esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
###### Abstract  
Network topology construction plays an important role in the application of large-scale unmanned aerial vehicle (UAV) swarm. Current researches usually perform the topology construction in terms of criteria of nodes energy consumption, transmission delay and network throughput, etc. However, another important criterion, the stability of swarm network topology, which is much critical for dynamic scenarios, has not been fully considered. In this paper, a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a novel Group Trend Similarity based double-head Clustering method(GTSC) is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account. The proposed method is effective when used to perform the network topology construction for UAV swarm, which is verified by the simulation results. Furthermore, in comparison with representative algorithms, the proposed GTSC method exhibits better performance on topology duration, network throughput, end-to-end delay and energy consumption balance especially in a large-scale swarm scenarios.  
UAV swarm; network topology construction; topology duration; double-head clustering; energy consumption balance +
Footnote †: journal: Applied sciences
## 4 Results
### Assessment of GPP in Response to Extreme Events  
To further evaluate the ability of the EF-LUE GPP to capture the impact of drought and heat-wave events, we analyzed the response of the estimated GPP by the EF-LUE model during different drought/heat-wave events. Four severe heat-wave and drought events that occurred in recent years, including the 2010 Russian heat-wave, 2009 India heat-wave, 2013 China drought, and 2012 US Corn Belt drought, were selected for analysis. Figure 11 showed the percentage of mean GPP anomalies in the period during the heat-wave and drought events with respect to multi-year monthly mean GPP over the same months of extreme events in the past two decades. The heat-wave that occurred in western Russia in the summer of 2010 resulted in the warmest July since 1880 in many regions of Russia [78], which increased sensible heat flux by about 10-15% and reduced latent heat flux by about 15-20% reported in the literature [79]. The impact of heat-wave on the GPP estimated by the EF-LUE model was pronounced with more than 30% reduction in GPP in most croplands as seen in Figure 11a. In 2009, a heat-wave occurred in Orissa, West Bengal, Bihar, Uttar Pradesh, Jharkhand, and Andhra Pradesh provinces in India. Our results showed a significant reduction in GPP during the Indian 2009 heat-wave period (Figure 11b). In 2013, different severe level of droughts occurred in Southwest China, the North China Plain, and south of the Yangtze River, resulting in crop yields reduction to the largest negative anomaly level since 1960 in these regions [80]. GPP estimated by the EF-LUE showed a significant decrease (10-20%) in part of the North China Plain (Figure 11c). In 2012, a severe drought occurred and spread over almost two thirds of the continental US, particularly in the Corn Belt [81], which resulted in 25% reduction in maize yields [82]. Our result showed a significant reduction in GPP (\(\sim\)30%) (Figure 11d).  
Figure 10: Comparison of annual mean GPP (2002–2018) from the EF-LUE model with other 5 GPP products in rainfed and irrigated croplands in tropical (**A**), arid (**B**), temperate (**C**), boreal (**D**), and polar (**E**) climate zones.
## 3 Experimental Study  
### Specimen Details and Fabrication  
In order to monitor the cracking characteristics of RC beams with NSM pre-stressed CFRP plates, two test specimens were designed with the same dimensions and reinforcement. The total length of each  
Figure 2: Three level decomposition diagram of wavelet packet.  
beam is 2700 mm and the cross section is 150 mm \(\times\) 250 mm. There are two CFRP plates with a spacing of 80 mm at the bottom of the beam, which are bonded by three sides of epoxy resin. The thickness of top and bottom protective layer is 50 mm and 30 mm respectively. The tensile area of the beam is divided into pre-stressed CFRP bonded section, non-pre-stressed CFRP bonded section and unbonded section. The boundary between the pre-stressed CFRP bonded section and the non-pre-stressed CFRP bonded section is called the tensioned end and the junction of the non-pre-stressed CFRP bonded section and the unbonded section is called the bonded end. The total bond length of CFRP plate in beam 1 is 1800 mm, the length of pre-stressed bond section is 1400 mm and the length of non-pre-stressed bond section on both sides of beam is 200 mm, as shown in Figures 3 and 4. The total bond length of CFRP plate in beam 2 is 2200 mm, the length of pre-stressed bond section is 1400 mm and the length of non-pre-stressed bond section on both sides of beam is 400 mm, as shown in Figures 4 and 5. The test specimens in the experimental program were made of concrete, CFRP plate, epoxy resin, longitudinal distributed reinforcement, steel stirrups and SAs. Strain gauges were symmetrically arranged on the outside of the CFRP plate at the non-pre-stressed CFRP bond section of the beam to measure the strain of the CFRP plate during the loading process, as shown in Figures 3 and 5. The cement used for casting the specimen is type 32.5 Portland cement [30]. The mixture ratio of the concrete is shown in Table 1. The average compressive strength of the concrete for 28 days is 30 MPa. The thickness and width of CFRP plate are 2 mm and 16 mm respectively. The initial prestress applied was 900 MPa. The mechanical properties of CFRP and epoxy resin are listed in Table 2. In the research, the steel stirrups are arranged with a spacing of 100 mm along the length of the test specimen. Table 3 shows the mechanical properties of the steel. The SA is installed in the tension zone of the beam, as shown in Figures 3 and 5.  
