esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 4 Our Construction  
This section introduces the detailed construction of the model. Based on the three-node star quantum communication network (Bob, Alice1, Alice2), through the quantum key distribution, Bob-Alice1 generates a symmetric quantum key, and Bob-Alice2 generates a symmetric quantum key. The video conference system server is placed at Bob; user1 is in Alice1; user2 is in Alice2. Bob, Alice1, and Alice2 are local area networks, respectively.
## 3 Proposed Methodology  
### Overview of the HAR Framework Used in This Study  
This research investigated S-HARs that are based on DL and use deep residual networks to extract the relevant abstract characteristics from raw sensor data. As seen in Figure 1, the examined S-HAR system comprises four key processes: data collecting, data preprocessing, model training, and model assessment.  
To begin, we compiled HAR datasets, including wearable sensor data from fundamental and transitional actions. Following a literature survey, we have chosen two publicly accessible datasets to analyze in this research, namely HAPT and MobiAct. Sensor data include a three-dimensional accelerometer, a three-dimensional gyroscope, and three-dimensional orientation data. The sensor data were then denoised, normalized, and segmented using a sliding window method to create sample data for training and evaluating DL models. These samples are prepared using the k-fold cross-validation methodology. Finally, we evaluated and compared the trained models using four main HAR measurements. Each procedure is described in depth in the following subsections.
## 5 The Application and Experiment  
### The Application of Question and Answer  
Knowledge graphs provide a more effective way to express, organize, manage, and utilize massive, heterogeneous, and dynamic medical big data in medical information systems, making the systems more intelligent and closer to human cognitive thinking. Currently, medical knowledge graph technology is mainly used in clinical decision support systems [18], medical intelligent semantic search engines [19], medical question and answer systems [20], chronic disease management systems, medical guidance systems, and adverse drug reactions [21]. Medical question and answer systems were an advanced form of medical information retrieval that could provide users with answers in an accurate and brief natural language form. [PERSON] et al. [22] proposed a medical question-and-answer system based on natural language processing, which combined medical domain knowledge, natural language processing-related techniques, and semantic relations to construct a knowledge graph for the automated question-and-answer of medical problems. [PERSON] et al. and [PERSON] [23] integrated TCM and EMR knowledge graphs such as disease database, symptom database, and herbal medicine database, and explored the automated construction method and standardized process of TCM knowledge graphs using text extraction, relational data conversion, and data fusion to realize the intelligent application of TCM knowledge graphs.  
Figure 7: The Medical Knowledge Graph. (**a**). Visualization of DIK Medical Knowledge Graph from Multi-Data Source; (**b**). Medical Data Fusion from Different Data Sources.  
In this paper, we used the Flask framework and Python language to construct an intelligent medical Q&A system based on the DIK medical knowledge graph, whose implementation flow is shown in Figure 8, which realized the conversion of \"natural language-to-Cyber-to-natural language\" (Cyber is the query language of Neo4j). The first \"natural-language-to-Cyber\" is the conversion of the user-entered question to the system's internal search statement. The second \"Cyber-to-Natural Language\" is the conversion of the system's internal search results to the user's understandable natural language form of the response. In this paper, the conversion of natural language queries and responses was used to improve the serviceability and friendliness of the intelligent medical Q&A system.
## 4 Materials and Methods
### Headspace Solid-Phase Microextraction-Gas Chromatography  
(HS-SPME-GC-MS)  
Chestnut male flowers were ground into powder by adding them to liquid nitrogen in a mortar, vortexing, and mixing evenly. Then, 500 mg of the powder was transferred immediately to a 20 mL head-space vial (Agilent, Palo Alto, CA, USA) containing 2 mL of saturated NaCl solution to inhibit any enzyme reaction. The vials were sealed using crimp-top caps with tetrafluoroethylene ( TFE)-silicone headspace septa (Agilent). At the time of SPME analysis, each vial was placed at 60 \({}^{\circ}\)C for 5 min, and then a 120 \(\upmu\)m DVB/CWR/PDMS fiber (Agilent) was exposed to the headspace of the sample for 15 min at 60 \({}^{\circ}\)C. A total of 20 \(\upmu\)L (10 \(\upmu\)g/mL) of internal standard (3-Hexanone-2,2,4,4-d) solution was added separately to the headspace vial, which was then sealed. Fully automated headspace-solid phase microextraction (HS-SPME) was simultaneously performed for sample extraction for GC-MS analysis.  
