esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, we have proposed a scheme to generate spatially separated attosecond pulses by irradiating a cone target with a \(p\)-polarized laser at an intensity of \(a_{0}=30\). The laser irradiates on the cone's inner side and reflects as an emission of HHG. Meanwhile, quite a large amount of electrons escape out of the inner side under the action of the laser's ponderomotive force, Lorentz force and the ions' electrostatic force, after which, the escaped electrons collide with the counter-propagating attosecond electromagnetic pulses and quantities of high-energy photons are generated to constitute attosecond \(\gamma\)-ray pulses. The cut-off energy of the \(\gamma\)-ray is 6 MeV, and the FWHM of a single pulse can reach 267 as; in particular, the corresponding peak brilliance \(>\)10\({}^{22}\) photons/(s-mm\({}^{2}\)-mrad\({}^{2}\)-0.1%BW) is obtained. A series of comparative simulations show that the laser-to-photon energy conversion efficiency at the cone target is 12 times higher than that at the planar target, and the energy conversion efficiency and photon cut-off energy at the cone target at 90\({}^{\circ}\) are the highest compared with the others.  
Studies on the effect of escaped electrons on the correction of the HHG power-law, the influence of CEP on distribution and the effects of other parameters, e.g., the laser intensity on \(\gamma\)-ray generation, is also valuable. They are beyond the scope of this paper and are worthy of being studied in the future.  
Conceptualization, simulation, data analysis and writing--original draft preparation, C.-W.Z., Y.-X.Z. and J.-F.L.; writing--review and editing, C.-W.Z., Y.-X.Z., J.-F.L. and B.-S.X.; project administration, B.-S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant No.11875007 and No.11935008.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
We would like to thank [PERSON] for his help with numerical calculations. We acknowledgment the open source PIC code Smilei. The computation was carried out at the HSCC of the Beijing Normal University.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### 2.4.1. Sorption Kinetic Models  
Sorption is a mass transfer process that transfers contaminants from the liquid phase to a solid sorbent. In order to accurately assess information related to heavy metal sorption rates, sediment properties and their mass transfer mechanisms, a kinetic model consistent with the sorption behavior of manganese on DX sediments needs to be determined. In this study, the pseudo-first-order (PFO) kinetic model, the pseudo-second-order (PSO) kinetic model, the Elovich model, and the Webber-Morris model were used to study the kinetic analysis of the interfacial processes. The four kinetic models mentioned above can be represented as follows.  
PFO kinetic model:  
\[Q_{t}=Q_{e}[1-\exp(-k_{1}t)] \tag{1}\]  
PSO kinetic model:  
\[Q_{t}=\frac{Q_{e}^{2}k_{2}t}{1+Q_{e}k_{2}t} \tag{2}\]  
Elovich mode:  
\[Q_{t}=\frac{ln(a\cdot b)}{b}+\frac{lnt}{b} \tag{3}\]  
Webber-Morris model:  
\[Q_{t}=k_{d}\cdot t^{\frac{1}{2}}+c \tag{4}\]  
where \(Q_{e}\) (mg\(\cdot\)g\({}^{-1}\))is the equilibrium value of adsorbed Mn(II), t (min) is the sorption time (min), \(k_{1}\) (min\({}^{-1}\)) and \(k_{2}\) (g\(\cdot\)mg\({}^{-1}\)) are the pseudo primary and pseudo secondary rate constants, respectively. In the Elovich equation, a (mg\(\cdot\)(g\(\cdot\)min)\({}^{-1}\)) is the constant on the initial sorption rate, b constant is the surface coverage and chemical Intraparticle diffusion equation, \(k_{d}\) (mg\(\cdot\)(g\(\cdot\)min\({}^{1/2}\))\({}^{-1}\)) is the rate constant for intraparticle diffusion, c is the intercept of the \(y\)-axis [28].
## 4 Experimental Setup  
### Dataset  
The experiments are conducted using three datasets: EdNet-KT4, XuetangX, and orthografietrainer. The data in EdNet-KT4 is sourced from Santa [13]. The original EdNet-KT4 dataset comprises data from 297,915 students with 131,441,538 learning interaction records in total.  