Two beams with different lengths of non-pre-stressed CFRP sections were fabricated and there were pre-stressed and non-pre-stressed sections. Therefore, the experimental procedure involves two different phases of comparative experimentation: (1) Monitoring the cracking characteristics; (2) Monitoring the crack development characteristics. The entire specimen is divided into four observation surfaces (OS), as shown in Figures 3 and 5. The signal received by the SAs is compared with the concrete cracking and crack development and failure at the bonded end region and tensioned end region of the beam.  
Figure 3: Test beam 1 details (unit: mm): (a) Side view (b) Horizontal view.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks
### Ad Hoc On-Demand Distance Vector (AODV)  
The Ad hoc On-Demand Distance Vector (AODV) routing protocol is classified as a reactive protocol for MANETs [20]. However, it exhibits characteristics of both the reactive and proactive approaches. The AODV shares similarities with reactive protocols like Dynamic Source Routing (DSR) in its route discovery and maintenance mechanisms [21]. However, before flooding the network with route request messages, the AODV results its cached routing table, similar to a proactive protocol like the Destination Sequenced Distance Vector (DSDV). This cached information helps the AODV avoid unnecessary route discovery overhead in scenarios where the route might already be known [22]. As a result, the AODV can be viewed as a hybrid protocol that leverages aspects of both reactive and proactive approaches. This routing decision process can be seen in Figure 3.  
The AODV initiates route discovery by broadcasting route request (RREQ) packets when no cached route exists. These packets propagate through the network until the destination is found or an intermediate node has a valid route. Upon receiving an RREQ, the destination or a suitable intermediate node responds with a route reply (RREP); see Figure 4. The source node selects the best route based on the sequence number and hop count and starts data transmission. The AODV also employs route maintenance using  
Figure 2: Topology-based MANET routing protocols.  
periodic Hello messages and Route Error (RERR) packets to detect and repair broken links, triggering route discovery when necessary [23].
## 4 Discussion
### Failure Mode and Effects Analysis (FMEA)  
This study applied analysis of more detailed datasets. Thus, there is the possibility of further studies applying FMEA. FMEA is the process of inspecting as many components, assemblies, and subsystems as possible to reveal the potential failure modes of a systemand their causes and effects. For each part, the failure mode and the resulting impact on the entire system are recorded in a risk priority number (RPN) scoring metric [32]. However, although RPN scoring is a quantitative method, subjective factors that vary depending on the situation of individuals and institutions performing RPN scoring are included. Although FMEA is a qualitative analysis, it might rely on a quantitative basis using a mathematical RPN scoring for each institution. FMEA is highly recommended in medical physics using the AAPM TG-100 guideline [8]. Thus, it provides a QA procedure for the successful operation and management of LINACs with a high safety potential. Potential failure mechanisms, which impact the system operation, can reveal the severity and occurrence. Therefore, it is possible to provide a stable treatment environment without interrupting the patient's treatment, and to minimize downtime.
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 3 Theoretical Background of Deep Learning Methods Used for Self-Driving Cars
### Recurrent Neural Network  
As the self-driving car functions depending on the information from the perception of constantly changing the surrounding environment, it is important to get a more complete representation of the environment by storing and tracking all the relevant information obtained in the past. Recurrent Neural Network (RNN) can be used to deal with this problem efficiently, which is mainly used to capture the time dynamics of video fragments.  
Recurrent Neural Network maintains the memory of its hidden state for a period of time through a feedback loop and models the dependence relationship between the current input and the previous state [33]. A special type of RNN is Long Short-Term Memory (LSTM), which controls the input, output and memory state, to learn long-term dependencies [34] (see Figure 6).  
In the RNN network, the current state of each loop unit is determined by the input at this moment and the previous state. Given the learning data input in sequence \(X=\{x_{1},x_{2},\cdots x_{t}\}\), the hidden state at the time \(t\) is updated by:  
\[H_{t}=\varphi\left(UX_{t}+WH_{t-1}+b\right), \tag{3}\]  
where the weight matrices \(U\) and \(W\) determine the importance given to the current input \(X_{t}\) and to the previous state \(H_{t-1}\) respectively; \(\varphi\left(\cdot\right)\) is an activation function and \(b\) is the bias matrix. Then the output \(O_{t}\) is calculated by:  
\[O_{t}=VH_{t}+c, \tag{4}\]  
Figure 6: The network structure of Recurrent Neural Network (RNN).  
where \(V\) is the weight matrix and \(c\) is the bias matrix.
