. To address issues such as gradient bias and prediction shift, which can lead to overfitting and reduce the accuracy and generalizability of the algorithm, CatBoost implements a symmetric boosting approach [34]. Previous research has shown that hybrid multi-time scale neural network models can be effective in feature extraction for electricity consumption data [35]. Therefore, the study used a hybrid neural network model in combination with CatBoost to extract high-level features from the data. Our implementation set the maximum number of tree iterations to 100, the learning rate to 0.005, and the tree depth to 2. Other parameters were set to their default values.
# Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
###### Abstract  
Novel coronavirus disease 2019 (Corona Virus Disease 2019, COVID-19) is rampant all over the world, threatening human life and health. Currently, the detection of the presence of nucleic acid from SARS-CoV-2 is mainly based on the nucleic acid test as the standard. However, this method not only takes up a lot of medical resources but also takes a long time to achieve detection results. According to medical analysis, the surface protein of the novel coronavirus can invade the respiratory epithelial cells of patients and cause severe inflammation of the respiratory system, making the cough of COVID-19 patients different from that of healthy people. In this study, the cough sound is used as a large-scale pre-screening method before the nucleic acid test. Firstly, the Mel spectrum features, Mel Frequency Cepstral Coefficients, and VGG embeddings features of cough sound are extracted and oversampling technology is used to balance the dataset for classes with a small number of samples. In terms of the model, we designed multi-headed convolutional neural networks to predict audio samples, and adopted an early stop method to avoid the over-fitting problem of the model. The performance of the model is measured by the binary cross-entropy loss function. Our model performs well on the dataset of the AICovidVN 115M challenge that its accuracy rate is 98.1%, and on the dataset of the University of Cambridge that its accuracy rate is 91.36%.  
COVID-19 detection; Mel Frequency Cepstral Coefficients; VGG embeddings; multi-headed convolutional neural networks; feature extraction; model training 2023
## 5 Signal and Noise Evaluation
### Illuminated Pixels  
Now we shall estimate the intensity of illuminated pixels \(I_{S}\). We can assume that the laser intensity \(l\) is constant for all the positive chips of modulation sequence (\(c(i)=1\)). Then the resulting intensity of illuminated pixels is the following:  
\[I_{S}=\sum_{i=0}^{N-1}(\bar{b}+\Delta b_{i}+lc_{i})v_{i}=\frac{lN}{2}+\delta \tag{4}\]  
As in Equation (3); the constant constituent is eliminated and only the sum of \(l\) coinciding with the positive chips of \(v\) remains. Again; the uncompensated remainder of background light deviations tends to zero in circumstances of the constant or slowly changing intensity of the external illumination. Hence; using _balanced_ sequence \(c\) the resulting pixels intensity equals to \(l\) multiplied by the number of ones in modulation sequence (\(N/2\)) plus the influence of uncompensated background light \(\delta\).
# Duality and Flavor of 'Aliza' Fruit: A Unique Pomelo \(\times\) Mandarin Hybrid
## 2 Materials and Methods
### Descriptive Sensory Tests  
Descriptive flavor tests were performed with the aid of a trained sensory panel comprised of 10 members, including four males and six females aged 25 to 65, who routinely perform flavor tests of citrus fruits [16; 17]. Each panelist assessed the various attributes according to an unstructured 100-mm linear intensity scale for each attribute. The scale ranged from'very weak' to'very strong' and sensory data were recorded as distances (mm) from the origin. All samples contained cut segments prepared from at least five different fruits and were assigned three-digit codes for identification. The sensory attributes were defined following preliminary tasting sessions. The sensory panel was trained by tasting standards of 0.5-1.5% sucrose to evaluate sweetness, 0-0.5% citric acid to evaluate sourness and 500-1000 mg L\({}^{-1}\) naringin to evaluate bitterness. Citrusy aroma was related to the smell of citrus peel and tropical aroma was related to the smell of bananas and tropical fruit.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & ‘**Aliza’** & **Grapefruit** & **Pomelo** & **Orange** & **Mandarin** \\ \hline