The identification and quantification of VOCs were carried out using an Agilent Model 8890 GC and a 7000D mass spectrometer (Agilent) equipped with a 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m DB-5 MS (5% phenyl-polymethylsiloxane) capillary column. The GC conditions were consistent with the work of [PERSON] [19]. Helium was used as the carrier gas, at a linear velocity of 1.2 mL/min. The injector temperature was kept at 250 \({}^{\circ}\)C, and the detector temperature was kept at 280 \({}^{\circ}\)C. The oven temperature was programmed from 40 \({}^{\circ}\)C (3.5 min), increasing at 10 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C, at 7 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, at 25 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C, and held for 5 min. Mass spectra were recorded in electron impact (EI) ionization mode at 70 eV. The quadrupole mass detector, ion source, and transfer line temperatures were set to 150, 230, and 280 \({}^{\circ}\)C, respectively. The MS was set in ion monitoring (SIM) mode for the identification and quantification of analytes. Volatile components were identified by comparison of the retention index (RI, determined by n-alkanes C7-C40) and the National Institute of Standards and Technology (NIST) mass spectral database in the NIST library ([[https://www.nist.gov/](https://www.nist.gov/)]([https://www.nist.gov/](https://www.nist.gov/)), accessed on 15 July 2024).  
The formula for calculating the relative content (\(\mu\)g/g) of volatile components is as follows [44]:  
\[\text{Content}\ (\mu\text{g/g})=\frac{\text{target volatile component peak area }\times\text{internal standard mass }(\mu\text{g})}{\text{internal standard peak area }\times\text{sample mass }(\text{g})}\]
## 3 Algorithms
### Parcel-Receiving Station and Public Route Planning Based on GA-MMAS  
In this section, the genetic algorithm (GA) is used to solve the upper-layer model of the logistics UAV parcel-receiving station location problem and determine the layout of the parcel-receiving station from alternative locations. Then, based on the current location scheme, determine the public air-route structure using the max-min ant system (MMAS). Use the GA-MMAS algorithm for nesting optimization to obtain the logistics UAV parcel-receiving station and public air-route planning results.
###### Abstract  
Every new generation of wireless communication standard aims to improve the overall performance and quality of service (QoS), compared to the previous generations. Increased data rates, numbers and capabilities of connected devices, new applications, and higher data volume transfers are some of the key parameters that are of interest. To satisfy these increased requirements, the synergy between wireless technologies and optical transport will dominate the 5G network topologies. This work focuses on a fundamental digital function in an orthogonal frequency-division multiplexing (OFDM) baseband transceiver architecture and aims at improving the throughput and circuit complexity of this function. Specifically, we consider the high-order QAM demodulation and apply approximation techniques to achieve our goals. We adopt approximate computing as a design strategy to exploit the error resiliency of the QAM function and deliver significant gains in terms of critical performance metrics. Particularly, we take into consideration and explore four demodulation algorithms and develop accurate floating- and fixed-point circuits in VHDL. In addition, we further explore the effects of introducing approximate arithmetic components. For our test case, we consider 64-QAM demodulators, and the results suggest that the most promising design provides bit error rates (BER) ranging from \(10^{-1}\) to \(10^{-4}\) for SNR 0-14 dB in terms of accuracy. Targeting a Xilinx Zynq Ultrasae+ ZCU106 (XCZU7 EV) FPGA device, the approximate circuits achieve up to 98% reduction in LUT utilization, compared to the accurate floating-point model of the same algorithm, and up to a 122% increase in operating frequency. In terms of power consumption, our most efficient circuit configurations consume 0.6-1.1 W when operating at their maximum clock frequency. Our results show that if the objective is to achieve high accuracy in terms of BER, the prevailing solution is the approximate LLR algorithm configured with fixed-point arithmetic and 8-bit truncation, providing 81% decrease in LUTs and 13% increase in frequency and sustains a throughput of 323 Msamples/s.  