The XuetangX dataset originates from \"XuetangX\" [14]. XuetangX includes 39 teaching mode courses and 112,448 learners in sum. The dataset contains 1,319,032 video activities, 10,763,225 forum activities, 2,089,933 assignment activities, and 7,380,344 web page activities.  
The dataset \"orthografietrainer\" originates from orthografietrainer.net, which serves as a resource for mastering German spelling ability. The platform's data encompasses 181,792 assignments activities, 164,580 user sessions, and 3,224,014 completed sentences, all contributed by 52,032 users.  
The processed datasets are illustrated in Table 1. Before training, the synthetic minority over-sampling technique (SMOTE) is employed to oversample the missing labels, balancing the ratio of missing labels to approximately 1:1.  
The dataset underwent the following processing and feature engineering steps:  
1. Filtering low activity learners: Removed data from learners with fewer than seven recorded behaviors.
2. Feature sequence completion: Mapped the learners' actions (e.g., courses, lectures, problem analysis) to corresponding project features, extending the learning behavior feature sequences as needed.
3. Answer evaluation: Judged the learners' responses (correct = 1, incorrect = 0) based on the standard answers for each question. We treat missing data as 0.4.
4. Cursor time: For each student action (pause, play), we subtract the timestamps of consecutive actions to obtain the cursor time.
5. Session split: Split learning behavior records into sessions using a 1 h threshold. The last interaction in each session was marked as a dropout interaction (dropout = 1).
6. Dataset split: Divided the preprocessed dataset into training, validation, and test sets in an 8:1:1 based on the number of learners.
# Towards Understanding Neural Machine Translation with Attention Heads' Importance
## 3 Attention Heads' Importance  
### Experimental Setup  
To perform a robust and reproducible evaluation of the disclosed approach, we chose a large-scale, publicly available dataset for our study. Specifically, for the Chinese-English translation task, we use the CWMT21 Chinese-English dataset, which contains 9.0M sentence pairs [27]. As detailed in Table 2, this dataset is segmented into training, testing, and validation sets in the ratio of 0.9:0.05:0.05. Following the standard NMT procedure, we adopt the standard byte pair encoding (BPE) [28] for all language pairs. We use the standard SacreBLEU score as the evaluation metric for translation performance. The BLEU score is a widely used metric for evaluating the quality of machine translation. It measures the similarity between the machine-generated translation and one or more reference translations based on n-gram precision and a brevity penalty [29].  
We employ the state-of-the-art Transformer, which is composed of two parts: an encoder and a decoder. Each part is divided into six layers, each layer has eight attention heads. A particularity of this model is that it features three distinct attention mechanisms: encoder self-attention (Enc-Enc), decoder self-attention (Dec-Dec), and encoder-decoder attention (Enc-Dec), all of which use multi-headed attention (MHA). Our study analyzed the importance of the attention head to all of these three attention mechanisms. We analyzed the learning of attention heads in the Transformer model, which includes information on individual words (part of speech), inter-word relationship information (dependency relations), and overall sentence structure information (syntax tree). Based on the importance ranking of the attention heads, we established a relationship between these three types of human-understandable, multi-granular linguistic knowledge (part of speech, dependency relations, syntax tree) and the attention heads. Most of the experiments were carried out on the test set, and the model was trained by fairseq [30] in standard settings.
# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 5 Results
### Benefits of Workload Distribution  
The workload distribution and its adaptation to RE production can be observed in Figure 8, which reports the number of VMs per DC for the whole simulation, whose duration was one week. The day-night pattern is clearly visible, as well as the temporal shift due to the different time-zones in which the DCs are located. The approach followed pretty closely the RE production patterns, showing a good capability of adaptation to operation conditions that were changing.  
Figure 6: Energy consumption, expressed as kWh, and energy cost, expressed as $, for the four DCs, versus time of the day.  
Figure 7: Energy cost and cost-saving. The DCs have the same PV panel size, reported on the \(x\)-axis.  
