esa-sceva/satcom-chunk-collection huggingface dataset:
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Physical Interpretation of Entropy Gradients  
The dissipative term \(T\
abla\overline{S}\) in Equations (1) and (2) encapsulates two key physical mechanisms:  
* **Environmental Coupling**: When the system interacts with a thermal reservoir (e.g., stochastic cooling), \(T\
abla\overline{S}\) represents heat exchange. For example, in beam stacking [34], entropy gradients model energy loss to electromagnetic fields.
* **Internal Irreversibility**: For systems with self-interacting particles (e.g., turbulent plasmas), the entropy production has two equivalent representations: \[\dot{S} =\gamma\,\text{Var}(x)\] (18) \[=\
abla\cdot(T\
abla\overline{S})\] (19) where \(\gamma\) is the damping coefficient, \(\text{Var}(x)\) measures velocity fluctuations, and the right-hand side matches the entropy production term \(\sigma\) in Equation (7) and shwon in Figure 1. This dual form connects particle-scale dynamics to continuum-scale irreversibility.
## 3 Experimental Details and Results
Therefore, the measurement of the gamma-rays as a function of time can be used to determine the muon lifetime in a particular element in the sample. As an example, we report here in Figure 5 the intensity of the 1528.3 keV gamma-ray (from muon capture in Fe at p = 28.6 MeV/c) as a function of the time of the Ge detector signals (in 100 ns wide windows). The fit of this plot results in a mean lifetime of muon in Fe to be 201(2) ns, which is in good agreement with the values of 201(4) [64], 207(3) [65], 206.7(2.4) [66] and 206.0(1.0) ns [45]. We note here that this muon lifetime determination was not our primary goal and that a measurement as short as 30 min already provides an excellent precision. Hence, it appears that the identification of the gamma-rays and the determination of the mean lifetime of the muon act as additional proofs for the elemental identification. And we stress again that the determination of the mean lifetime of all elements is only possible due to the continuous muon beam, where each muon arrival time is measured. Figure 4: The prompt counts (N) of the muonic X-ray spectrum ((i) \(-\)50\(<t_{\rm diff}<\)50 ns) and delayed counts (N) of the gamma-ray spectrum from the nuclear capture of muons ((ii) 100\(<t_{\rm diff}<\)200 ns) in the Fe layer of the three-layered sandwich sample at \(p=28.6\) MeV/c within the energy ranges of (**a**) \(0<E<200\) keV, (**b**) \(200<E<500\) keV, (**c**) \(500<E<1000\) keV, (**d**) \(1000<E<2000\) keV, and (**e**) \(2000<E<3100\) keV. The \(\mu\)-X ray energies are written in red, while the gamma-ray energies are in blue. A statistical error of the data points (\(\sqrt{N}\)) is not shown for visibility of the smaller peaks.
## 4 Conclusions  
This paper presents a video quality assessment model that fully accounts for the influence of video quality fluctuation caused by video quality switching on QoE during network transmission. Moreover, various factors, such as video quality, fluency and volatility, are combined with deep spatial-temporal features of video, average bitrate, re-buffering duration and count, initial buffering time and video quality switching count to form the feature parameters vector, and use the ridge regression method to establish the mapping relationship between the feature parameters vector and MOS. The experimental results on the public datasets, namely, Waterloo Streaming QoE Database-III and LIVE-NFLX-II show that, compared with the existing video QoE assessment models, the proposed QoE model established in this paper can achieve higher prediction accuracy. The proposed model can be used to guide the client's bitrate selection to improve the user's QoE for DASH standard as well as other dynamic adaptive streaming technologies.  
However, compared with the existing methods, to effectively express the content characteristics of the video, the dimension of the features selected in this paper is relatively high, and the established model is relatively complex. In further work, the complexity of the model will be optimized.  
Finally, thanks to the support and help of the author of the Waterloo Streaming QoE Database-III [PERSON].  