**Physical Traits** & & & & & \\ \hline Weight (g) & 541 b & 484 b & 1035 a & 281 c & 151d \\ Height (mm) & 87.89 b & 85.22 b & 144.5 a & 84.60 b & 54.84 c \\ Diameter (mm) & 100.5 b & 104.1 b & 129.3 a & 80.10 c & 69.37 d \\ Shape (height/diameter) & 0.88 b & 0.82 bc & 1.12 a & 1.06 a & 0.79 c \\ Albedo thickness (mm) & 3.72 c & 5.44 b & 16.57 a & 5.30 b & 2.55 c \\ Flavedo thickness (mm) & 0.89 bc & 1.78 ab & 2.43 a & 1.10 bc & 0.85 c \\ Peel thickness (mm) & 4.61 c & 7.22 b & 19 a & 6.40 b & 3.40 d \\ Juice content (\%) & 52.75 a & 42.55 b & 17.62 d & 37.64 c & 44.39 b \\ Seed number & 0 b & 1.11 b & 82.86 a & 0 b & 2.5 b \\
**Peel Color** & & & & & \\ \hline Hue angle (\({}^{\circ}\)h) & 99.40 a & 64.30 b & 95.92 a & 62.98 b & 64.80 b \\ Lightness (1–100) & 78.90 a & 66.30 c & 74.51 b & 65.59 c & 62.50 d \\ Chroma (1–100) & 52.10 c & 45.80 d & 42.25 d & 60.97 b & 68.60 a \\
**Biochemical Parameters** & & & & & \\ \hline TSS (\%) & 12.10 ab & 11.93 b & 11.80 b & 12.56 ab & 13.27 a \\ Aidity (\%) & 1.21 b & 1.59 a & 1.55 a & 0.83 c & 1.17 b \\ Ripening ratio (TSS/acidity) & 10.5 b & 7.5 c & 7.6 c & 15.2 a & 11.34 b \\ Vitamin C (mg 100 mL\({}^{-1}\)) & 54.0 b & 56.7 ab & 38.2 c & 59.1 a & 56.6 ab \\ \hline \end{tabular} Data concerning physical traits and peel color are means of 10 replications; data from biochemical analyses are means of five replications. Different letters within rows indicate significant differences at \(p\leq 0.05\). Bold fonts indicate citrus fruit not significantly different from ‘Aliza’.  
\end{table}
Table 1: Fruit quality traits of different citrus fruits.
## 3 LED-PHOTON AE Implementation
### Summary of Resource Reusability  
The authenticated encryption architecture being proposed leverages the common logic resources shared by both the LED block cipher and the PHOTON hash function. The state matrices of both primitives are merged into a unified 100-bit register in the authenticated encryption shared architecture.  
In the AddConstants module, part of the internal state is directly modified using XOR operations. The sharing ability reduces the number of XOR gates used in this module by half. The four-bit LFSR of PHOTON is considered a part of the six-bit LFSR of LED, as it takes its four LSB bits. Therefore, these two LFSRs are combined into one. For the SubCells module, since it is generated from the same substitution box for both LED and PHOTON, it may be used by both, with the exception that LED's state is now 100 bits long rather than 64 bits long, to match PHOTON. The ShiftRows module is applied to both LED and PHOTON but, since the number of cells in the state is not the same for LED and PHOTON, there are some mismatched cells, and they are treated individually. Therefore, the ShiftRows module is partially shared. MixColumns, on the other hand, consumes more logic resources than the other modules, even though look-up tables are used to replace the finite intensive multiplications. Since the sizes of the state matrices and pre-defined matrices are not the same for LED and PHOTON, the two modules are not fully merged. However, some similar cells of the pre-defined matrices were exploited to reuse the mask for the LUTs. Generally, the similarity of the operations and the network structure between the LED block cipher and the PHOTON hash function allows for the reusability of the logic resources.
