esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 4 Experimental Results and Discussion
### The Stay Cable with Electromagnetic Damping Alone
The inertial mass is generated from the rotary part (i.e., rotor) of the generator. Equation (8) indicates that the negative stiffness of the rotary DC generator is proportional to the natural frequency of the cable, demonstrating frequency-dependent behavior. In other words, a higher mode of the cable corresponds to a larger negative stiffness. Accordingly, experimentally identified equivalent damping coefficients and stiffness coefficients are shown in Figure 12. As expected, the damping coefficient almost remains a constant with the increase of the mode order of the cable for each circuit, while the negative stiffness increases with the increase of the mode order. However, the optimum negative stiffness for a stay cable does not depend on the mode number [36; 37]. Hence, the DC generator with frequency-dependent negative stiffness may be better than viscous damping only, but is still suboptimal when compared with optimum viscous damping with negative stiffness for cable vibration control.
###### Abstract  
Viroids are small infectious pathogens, composed of a short single-stranded circular RNA. Hop (_Humulus lupulus_ L.) plants are hosts to four viroids from the family _Pospiviroidae_. Hop latent vivid (HLVd) is spread worldwide in all hop-growing regions without any visible symptoms on infected hop plants. In this study, we evaluated the influence of HLVd infection on the content and the composition of secondary metabolites in matured hop cones, together with gene expression analyses of involved biosynthesis and regulation genes for Saaz, Sladek, Premiant and Agnus cultivars. We confirmed that the contents of alpha bitter acids were significantly reduced in the range from 8.8% to 34% by vivid infection. New, we found that vivid infection significantly reduced the contents of xanthohumol in the range from 3.9% to 23.5%. In essential oils of Saaz cultivar, the contents of monoterenes, terpene epoxides and terpene alcohols were increased, but the contents of sesquiterpenenes and terpene ketones were decreased. Secondary metabolites changes were supported by gene expression analyses, except essential oils. Last-step biosynthesis enzyme genes, namely humulone synthase 1 (HS1) and 2 (HS2) for alpha bitter acids and O-methyltransferasease 1 (OMT1) for xanthohumol, were down-regulated by vivid infection. We found that the expression of ribosomal protein L5 (RPL5) RPL5 and the splicing of transcription factor IIIA-7 ZF were affected by vivid infection and a disbalance in proteosynthesis can influence transcriptions of biosynthesis and regulatory genes involved in of secondary metabolites biosynthesis. We suppose that RPL5/TFIIA-7 ZF regulatory cascade can be involved in HLVd replication as for other viroids of the family _Pospiviroidae_.  
hop; _Humulus lupulus_; hop latent vivid; HLVd; bitter acids content; xanthohumol; essential oils; differential gene expression
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 4 Current Consumption Model of an LR-FHSS ED
### Experimental Scenario  
The experimental scenario for which we carry out the measurements is a complete LoRaWAN network. To the best of our best knowledge, this is the first time in the academic literature that a complete LoRaWAN network--comprising an ED, GW, and NS, all supporting LR-FHSS--has been tested in this manner. For the ED, we use a LR1121 DVK1 TBKS development kit from Semtech [29], which is composed of a Nucleo L476 board alongside a LR-FHSS-capable radio interface based on the LR1121 chipset [30]. For the GW, we use a Kerlink Wirnet iBTS Compact, which is LR-FHSS-compatible after a firmware update [31]. We deploy an instance of Chirpstack version 4.8.1 on-premise to act as the NS [32]. To perform the measurements, we use the Keysight N6705A DC power analyzer [33], which includes two power supply outputs (cf. Figure 5). In this scenario, the ED is supplied 5 V via the USB interface at one of the outputs to feed the Nucleo L476 board, and the energy consumption of the radio interface is measured through a 3.3 V second power supply that only feeds this part of the system. In the measurements, the transmit power of the ED is configured to +14 dBm, which is the maximum value for the band in the EU868 region [28]. This is the transmit power that would be used to achieve the longest link range. Therefore, our work allows us to determine the energy performance in scenarios where an end device is configured to achieve the longest link range. In this study, as the link range does not affect the measurements, both the ED and the GW are located in an indoor environment and are at a distance of around 2 m from each other.