Conceptualization, L.D. and L.Z.; methodology, L.Z. and L.D.; software, L.D.; validation, L.D. and L.Z.; formal analysis, L.D. and L.Z.; investigation, L.D. and L.Z.; resources, L.Z., J.Z., H.Z., J.L. and X.L.; data curation, L.D.; writing--original draft preparation, L.D.; writing--review and editing, L.Z., L.D. and J.Z.; visualization, L.D.; supervision, J.L. and L.Z.; project administration, L.Z. and J.Z.; funding acquisition, L.Z., J.L., J.Z., X.L. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 61531006, No. 61971016). The authors would like to thank all the colleagues who contributed to this research work. The authors declare no conflict of interest.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 6 Power Transfer
### Power Transmission over a Multi-Hop Channel  
For power transfer inside a cave where the shape of the cave and obstacles makes it impossible to maintain a direct-line-of-sight connection over distance, a multi-hop channel is proposed where the laser beam is relayed from one point to another point through multiple sensors in between. The laser power transmitted by the source, \(P_{l(0)}\) for supplied power, \(P_{s}\) is expressed as Equation (14).  
\[P_{l(0)}=\eta_{E-L}P_{s} \tag{14}\]  
The incident laser power received by the \(n\)th sensor is expressed as Equation (15).  
\[P_{i(n)}=\ \eta_{T}P_{l(n-1)} \tag{15}\]  
The electrical power produced by the \(n^{th}\) sensor is then expressed as Equation (16).  
\[P_{e(n)}=\eta_{L-E}P_{l(n)} \tag{16}\]  
Each sensor receives the incident laser power from its previous neighbor, then it converts a fraction of it for storage and reflects the rest to the next neighboring sensor. If the efficiency of reflection is defined as \(\eta_{R}\), the laser power transmitted by the \(n\)th sensor to the \((n+1)\)th sensor is expressed as Equation (17).  
\[P_{l(n)}=\eta_{R}\left(P_{e(n)}-\frac{P_{sensor}}{\eta_{L-E}}\right) \tag{17}\]  
Figure 17 shows the variation of transmitted power by each sensor placed at a distance of \(d\) = [50, 100, 250] m, over a total distance of 1000 m for lasers with wavelength 810 nm  
and 1550 nm, respectively. For each distance \(d\) and wavelength, the model was simulated for three values of initial power supply \(P_{s}=[100,\,250,\,500]\) W with each sensor storing power at \(P_{sensor}=10\) W. Table 1 shows the description of the legend for Figure 17.
## 3 Results
### Characterization of Spatiotemporal Variations
#### 3.1.3 The Significance Test Analysis of WUE Variation Trend  
In this paper, the Mann-Kendall significance test was performed to test the significance of the WUE interannual rate of change in order to obtain the trend of significance change in WUE in the YRB from 1982 to 2018 (Figure 4 and Table 2). It was revealed that there was spatial heterogeneity in the trend of WUE changes in the YRB from 1982 to 2018, with a significant decrease (14.64%, Slope \(<\) 0, \(p\) \(<\) 0.05) of WUE in the southern and southeastern regions and a significant increase (47.07%, Slope \(>\) 0, \(p\) \(<\) 0.05) in some regions of the upstream and midstream. The areas with a significant increase in WUE were mainly distributed in Gansu and northern Shaanxi, while the areas with a significant decrease in WUE are primarily located in southeastern Shanxi, southern Shaanxi, Henan, and Shandong. Considerable variations were detected in the geographical patterns of WUE trends during the periods 1982-2000 and 2000-2018. The WUE exhibited a significant downward trend (Slope \(<\) 0, \(p\) \(<\) 0.05) in the midstream and downstream of the YRB, mainly in the southern and southeastern parts of the YRB from 1982 to 2000. However, the WUE exhibited a significant upward trend from 2000 to 2018 in most regions (Slope \(>\) 0, \(p\) \(<\) 0.05), mainly including Gansu, Ningxia Hui Region, northern Shaanxi, northern Shanxi, and most of southern Inner Mongolia.  
Figure 4: Spatial characteristics of significant variation trend of the WUE in different time periods.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 4 Proposed Method
### Protocol for NFV/SDN-Based Constructions of the Proposed Networks  
Based on the algorithm discussed in previous sections, we discuss the network configuration for dynamically changing the frequency assignment of the access-side UAVs. Figure 6 shows the proposed NFV/SDN-assisted UAV network architecture. It can be seen that UAV holds two interfaces: the access side for user communication and the backhaul side for communication with other UAVs or base stations. Meanwhile, the NFV/SDN controller, which controls the NFV/SDN agent in UAV, is located at the base station. The FFR partition allocation used on the access side is determined by the algorithm proposed in Algorithm 2 and controlled by SDN through cooperative coordination with UAVs and backhaul in the C-Plane (control plane). The function that determines the algorithm is the Config Manager, as shown in Figure 6. Furthermore, the Config Manager in UAV manages the CNF (Containerized Network Function) image and deploys it on the NFV platform by the NFV controller. Here, since we assume that UAV is lightweight, the computing resources of the MEC in UAV are limited. Hence, the virtualization in the MEC is not a VM (Virtual Machine) but a process bootable CNF.  
