esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Considerations  
A common challenge in applying machine learning techniques to problems is the exponential increase of the size of training data and the growing model complexity. It is true that the availability of large sized data requires the use of more complex machine learning models so as to discover finer structures in the data. However, more complex models are often associated with higher computational costs. Furthermore, deeper NNs also bring in more complex computational patterns [79]. Techniques such as, shrinking the model, data parallelism and model parallelism are being explored as possible solutions to accelerate the performance of deep learning models. In [80] it was shown that about 80% of the computational cost in NNs results from the convolution operation [81]. Hence, recent studies on NN model designs have focused on how to configure convolutional layers [82]. Thus to control the execution overhead of an NN model, in [83], the authors mathematically formulate the convolutional overhead. Without such a formal overhead formulation, an NN model structure is mainly configured based on a designer's experience with balancing the execution overhead and the model's accuracy. The designer manually selects a configuration (based on experience or some public models) and then trains the model. In the case of low training accuracy, a different configuration will be tested. Clearly this approach lacks a systematic way to configure the model. On the contrary, with the execution overhead formulation, this configuration can be quantitative and effective. Denote \(\mathcal{O}\) as the convolutional overhead, such that:  
\[\mathcal{O}\leq\alpha\times\mathcal{B}, \tag{16}\]  
where \(\mathcal{B}\) is the preferred or predefined resource budget and \(\alpha\) is the percentage of computations due to convolution. Hence, the execution overhead of layer \(k\), denoted \(\mathcal{O}_{k}\), needs to satisfy:  
\[\mathcal{O}_{k}=\mathcal{O}\times\beta_{j}\leq\alpha\times\mathcal{B}\times \beta_{j}, \tag{17}\]  
where \(\beta_{j}\) indicates the computation percentage of each layer, \(j=1,2,3,\cdots,m\). For a detailed discussion on this method we refer the reader to [83].
## 4 Case Study  
The Italian company _Mega Italia Media_, acting in the e-learning sector, is the owner of _DynDevice_, a cloud-based platform which provides online courses about \"workplace safety\" for thousands of users. While a messaging system which allows the users to contact human operators is already present on their platform, the recent increase in the demand of online courses lead them to decide to integrate their service with a chatbot which can answer the most frequently asked questions. Aiming at developing a chatbot that satisfies the company requirements, we have to find the best cloud-based NLU service on the market. Therefore, we analyzed the performance of different platforms exploiting real user requests (made in the Italian language), provided by the company.  
Below, we show two examples of requests and their translation expressing the same intention. The first one is simple and the second one is more complex in order to show the variety and the complexity of the sentences coming from a real case of study:  
* _\"Corso scaduto e possibile riattivarlo?\"_ Course expired; is it possible to reactivate it?
* Formazione specifica Basso rischio Ufficii\" probabilmente perche e scaduto e purtroppo non me ne sono resco conto. Cosa posso fure? Grazie\"_ Good morning, I sent a message this morning because I cannot join \"Workers
- Specific training course about Low risk in Offices\", probably because it is expired and unfortunately I did not realise it. What should I do? Thank you  
In general, the requests made by the users are quite short, in fact 75% of them contains less than 50 characters. However, there are a few outliers with more than 500 characters (such as the example above) or less than 10.
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 2 Materials and Methods
### Walk Error
TDC7200, the TDC used on the selected ranging device, has the ability to measure time from the start pulse to multiple stop pulses if a sufficient amount of time passes between individual pulses. However, all stop pulses must have the same direction (rising or falling edge). By transmitting a 100 ns light pulse and converting the returned signal into two 10 ns pulses, one for rising and one for the falling edge of the return signal after passing through a comparator, we ensure that the condition for measuring time to multiple stop pulses is met. Figure 4b shows the pulse shape of the transmitted pulse, as measured on the shunt resistor (blue), reflected pulse, as measured after TIA (orange), pulse shape after the first comparator (green, and the pulse pair after the pulse shaper (red). The comparator's and the pulse shaper's waveforms were attenuated by 50% to make the graph more comprehendible. To obtain the compensation function, multiple measurements were taken off of a spinning color wheel with varying albedo, set at multiple different known distances. Both the time to the first stop signal and TOT were recorded for all samples. The difference between measured distance and the expected value (error) versus TOT was plotted, and a piecewise linear (PWL) function was fitted to the resulting data cluster, as shown in Figure 5. The yellow points correspond to single measurements, the green points are for the data captured with running averages on eight samples, and the solid blue line represents the manual PWL fit. Beyond calibration range, linear continuation with the same coefficient as the last segment within the calibration range is assumed. After the time from the transmitted to received pulse and the corresponding TOT are measured, walk error compensation is applied. As observed in Figure 5, a wider pulse means that less time is subtracted from the first stop signal and vice versa. Walk error compensation has to be applied either immediately on the microcontroller or afterwards on the master computer. The later was chosen for our experiments, as we wanted to obtain raw as well as compensated measurements. Figure 4: (a) Reflected pulse after TIA at different target distances, measured after receiving TIA. All pulses are synchronized to the rising edge of the transmitted pulse. (b) Pulse shape of the transmitted pulse, as measured on the shunt resistor (blue), reflected pulse, as measured after TIA (orange), pulse shape after the first comparator (green, and the pulse pair after the pulse shaper (red). The comparator’s and the pulse shaper’s waveforms were attenuated by 50%.
## 4 Discussion
### Challenges in Simulating Complex and Heterogeneous Canopies  
The model exhibits significant biases in ecosystems with complex canopy structures and heterogeneous compositions, revealing limitations in its underlying assumptions.  
