esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Implementation and Evaluation
### Multimedia Workflows
#### 7.4.3 Service Monitoring  
The instantiation of a suitable service chain for an individual user is done by a Monitoring Service. This service also monitors the workflow and is responsible for service replacements. The client device only interacts with the Monitoring Service using Web service calls, as previously visualized in Figure 9. Beside different multimedia adaptation services, we also developed the display service which is responsible to show the adapted content on the client side. Furthermore, we developed a content provider which provides a multiplicity of different media files.  
In this way we are transferring the media file chunk by chunk via the stream connection. These chunks are then stored in a cache and processed by the adaptation process. The adapted chunks are cached again and transferred to the next service. In case of a service failure, this service is replaced by a similar one immediately and the processing continues with the next required chunk. By storing a number of chunks on each multimedia service provider, we assure that no chunk gets lost. The user does not notice a service failure; because the replacement takes only a few seconds (see [11]). Thus, we are able to use different services in a chain to change different aspects of the stream and display it on a client device.
## 2 Advances and Achievements in Seed Biotechnologies in the Decade 2014-2024
### Seed Quality Improvement Through Biotechnological Innovations
#### 2.2.4 Determination of Seed Traits via Non-Coding RNAs  
Seed is a key target for crop improvement, attributed to an important source of biofuels, nutrition, and edible oil, and features like weight, shape, size, quality, oil content, etc., are targeted for biotechnological initiatives. The emerging research on seed improvement has largely focused on gaining insights into the regulatory aspects of seed development. Furthermore, studies have highlighted that long non-coding RNAs (lncRNAs) are key regulators in seed development [143]. lncRNAs are greater than 200 bp in length but lack protein-coding function and affect seed traits. RNA polymerase (Pol) II/III enzyme is involved in the transcription of LncRNAs, while plant-specific RNA polymerases IV/V may also function in transcription in plants [144]. These are classified as small non-coding RNAs (microRNAs are involved in seed development) [145; 146], and recent work has discussed the potential of long non-coding RNAs for genetic manipulation. Mutational studies on LncRNAs demonstrated the regulation of seed traits and development via mimicking the target, chromatin remodeling, the repression of the target, and as a small non-coding RNA precursor [143]. In this direction, genome-wide studies in crop plants, namely rice, castor bean, rapeseed, pigeon pea, maize, etc., have established LncRNAs and lncRNA-miRNA-mRNA networks as the regulators of seed development. The LncRNAs, such as LHR, MISSEN, osa-eTM160, etc., have been documented as the key regulators of seed traits and have immense potential for improving seed traits in agricultural crops. Furthermore, 21 different microRNAs were demonstrated to regulate key seed traits--seed vigor, nutritional profiles, size, etc.--contributing to agronomically important seed traits [145].
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
###### Abstract  
The stereo-vision system plays an increasingly important role in various fields of research and applications. However, inevitable slight movements of cameras under harsh working conditions can significantly influence the 3D measurement accuracy. This paper focuses on the effect of camera movements on the stereo-vision 3D measurement. The camera movements are divided into four categories, viz., identical translations and rotations, relative translation and rotation. The error models of 3D coordinate and distance measurement are established. Experiments were performed to validate the mathematical models. The results show that the 3D coordinate error caused by identical translations increases linearly with the change in the positions of both cameras, but the distance measurement is not affected. For identical rotations, the 3D coordinate error introduced only in the rotating plane is proportional to the rotation angle within 10\({}^{\circ}\) while the distance error is zero. For relative translation, both coordinate and distance errors keep linearly increasing with the change in the relative positions. For relative rotation, the relationship between 3D coordinate error and rotation angle can be described as the nonlinear trend similar to a sine-cosine curve. The impact of the relative rotation angle on distance measurement accuracy does not increase monotonically. The relative rotation is the main factor compared to other cases. Even for the occurrence of a rotation angle of 10\({}^{\circ}\), the resultant maximum coordinate error is up to 2000 mm, and the distance error reaches 220%. The results presented are recommended as practice guidelines to reduce the measurement errors.  
