esa-sceva/satcom-chunk-collection huggingface dataset:
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## 2 Generalized Multicarrier Radar-Communication Coexistence Model  
In this section, we extend a generalized multicarrier radar signal model [20] to more general settings, where radar and communication systems coexist and share the same radio spectrum. We start from a single-input single-output (SISO) point-to-point generalized multicarrier signal model to capture multicarrier radar and multicarrier communication signals. Then, the signal model is applied for \(K+1\) radar and communication users, where a radar user coexists with \(K\) communication users.
## 2 Related Works
### Emotional Perception of Children with Autism  
Research has indicated that facial expressions account for 55% of human emotional expressions, whereas voice and words account for 38% and 7%, respectively [18]. This shows that an individual's emotional state can be judged mainly from facial expressions and speech. Hence, data analysis based on facial expressions and speech features has been widely used in the field of emotion recognition for children with autism.  
Facial expression is an important factor in evaluating an individual's emotional state. Using electromyography, [PERSON] et al. [19] analyzed facial muscle movements under different emotions in children with autism and found that their zygomatic and frowning muscles show less variation than those of typically developing children. [PERSON] et al. [20] used Inception-ResNet-V2 to extract facial expression features such as fear and anger in the meltdown state of children with autism, and developed a system for detecting and alerting meltdown emotions. [PERSON] et al. [21] developed a CNN-based emotion recognition system for children with autism that recognized six facial expressions in real time through a camera while children were playing games, and they generated analysis reports that can be used for the development of targeted therapeutic interventions.  
Another critical factor in identifying the emotions of children with autism is speech characteristics. [PERSON] et al. [22] conducted a study on the application of emotion recognition techniques for children with autism. They found that SVM and neural networks are the most frequently used techniques, with the best effects. [PERSON] et al. [23] developed a speech emotion database for children with autism that is based on the Tamil language, retrieved acoustic features such as MFCCs using SVM, and identified five emotions, including anger and sadness. [PERSON] et al. [24] created a speech-based emotion prediction system for children with autism by extracting two acoustic features, namely MFCCs and spectrograms, and using the multilayer perceptron classifier to predict children's seven basic emotions. Emotion perception methods for children with autism have been progressing towards intelligence, with developments in deep learning and large data analysis technologies. Neural networks are increasingly becoming the dominant technology in this field. Nevertheless, most current studies have been limited to analyzing single-modal data such as facial expressions or speech characteristics. Few studies have investigated the emotion perception of children with autism from the perspective of multimodal data fusion.  
For MFCCs, [PERSON] et al. [25] conducted a comparative analysis of MFCC, MEL, and Chroma features for emotion prediction from audio speech signals, finding that MFCCs exhibited superior performance in distinguishing between emotional states due to their ability to model the human auditory system's frequency response.