In Figure 9 the impact of migrations on the scenario can be assessed by observing the energy cost-saving for different sizes of the PV panels installed in each of the four DCs, either without any storage (Figure 9a) or with battery units providing a storage capacity of 200 kWh and 400 kWh (Figure 9b,c). The lowest curve refers to the case in which no migration was performed (orange curve). For comparison purposes, two other cases were considered as well: workload that was moved from the most costly DC randomly to the other DCs (blue curve) and workload that was moved targeting energy-saving (red curve). Observe from Figure 9a that the possibility to migrate some VMs can improve cost-saving by up to 17%, even without any storage, with the energy-saving migration policy only slightly outperforming the random migration policy. The introduction of a storage of capacity 200 kWh (Figure 9b) provided similar cost-saving percentages under VM migration, but its impact in terms of absolute decrease of the energy bill was higher than in the case without any storage. The same curves are shown in Figure 9c for a battery capacity that is double the value considered in Figure 9b; i.e., the battery is 400 kWh instead of 200 kWh. Saving reached 100%, for large values of the PV panels, meaning that the DCs were powered all the time with RE. In this case, migrations were useless. For intermediate values of the PV panels, migrations helped in improving saving, similarly to the previous case.
# [PERSON] and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution
## 5 Results  
### Error  
The errors in the estimates returned by each method are shown in Figure 1 for a single'replication', i.e., an application of each algorithm to return a single (convergent) estimate. The figure illustrates the qualitatively different behavior of the two estimation procedures--the deterministic approximation returned by the me algorithm, and the stochastic estimate returned by the Genz mc algorithm.  
Estimates from the mc algorithm are well within the requested maximum error for all values of the correlation coefficient and throughout the range of dimensions considered. Errors are unbiased as well; there is no indication of systematic under- or over-estimation with either correlation or number of dimensions.  
In contrast, the error in the estimate returned by the me method, though not generally excessive, is strongly systematic. For small correlations, or for moderate correlations and small numbers of dimensions, the error is comparable in magnitude to that from mc estimation but is consistently biased. For \(\rho\,{}_{>}\atop{}\atop{}\atop{}\atop{}\atop{}\atop{}\atop{}\atop{}\atop{} \atop{}\atop{}\atop{}\atop{}}\atop{}\atop{}\atop{}\), the error begins to exceed that of the corresponding mc estimate, and the desired distribution can be significantly under- or overestimated even for a small number of dimensions. This pattern of error in the me approximation reflects the underlying assumption of multivariate normality of both the marginal and conditional distributions following variable selection [1; 8; 17]. The assumption is viable for small correlations, and for integrals of low dimensionality (requiring fewer iterations of selection and conditioning); errors are quickly compounded and the approximation deteriorates as the assumption becomes increasingly implausible.  
Although bias in the estimates returned by the me method is strongly dependent on the correlation among the variables, this feature should not discourage use of the algorithm. For example, estimation bias would not be expected to prejudice likelihood-based model optimization and estimation of model parameters, which are determined by the _location_ of likelihood extrema. However, estimation bias could conceivably vitiate likelihood-ratio tests involving functions of the actual likelihood values. The latter may become of particular concern in applications that accumulate and compare likelihoods over a collection of independent data under varying model parameterizations.
## 5 Efficiency Analysis
### Efficiency of the Cryptographic Primitives
#### 5.1.2 Encryption Schemes  
The following times represent the encryption and decryption of a 50 characters random text with an IND-CPA secure version of RSA and ElGamal. We use RSA for the public key encryption schemes implemented by the SA, the drivers and the RSUs.
# A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
## 2 Materials and Methods
### Fleet Utilization Using Reinforcement Learning and Traveling Salesman Problem  
The core of the solution is the intelligent approach of identifying the efficient utilization of resources within a logistics environment. This is where the RL recommendation framework for route and job assignment optimization comes in. Having defined the problem, we use the MDP to formulate the case study as an RL problem, as follows [15].  
* State (S): current location of a vehicle and the status of each job (assigned or unassigned).
* Action (A): decisions relating to job assignment such as assigning a vehicle or leaving a job unassigned.  