## 2 Materials and Methods
### Protein Identification and Quantitation  
Raw data were captured from the mass spectrometer and converted to MGF (Mascot Generic Format) files using ProteoWizard with the following parameters: peakPicking true 2, msLevel 2, zeroSamples removeExtra [23]. For thorough protein identification, the generated peak lists were searched with the MASCOT (v 2.5.1, Matrix Science Ltd., London, UK) and X! Tandem (VENGEANCE, 2015.12.15, The Global Proteome Machine Organization) search engines. Database-searching parameters were as follows: tryptic hydrolysis, no more than one missed site, the precursor and fragment mass tolerance were set at 20 ppm and 50 ppm, respectively. Oxidation of methionine was set as a possible modification, carbamidomethylation of cysteine as a fixed. For X! Tandem, parameters that allowed a quick check for protein N-terminal residue acetylation, peptide N-terminal glutamine ammonia loss or peptide N-terminal glutamic acid water loss were selected. Resulting files were submitted to the Scaffold 4 software (v 4.2.1, Proteome Software, Inc., Portland, OR, USA) for validation and further analysis. For protein identification, the proteomic databases for the _M. smegmatis_ mc\({}^{2}\) 155 (RefSeq:NC_008596.1) genome were used. The local false discovery rate scoring algorithm with standard experiment-wide protein grouping was used. A 1% FDR threshold was applied to search results from individual datasets. For all detected proteins, functional categories (Mycobrowser v 3.0 ([[https://mycobrowser.epfl.ch/](https://mycobrowser.epfl.ch/)]([https://mycobrowser.epfl.ch/](https://mycobrowser.epfl.ch/)), accessed on 25 September 2023)) and subcellular localizations (PSORTdb v 4.0 ([[http://db.psort.org/](http://db.psort.org/)]([http://db.psort.org/](http://db.psort.org/)), accessed on 25 September 2023), proteomic data were aligned to _M. tuberculosis_ orthologs) were established.  
For label-free quantitation, raw MS data files (.wiff files) were imported and processed in Progenesis LC-MS software v.4.1 (Nonlinear Dynamics, Newcastle, UK). The results of peptide quantitation were normalized using an iterative median-based normalization as implemented in the Progenesis software v.4.1. Differences in the abundance of a protein between the three biological replicates of all strains were evaluated using a two-sided unpaired Student's \(t\)-test. \(p\)-values \(<\) 0.05 were considered statistically significant. Adjusted \(p\)-values for multiple tests (q-values) were generated using the Benjamini-Hochberg method [24].
###### Abstract  
The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant _EGFR_ mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the _EGFR_ mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.  
radiogenomics; machine learning; feature selection; lung cancer; _EGFR_ prediction  
Accepted: 29 March 2021  
Submitted: 6 April 2021
# Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan
## 3 Automatic System Design
### Front-End and Back-End Communication  
The core of automatic SG lies in the real-time data transfer between the HCI front-end interface and the PreScan back-end. This paper employs the WebSocket for seamless communication between the front-end and back-end. WebSocket, a full-duplex communication protocol, enables a persistent connection between client and server. Through WebSocket, the front-end is capable of transmitting data to the back-end in real time. Concurrently, the back-end can relay results and visualized scenarios back to the front-end instantaneously. This real-time, bi-directional communication facilitates continuous data interaction between the front-end and back-end, obviating the need for frequent opening and closing of connections, thereby ensuring a more efficient and uninterrupted connection.  
In order to accurately facilitate data transfer between different programming languages, this paper chooses JavaScript object notation (JSON) as the data exchange format, which has the characteristics of easy parsing and generation, and can be used for data exchange and communication between different programming languages and operating systems. As shown in Figure 4, after the user specifies the scenario parameters, the front-end sends the input data to the back-end. After receiving the data, the back-end first determines the appropriate scenario category, selects the appropriate unit class, and then selects the API according to the elements in the unit class to generate the corresponding simulation scenario. At the same time, the data in the simulation scenario is transferred back to the front-end in real time and displayed in the front-end visualization component. The real-time information transfer between the front-end and back-end effectively improves the efficiency of data transfer.  
Through the above process, users can design the required scenario test cases according to the scenario parameterization, and only need to enter the set parameters to communicate with the back-end to display the generated results on the front-end. This ensures both the speed of building scenarios and the accuracy of the task.  