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 3 Analysis of Long-Term Clock Bias, Frequency, Drift Rate, and Noise
### Analysis of Clock Bias Series  
The long period series of the precise clock offset of each BDS-2 and BDS-3 satellite are plotted in Figures 1 and 2, respectively. It is obvious that there are clock offset jumps in both BDS-2 and BDS-3 onboard clocks. Generally speaking, the clock offset jumps are mainly caused by the adjustment of the ground control system, the purpose of which is to control the time synchronization error between stations and satellites within a preset threshold [5]. However, these adjustments destroy the continuity and stability of the clock products, resulting in reduced performance for positioning and timing accuracy.  
Figure 1: Clock bias series of the BDS-2 onboard clocks from DOY 1 to 300, 2021 (units: \(10^{-4}\) s).  
For the BDS-2 onboard clocks shown in Figure 1, the number of jumps of GEO satellite clocks are more than those for IGSO and MEO satellite clocks. In terms of GEO satellites, C01, C03, and C05 are adjusted more than four times, while C04 is adjusted even more than 10 times. In contrast to the GEO satellites, the clock stability of the IGSO and MEO satellites clock bias series are comparable. C06 and C14 are both adjusted fewer than two times. Some satellites, such as C10 and C13, do not even require modulation operations.  
In the case of the onboard clocks carried by BDS-3 satellites, it can be seen from Figure 2 that only C59 and C60 (both are PHMs carried on GEO satellites) have jumps, and none of the PHMs and Rb clocks carried on IGSO and MEO satellites experienced jumps. In addition, it can also be seen that the clock bias series of most BDS-3 Rb clocks show a quadratic trend, while those of most of the PHMs show a linear trend. These phenomena indicate that the long-term performance of PHMs is better than Rb clocks.  
Comparing Figures 1 and 2, it can be found that the number of jumps of the BDS-2 clocks is far greater than those of the BDS-3 clocks during the research period. This may be a result of clock aging on the BDS-2 satellite, and subsequent cumulative deviations from working in orbit over a longer time.
## 4 Analyses of the Applications of Digital Twin Models
### Manufacturing  
In the manufacturing domain, DT integration is a significant stage and is closely related to concepts such as Product-Service Systems (PSSs) and Cyber-Physical Systems (CPSs). The synergistic interconnection between DT, PSS, and CPS is envisioned to redefine how products are designed while also opening new horizons in providing personalized services and in optimization of resources.  
The results of the search for the concept of \"Digital Twin in manufacturing\" show a minimum number of articles on IEEE Explore--1497, followed by the Web of Science--2272, Scopus--20,205, and Google Scholar--213,000. The number of articles highlights the fact that the applications of DT in manufacturing are a field of interest.
## 4 Experimental Design
### Experiment Setup  
#### 4.2.1 Dataset  
The dataset used for pretraining the model is VoxCeleb2 (Vox2), which is sourced from publicly available video resources, covering various scenarios such as interviews, news, and conversations. It contains around 1100 h of audio data from more than 6000 speakers worldwide, encompassing diverse accents, genders, and background noises. The air-ground communication dataset used in the experiments (shown in Figure 5) comes from a national key research and development program. It includes control recordings from frontline airports in southwestern China and control simulation training recordings from the Civil Aviation Flight University of China. The dataset consists of 5841 short audio segments, totaling 600 min of air-ground communication records. The audio in this dataset was collected during periods of high aircraft traffic, so it contains very few non-speech segments, with most of the audio being valid speech segments. The dataset includes both Chinese and English communication. The audio content comprises air traffic control (ATC) commands, which can be categorized into three key components: call signs, action commands, and action parameters. Call signs are composed of the airline abbreviation and flight number. Action commands refer to the specific directives within ATC communications, such as ascent, descent, or holding. Action parameters offer critical supplementary details for these directives, including speed, altitude, heading, and waypoints, all of which are vital for the accurate execution of the commands. After manual labeling, there are a total of 476 speakers in this dataset, including 25 air traffic controllers (ATCOs) and 451 pilots. After audio segmentation, there are 5438 speech segments, with 2753 belonging to air traffic controllers and 2685 belonging to pilots. The training set (ATC-Communication-train) contains 4078 audio samples from 357 speakers, while the test set (ATC-Communication-test) contains 1360 samples from 119 speakers. The speakers in the training and test sets are mutually exclusive and the speech samples are distinct.
# Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic
## 4 Formalizing Cryptographic Protocol  
### The Verification Problem of the Cryptographic Protocol  
**Protocol description**: We use \(Crypto\) to denote the cryptographic protocol. Actually, the execution process of a protocol is a sequence of actions. In cryptography, a protocol is described as the form \(A\to B:m\) (agent '\(A\) sends message \(m\) to \(B\)'), where \(m\in M\) is a message that has a certain pattern. These patterns have been defined as definition 1 in the specific system.  
**Instantiations**: Action pattern is an important part in the execution of the cryptography. We usually instantiate action patterns by _instantiation_\(\theta\). _Instantiation_\(\theta\) is a map from the parameters of the protocol to their respective domain. An execution of a protocol can have several instantiations interleaving. In this system, we employ \(\Theta\) to denote a set of all instantiations. In this protocol, the number of parameters is finite, and \(\Theta\) is too.  
**Network environment**: We assume that the network on which the protocol can be run consists of an input-buffer and an output-buffer just as that defined in the work [52]. We take the two partsas two special agents \(In\) and \(Out\). In the protocol, the trusted agents can only send a message to \(In\). The attacker or intruder could eavesdrop messages from \(In\). The trusted agents receive messages from \(Out\).  
**The verification model**: The verification model is the model for this protocol to archive the goal pattern. After running of the protocol \(Crypto\), a requirement \(\phi\) (\(\phi\in\mathcal{L}^{A,B}_{crypt}\)) should hold. So, we formalize the verification model as below:  
\[\mathfrak{M}\models\bigwedge_{\theta\in\Theta}\left[Crypto_{Intr},\sigma( \theta)\right]\phi(\theta)\]  
where  
* \(\mathfrak{M}\) is a model that we have defined previously, including all assumptions at the initial state, and all epistemic information for all the agents.
* \(Crypto_{Intr}\) denotes the action model. \(Intr\) denotes intruder's actions. This action model consists of all the actions in the protocol \(Crypto\) and all possible intruder's actions.
* \(\sigma\) is an action in the action model \(Crypto\) (generally the final one).
* \(\phi\) is an \(\mathcal{L}^{A,B}_{crypt}\)-formula. The formulas are either static epistemic formulas or propositions.  
\(\bigwedge_{\theta\in\Theta}\left[Crypto_{Intr},\sigma(\theta)\right]\phi(\theta)\) means after an execution of all possible actions in the model \(Crypto\), \(\phi\) holds. This is the model of the protocol goals.
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
## 3 Experiment and Results
### Experimentnal Determination of the Dynamic Mechanical Properties of Materials
Figure 16 shows the three-dimensional surface map of stress, strain, and strain rate through the interpolation fitting algorithm. In the field of dynamic mechanics, a series of experiments to study the dynamic mechanical properties of materials are arranged according to the strain rate: medium strain rate experiments (\(10\sim 10^{2}\)/s), high strain rate experiments (\(10^{2}\sim 10^{4}\)/s), and ultra-high strain rate experiments (\(10^{4}\sim 10^{6}\)/s). As can be seen from the figure, the experiment we set belongs to the second case. Note that the measurement system of Kolsky bar with temporal speckle interferometry that we give here can also be used for ultra-high strain rate experiments by reducing the diameter of the bar and increasing the velocity of striking.
# Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage
## 4 Accessible Cultural Heritage through Explainable Artificial Intelligence
### Art and Robotics  
In terms of accessibility, technological advancements have brought \"telepresence\" or mobile remote presence (MRP) systems as another opportunity for bridging social and spatial barriers for people with mobility constrains. MRPs are designed to be teleoperated and are used to improve communication between individuals. They were found to have the potential to assist challenged individuals in instrumental activities of daily living as well as to foster social interaction between people. A number of qualitative studies where people with mobility constrains used an MRP system identified benefits for the participants such as being able to see and to be seen, reducing costs and hassles associated with traveling, and reducing social isolation [126].  
Robotics can be thought of as delivery means for art explanations. For instance, a robot can sense when the group he is leading in Seville's Alazaba tour is getting bored, and change, e.g., the length of its explanations based on the movement of the visitors [127; 128]. In this context, it is worth investigating the utility of such robots in terms of:  
* Do remotely operated mobile robots increase virtual visits to a cultural site, with respect to static browser-based virtual tours?
* Do robot guides [127] improve the visitors rating when no human guide is available? Is their user experience rated better than _walkyatlky_ guides?
* Can AI provide guide explanations that reduce the boredom of the visitors?  
There could be a value in having a AI-empowered robots visiting together the cultural heritage site with the humans as well. One potential application and advantage of using robots and AI in cultural heritage is with respect to language: e.g., a robot like C-3 PO that speaks all languages can make the tour anytime in any language, including sign language. This has a value with respect to a human tour guide and can be seen as a next step in innovation in the field of guide systems, as the incarnation of audio guides.