esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring ISAC: Information-Theoretic Insights
## 3 Mono-Static ISAC with Sensing Distortion  
In this section, we introduce a first simple information-theoretic model of ISAC that allows the obtaining of a convenient expression for the information-theoretic limits and tradeoffs for ISAC point-to-point channels with a single Tx and a single Rx. Sensing performance is measured by an arbitrary distortion function, as is typically used by information theorists in rate-distortion theory to formalize lossy compression systems or joint source-channel coding. The model is powerful in the sense that it allows the inclusion of desired properties both from a sensing and a communication perspective. For example, any arbitrary number of sensing targets can be modeled, as well as a wide range of sensing metrics. Similarly, depending on the specific situation one wishes to analyze, this model can describe arbitrary communication and radar channels, which may or may not depend on the sensing targets. Moreover, the model allows the inclusion of arbitrary (perfect or imperfect) channel state information (CSI) at the Rx and arbitrary instantaneous but causal CSI at the Tx, which typically are obtained by transmission of independent pilot signals. However, it should be noted that the proposed model can only model channels and targets that evolve in a memoryless fashion. Moreover, certain sensing performances like detection probabilities cannot be described using distortion conditions. To remedy this latter drawback, in Section 7, we introduce a related problem where sensing performance is measured in terms of detection error probabilities.  
It should be noted that a very similar model was also considered in [53] for a multi-antenna Gaussian fading channel. In this related work, sensing performance is, however, not measured in terms of distortion but by an averaged inverse Fisher information, which is motivated by the well-known Cramer-Rao bound. In particular, in [53], I-MMSE is introduced as a unifying relation between distortion-based sensing (MMSE) and communications (mutual information). It seems more difficult to determine the fundamental limits of this basic ISAC model under this related sensing criterion; however, the authors of [53] were able to determine some extreme points of this tradeoff: the points of optimum communication performance or optimum sensing performance.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.6 Characteristics of Driver Applying Information
explored the correlation between the memory of relative velocity with different time steps and the car-following behavior based on the grey correlation analysis method and field data [89]. Based on this, they introduced an item about the difference between the relative velocity and its historical values with different time steps. Like [PERSON]'s approach, [PERSON] et al. also employed the grey correlation analysis method based on the field data and explored the correlation between the memory of acceptable risk and car-following behavior [91]. Based on this, they proposed an extended Desired Safety Margin (DSM) model considering the drivers' memory of the historical acceptable risk. Later, [PERSON] et al. replaced the items about optimal velocity and velocity difference, respectively, with the integral items about the continuous memory of optimized velocity and velocity difference over a period in the FVD model, and established a new extended model [97]. [PERSON] et al. extended the FVD model by incorporating items about the memory, its velocity and the headway between the object vehicles and the preceding vehicle over the last time period \(m\)[98]. At the same time, the hysteresis in the traffic flow was recognized based on the field data, and its causes were discussed by employing a deep learning model, of which the input was various pieces of historical information with different time steps, by [PERSON] et al. [99]. The results reveal that considering the long memory is important for reproducing the hysteresis in the simulation of traffic flow. Then, [PERSON] et al. extended the previous research [100] with consideration of the availability and ease-of-storage of information in the V2X environment and proposed a new model incorporating the historical information of each one of the preceding vehicles [101]. Recently, [PERSON] et al. incorporated the drivers' memory of the second headway with the preceding and following vehicle, respectively, to further extend the BLFVD model [102]. Differences between the vehicle's current and historical motion state can present the varying tendency in the motion state. It is obvious that the drivers will consider the varying motion state tendency in other vehicles in the car-following process. The results of the above studies confirm that there is a correlation between the car-following behavior in the time \(t+1\) and the historical information over the last period \([t-\tau,t]\). The impacts of the memory of various information sources, whether continuous or not, on car-following behavior were discussed, although it was not pointed out in part of the works that they were for the V2X environment. The results of these achievements can all contribute to the research on car-following behavior in the V2X environment and construction, as well as the improvement of V2X technology.
## 3 Methodologies
### Logistic Map  
In this subsection, we demonstrate the use of the logistic map for generating the keystream in our steganographic algorithm. To ensure the chaotic behavior required for secure keystream generation, we first determine the appropriate range for the logistic map parameters using the Lyapunov exponent and bifurcation diagram. By carefully selecting these parameters, we can generate a highly unpredictable and secure keystream.