Figure 4: Front- and back-end communication.
## 2 Materials and Methods
### Improved Deeplabv3+ Network Architecture
#### 2.3.2 SENet Module  
The SENet module learns feature weights through training loss, enhancing the effectiveness of important features while suppressing irrelevant or uninformative weights. Within the realm of tilapia body part segmentation tasks, practical farming scenarios can introduce certain disruptions, such as fluctuations in lighting conditions or interferences from the background. The introduction of SENet can effectively alleviate channel redundancy issues in the feature maps of the Deeplabv3+ network, improving both parameter and computational efficiency. By precisely controlling the weights of each channel, the network can adaptively adjust the expressive power of the feature maps at different levels. This improves the model's perception of tilapia body part boundaries, textures, shapes, and other features, enabling it to better adapt to variations in tilapia morphology.  
The structure of the SENet module is shown in Figure 8, and it recalibrates the output features through three operations: Squeeze, Excitation, and Reweight. First, the Squeeze operation compresses the feature maps of each channel into a scalar value using global average pooling. Then, a series of fully connected layers map this scalar value to a smaller vector. Next, the Excitation operation treats this vector as the importance weights for each channel and uses these weights to reweight the feature maps of each channel. Finally, the Reweight operation considers the weights outputted by Excitation as the importance of each feature channel after feature selection. It then performs element-wise multiplication  
Figure 7: CBAM module structure.  
to reweight the original features along the channel dimension. The mapping formula is as follows:  
\[z_{c}=F_{sq}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c}(i,j) \tag{5}\]  
\[s=F_{ex}(z,W)=\sigma(g(z,W))=\sigma(W_{2}\delta(W_{1}z)) \tag{6}\]  
\[\widetilde{x}_{c}=F_{scale}(u_{c},s_{c})=s_{c}\cdot u_{c} \tag{7}\]  
In the given context, \(u_{c}\) represents each feature channel, \(W_{1}\) and \(W_{2}\) denote weights, while \(\sigma\) and \(\delta\) represent activation functions.
## 1 Introduction  
Glass fiber reinforced epoxy is one of the composite materials used throughout the globe for its advantages, such as corrosion resistance, high strength, light weight, and design flexibility [1]. Although composites are more expensive compared to common metals, such as aluminum and steel, new applications are increasing with time in various fields, including aerospace and civil structures. Composite material is more difficult to evaluate in terms of quality control than traditional materials (such as steel) as it is made with materials with different mechanical properties. When using composites, defects, such as debonding, delamination, and cracks, can occur due to impact or fatigue stresses, and detecting these damages at an early stage is critical to ensure safety. For detecting damage, various non-destructive testing (NDT) techniques have been developed for testing composite structures. Damage detection methods can be categorized into contact and non-contact methods, where contact methods include electromagnetic testing, eddy current testing, and ultrasonic testing [2; 3; 4; 5]. Non-contact methods include holography, infrared, and thermography. It is usually used for situations where physical contact between the target structure and sensor is not possible [6; 7; 8].  
In this study, a new way of conducting one of the contact methods known as the electromechanical impedance (EMI) technique is investigated. The EMI technique requires one to permanently attach the piezoelectric transducer onto the target where an impedance measuring device is used to measure impedance, usually before and after damage, to identify the existence of damage [9; 10; 11; 12]. Permanently attaching the PZT transducer can allow one to monitor a structure in real-time. However, for large structures, it can be costly as the sensing range of the EMI technique is small, and many PZT transducers would be required. Thus, to overcome this problem, attempts have been made to create the EMI technique that allows one to temporarily attach a PZT transducer to cover a large area with a single PZT device [13]. Although more research is required to solve the existing problem of the temporary attachment EMI technique, the previous research required one to attach and detach the PZT device manually. In this study, the proposed roller device is designed to be simply rolled onto the surface of a composite structure for detecting damage. Here, tests were conducted to evaluate the performance of the proposed idea against one of the temporary attachment PZT devices mentioned previously.