Figure 3: Nearest trucks to the trailer based on the geozone.  
* Transition (T): the probability of transitioning to the next state after taking an action such as vehicle movement and job assignment statuses.
* Reward (R): the reward for each state action fair, such as positive rewards for efficiently completed jobs and negative rewards for late pick-ups/drop-offs and empty runs. The DT framework also incorporates feedback from the human transport planners as rewards.  
Figure 4 below describes the RL algorithm and the transition function given by:  
\[T(s^{\prime}|\ s,a,Jh)=\text{P}(s_{t+1}=s^{\prime}\ |\ s_{t}=s,a_{t}=a,h_{t})\]  
where \(T(s^{\prime}|\ s,a,Jh)\) gives the probability of transitioning to state \(s^{\prime}\) given the current state \(s\), action \(a\), and human input \(h\), all factors at time \(t\). To achieve the optimized policy, the agent will traverse and utilize the environment to discover the optimal policy. Having formulated the case study into an RL problem, the approach for the model is the application of RL to a TSP. The model was enhanced to achieve advantages better than the use of classical optimization techniques. With this design in the DT framework, we can tweak the constraints, change rewards, add information such as the expected time of arrival (ETA) in real time and integrate machine learning (ML) prediction techniques [31]. Solving the problem in our case study also requires an optimization approach to find the best routes while tackling the empty-running problem. For this, we use a TSP solution [31], within the logistics DT. Figure 5 depicts a typical day's job for a single driver modeled as graph instances.  
Figure 4: RL algorithm with a human in the loop.  
Figure 5: A driver’s (Driver_A) route as graph instances.
## 3 Numerical Simulation
### Numerical Modeling Assumptions  
Based on the actual engineering profile and model calculation theory, the following assumptions are made for the hydrothermal coupling model of this freezing project:  
1. The investigation report for this project indicates that powdery clay with high water content produces a large freezing rate during the freezing process. Therefore, this study simplifies the soil body to a homogeneous, continuous, isotropic, and linearly elastic soil layer with the most unfavorable soil parameters. Each parameter is derived from weighted values based on the investigation report, and the soil body has an initial temperature of 18 \({}^{\circ}\)C.
2. Based on existing research [35], it is assumed that the soil body begins to freeze at \(-1\) \({}^{\circ}\)C and forms a stable permafrost structure at \(-10\)\({}^{\circ}\)C.
3. Neglecting the loss of cold during the brine circulation process and the influence of heat convection and radiation during heat transfer, the brine temperature is directly applied to the wall of the freezing pipe as a temperature load with heat transferred solely by conduction.
4. It is assumed that moisture migration in the soil conforms to Darcy's law.
5. The migration of salts and other solutes in the soil is not considered. Water migration is assumed to occur primarily in liquid form, ignoring the movement of gaseous water. The soil is considered saturated, containing only liquid water and solid ice in the pore space, with the saturated water content equating to the soil's porosity.
6. It is assumed that ice crystals and the solid phase of the soil are incompressible.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 4 System Validation  
### Experimental Comparison between Passive and Active Technologies  
This section describes several preliminary experimental tests with the objective to present a comparison between passive and active technologies, considering the latter as the gold standard or the technology that produces the bests results (though at a much higher cost). Therefore, once we have proposed the use of passive RFID, the objective of this Section 4.1 is to validate this choice by performing similar tests to both technologies, with the objective to corroborate that, although active RFID should provide better performance (regarding coverage range or anti-collision response), using passive RFID is still possible to meet the requirements for this application, exploiting the benefits inherent to this technology. Thus, this section is organized as follows: Section 4.1.1 describes the selected equipment for both technologies, Section 4.1.2 details the performed experiments and, finally,Section 4.1.3 provides some preliminary conclusions regarding the convenience of using passive RFID, which will be corroborated later for in-motion scenarios at simulation (Section 4.2) and experimental (Section 4.3) levels.