## 2 Experimental Section
### Screening of Compound **1** Producing Microbes  
Bacteria and fungi were isolated from the fermented tea leaves using the plate dilution method. Tea leaves (25 g) were suspended in 225 mL of sterile 0.9% NaCl (Xilong Chemical, Guangzhou, China), diluted to 10\({}^{-2}\), 10\({}^{-3}\), 10\({}^{-4}\), and 10\({}^{-5}\) with sterile water, and spread on Rose Bengal Medium Agar (RBMA; 5 g peptone, 10 g glucose, 1 g KH\({}_{2}\)PO\({}_{4}\), 0.5 g MgSO\({}_{4}\) 7H\({}_{2}\)O, and 20 g agar per liter) with 0.1 g/L chloramphenicol and Nutrient Agar (NA; 5 g peptone, 3 g beef extract, 5 g NaCl, and 20 g agar per liter) for fungal and bacterial isolations, respectively. RBMA and NA plates were incubated at 28 \({}^{\circ}\)C for 5 d and 37 \({}^{\circ}\)C for 2 d, respectively. Bacterial genomic DNA was extracted using the Rapid Bacterial Genomic DNA Isolation Kit (Sangon Inc., Shanghai, China) and used as a template for the Polymerase Chain Reaction (PCR) amplification of bacterial 16s rRNA genes using general bacterial primers 8F (5'-AGA GTT TGA TCC TGG CTC AG-3') and 1492R (5'-GGTTACCTTGTTACGACT T-3') [29]. Fungal genomic DNA was extracted using the Rapid Fungal Genomic DNA Isolation Kit (Sangon Inc., Shanghai, China) and used as a template for the PCR amplification of fungal 18S rRNA genes using general primers ITS1F (5'-CTTGGTCATTTAGAGGAGTAA-3') [30] and ITS4R (5'-TCCTCCGTTATTGATATGC-3') [31]. PCR amplification was performed using TaKaRa Ex Taq polymerase (DRR100A, TaKaRa Biotechnology Co., Ltd., Dalian, China). The reaction mixture (50 \(\upmu\)L) contained 2 \(\upmu\)L of DNA template, 5 \(\upmu\)L of Ex PCR buffer, 3 \(\upmu\)L of dNTP mixture (25 mM each), and 0.25 \(\upmu\)L of TaKaRa Ex Taq polymerase (5 units/\(\upmu\)L). The amplification program consisted of an initial denaturation step at 95 \({}^{\circ}\)C for 5 min, followed by 35 cycles at 95 \({}^{\circ}\)C for 30 s (denaturation), 55 \({}^{\circ}\)C for 30 s (annealing), and 72 \({}^{\circ}\)C for 60 s (extension), and a final extension at 72 \({}^{\circ}\)C for 5 min. The PCR fragments were sequenced by Shanghai Sangon Inc. (Shanghai, China) and identified using EzTaxon server [32] and Blastn search.  
The bacterial and fungal strains isolated from the fermented tea leaves samples were inoculated into sterile sun-dried green tea and fermented for 14 d at 28 \({}^{\circ}\)C. The fermented tea leaves were air-dried and subjected to HPLC analysis.
# LSTM Accelerator for Convolutional Object Identification
## 4 Experiments and Results
### Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy (Percentage)** & **Loss (Cross-Entropy)** & **Time (s)** \\ \hline \multicolumn{4}{c}{**Epoch = 1**} \\ \hline C1 + C2 & 87.7 & 0.43 & 104 \\ C1 + C2 + B\_N & 92.8 & 0.2 & 107 \\ C1 + C2 + B\_N + LSTM & 90.8 & 0.314 & 78 \\ C1 + C2 + C3 & 87.7 & 0.38 & 149 \\ C1 + C2 + C3 + B\_N & 95 & 0.16 & 149 \\ C1 + C2 + C3 + B\_N + LSTM & 87.1 & 0.41 & 111 \\ \hline \multicolumn{4}{c}{**Epoch = 5**} \\ \hline C1 + C2 & 94.3 & 0.15 & 448 \\ C1 + C2 + B\_N & 97.1 & 0.097 & 519 \\ C1 + C2 + B\_N + LSTM & 95.8 & 0.137 & 278 \\ C1 + C2 + C3 & 95.6 & 0.145 & 595 \\ C1 + C2 + C3 + B\_N & 97.2 & 0.084 & 768 \\ C1 + C2 + C3 + B\_N + LSTM & 96 & 0.11 & 467 \\ \hline \multicolumn{4}{c}{**Epoch = 10**} \\ \hline C1 + C2 & 95.3 & 0.15 & 956 \\ C1 + C2 + B\_N & 97.1 & 0.097 & 1048 \\ C1 + C2 + B\_N + LSTM & 95.8 & 0.137 & 756 \\ C1 + C2 + C3 & 96.2 & 0.13 & 1262 \\ C1 + C2 + C3 + B\_N + LSTM & 97.5 & 0.095 & 1356 \\ C1 + C2 + C3 + B\_N + LSTM & 97.3 & 0.09 & 810 \\ \hline \multicolumn{4}{c}{**Epoch = 20**} \\ \hline C1 + C2 & 96.1 & 0.101 & 1961 \\ C1 + C2 + B\_N & 97.2 & 0.085 & 2174 \\ C1 + C2 + B\_N + LSTM & 97.3 & 0.081 & 1378 \\ C1 + C2 + C3 & 97 & 0.12 & 2510 \\ C1 + C2 + C3 + B\_N & 97.6 & 0.092 & 2741 \\ C1 + C2 + C3 + B\_N + LSTM & 97.4 & 0.09 & 1696 \\ \hline \multicolumn{4}{c}{**Epoch = 40**} \\ \hline C1 + C2 & 97 & 0.101 & 3866 \\ C1 + C2 + B\_N & 97.7 & 0.098 & 3887 \\ C1 + C2 + B\_N + LSTM & 97.5 & 0.088 & 2484 \\ C1 + C2 + C3 & 97.3 & 0.13 & 5080 \\ C1 + C2 + C3 + B\_N & 97.8 & 0.097 & 5395 \\ C1 + C2 + C3 + B\_N + LSTM & 97.6 & 0.09 & 3007 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy, loss and time for different numbers of epochs (10% sample) for the MNIST dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline C1 & First Convolutional Layer \\ C2 & Second Convolutional Layer \\ C3 & Third Convolutional Layer \\ B\_N & Batch Normalization \\ LSTM & Long Short-Term Memory Neural Layer \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Abbreviations for different methods utilized. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Accuracy (Percentage)** & **Loss (Cross-Entropy)** & **Time (s)** \\ \hline \multicolumn{4}{c}{**Epoch = 50**} \\ \hline C1 + C2 & 96.9 & 0.102 & 4790 \\ C1 + C2 + B\_N & 97.7 & 0.097 & 4794 \\ C1 + C2 + B\_N + LSTM & 97.6 & 0.082 & 3045 \\ C1 + C2 + C3 & 97.4 & 0.12 & 6351 \\ C1 + C2 + C3 + B\_N & 97.8 & 0.097 & 6809 \\ C1 + C2 + C3 + B\_N + LSTM & 97.8 & 0.091 & 3812 \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