## 4 Numerical Results
Results of the buckling analysis are shown in Table 3, where the (dimensional) critical load values are reported for different values of \(m\), namely \(m\in[2,11]\); both the critical buckling loads of the coarse equivalent model, namely \(P_{c}^{EQ}\), and of the refined finite-element model, \(P_{c}^{FE}\), together with the percentage error \(e_{\%}:=100\left(P_{c}^{FE}-P_{c}^{EQ}\right)/P_{c}^{FE}\) are reported in the same Table 3. It is seen that: (i) the larger is \(m\) the larger is the critical load, coherently with the fact that the elastic stiffnesses of the equivalent beam model (\(c_{11}\), \(c_{22}\) and \(c_{33}\) defined in Equation (17)) increase with \(m\); (ii) the magnitude of the percentage error is small, thus confirming the effectiveness of the proposed Timoshenko equivalent beam model. Figure 4: Critical mode in the case study I, when \(n=50\), and for different boundary conditions: (a) H-R; (b) C-F; (c) C-R; (d) C-S. Continuous green curve: Timoshenko model; blue dots: FE solution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \(\mathbf{P_{c}^{EQ}}\) & \(\mathbf{P_{c}^{FE}}\) & \(\mathbf{\epsilon_{\%}}\) \\  & \([\mathbf{N}]\) & \([\mathbf{N}]\) & \([-]\) \\ \hline
2 & 8.55 & 8.60 & 0.60 \\ \hline
3 & 29.59 & 29.53 & \(-0.21\) \\ \hline
4 & 62.66 & 61.90 & \(-1.22\) \\ \hline
5 & 104.91 & 102.71 & \(-2.15\) \\ \hline
6 & 153.27 & 149.00 & \(-2.86\) \\ \hline
7 & 205.30 & 198.60 & \(-3.37\) \\ \hline
8 & 259.34 & 250.08 & \(-3.71\) \\ \hline
9 & 314.37 & 302.55 & \(-3.91\) \\ \hline
10 & 369.78 & 355.49 & \(-4.02\) \\ \hline
11 & 425.20 & 408.61 & \(-4.06\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Critical loads of a sample H-R grid beam with \(n=50\), for different values of \(m\). Figure 5: Non-dimensional critical load \(\mu\) vs. the number of cells \(n\), for the case study II and for different boundary conditions: (**a**) H-R; (**b**) C-F; (**c**) C-R; (**d**) C-S. Green curve (A): Timoshenko model; blue curve (B): FE solution; yellow (C) and red (D) curves: Timoshenko and Euler–Bernoulli models without constitutive geometric effect.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 3 Production of Ultra-Short e-Bunches  
Particles traveling in an accelerator are subject to synchrotron oscillations in their longitudinal phase space [31]. To achieve bunch compression in a linear accelerator, the rotation of the particles comprising the bunch in the longitudinal phase space has to be tuned in such a way to reduce the size of the projection of the particles in time.  
In order to allow such rotation a non-zero momentum compaction factor \(\alpha_{c}\) must be present, defined as \(\alpha_{c}=\frac{\Delta L/L}{\Delta p/p}\), where \(\Delta L\) is the variation of the path length w.r.t. the reference particle traveling a distance \(L\) and \(\Delta p\) is the difference in momentum w.r.t. the reference particle momentum \(p\).  
The first order momentum compaction factor is often referred to as \(R_{56}\), from the name of the linear transport matrix element coupling momentum and longitudinal coordinates of a particle. An \(R_{56}\
eq 0\) can be provided by a magnetic lattice, such as a chicane or a dogleg, or, at low electron velocities, i.e., when \(v_{e}<<c\), by the ballistic motion of particles according to \(R_{56}\simeq-L/\gamma^{2}\), where \(\gamma\) is the well-known relativistic factor.  