stereo-vision system; camera movement; noisy or outdoor environment; 3D coordinate error; distance error +
Footnote †: journal: Applied Sciences
# Design and Experiment of Variable-Spray System Based on Deep Learning
## 4 Conclusions  
In this study, weed density was calculated based on the improved DeepLab V3+ semantic segmentation model, and a variable spraying-level model based on real-time weed density and machinery speed was constructed for variable-spraying experimental research. The main conclusions are as follows:  
1. The overall parameter count and computational load were reduced by the improved model, and the accuracy of image segmentation was enhanced. By substituting the original Xception backbone network with the lightweight MobileNet V2 network and integrating the CA attention mechanism module, the results indicated that the MIoU and MPA of the modified model were 73.34% and 80.76%, respectively, with a single-image segmentation time of 0.09 s.
2. Within a certain distance, the atomization parameters of droplets sprayed at the same speed by constant and variable spraying were compared. The results indicated that variable spraying can refine droplet diameter and increase droplet density. The median volume diameter decreased by 138.13 um, and the droplet density increased by 38.87 droplets/cm\({}^{2}\), with an average drug-saving rate of 46.3%.  
Figure 13: Map of droplet coverage and droplet density. (**a**) Droplet coverage; (**b**) droplet density.  
By utilizing PWM variable-spraying technology, online variable spraying was achieved for different weed densities and speeds. At the same speed, the average droplet-coverage rate in sparse-weed areas decreased by 13.98% compared to dense areas. Under the same plant density, the average coverage rate at a forward speed of 0.5 m/s increased by 2.91% and 6.59% compared with 1 m/s and 1.5 m/s, respectively. The results indicated that, under the standards of spraying, the spray system can automatically adjust the spray volume based on different travel speeds and weed densities, exhibiting excellent spray performance.  
Conceptualization, Z.H., J.J. and X.J.; Methodology, Z.H., L.D. and J.J.; Software, Z.H., L.D. and J.J.; Validation, Z.H., L.D. and J.J.; Formal analysis, Z.H., L.D. and J.J.; Investigation, [PERSON] and [PERSON]; Resources, [PERSON], [PERSON] and [PERSON]; Data curation, [PERSON], [PERSON], [PERSON] and M.H.; Writing--Original Draft Preparation, [PERSON]; Writing--Review and Editing, [PERSON], [PERSON] and J.J.; Visualization, [PERSON]; Supervision, [PERSON] and X.J.; Project Administration, [PERSON] and X.J.; Funding Acquisition, [PERSON], [PERSON] and X.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Science and Technology Project of Henan Province (No. 231100110200); Major Science and Technology Project of Henan Province (No. 221100110800); Longmen Laboratory Major Projects (No. 231100220200).  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
### Aims and Motivation  
We aim to show that the digital twin can also be built to support cyber security concerns. Consequently, a digital twin of relevant parts of the food supply chain is described. This study aims to describe the implementation of the food production cyber arena, which meets the eight high-level requirements mentioned above. This digital representation facilitates the creation of a simulated supply chain for a cyber exercise aimed at organizations working in the domain of food production and processing. For example, food production companies could exercise their response to cyber attacks targeted at critical points in their processes. A cyber exercise in the food supply domain could include participants from a food processing factory. Staff members from departments such as management, communications, and operations could participate. The scenario includes a cyber attack to the factory, to which the participants should react. The resolution of the problem will provide resilience information to the departments. In this article, we describe the design of the digital twin for this purpose.  
The following key questions in this study can be identified:  
* RQ1: How can we develop a digital twin of the food supply chain for cyber exercises?
* RQ2: What kind of simulation concept fulfills the requirements of the food supplychain digital twin?  
This article is structured in the following manner. After the introduction, the cyber range used in this study, the Realistic Global Cyber Environment (RGCE), is described in Section 2. Section 3 describes the proposed design, the design science approach used in this study, the requirements of the system, architecture, implementation, and simulation concerns. The results are discussed in Section 4, describing the use of digital twins in RGCE, and evaluating how the system fulfilled the requirements. Finally, Section 5 concludes the article.