###### Abstract  
To investigate the effects of long-term freeze-thaw cycles on the physical and mechanical properties as well as the attenuation trend of rocks, this study conducted saturated freeze-thaw tests on coarse sandstone and fine sandstone samples collected from the slopes of Muli Coal Mine in Qinghai Province. The samples underwent different numbers of freeze-thaw cycles, and their porosity, longitudinal wave velocity, and uniaxial compression strength were studied. The variations in the physical and mechanical properties of the two types of sandstone with respect to the number of freeze-thaw cycles were analyzed. Take uniaxial compressive strength (UCS) as the integrity index, and decay laws of rock integrity were analyzed based on the decay equation suggested in previous studies. We found that the decay index \(\lambda\), which is commonly assumed to be constant, varies with the number of freeze-thaw cycles. Furthermore, the \(\lambda\) values varied between different rock types. For fine sandstone, the \(\lambda\) decreases with an increase in the number of freeze-thaw cycles, ranging from 0.00385 to 0.005. However, for coarse sandstone, the \(\lambda\) initially decreases and then increases with an increase in the number of freeze-thaw cycles. The range of \(\lambda\) for coarse sandstone is between 0.00376 and 0.00481. Finally, we established a relationship between the decay index, porosity, and longitudinal wave velocity in the fine sandstones. This relationship provides a more straightforward way to evaluate the integrity of fine sandstones subjected to different numbers of freeze-thaw cycles.  
rock; freeze-thaw cycling; decay equation; decay index \(\lambda\); uniaxial compressive strength (UCS)
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 1 Introduction
Without completeness, a few important milestones are listed as follows:  
* Convergence of fixed and mobile networks (FMC, Fixed-Mobile Convergence);
* Sunset of earlier Radio Access Technologies (RATs), e.g., 3G systems in Europe and 2G (second-generation) and 3G in America and Asia [3];
* Introduction of new RATs, i.e., 4G (fourth-generation), LTE-A (Advanced Long-Term Evolution), and 5G [4, 5, 6, 7];
* Intensive research and standardization of 5G and beyond and future 6G (sixth-generation) mobile systems and Open RAN (Radio Access Network) solutions [4, 5, 6, 8, 9];
* Access-agnostic solutions expanding the coverage of various 'trusted' mobile generations with WiFi (Wireless Fidelity) access, which has traditionally been treated as 'untrusted' [10, 11];
* Continuous introduction of new protocols and interfaces (e.g., Diameter for Ro/Rf to replace the legacy Bi/Bc charging interfaces) [7];
* Replacement of legacy transmission and transport solutions and legacy protocols (e.g., TDM/PDH/SDH/ATM) in all segments, including User Plane (UP), Control Plane (CP), management plane, and synchronization [3, 12, 13];  
Figure 1: The evolving telecommunication network as a mesh of various fixed and mobile access technologies and core networks. * Modernization projects targeting 'all-IP' networks and Software-Defined Networking (SDN) [13; 14; 15; 16];
* Replacement of earlier 'non-cloud friendly' interfaces and protocols and introduction of Service-Based Architecture (SBA) and service discovery instead of rigid 'fixed configurations' that are used in legacy telecommunications [17];
* Protection of network functions and interfaces to achieve more resilient operation and high availability (from redundancy methods and anti-affinity rules to distribution in a geo-redundant Telco cloud) [18; 19];