The minimum bunch length will be given by the projection of the phase space on the t coordinate axis after the electrons have rotated by roughly 90 degrees w.r.t. their production in the photo-injector. In a perfectly linear system, this matching condition is reached when \(k_{1}=-1/R_{56}\), where \(k_{1}\) represents the energy chirp of the beam at the entrance of the bunch compressor (magnetic or RF). The ultimate limit of the Full Width at Half Maximum (FWHM) of this distribution is influenced by several factors such as:  
* the initial uncorrelated energy spread of the beam (analogous to the slice energy spread of the beam);
* non-linear effects on the phase space distribution, e.g., RF curvature;
* the deterioration of the phase space distribution due to space charge and Coherent Synchrotron Radiation (CSR) effects.  
These effects must be minimized to guarantee the best performance, i.e., to maximize the beam current. In the next paragraphs we will give an overview of the methods to achieve that.
## 4 Identification of Some Technical Barriers and Further Research Targets
Hence, the DSOs should be involved in the evaluation of the technically feasible amount of each AS, which can be offered at any time interval, even in the cases an aggregator is responsible for the trading of these AS. Hence, the aggregation of the AS, particularly those related to frequency deviation, and their secure and controllable provision to the upstream TSO, still remains an open research issue. This ongoing discussion has already been reflected and comprehensively presented in the recently published \"TSO-DSO Report: An integrated approach to active system management\" [4]. As a starting point, in [113] a method is developed to (_i_) aggregate the inertial response by several DRES within a distribution system and (_ii_) to optimally allocate a given amount of inertial response to the DRES within the distribution system. This two-stage method takes into account some of the technical constraints within the distribution system mentioned above as well as specific constraints of the DRES, e.g., converter temperature increase and power losses. A broader identification of relevant technical, financial and regulatory barriers can be found in [18].
## 3 Results and Discussion  
### Influence of Stirring Speed  
The influence of stirring speed was studied in order to achieve a uniform mixture of the aqueous solution and the adsorbent, and to minimize the thickness of the aqueous boundary layer, with the aqueous solution and adsorbent conditions being maintained as follows: Cr(III) 0.01 g L\({}^{-1}\) in 0.1 M NaOH, and adsorbent dosage of 1g L\({}^{-1}\).  
The results derived from this investigation revealed that the stirring speed had no influence on the time required for the system to reach equilibrium (Figure 1), since for every stirring speed investigated here, at 30 min of contact time between the aqueous solution and the adsorbent, 85% of the chromium(III) from the solution had been adsorbed onto the carbon nanotubes, whereas equilibrium was reached after 120 min at all stirring speeds. However, the stirring speed had an influence on the maximum metal uptake onto the nanotubes (Table 2). As shown from these results, a maximum chromium(III) uptake was achieved at 1000-1500 min\({}^{-1}\), with this being attributable to the fact that at these stirring speeds, the minimum of the aqueous layer was reached and adsorption was maximized. A stirring speed of 1000 min\({}^{-1}\) was kept constant throughout the experiments.  
The data at 1000 min\({}^{-1}\) were used to estimate the rate law in the present system. The experimental results fit best with the particle diffusion model, as represented by Equation (1), with a rate constant of \(3.8\times 10^{-2}\) min\({}^{-1}\).  
Figure 1: Dimensionless \([\mathrm{Cr(III)}]\mathrm{cnt,t}/[\mathrm{Cr(III)}]\mathrm{cnt,e}\) versus time at various stirring speeds. (\([\mathrm{Cr(III)}]\mathrm{cnt,t}\) and \([\mathrm{Cr(III)}]\mathrm{cnt,e}\) are the chromium concentrations in the nanotubes at elapsed time and at equilibrium, respectively).
# Enhancing Human-Agent Interaction via Artificial Agents That Speculate About the Future
## 5 Discussion
### Implications
#### 5.2.3 Physical vs. Virtual Interaction in HRI  
There is ongoing debate in the scientific community about the difference between virtual interaction and physical interaction, not only in HRI but also in fields such as HAI, CUI, etc. Nevertheless, there is also increasing interest within those fields in \"mixed-reality\" platforms that combine the virtual and physical, as well as virtual simulations of physical interactions for design purposes [27; 79; 80]. Critically, these approaches can allow us to more rapidly prototype social interaction scenarios. Such interest was underscored by the recent, first-ever CUI workshop at the ACM HRI flagship conference in 2023, which drew a large audience from multiple overlapping scientific fields [81].  