# Airline Network Planning Considering Climate Impact: Assessing New Operational Improvements
## 2 Materials and Methods
### Integrated Network Planning Model  
Airline network planning involves designing the network structure commonly aimed at maximum profitability. The network is defined by computing the frequencies per route. The frequencies should be computed according to the availability of resources and the possible allocation of fleets to the network. To address these inter-dependencies, we developed an _integrated network planning model_ in which network design is combined with fleet allocation decisions. The result is a flight schedule presenting the flight times and respective frequencies per route and the aircraft routes to be followed to provide flight capacity.  
The _integrated network planning model_ uses a multi-agent approach to solve a multi-fleet and multi-objective network optimisation problem. The problem is solved for a representative week for a given season, in which the average passenger demand in all ODs is considered. It was also assumed that the resulting network structure and flight schedule would be in place for the entire season. Each fleet type was assigned, in parallel, through a fleet allocation agent to promote the concurrency of calculations. Moreover, the climate impact associated with the route selection decisions was incorporated into the network planning objective function of the agents. Agents use a dynamic programming algorithm to solve their problems.  
The airline network was represented as a time-space network [42]. The optimal aircraft assignment to that network was determined after the mediator agent compares the solutions of the different fleet allocation agents provided. The coupled time-space network and iterative fleet allocation approach offer the opportunity to simultaneously solve network planning and flight scheduling problems and capture the interdependencies in the final solution. This integrated approach also helps improve the run time. The final result delivered by the framework includes the network design, flight schedule, aircraft routes, and a set of climate and non-climate KPIs to facilitate an in-depth evaluation of output.  
More detailed information on the pre-processing data utilised by the model framework, the functions performed by the fleet allocation agents, and the purpose of the mediator agent is provided in the subsequent subsections.
## 2 OFDM Based Multicarrier Modulation Schemes
### Spread OFDM  
[PERSON] _et al._[PERSON] _et al._ (2014) proposed Walsh-Hadamard Transform-Spread OFDM (WHT-OFDM) scheme to reduce PAPR of conventional OFDM. Properties and advantages of WHT-OFDM method were further studied in [PERSON] _et al._ (2014); [PERSON] _et al._ (2014); [PERSON] _et al._ (2014). In WHT-OFDM, Walsh-Hadamard Transform is added to the OFDM system in order to achieve diversity gain in a mobile environment through spreading of data and hence, it improves the BER performance of the system in multipath fading channels.  
[PERSON] _et al._[PERSON] _et al._ (2015) proposed two novel spread coding schemes for NC-SOFDM for cognitive radio in a DSA network. The proposed spreading code sets eliminate the loss of orthogonality problem in WHT spread OFDM system and maintain the same data rate as that of the traditional OFDM. In one scheme, the whole multicarrier system is divided into multiple subsystems and variable-length orthogonal WH codes are used for each subsystem to maintain the data rate. In the other scheme, orthogonal carrier interferometry (CI) codes are used for spreading. CI codes are complex spreading codes and they exist for code length of any integer. Thus, Non-contiguous SOFDM (NC-SOFDM) systems with CI spreading code can eliminate the loss of orthogonality among spreading codes caused by deactivating subcarriers and hence CI-SOFDM systems are more suitable for DSA network.  
[PERSON] _et al._ (2015) introduced Non-Continuous CI (NCI) codes into the NC-OFDM system to improve the performance in terms of Bit-Error-Rate (BER) and PAPR reduction. Moreover, the number of users supported in a DSA network can be increased by using Pseudo Carrier Interferometry (PCI) code sets, which have twice the number of codes compared with traditional CI code sets [PERSON] _et al._ (2015). Discrete Fourier transform-spread OFDM (DFT-S-OFDM) was proposed to be one of the most promising modulation schemes for cognitive radio systems in [PERSON] _et al._ (2016). Recently, DFT-S-OFDM has been proposed for 3 GPP Long Term Evolution (LTE) systems [PERSON] _et al._ (2016). In DFT-S-OFDM scheme, data symbols are pre-coded using a discrete Fourier transform operation at the transmitter before being sent to the OFDM modulator, in order to reduce PAPR.  
Figure 4: Spectrum of NC-OFDM signal.