## 1 Introduction
However, commercially available VUV lamps have a main output at 254 nm with only a small amount (<10% of the total intensity of irradiation) at 185 nm. Therefore, semiconductors, such as titanium dioxide (TiO\({}_{2}\)), can be used to produce more hydroxyl radicals via photocatalysis [23]. TiO\({}_{2}\) is a well-known photocatalyst, because of the advantages of a superior photocatalytic oxidation ability and optical and electronic properties [24]. Additionally, the TiO\({}_{2}\) photocatalyst is inexpensive, commercially available, non-toxic, and photochemically stable [25]. The efficacy of decontamination techniques is highly affected by the physical properties and exterior morphology of a surface, such as fresh produce [26]. Studies have also demonstrated that internalization and colonization of bacterial cells in damaged tissues or in niches on complex matrices also make removal from vegetables difficult [2]. In addition, several studies have reported that the reduction degree of pesticide residues varies, depending on the contact area, tissue structure, compactness, surface area, thickness, and the amount of wax on the cuticle [27; 28]. Hence, a standardized model matrix with a regular size and shape and homogeneous surface characteristics is needed. The objectives of this study included evaluation of the effects on the degradation of pesticides and microorganisms in aqueous solutions of UV photolysis and UV-TiO\({}_{2}\) photocatalysis under VUV and UVC light sources. The removal efficiencies of pesticides and microorganisms on model matrices prepared to represent fresh produce were also studied for their simultaneous decontamination.
# Spatiotemporal Evolution of Gas in Transmission Fluid under Acoustic Cavitation Conditions
## 4 Discussions  
### Spatiotemporal Evolution of Gas Void Fraction  
To gain deep insight into the cavitation and pseudo-cavitation zones, as well as the spatiotemporal evolution of gas void fraction, the three-dimensional representation and mapping projection of the time direction as the \(x\)-axis, along the axial direction of the tube as the \(y\)-axis, and the gas void fraction as the \(z\)-axis are plotted in Figure 8. As the figure shows, they can be divided into three zones. The dark yellow zone represents the air cavitation zone, mostly occurring in the 0-0.04 s and 0.2-0.24 s time intervals of the whole tube. When \(x\) = 0-0.51 m, a few occur in the time intervals of 0.1-0.14 s. Therefore, the pressure drops rapidly below the air separation pressure, and bubbles are generated quickly. The maximum air void fraction is approximately 1.425%. The orange-red zone denotes the vapor cavitation zone. This process mainly occurs in the time intervals of 0.1-0.14 s in \(x\) = 0-0.51 m. The cavitation zone moves to the middle of the tube over time and continuously shrinks, and the vapor cavitation degree reaches the maximum when is almost 0.45 m. Also, the gas void fraction reaches 1.925% at the maximum point. The purple zone represents the pseudo-cavitation zone, occupying most of the space-time area. The void fraction decreases with the increase in pressure, and the lowest void fraction is about 0.05%. These results show that the numerical calculation algorithm is relatively stable and does not produce obvious oscillation.
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 2 Urban Microcellular Measurement Campaigns  
To provide a data basis for model refinement, several measurement campaigns have been performed in Berlin at 60 GHz and 10 GHz. Two of them are related to UMi street canyon scenarios, namely \"street canyon, city center\" (SC-CC) [24] and \"street canyon, residential area\" (SC-RA) [24; 25]. The third UMi scenario is \"open square, city center\" (OS-CC). Table 1 summarizes information on the environments and the collected data. The OS-CC measurement campaign is briefly described in Section 2.2.