Figure 6: Dynamic control NFV/SDN-assisted UAV networks.  
On the other hand, the SDN controller controls each UAV's access and backhaul OVS based on the network topology. In the case of CNF used by users, connecting between access and backhaul OVS with packet-through is unnecessary. Furthermore, OVS is created as an overlay network between other OVS to pass through data traffic. In other words, it is possible to construct a secure network where traffic cannot be intercepted from the outside because it is a traffic-capacitated network. Hence, access OVS is needed to communicate with CNF only in secure networks, and the SDN controller updates the flow table in OVS in UAV. Otherwise, access and backhaul OVS are needed to connect and pass through the user traffic to other UAVs or base station sides. This proposal architecture supports a dynamic control for OVS and achieves a secure platform.
# Synthetic Jet Actuators with the Same Cross-Sectional Area Orifices-Flow and Acoustic Aspects
## 3 Results and Discussion
### The Centerline Velocity Measurements  
The centerline exit (\(x=r=0\) mm) momentum velocity as a function of the frequency and the real power of SJA is presented in Figure 6. The course of the velocity charts (Figure 6a) is similar to the Reynolds number charts (Figure 3). This confirms the correctness and compatibility of the methods used because these values are directly proportional to each other--Figure 3 has been drawn from a time-mean force measurement and Figure 6 from hot-wire velocity measurement. Additionally, note that the velocity measured at the center of the cross-section area is representative and can be used to calculate the mean velocity value [40; 46].  
The centerline momentum velocity of SJ as a function of real power was similar for each type of orifice. However, the velocity for the slot orifice was higher than others, especially at small real power, \(P<2\) W. The centerline momentum velocity for slot orifice was even 12.5% higher at \(P=0.1\) W than the velocity for circular orifice. At \(P>2\)W difference between velocity for slot and square orifice was circle 2% and it is the value equal to measurement uncertainties. The minimal difference between the velocity for slot and circular orifice was 3.6%.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Transmission Latency (L)  
The transmission latency (L) is defined as the time required, expressed in milliseconds, to transmit a payload (\(P_{b}\)) in bytes, from a transmitting vehicle to a receiving vehicle, over a wireless medium. Calculating the L is fundamental to guaranteeing the effectiveness and reliability of communications, from road safety to traffic management and immediate response to emergencies. In this work, the term payload refers to the size of the data transmitted between vehicles that are encapsulated in a frame.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 3 Field Testing  
As described in this section, two field testing sites were used to assess the accuracy in estimating the rapid-moving load and natural vehicle load of concrete pavements. The concrete pavements at the two sites were designed to be different sizes, in order to study the effect of pavement size on the estimation.
# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 2 Related Work
### Generative Network  
Generative models play an important role in this work. They are models that can simulate or generate distributions of real-world data. In particular, with recent advances in deep learning, generative models have attracted attention for overcoming distributional differences between domains and generating or transforming data in new domains. In the past, various generative networks such as Variational Auto-Encoders (VAEs) [8] and Generative Adversarial Networks (GANs) [10; 28] have been introduced in related research. VAEs are used to learn the distribution of data in a latent space to generate different variations, while GANs use a competitive network of generators and discriminators to produce data mimicking the actual data distribution.  
Autoencoder has been used in many fields for a while because it has a wide range of applications, such as utilizing convolutional layers for its application and having deep hidden layers by stacking multiple layers. In this paper, we used convolutional autoencoder (CAE), an autoencoder that utilizes convolutional layers [4]. In addition, GANs generate fake data that is almost indistinguishable from real data through competitive learning between generators and discriminators, which has the great advantage of reducing distribution differences between domains, and converting while maintaining features of the data. As a result, GANs have become a key tool in the field of domain adaptation. Recent research trends are moving towards combining GANs with VAE or CAE to develop more robust generative models. In addition, various variants that take into account characteristics of the data and the relationship between domains are proposed, which are utilized to better simulate distributional differences between domains and perform data transformations.  