One aspect that cannot be ignored, especially when creating conversational agents and user interfaces that appear through a screen rather than a physical robot (as we did here), is the importance of non-verbal social cues [82; 83]. This obviously leads to the question of how virtual interaction paradigms can be utilized to design such physical social cues. There are some examples of this--e.g., [PERSON] et al. (2015) looked at using Unity to visualize and create more realistic movements for social robots [84]. Other researchers have studied the use of physical haptic feedback as a social cue while users wear a virtual reality (VR) headset during a virtual interaction with a physical robot [85], while others have examined applying general UX design principles of digital interfaces to reimagine physical interactions as a kind of step-by-step \"interface\" [86]. Another common area of research is using VR systems for multi-robot control in human-in-the-loop scenarios, where otherwise coordinating the actions of multiple robots is quite difficult for a human user [87].  
There are also many examples of using virtual interaction as a substitute for physical interaction during human-robot communication. A common problem in HRI is having our robot indicate its intent to human users somehow through mainly physical means (e.g., gaze), leading to some ambiguity that often leaves the human guessing [88]. That can sometimes be augmented through verbal cues towards specific objects, though the scalability of such systems remains unclear. However, another avenue is to provide so-called \"mixed reality\" interfaces combining virtual reality and other _digital interfaces_ into the system. We are already seeing some HRI platforms adopt such an approach. For instance, many robots now include digital interfaces (e.g., screens) that allow us to incorporate both embodied interaction and virtual interaction on the same platform [89; 90; 91]. A great example of the above is the Furhat, which is a 3D digital image of a face retro-projected from behind onto a plastic screen made in the shape of a human face, but a virtual image nonetheless [92; 93].
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 2 System Model
### RIS-DH Scheme
Hence, we compute SNR at \(D\) as follows [59]:  
\[\gamma_{D}=\frac{P_{S}\Big{|}\sum\limits_{n=1}^{N}h_{R,n}h_{D,n}e^{i\phi_{n}- j\theta_{n}-j\theta_{n}}\Big{|}^{2}\omega_{1}}{P_{S}\Big{|}\sum\limits_{n=1}^{N}h_{R, n}h_{D,n}e^{i\phi_{n}-j\theta_{n}-j\theta_{n}}\Big{|}^{2}\omega_{2}+d_{1}^{ X}d_{2}^{X}N_{0}\omega_{3}}, \tag{4}\]  
where \(\omega_{1}=\big{|}\beta_{D}\beta_{S}+\tilde{\beta}_{D}\tilde{\beta}_{S}^{*} \big{|}^{2}\), \(\omega_{2}=\big{|}\beta_{D}\tilde{\beta}_{S}+\tilde{\beta}_{D}\tilde{\beta}_{ S}^{*}\big{|}^{2}\) and \(\omega_{3}=\big{|}\beta_{D}\big{|}^{2}+\big{|}\tilde{\beta}_{D}\big{|}^{2}\). Based on [57], we can approximate \(\omega_{1}\approx\big{|}\beta_{D}\tilde{\beta}_{S}\big{|}^{2}+\big{|}\tilde{ \beta}_{D}\tilde{\beta}_{S}^{*}\big{|}^{2}\) and \(\omega_{2}\approx\big{|}\beta_{D}\tilde{\beta}_{S}\big{|}^{2}+\big{|}\tilde{ \beta}_{D}\tilde{\beta}_{S}^{*}\big{|}^{2}\). Next, we set \(\phi_{n}=\theta_{n}+\varphi_{n}\) as [59]. Then, the maximum amount of \(\gamma_{D}\) is given as follows:  
\[ \begin{split}&\gamma_{D}=\frac{P_{S}\Big{[}\sum\limits_{n=1}^{N}h_{R, n}h_{D,n}\Big{]}^{2}\omega_{1}}{P_{S}\Big{[}\sum\limits_{n=1}^{N}h_{R,n}h_{D,n} \Big{]}^{2}\omega_{2}+d_{1}^{X}d_{2}^{X}N_{0}\omega_{3}}\\ &=\frac{\eta\sigma_{1}}{\eta\omega_{2}+\omega_{3}},\end{split}  \tag{5}\]  
where \(\rho_{S}=\frac{P_{S}}{N_{0}}\) denotes the average SNR, \(Z=\sum\limits_{n=1}^{N}h_{R,n}h_{D,n}\), and \(\eta=\frac{\rho_{S}}{d_{1}^{2}d_{2}^{X}}Z^{2}\). Figure 1: System model for the proposed RIS-aided dual-hop communication scheme. The above-derived SINR expressions are necessary to compute other main system performance metrics. It is worth noting that the transmit SNR plays an influential role in varying performance at destinations. However, the system needs to adjust parameters at the transmit side, for example, the transmit SNR, so as to achieve the expected performance. We will present the closed-form expression to illustrate performance in terms of outage probability and ergodic capacity in Sections 3 and 4.