* Cloudification: migrating physical network elements and Physical Network Functions (PNFs) to Virtualized Network Functions (VNFs) and Cloud-native Network Functions (CNFs) [17; 18; 19; 20; 21; 22; 23; 24];
* Flexible resource management of cloud-based VNFs and CNFs, scaling, and network slicing [24; 25; 26; 27; 28; 29; 30];
* Continuous capacity expansions and upgrades in both access and core network domains, introducing new business models, such as private and non-public networks [3; 5; 12; 23; 31; 32; 5];
* Convergence of Data Center and IT (Information Technology) with telecommunications on HW and SW infrastructure and service layers [18; 19; 20; 21; 22; 33; 34; 35]. After this short introduction, in Section 2, the development of legacy telecommunication services is reviewed. Section 3 shows replacement options for existing Intelligent Networks (INs) services. Section 4 discusses the fusion of IT and telecommunication domains, which is illustrated with Nokia TAS. Several Open API and IMS Data Channel use cases are given. Finally, Section 5 concludes this paper, and useful references are listed.
## 5 Results and Discussion
### Discussion of Results
#### 5.3.5 Data Transfer Security  
The measured data did not suffer any type of interference from nearby radiofrequency sources. Although there were other WiFi communications in areas close to the test, the selection of distant channels within the 2.4 GHz band, specifically channels 1 and 11, avoided any type of coupling. The signals reached the computing center with sufficient intensity, registering coherent values and with normal noise levels. The security of the data transmissions was guaranteed by means of the SSL protocol executed over the wireless signals [58; 59].  
The results of this study indicate that AIoT wireless technology is a mature and highly effective solution, demonstrating superior performance under fatigue test conditions. This is validated by its accuracy in measurements, advanced analytical capabilities, operational efficiency, integration of advanced services, and robust data transfer security.  
The measurement capabilities, coupled with the significant advantages provided by AIoT, confirm the feasibility of utilizing this technology as a monitoring system in high-latency aeronautical structural testing and other types of tests. The sensors within the proposed network achieved measurement accuracy that met the test requirements for the three monitored variables. The system's sampling rate exceeded the demands of real scenarios, with no data loss or duplication, establishing it as a robust and reliable solution. Additionally, IoT technologies significantly reduce cabling requirements and deployment time while enhancing flexibility and scalability.  
These results have the potential to profoundly improve the efficiency and competitiveness of aeronautical testing laboratories by leveraging this technology. The proposed methodology also creates opportunities to incorporate advanced algorithms based on artificial intelligence techniques for real-time analysis of structural testing outcomes.
## 3 Results
### Study on the Unconfined Compressive Strength of Steel Slag-Cement-Bentonite Barrier Materials
#### 3.1.2 Effect of Bentonite Dosage on the Unconfined Compressive Strength of Barrier Materials  
In Figure 4, the unconfined compressive strength increases with the bentonite dosage at the same steel slag substitution rate and age. For example, the unconfined compressive strength of steel slag-cement-bentonite barrier materials with an age of 28d, a steel slag substitution rate of 25%, and bentonite dosages of 46%, 50%, and 54% were 72.80 kPa, 109.66 kPa, and 206.93 kPa, respectively. The larger the bentonite dosage, the more the pores of the cement skeleton in the steel slag-cement-bentonite slurry are filled, and the other part reacts with the cement steel slag and becomes part of the overall structure. With the increase in bentonite, as more pores of the cement skeleton are filled, the strength increases.  