This paper emphasizes the role and importance of generative networks and proposes a new methodology to apply to the domain adaptation problem. In this paper, we combined autoencoder and GAN to develop a generative model considering the distribution of real data. We then applied it to the domain adaptation problem to overcome the distribution difference between the domains. In this way, realistic and stable adaptation results were obtained. The effectiveness of the proposed method was demonstrated through comparison with related studies. As a result, the accuracy of the generative network on the dataset was 98.5\(\sim\)99.23%, which showed a very high classification accuracy. An error of 0.77\(\sim\)1.5% was assumed to be some noise. The model was designed and tested to be robust against noise. This shows an innovative solution in the field of domain adaptation. It is expected to provide an important direction for strengthening the stability and generalization ability of models not only in domains, but also in the face of noise.
## 7 Discussion  
Advancements in computational resources have enabled the application of MPC in systems requiring high dynamic performance, such as VSI control. Unlike conventional control strategies, MPC can predict system behavior and incorporate operational constraints directly into control design. In this context, the proposed MPC computes the optimal control action to achieve the voltage magnitude reference while considering filter current and voltage modulation constraints. Additionally, the method leverages an augmented system to incorporate integral action, improving steady-state accuracy, and utilizes the system's steady-state behavior to enhance the transient response, ensuring faster convergence.  
This article presents an offset-free MPC for the VSI using the OSQP solver, which efficiently solves the resulting QP problem. The computational cost is evaluated on the dSPACE MicroLabBox platform, demonstrating the feasibility of this approach via the condensed problem formulation. The proposed methodology is applicable to voltage control in DERs within microgrids, meeting the growing demand for advanced control strategies in modern decentralized power systems. This control approach could be extended to higher-level control loops in microgrids [38], such as secondary control, to achieve reference tracking of voltage and operating frequency while also incorporating operational constraints.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 5 Detailed System Analysis
### Protocol Description
#### 5.3.1 Report Form Collection Sub-Protocol  
The Report Form Collection sub-protocol that is depicted in Figure 10 is run when the user needs to acquire a new Report Form.  
1. The user calculates a pair of asymmetric keys \(U_{EK}\) and \(U_{DK}\). The encryption key \(U_{EK}\) is composed into an _RF-Request_ and the decryption key \(U_{DK}\) is temporarily stored.
2. The _RF-Request_ is encrypted with the Broker's public key \(B_{P_{DK}}\) and sent to one of the Ad-Dealers, either directly or via an Agent.
3. Upon receiving the _RF-Request_, the Ad-Dealer forwards it to the Broker.
4. The Broker decrypts the _RF-Request_ with his private key \(B_{P_{FK}}\) and obtains the user's encryption key \(U_{EK}\). The Broker then creates an entry in the _SC-Board_ with a new _ARC-ID_ and the identity of the issuing Ad-Dealer and afterwards computes a pair of keys \(sk\) and \(vk\). The Verification Key \(vk\) is stored securely while the Signing Key \(sk\) and the _ARC-ID_ are composed into a Request Form _RF_.
5. The _RF_ is first encrypted with the \(U_{EK}\) which was provided by the user and then is returned to the issuing Ad-Dealer who filed the request.
6. The **issuing** Ad-Dealer verifies his identity on the _SC-Board_ by providing this signature.
7. The Ad-Dealer forwards the _RF_ to the user either directly or via the same Agent who delivered the original _RF-Request_.
8. The user receives the encrypted _RF_ and decrypts it with his \(U_{DK}\) in order to obtain the _ARC-ID_ and _vk_.  
Figure 10: Report Form Collection sub-protocol.
## 3 Method
### SOTA LLM Evaluation  
In this section, we detail the LLM-based model evaluation method (the model used in this experiment is GPT-4o), which is specifically designed for evaluating model performance in the Named Entity Recognition (NER) task. The evaluation process follows a comprehensive framework that considers several key factors. The overall architecture of the SOTA LLM evaluation is shown in Figure 2.  