Keywords:QAM demodulation; approximate computing; approximate arithmetic; FPGA; design space exploration; log likelihood ratio; maximum likelihood
## 4 Harmonic Emission Profiles  
Several case studies of measurements in LV networks have analyzed the emission of harmonic currents under different impacts, like PV inverters or lamp technologies [16; 17; 18]. The results showed that the harmonic emission of different consumer configurations is strongly related to the typical usage of the connected equipment. The analysis of the 40 measurements sites within the German measurement campaign showed that characteristic emission profiles, especially of the 3 rd harmonic current are most suitable for representing different consumer configurations.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 4 Experimental Validation
### System Architecture Overview  
The cloud system in Figure 2 includes a centralized command and control unit, a core network, and a high-performance computing server. It handles model training, real-time inference, task scheduling, and secure bidirectional data exchange. A resource orchestration framework ensures dynamic workload allocation, parallel task execution, and fault tolerance. Modules for resource monitoring, task tracking, data management, and network status evaluation are integrated to support scalable and resilient operation.  
The edge system in Figure 2 includes one UCDU, two EDUs, and four RRUs, interconnected with the cloud via fiber-optic links. The UCDU performs modulation/demodulation and handles channel encoding/decoding. The EDUs allocate baseband processing resources dynamically according to traffic load, while the RRUs upconvert baseband signals to RF for long-range CF-mMIMO transmission to UAVs. The edge layer also manages mission scheduling based on UAV status and supports real-time coordination.  
The UAV system in Figure 3 consists of nine quadrotors equipped with onboard FCUs, gimbal-mounted cameras, and edge computing modules. The FCU provides real-time state information including position, velocity, and orientation. The gimbal captures live video streams for visual perception. Onboard processors handle lightweight inference or feature extraction and transmit both raw and processed data to the edge/cloud servers. This architecture supports autonomous operation and efficient task execution in dynamic environments.
# Modifying Orthogonal Drawings for Label Placement
## 3 The OSLP Problem  
The problem of modifying a graph drawing to open up space that can be used to assign labels without compromising the quality of the drawing is an optimization problem. We want to minimize the extra area needed to resolve overlaps, while preserving the constraints and aesthetics of the drawing. In any approach to solve the OSLP problem, two things are of particular importance:  
* We must not change the embedding of the drawing since we want the drawings before and after opening extra space to look similar. The reason is:
* we want to maintain the mental map of the drawing;
* we do not want to reduce the quality of the drawing by violating any of the layout constraints.
* Opening up the minimum amount of space.  
The notion of the mental map of a drawing has been introduced in [11]. By preserving the user's mental map of a drawing, we generally mean that a user that has studied a drawing will not have to reinvest significant time and effort to visualize or understand the same drawing after some changes. For orthogonal drawings, we can preserve the mental map of a drawing by requiring that the orthogonal representation of a drawing remains the same after opening space. This is possible since an orthogonal representation of a graph \(G\) describes an equivalence class of planar orthogonal drawings of \(G\) with similar shapes [5]. In Figures 1 and 2 we have orthogonal drawings of the same graph. The drawings in Figure 1a,b have the same orthogonal representation. It is obvious that both drawings have similar shapes, and the mental map between those two drawings is similar.  
The problem we propose to solve is the following:  
**The opening space label placement problem:**  
**Instance:** Given an orthogonal drawing with a label associated with each edge of the drawing.  
**Question:** Is there a label assignment where each edge has an overlap-free label assigned to it by minimally modifying the drawing?  
This is a very challenging problem, since most of its variants are computationally hard, as we show in the next section.