# Feasibility of Obtaining Surface Layer Moisture Flux Using an IR Thermometer
###### Abstract  
This paper evaluates the feasibility of a method using a single hand-held infrared (IR) thermometer and a mini tower of wet and dry paper towels to psychometrically obtain surface layer temperature and moisture gradients and fluxes. Sling Psychometers have long been standard measuring devices for quantifying the thermodynamics of near-surface atmospheric gas-vapor mixtures, specifically moisture parameters. However, these devices are generally only used to measure temperature and humidity at one near-surface level. Multiple self-aspirating psychrometers can be used in a vertical configuration to measure temperature and moisture gradients and fluxes in the first 1-2 m of the surface layer. This study explores a way to make multiple vertical psychrometric measurements with a single non-contact IR temperature sensor rather than using two in situ thermometers at each level. The surface layer dry- and wet-bulb temperatures obtained using an IR Thermometer are compared to Kestrel 4000 Weather Meter and Bacharach Sling Psychrometer measurements under various atmospheric conditions and surface types to test the viability of the method. To evaluate the results obtained using this new approach, standard meteorological surface data are collected during each experiment, and moisture parameters are derived via psychrometric equations. The results indicate that, not only is the method possible and practical, but they suggest that the IR Thermometer method may provide more surface layer temperature and moisture gradient and flux sensitivity than other single instrument methods.  
infrared thermometer; wet-bulb temperature; moisture flux +
Footnote †: journal: Applied Sciences
## 4 Experimental Validation  
### Comparison between Original and Modified Boring Bars When the Control Is Off  
At first, experimental tests were carried out in order to compare the standard boring bar with the realized active boring bar when the control is off. The aim of this validation is to verify that the designed active boring bar presents an equivalent static and dynamic behavior of the standard one (requirement 3 in Section 2.1). Experimental modal analysis was carried out on both boring bars in free-free (Figure 18a) and constrained (fixed-free) conditions (Figure 18b).  
Experiments were performed by means of impact testing using a PCB 08C03 impact hammer and two mono-axial accelerometers (PCB 352C22); a roving hammer strategy was adopted, and required FRFs were computed by means of the dedicated acquisition system (SCADAS SCM202V). Using LMS software, FRFs were analyzed and dominant modes characteristics (mode shapes, natural frequencies) were extracted by means of Polymax algorithm [21].  
Figure 17: (**a**) Original boring bar dominant mode, (**b**) active boring bar dominant mode and (**c**) comparison between original (i.e., standard) and active boring bar in terms of FRF.  
Results in terms of dominant mode natural frequencies are reported in Table 7. The difference in terms of natural frequency between original and active boring bar (less than 5%) indicates a good match between the two systems. As a consequence, the effectiveness of design approach to achieve dynamic equivalence was demonstrated.
## 3 Demand-Side Resources in Active Distribution Systems
### Demand Responses  
Demand Response (DR) refers to the strategy of encouraging consumers to reduce energy usage in the near term in response to a price signal from the hourly electricity market or a trigger set by the electricity grid operator. DR activities typically last 1 to 4 h and involve turning off or lowering lighting banks, altering HVAC settings, or shutting down a segment of a manufacturing process. In addition, onsite generating can be utilized to displace load pulled from the power grid [30].  
The quest to reduce the electricity generation sector and consumption is paramount. This can be achieved by increasing smart energy solutions (SES), which constitutes one way that could be achieved. This, however, cannot be fostered for its own sake, but rather as a tool for attaining common goals [31]. The DR activities are very complex, but the introduction of the Internet of Energy (IoE) revolutionized power production and supply, and Figure 1 below depicts the IoE with energy trading.  
Figure 2 shows automated demand response operability in the different layers. As seen in Figure 2, the evolution of communication and metering equipment has transformed the traditional distribution grid into the Smart Distribution Grid (SDG). Two-way communication between utilities and consumers facilitates the sharing of metering information, allowing them to better utilize their energy resources and engage in automated demand response. To deploy DR in an automated way, smart metering infrastructure, communication technologies, flexible energy supplies, and efficient decision-making tools are of utmost importance. ADS operates in three layers, namely the physical layer, the communication layer, and the software layer, otherwise known as the Automated Demand Response layer, as shown in Figure 2.  
Figure 1: IoE with Energy Components [31].  
The wide area network (WAN) is managed by the utility or companies, while the neighborhood area network is managed by the aggregator. The home area network, building area network, and industrial area network are of the same level and are managed by the consumers themselves.