1. Generate Evaluation Prompt  
The LLM is employed to generate the prompts required for the assessment. The prompts will direct the model to combine the prediction results from the model M\({}_{\text{o}}\) and the dataset D\({}_{\text{o}}\) to calculate the contextual relevance (CR) by evaluating whether the NER results of model and dataset align and whether model and dataset are linguistically congruent. The corresponding weight parameters, \(\alpha\) and \(\beta\), are also set for both aspects in the prompt.  
Figure 2: Details of SOTA LLM Evaluation. The framework systematically integrates model output, dataset feedback, and dynamic weighting mechanisms to provide a quantifiable and scientific evaluation path for optimizing the performance of large language models.  
Calculate the contextual relevance (CR) between the model output and the ground truth output. This step involves evaluating two dimensions: entity matching and linguistic consistency.  
\[\mathrm{Match(M_{o},D_{o})}=\frac{|\{\mathrm{\alpha_{i}\in M_{o}|\exists g_{i}\in D _{o},o_{i}=g_{i}\}\}|}{|M_{o}|}. \tag{2}\]  
The Match function measures how accurately the model identifies entities compared to the ground truth, based on exact type and content matching. \(M_{o}\) is the model prediction result, \(D_{o}\) is the true value of the dataset, and \(o_{i}\) is the type and value corresponding to each entity in the set of entities identified by the model. \(g_{i}\) is the type and value corresponding to each entity in the set of entities in the true value of the dataset.  
\[\mathrm{Language(M_{o},D_{o})}=\frac{V_{M}\cdot V_{D}}{\|V_{M}\|\|V_{D}\|}. \tag{3}\]  
The Language function measures the linguistic consistency between the model's output and the dataset, using a vector-based similarity measure (cosine similarity). \(V_{M}\) is the linguistic feature vector of the model output \(M_{o}\), and \(V_{D}\) is the linguistic feature vector of the dataset result \(D_{o}\); \(\|V_{M}\|\|V_{D}\|\) is the modulus of the vector, respectively.  
\[\mathrm{CR(M_{o},D_{o})}=\alpha\cdot\mathrm{Match(M_{o},D_{o})}+\beta\cdot \mathrm{Language(M_{o},D_{o})}. \tag{4}\]  
\(\alpha\), \(\beta\) are weight coefficients that balance the importance of entity matching and language consistency in the final assessment. These coefficients can be adjusted based on specific task requirements to reflect the relative influence of each factor.  
Performance Evaluation  
\(n\) denotes the number of evaluation datasets. If we have n samples with scores CR, Then the average score \(\mathrm{E(P)}\) can be expressed as:  
\[\mathrm{E(P)}=\frac{1}{n}\sum\mathrm{CR(M_{o},D_{o})}, \tag{5}\]  
\(\mathrm{E(P)}\) is the overall performance evaluation score of the model.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 1 Introduction
### Main Contributions  
Motivated by the above-mentioned challenges, this article explores the integration of communication and sensing from a pragmatic perspective, with a focus on the role of JSC in real-world applications like vehicular technology. We outline the key challenges and benefits associated with different sensing configurations and present a case study that addresses some of these issues. Specifically, we introduce a data fusion technique that enables cooperative sensing among multiple monostatic JSC BSs within an MIMO-OFDM framework by exchanging soft maps with an fusion center (FC) at the network edge. The proposed system is evaluated in a challenging urban environment, employing a channel model that captures complex propagation effects, including multipath and multiple scattering points, to accurately represent extended targets such as vehicles. A key point of novelty of this work is the integration of extended target recognition using convolutional neural networks (CNNs), which is supported by a tracking algorithm that enhances the detection and estimation of both extended targets, like vehicles, and dim targets, such as pedestrians--an approach not previously explored in the literature for JSC MIMO-OFDM systems. Finally, the sensing/communication trade-offs are analyzed by varying the fraction of resources--power, frequency, time, and number of cooperating BSs--to provide insights into their impact on both the sensing and communication performance of the network. To narrow our focus within the vast JSC landscape, Figure 1 illustrates a typical urban environment where various JSC deployments can serve different purposes, such as detecting unauthorized unmanned aerial vehicles (UAVs), assisting in the preemptive detection of beam blockages, and enhancing road safety by detecting and locating vehicles and VRUs, including pedestrians and cyclists.