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 3 Results
### Gene Expression Profile  
We next profiled the gene expression in the Ashmead insects maintained with the fruit flies fed with the three kinds of fruits. After lowly expressed genes (count \(<\) 5) were filtered, RSEM identified a total of 63,627 transcripts in the Ashmead animals, of which 46,607, 49,253, and 53,558 transcripts were distributed in G, M, and C, respectively (Table S3). The Venn diagram (Figure 2A) revealed 38,009 transcripts commonly expressed in all samples while 3115, 4571, and 8159 were specifically detected in G, M, and C, respectively. Interestingly, not all the olfactory genes were expressed in the insects and we found 39 OBPs, 65 CSPs, 29 GRs, 382 ORs, 15 IRs, and 13 SNMPs. Figure 2B,C showed the expression levels of these olfactory transcripts in the insects, and revealed that different olfactory genes of Ashmead insects are responsible for the fruit flies with different fruits. According to the average expression levels, we showed the top five highly expressed olfactory transcripts identified in this study (Table 3) and it was revealed that the identities of highly expressed olfactory transcripts were shared by the parasitoid wasps of the fruit flies fed with different fruits. Notably, OBP56 and OBP69 were highly expressed in the insects; IR25a and SNMP1 were the only highly expressed transcript for the IR and SNMP groups, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Type** & **Transcripts** & **Sub-Family** & **Intact ORF** & **SignalP** & **TMHMM** \\ \hline OBP & 43 & 21 OBP-56, 7 OBP-69, 4 OBP-72, 11 OBP-83 & 35 & 28 & 15 \\ \hline CSP & 69 & 5 CSP-1, 3 CSP-3, 2 CSP-4, 5 CSP-5, 10 CSP-6, 3 CSP-7, 4 CSP-8, 3 CSP-8 & 63 & 50 & 13 \\ \hline  & & 48 OPR-1, 6 OPR-12, 12 OPR-10, 110, 15 OB-13, 43 OPR-2, 46 OPR-22, 20 OR-23, 9 OPR-24, 2 OR-245, & & & \\ OR & 108-26, 20 OR-26, 10 OR-27, 25 OPR-30, 30 OPR-3, 31 OPR-4, 5 OPR-42, 14 OUR-43, 2 OR-45, 24 & & & \\ OR & 689 & OR-46, 2 OUR-42, 2 OR-69, 2 OR-89, 2 OR-89, 1 OUR-5, 1 OR-63, 4 OUR-67, 1 OR-69, 6 OUR-71, 13 & 347 & 25 & 276 \\  & & OR-17, 1 OR-81, 2 OR-82, 37 OR-85, 11 OR-92, 11 OUR-94, 5 OUR-88, 4 OUR-9, 9 OUR-co, 1 & & & \\ \hline IR & 26 & 3 IR-21, 18 IR-25, 4 IR-68, 2 IR-93 & 18 & 4 & 12 \\ \hline GR & 60 & 6 GR-2, 5 GR-107, 3 GR-15, 1 GR-23, 19 GR-28, 4 GR-2, 8 GR-43, 2 GR-64, 3 GR-66, 8 GR * & 26 & 1 & 25 \\ \hline SNMP & 14 & 11 SNMP-1, 3 SNMP-3 & 10 & 1 & 9 \\ \hline Others * & 54 & 16 CR, 38 GOBP & 10 & 12 & 22 \\ \hline \hline \end{tabular}  
* Transcripts containing the chemosensory receptor or the GOBP domain, by PFAM annotation.
* Products are not specified.  
\end{table}
Table 2: Olfactory genes identified in the Ashmead transcriptome.
# Line Beam Scanning-Based Ultra-Fast THz Imaging Platform
###### Abstract  
In order to realize rapid THz detecting and imaging, a line beam scanning-based ultra-fast THz imaging platform is designed combining simple optical components and lightweight mechanical system. The designed THz imaging platform has the resolution of 12 mm, the scanning angle range of \(\pm\)10.5\({}^{\circ}\), the scanning speed of 0.17 s/frame, and the scanning range of 2 m \(\times\) 0.8 m; moreover, it can realize rapid human body THz imaging and distinguish metallic objects. Considering its high-quality performance in THz imaging and detecting, it is believed the proposed line beam scanning-based ultra-fast THz imaging platform can be used in the future in various safe screening applications.  
Ultra-fast THz imaging; security screening platform; multiple-input multiple-output; line beam scanning; human body imaging +
Footnote †: journal: Applied
## 2 Materials and Methods
### 3D Mapping and Flight Data Logging  
A 3D mapping system was implemented to visualize the drone's flight path and detect obstacles. The system recorded the drone's x, y, and z coordinates at 0.1 s intervals, logging its movement throughout the flight. Obstacle detection data from the Multi-Ranger Deck were incorporated into the map, with obstacles represented as rectangular prisms positioned at a distance from the detection point based on the safety threshold.  
The collected flight data were processed and visualized using Python's Plotly library, generating an interactive 3D representation of the environment. The resulting visualization provided a clear depiction of the drone's trajectory and mapped obstacles, aiding in post-flight analysis and system evaluation.