# Using Enhanced Representations to Predict Medical Procedures from Clinician Notes
## 3 Proposal  
We propose an approach for predicting medical procedures from clinical notes by combining these with their known diagnoses as ICD codes. To our knowledge, no other study has exploited this relationship in predicting clinical procedures. Since procedures and diagnoses are often described by following the ICD coding standard, it is common in the SOTA to treat the joint prediction task of both groups, ignoring the clinical importance of each individually. Our method revolves around the premise that some diagnoses often trigger specific medical treatments; therefore, incorporating the information about this underlying relationship together with the clinical annotations can, in turn, produce better results in the task of procedure prediction compared to using either source of information alone.  
The proposed process depicted in Figure 1 comprises three main stages: diagnosis ICD code detection, diagnosis ICD code expansion, and procedure ICD code detection.  
The first step consists of automatically detecting ICD codes based on textual analysis of medical notes. Usually, this is the only task considered when detecting ICD codes. Our solution innovates by proposing an extended process.  
The second step is optional and consists of the expansion of the codes detected in the previous step. This expansion consists of adding ICD codes from the hierarchy. For example, under the ICD-9 standard, the diagnosis \"presenile dementia with delirium\" belongs to the general category of \"dementias\", which are defined with the codes 290.11 and 290, respectively. During this step, we resolve and combine both definitions to use them later in  
Figure 1: Approach to automatic procedure prediction combining text and diagnosis information.  
the procedure detection step. Some authors have also exploited the taxonomic structure of ICD codes to calculate patient similarity [32]. Our work proposes an expansion mechanism to improve the procedure of ICD code detection, which, in a nutshell, might improve the overall performance when predicting procedures by enriching the information about known diagnoses.  
Finally, the third step is the detection of procedure ICD codes, taking the diagnosis ICD codes (potentially expanded) and the original text as input. This step aims to provide a new perspective for procedure ICD code prediction by considering it as a first-class task rather than a task included in general ICD code prediction. This consideration is also supported by the fact that ICD-10, a newer revision of the standard, already defines different classification schemas for diagnosis codes (ICD-10-CM) and procedure codes (ICD-10-PCS) [33].  
Our experiment tested our assumptions regarding the hierarchy of ICD codes and treated procedure prediction as a separate task.
## 3 Results  
### NDVI and VV + VH Backscattering Time Series  
The time series of mean NDVI and VV + VH backscattering for the selected crop types are shown in Figure 6. As observed in this figure, the average VV backscatter is higher than the average VH backscatter. The dynamic range of the VV backscatter is \(-12.31\) dB to \(-5.49\) dB, while the range of VH backscatter is \(-19.37\) dB to \(-11.97\) dB. NDVI values range from 0.23 to 0.73.  
Because the crop parameters were estimated from the NDVI and VV + VH time series, it can be seen in Figure 6 that the period covered by EO data corresponds to the second half of the growing season. The lack of data for the beginning of the growing season prevents the estimation of the seasonal crop irrigation requirements and its respective analysis. Due to this limitation, the focus of this study was only on crop water requirement estimation.  
The behavior of the Sentinel-1A signal was also analyzed as a function of the NDVI. The backscattering coefficients were compared to the NDVI index calculated from SPOT-5 data with acquisition dates close to those of the Sentinel-1A acquisitions (Table 5). Most of the image pairs used have a gap of only one to two days, yet for the pair Sentinel-1A 19 April and SPOT-5 30 April, there is a gap of 11 days. A square of the Pearson product-moment correlation coefficient (\(R^{2}\)) of 79% was obtained when comparing the VV backscatter with the NDVI values, while a slightly lower value of approximately 71% was obtained when comparing the VH backscatter and NDVI values (Figure 7).  
Figure 6: Mean NDVI (**a**) and gamma VV (**b**) and VH (**c**) backscattering time series for maize, soybean, pasture and bean.  