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 5 Experimental Results and Analysis
### Performance Analysis of Imbalanced Scenario (Figures 16-18)  
In an imbalanced scenario, where the workload distribution favors either enqueue or dequeue operations based on a parametric ratio, we simulate real-world conditions by varying the workload distribution. In one case, we maintain an equal ratio of enqueues and dequeues to observe an equal number of boundary condition hits (Figure 16). In another scenario, where enqueues dominate (70% enqueues and 30% dequeues) (Figure 17), we observe more frequent hits on full queue conditions. Conversely, when dequeues outnumber enqueues (40% enqueues and 60% dequeues), we see more frequent hits on empty queue conditions (Figure 18). This allows us to analyze how the algorithm handles different types of imbalances in workload distribution.  
advantage allows BACQ to outperform the BWD Queue by 3\(\times\), the broker queue by 3.5\(\times\), and the Gottlieb queue by 9\(\times\).
## 1 Introduction
In recent decades, there have been increasing efforts to quantify GPP using satellite measurements, such as solar-induced chlorophyll fluorescence (SIF) [29], the updated two-leaf LUE model (GPP derived from sunlit and shaded leaves) [30], the P-model [31], and the BEPS model [32; 33]. As a whole, these models combine process- and data-driven approaches to sidestep some of the more unsettling limitations of both complex process-based models and observed data-driven models. However, uncertainty in GPP predictions makes their spatio-temporal patterns and trends incomprehensible. As such, it is difficult to define the potential consistency or inconsistency of the same cluster of GPP models when assessing the spatio-temporalt trends and inter-annual variability of terrestrial GPP based on a single or even numerous models [16; 30; 34]. In this context, when recalling that droughts can affect the global carbon balance by retarding vegetation growth at various stages or within a wide range of tree mortality and ecosystem fires [35; 36; 37], it is difficult to accurately evaluate the response of terrestrial ecosystems to drought at different timescales, especially with the varying physiological and biochemical activities of plant growth and cultivated crops [38; 39; 40]. Due to its significant impact on terrestrial ecosystems and political, social, and economic systems, including hydrological processes [41], vegetation growth [42], and food security [43], drought and its spatial repercussions have frequently been the focus of several studies in the Middle East, which is prone to drought due to persistent water scarcity caused by a climate that is nearly transitional between hot-dry and cold-humid. As a result, the Middle East is a critical region for the understanding of global drought [44]. To better understand the ecosystem response to current and future climate change scenarios and to forecast possible changes in carbon cycle dynamics in the Middle East, it is crucial to evaluate the response of ecosystem productivity to drought and its long-term patterns and trends. Based on four state-of-the-art GPP datasets, including the revised EC-LUE algorithm-driven GPP (GLASS-GPP), the EC flux tower upscaling-based GPP (FluxCom-GPP), the MODIS algorithm-driven GPP model (GIMMS-GPP), and the vegetation photosynthesis model-GPP (VPM-GPP), this study aimed to: (a) detect the spatio-temporal annual trends of GPP in the Middle East and explore whether these trends varied as a function of model parameterizations; and (b) detect GPP sensitivity to dry and wet climatic conditions and assess ecosystem resilience to anomalous drought events on the regional scale and how this resilience could vary in response to dominant land-cover types. The findings of this work, as based on these four global long-term GPP datasets, can be relevant to ecosystem management in the Middle East because they can provide insights into how different ecosystems in the region can respond to drought perturbations.