## 1 Introduction  
The Gangdese region in the southern part of Xizang province is one of the most important metallogenic regions in China. The Nyainqentanglha lead-zinc metallogenic belt (NLMB) within this region is a major Pb-Zn (Ag) ore belt, hosting multiple large-scale ore deposits, including the Yaguila, Mongyai, and Narusongduo deposits. The Dongzhongla (DZL) ore deposit, which is the focus of this study, is a medium-sized Pb-Zn ore deposit located in the eastern segment of the NMLB [1, 2, 3, 4, 5, 6, 7]. DZL is situated approximately 90 km northeast of Lhasa city, Xizang province, and was discovered in 2002, with formal production commencing in recent years. Within a radius of several kilometers around the DZL deposit, multiple ore deposits, including the Mengya'a, Yaguila, Dongzhongla, and Xiangla deposits, have been discovered, forming a polymetallic ore cluster, with favorable geological conditions for mineralization.  
Previous studies have investigated the ore-forming model, ore-forming age, and material sources of the DZL deposit at the ore-body scale [8, 9, 10, 11]. Currently, the deposit owners plan to assess the mineral potential within the exploration area and conduct detailed exploration work. However, the harsh natural environment and high altitude of the study area pose significant challenges and risks to ground-based exploration. Aeromagnetic surveys can effectively eliminate these risks and provide rapid, area-wide measurements,supporting the identification of target areas for ground-based exploration [12; 13; 14; 15; 16]. Skarn deposits, such as the DZL deposit, are formed through the interaction of magmatic and hydrothermal fluids with carbonate rocks, resulting in the formation of skarn minerals, including garnet and pyroxene. The close spatial, temporal, and genetic relationships between the ore body and skarn rocks make airborne magnetic surveys a viable method for identifying potential ore bodies, as most economic skarn deposits are related to magmatic activity, which can produce magnetic anomalies [17]. We successfully applied airborne magnetic surveys to identify potential iron ore deposits in 2018 [18]. We were commissioned by Xizang Xinhu Mining Co., Ltd. (Ihasa, China) of Yunnan Chihong Zinc & Germanium Co., Ltd. (Qujing, China) to conduct an airborne magnetic survey over the DZL deposit and its surrounding area in 2021. This survey acquired high-resolution airborne magnetic data, which were analyzed using derivative calculations and 3D magnetic inversion to identify the magnetic characteristics of the DZL area, highlighting the significance of airborne magnetic surveys in guiding mineral exploration in the surrounding area.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 2 Basic Theory and Solution Method
### Linear Cumulative Damage Theory  
According to the fatigue cumulative damage theory, the cyclic action of the load causes structural fatigue damage. The fatigue damage under different stress amplitudes is affected by the number of cycles under this amplitude and the total number of cycles.  
Miner theory [11] holds that the fatigue damage of the structure is independent of each other under various stresses, and the total damage can be obtained by the linear superposition of independent fatigue damage. According to the linear cumulative damage theory, the cumulative fatigue damage of components under multiple load cycles can be expressed as follows:  
\[D=\int_{0}^{\infty}\frac{n_{s}}{N_{s}}ds \tag{1}\]  
where \(n_{s}\) is the number of cycles when the stress amplitude is \(s\) and can be expressed by the probability density function \(p\)(\(s\)) of the stress amplitude. The number of failure cycles \(N_{s}\) with stress amplitude \(s\) can be determined using the S-N curve equation:  
\[n_{s}=E(p)\cdot p(s)\cdot T \tag{2}\]  
\[s^{m}\cdot N_{s}=C \tag{3}\]  
where \(E(p)\) is the expected peak frequency of the random stress response signal, \(T\) is the action time of the random response, and \(m\) and \(c\) are the S-N curve constants related to the characteristics of structural materials.  
By substituting Equations (2) and (3) into Equation (1), the linear fatigue cumulative damage equation is obtained:  
\[D=E(p)\frac{T}{C}\int_{0}^{\infty}s^{m}p(s)ds \tag{4}\]  
When the cumulative damage of the cycle reaches the structural fatigue damage threshold D under various stress levels, the structure will suffer from fatigue failure. Numerous random vibration fatigue tests in engineering applications have demonstrated that the fatigue threshold set by the Miner fatigue criterion is close to 1. The formula is simple in principle and can provide an acceptable accuracy range for engineering applications.