Based on this linear regression, two equations are proposed to compute the \(K_{cb}\) values for epochs for which no optical images are available due to the existence of clouds. One equation uses the VV backscatter instead of the NDVI values (Equation (6)), while the other uses the VH backscatter values (Equation (7)):  
\[K_{cb}(VV)=0.116875\times\gamma_{VV}(dB)+1.757031 \tag{6}\]  
\[K_{cb}(VH)=0.087813\times\gamma_{VH}(dB)+1.984063 \tag{7}\]  
where \(\gamma_{i}\) is the gamma-calibrated backscattering coefficient in dB obtained from Equation (2) for each pixel of the SAR images.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### PLS Schemes for IoT: State of the Art  
So far, plenty of PLS schemes have been developed in literature, mainly including the artificial noise injection, the secure beamforming/precoding, the anti-eavesdropping signal design, the cooperation-based secure transmission techniques, power allocation and resource allocation schemes, etc. In this subsection, we would like to present a literature review of the PLS schemes that are applicable to IoT. For readers that are interested in more details about the existing PLS techniques, please refer to [9; 26] and references therein.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 2 Implementation of a Dual-Band RF Wireless Power Transfer (WPT) System
### Implementation of the Dual-Band Tx System  
The dual-band Tx system including the proposed Tx array antenna and a Tx module is implemented as shown in Figure 9. The Tx array antenna is the optimized aperture-sharing dual-band array antenna shown in Figure 5. The Tx module, which is developed such that it can be divided into a 2.4 GHz circuit part with 32 Tx channels and a 5.8 GHz circuit part with 32 Tx channels, controls the magnitude and phase of the signals fed to each radiating element in the Tx array antenna. A Tx channel is composed of a power divider, a 6-bit digital phase-shifter, a 6-bit digital attenuator, and amplifiers and is connected to a radiating element in the Tx array by means of an RF cable. In order to generate signals at 2.482 GHz and 5.73 GHz, two signal generators are connected to the Tx module, with this module controlled by a GUI program on a control PC. When the RF signal of 2.482 GHz is entered by the signal generator to the Tx module, the signal is divided into 32 channels through the 4-way and 8-way power dividers and amplified through the amplifiers in each Tx channel circuit. The phase and magnitude of the signal are varied by the phase-shifter and the attenuator, respectively. The output signal of 2.482 GHz from the 2.4 GHz Tx channel in the Tx module is delivered to the 2.4 GHz patch-radiating element connected by a RF cable, and the exited patch antenna finally radiates the RF signal into the air. This process of feeding the 2.4 GHz radiating elements is identical for the 5.8 GHz radiating elements.  
Figure 8: Single-band array antennas with performance equivalent to the proposed shared-aperture array: (a) 2.4 GHz array; (b) 5.8 GHz array.  
Figure 9: Implemented dual-band Tx system.
## 3 Materials and Method
### Hardware  
The two core components of the system, i.e., hardware and software, are illustrated in Figure 2. The hardware segment encompasses an ADXL345 accelerometer and an Arduino UNO micro-controller serving as the central processing unit. Conversely, within the software realm, MATLAB is harnessed for the presentation and analysis of the acquired data. The ADXL345 accelerometer sensor is utilized to gauge neck angles, while the Arduino UNO micro-controller interface is responsible for capturing accelerometer data and transmitting it to the computer. In the software domain, MATLAB assumes a dual role: (a) providing real-time data visualization on the monitor, represented as a red circle, and (b) registering signals to facilitate subsequent data processing. The Arduino UNO is a popular micro-controller board featuring an AT-mega328P processor running at 16 MHz. It offers 32 KB of flash memory for program storage and 2 KB of SRAM for data. The board has 14 digital input/output pins, 6 analog inputs, and supports PWM [43].  
The ADXL345 is a digital accelerometer sensor by Analog Devices, boasting good versatility. It can measure acceleration in three axes (\(x\), \(y\), and \(z\)) with a resolution of up to 13 bits [44]. To calculate the parameters using the accelerometer, the angle of deviation from the central state is determined in degrees using Equation (1):  
\[\text{Angle}=\cos^{-1}\left(\frac{x\cdot x_{0+}y\cdot y_{0+}z\cdot z_{0}}{ \sqrt{x_{0}^{2}+y_{0}^{2}+z_{0}^{2}}\cdot\sqrt{x^{2}+