###### Abstract
Despite the wide use of FA in SCC mixes in the earlier studies, an accurate model could not be identified for effective use by the construction industry. As a result, it was attempted in this study to analyze and quantify the effects of a wide range of mixture proportions, including FA content, cement content, w/b ratio, SP content, fine aggregate content, and coarse aggregate content, on the CS of SCC up to 365 days of curing. Different model approaches, such as nonlinear regression, multiregression, and ANN models, were used to predict the CS of SCC modified by FA using 400 data samples from the literature. The main goals are to (i) conduct statistical analysis and assess how mixture compositions such as FA content, C content, G content, w/c ratio, SP content, and curing time affect the CS of SCC; (ii) examine and design a reliable model for estimating the CS among all models (NLR, MLR, and ANN models) using appropriate statistical measures; and (iii) reduce the overall cost by designing mixes with any necessary specifications without the need for trail mixes.
# Structuring Reference Architectures for the Industrial Internet of Things
## 5 Considered Reference Architectures
### Industrial Value Chain  
The Industrial Value Chain Initiative (IVI) [28] defines a framework for industrial components and services. Independent Smart Manufacturing Units (SMU) serve as atomic building blocks for further integration and collaboration between the physical world on the one hand and the virtual world on the other. This view on 'things' corresponds to the concept of administration shells as propagated by RAMI4.0 (Asset Layer). In contrast to RAMI4.0, the IVI reference architecture also models the exchange of every resource of interest by Portable Loading Units (PLU). A PLU can contain a physical thing or product but also data and transaction information, therefore creating a general bracket for any transaction in both the physical and the virtual world.  
The strong focus on the combined modeling of physical, real-world objects and the activities happening to them underlines the perspective of the Industrial Value Chain. Similar to the Platform Industrie 4.0, the Industrial Value Chain Initiative examines how to connect the various implicit aspects of IIoT into describable and exchangeable SMUs. The proposed considerations concern in particular the industrial purpose of assets (see also RAMI4.0 Business Layer) and rather their virtual characteristics and connectivity mechanisms. In contrast to for example, the Industrial Internet Consortium, data security and interoperability methods are less prioritized in comparison to the modeling and description of processing activities and operations.  
The generically defined specifications allow the classification of very heterogeneous settings in terms of IVI, which at the same time implies a limited degree of expressed details. The Industrial Value Chain Reference Architecture aligns therefore mainly with the upper-level layers of other IIoT architectures, for example, the business- and function-related layers and viewpoints. Other frameworks can nevertheless be used to further specify connectivity or security concerns, as for example, the Industrial Data Space or FIWARE.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 6 Discussion
### Comparison with the State of the Art
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & [10] & [11] & [12] & [48] & [45] & [47] & [46] & **This Work** \\ \hline Data rate & 100 Mbps & 90 Mbps & 500 Mbps & 100 Mbps & 3 Mbps & 30 Mbps & 0.14 Mbps & 50 Mbps \\ \hline Power consumption & 2.1 mW & 1.6 mW & 5.4 mW & 0.26 mW & 5.4 mW & 30 mW & 0.085 mW & 0.3 mW \\ \hline Frequency & Light & 3–5 GHz & 3–7 GHz & 6.8–9 GHz & Sub-GHz & Sub-GHz & 3–5 GHz & 3–5 GHz \\ \hline Modulation & - & UWB & UWB & UWB & UWB & UWB & UWB \\ \hline Technology & - & 350 nm & 130 nm & 180 nm & 180 nm & 350 nm & 90 nm & 180 nm \\ \hline Supply Voltage & - & 1.65 V & 1.8 V & 1.5 V & - & 3.3 V & - & 1.3 V \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison with other medical implant transmitters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & [16] & [8] & [17] & [18] & [56] & [49] & **This Work** \\ \hline Data rate & 2 Mbps & 100 kbps & 13.56 Mbps & 8 Mbps & 2 Mbps & 0.01 Mbps & 1 Mbps \\ \hline Power consumption & 6.2 mW & - & 2.2 mW & 0.6 mW & 1.1 mW & 0.092 mW & 0.2 mW \\ \hline Frequency & 20 MHz & 5 MHz & 13.56 MHz & 902–928 MHz & 20–120 MHz & 413–419 MHz & 4 MHz \\ \hline Modulation & DPSK & - & PDM & FSK-ASK & P-OFDM & OOK & BPSK \\ \hline Technology & 350 nm & - & 350 nm & 130 nm & 65 nm & 180 nm & 180 nm \\ \hline Supply Voltage & - & 1 V & 1 V & 3 V & 1.1 V & 1.5 V & 1.3 V \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison with other medical implant receivers. (P-OFDM—Pseudo orthogonal frequency-division multiplexing). 