Figure 3: Change in unconfined compressive strength of steel slag–cement–bentonite material with substitution rate of steel slag under different bentonite dosages.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 4 Results and Discussion
### The Environmental Noise  
In practical applications, the acoustic noise delivered by the rolling bearing is combined with the environmental noise, which causes the detection of faulty bearing to be adversely affected. Thus, it is important to include such a noise in the experimental validation of the proposed module. Indeed, in most works, the proposed monitoring techniques are tested considering healthy and faulty rolling bearings, which are connected to an electric motor and a load. However, the environmental noise is almost always neglected. Thus, in this work a different experimental setup is proposed. The wheel bearings of a car, which connect the wheel hubs to the constant velocity joints, are used as a test casein what follows. Such an application requires a highly reliable monitoring system, indeed also the driver safety can be affected by a damaged bearing. Moreover, such test case represents an harsh environment from the acoustic point of view, since the background noise is time-independent and it really depends on the driving conditions. As a consequence, the fault detection may be challenging. By means of an extra microphone patched on the car body over the front-left wheel, the sound of a moving vehicle was recorded in different scenarios. The frequency spectrum of such noise (\(A_{n}\)) was evaluated using Matlab and shown in Figure 8. More precisely, Figure 8a refers to the car started but not moving, Figure 8b to a straight with asphalt, and Figure 8c to a straight with gravel. It is worth noticing that such spectra are significantly populated up to 1 kHz, and that in every driving condition the environmental noise is far from being closed to a white noise.  
Figure 5: Relative error affecting the (**a**) fundamental frequency and (**b**) amplitude of the input sinusoidal signal, when no windowing is used.  
Figure 6: Relative error affecting the (**a**) fundamental frequency and (**b**) amplitude of the input sinusoidal signal, when the order 2  Hann window is used.  
Figure 8: Frequency spectrum of the environmental noise in the proximity of the left front wheel when (**a**) the car is started but not moving, (**b**) the car is traveling along a straight with asphalt at 60 km/h, and (**c**) the car is traveling along a straight with gravel at 20 km/h. The amplitude of the time-domain signal was normalized between [\(-\)1,1].  
Figure 7: Relative error affecting the (**a**) fundamental frequency and (**b**) amplitude of the input sinusoidal signal, when the top flat window is used.
## 3 Results and Discussion
### The Grating Slant \(\Phi\)
#### 3.2.2 Impact of the Exposure Duration: \(\Phi\)(\(t_{\text{exp}}\))  
To measure the strength of the deformation effects perpendicular to the sample surface, the parameter \(\Delta\Phi\) is introduced. \(\Delta\Phi\) is defined as the difference of the left side and right side slant angles, as depicted at the lower left of Figure 3. With the help of this quantity, a comparison of the individual performances is facilitated. Figure 4 demonstrates the impact of the exposure duration on the observed grating slant difference \(\Delta\Phi\) with reference to the material viscosity.  
Notwithstanding the different viscosity, the grating slant difference shows qualitatively the same curve progression. In fact, \(\Delta\Phi\)(\(t_{\text{exp}}\)) describes an initial decrease up to a minimum and a subsequent increase with increasing exposure duration. Thereby \(\Delta\Phi\) reaches higher values in case of long exposure duration. Once again, these results are in compliance with findings from preliminary sections. As already observed in view of \(\Lambda\)(\(t_{\text{exp}}\)), an increase of the exposure duration enhances the effect of optical shrinkage.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
###### Abstract  
Uninterruptible power supplies (UPS), represented by lead-acid batteries, play an important role in various kinds of industries. They protect industrial technologies from being damaged by dangerous interruptions of an electric power supply. Advanced UPS monitoring performed by a complex battery management system (BMS) prevents the UPS from sustaining more serious damage due to its timely and accurate battery-fault detection based on voltage metering. This technique is very advanced and precise but also very expensive on a long-term basis. This article describes an experiment applying infrared thermographic measurements during a long term monitoring and fault detection in UPS. The assumption that the battery overheat implies its damaged state is the leading factor of our experiments. They are based on real measured data on various UPS battery sets and several statistical examinations confirming the high relevancy of the thermal features with mostly over 90% detection accuracy. Such a model can be used as a supplement for lead-acid battery based UPS monitoring to ensure their higher reliability under significantly lower maintenance costs.  
Keywords:battery overheat; fault detection; thermal feature processing; relevance estimation +
Footnote †: journal: Applied Sciences