1000 mW was transmitted in SAR simulations resulting in 10 g averaged SAR levels of a maximum of 1.92 W/kg are obtained. This value is below the specified 2 W/kg safety level. With the total power consumption of our IC below 1 mW, the data communication through the skin will be far below the SAR limit. The implanted transceiver itself can also dissipate power, causing the tissue directly surrounding it to heat up. The maximum temperature increase in the cortex has to be smaller than 1 \({}^{\circ}\)C [58; 59]. This corresponds to a maximum power density of 0.8 mW/mm\({}^{2}\) of exposed tissue area [20; 58; 59]. Our measured implanted transceiver IC consumes 0.5 mW in a 25 mm\({}^{2}\) QFN package, which brings the power density to 0.02 mW/mm\({}^{2}\), which is well below the safety limit. In [60], research into electromagnetic and thermal effects on IR-UWB on the human head was reported. From this work, it was also confirmed that the power levels for our IC are well within the control of thermal regulatory mechanisms of the human body.
## 3 Results and Discussion
### Best Practices Evaluation for Sustainability
This fact might be explained by the existence of catalogs that aim to simplify the reuse of requirements, such as the Simple Reuse of Requirements catalog (SIREN) proposed by the GIIS, which saves time in companies and has a beneficial effect on sustainability. Moreover, the need to internationalize to be able to sell abroad is important for companies, especially for technology companies, if they are to survive. Certifications (11.7%), consider sustainability when purchasing or outsourcing (4.26%), and the usage of sustainable infrastructures (4.26%) were practices that were carried out less in the companies. This reflects the fact that even when there are good intentions to apply best practices for sustainability (with an agreement among practitioners of between 60% and 80%) there seem to be difficulties in putting these alternatives into practice. The reason may be that the costs of certification could very well hold the companies back from implementing the practices, because there are not immediate benefits. The lack of companies certified in regulations for sustainability leads to the impossibility of considering sustainability when purchasing or outsourcing products, or of choosing to use green datacenter infrastructures. There should therefore be a promotion of certification among the companies in the quest to enhance sustainability. Finally, the group of practices with the least conviction about sustainability comprised \"Software product lines\" (52.13%), \"Big data and data mining\" (46.81%), \"Internet of things (IoT)\" (45.74%), and \"Virtual and augmented reality\" (28.72%). The level of use of these practices was even lower: \"Software product lines\" (21.28%), \"Big data and data mining\" (17.02%), \"Internet of things (IoT)\" (17.02%), and \"Virtual and augmented reality\" (3.19%). Practices such as software product lines, big data mining, and the use of IoT have an acceptable reception among professionals, as these technologies are proving productive for client companies, making it possible to produce more specific goods, in less time. However, other technologies such as virtual reality (VR) and augmented reality (AR) create more doubts regarding sustainability. Even though these technologies have traced out a certain path in the market, they are still in their infancy, and there is a lot of work to be done [27]. Figure 2: Comparison of results. Application of technical practices vs. the perceived helpfulness of technical practices in being more sustainable at work. There was generally a good level of willingness to engage in sustainable behavior at work on the part of the practitioners. This may be observed in the data set out above. The percentage of the perceptions always tends to be higher than the actual usage of the practices. For this reason, practitioners need to be provided with ways of proceeding in a sustainable manner at work.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 3 Algorithm Performance Test
### Template Matching Algorithm Comparison  
To demonstrate the advantages of the proposed algorithm, a comparison was performed with the template matching algorithms proven by OpenCV 3.0. Those algorithms were tested with the labeled template without compressing the template and category matrix. Gray template and color template tests were not performed because of their low success rates in changing environments. Three typical situations were tested: large angle, background changes, and obstacle obstruction. The figures below present comparisons of these situations.