The consistent underestimation of ET in ENF points to deficiencies in the model's radiation transfer scheme. Noah-MP's default single-layer, \"big-leaf\" approach treats the canopy as a single, homogenous entity. This simplification fails to capture the intricate three-dimensional structure of dense, multi-layered coniferous canopies. It cannot adequately represent processes like self-shading or the vertical gradients in light and microclimatic within the canopy. Consequently, the contribution of lower, shaded parts of the canopy to total ecosystem transpiration is likely underestimated, leading to a systematic low bias in total ET.  
The poor performance in WSA highlights the model's difficulty in simulating resource competition between different PFTs within a single grid cell. Savannas are composed of co-existing trees and grasses, which compete for light, water, and nutrients. These life forms have different rooting depths, phenological timings, and physiological responses to stress. A single-grid-cell model, like Noah-MP, struggles to resolve this sub-grid heterogeneity, particularly the asymmetric competition for soil water between deep-rooted trees and shallow-rooted grasses. Inaccuracies in partitioning these resources lead to significant errors in the simulated water and carbon fluxes for the ecosystem as a whole [51; 52].
## 3 Road-Based Multi-Metric Forwarder Evaluation Scheme
### Information Collection Stage
#### 3.1.5 Signal to Interference Plus Noise Ratio  
Signal to Interference plus Noise Ratio (SINR) is also an essential parameter, most especially when considering a city/urban vehicular network settings. The SINR evaluates the level of interference and channel noise of the candidate NFV. This is important considering the obstructions in the road area such as tall vehicles and tall buildings. The different source of noise, which are from other communication devices is also estimated in SINR [25]. Meanwhile, the SINR has been employed, which is formulated as given in Equation (15). Therefore, considering the type of scenario employed in this study, which is an urban scenario, the interference and the noise have been adequately considered when selecting an NFV for quality video streaming transmission.  
\[\text{SINR}_{\left(\text{P}_{\text{i}}\cdot\text{P}_{\text{j}}\right)}=\frac{ \text{S}_{\text{PW}}\text{F}_{\text{RD}\left(\text{P}_{\text{i}}\cdot\text{P} _{\text{j}}\right)}\times\text{I}\left(\text{P}_{\text{i}}-\text{P}_{\text{j} }\right)}{\varphi+\sigma\left(\text{P}_{\text{j}}\right)} \tag{15}\]  
where \(\text{P}_{\text{i}}\) is the transmitting vehicle position and \(\text{P}_{\text{j}}\) signal receiver vehicle's position; \(\text{S}_{\text{PW}}\) is the transmitting signal power and \(\text{F}_{\text{RD}}\) is the random fading between the transmitting vehicle and the receiving vehicle; \(\text{I}\) is the distance between \(\text{P}_{\text{i}}\) and \(\text{P}_{\text{j}}\), while \(\varphi\) and \(\sigma\) represent the external noise and small short-noise of \(\text{P}_{\text{j}}\), respectively. Thus, the SINR is employed when selecting the best NFV for video streaming transmission.  
The metrics discussed in Sections 3.1.1-3.1.5 are collected through the hello message broadcasted to all neighbor vehicles. The neighbor vehicles send information including their road-id and their directions, which are used to compute the VDR metric. The information based on position, speed and direction are used to estimate the VTJ metric of the C-NFV to the DVN. The information extracted from the hello message count is used to estimate the \(\text{H}_{\text{m}}\text{R}_{\text{r}}\) of the neighbor vehicles, which can improve on the playback freezes and the startup delay of the video streaming. Further, the time when the first hello message arrived to the PFV and the total quantity of hello messages received from an individual vehicle are employed to estimate the idle time and video packet losses, thus estimating the ABE of the wireless link. Finally, the positions, transmitting signal power based on the distance between positions are also employed to estimate the SINR of the link between the PFV and the C-NFV in the vehicular communication. The next section discussed the video streaming data forwarding stage.
## 3 Materials and Methods
### Kinetic Study and Isotherm Modeling  
To perform the kinetic study, it was mixed 100 mL of Pb(II) solution at 100 ppm with 0.5 g of bioadsorbents under continuous agitation at 200 rpm. Aliquots were taken every 5 min during the first 30 min because of the rapid adsorption mechanism before saturation. Then, the samples were taken each hour for 5 h. The experimental data was adjusted to kinetic models (pseudo-first, pseudo-second, and Elovich) using the OriginPro 8.5 software (developed by OriginLab Corporation) to calculate the fitting parameters and correlation coefficient (R\({}^{2}\)). Isotherm modeling was performed for 24 h by varying the initial concentration of heavy metal ions (25, 50, 75, and 100 ppm) at fixed conditions: 30 \({}^{\circ}\)C, 100 mL of solution, and 0.5 g of bioadsorbent. The Langmuir and Freundlich isotherms were selected to fit the experimental results in batch adsorption mode [14]. Tables 1 and 2 report the fitting equations used to determine both the kinetic and isotherm parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Kinetic Model** & **Equation** & **Parameters** \\ \hline Pseudo-1 st-order & \(q_{t}=q_{e}\big{(}1-e^{-kt}\big{)}\) & \(q_{e}\), Adsorption capacity at equilibrium (mg/g) \\  & \(k_{1}\), pseudo-1 st-order constant (min\({}^{-1}\)) \\ \hline Pseudo-2 nd-order & \(q_{t}=\frac{t}{\left(\frac{1}{2q_{e}^{2}}\right)+\left(\frac{t}{\omega} \right)}\) & \(k_{2}\) pseudo-2 nd-order constant (g/mg\(\cdot\)min) \\  & \(q_{e}\), Adsorption capacity at equilibrium (mg/g) \\ \hline Elovich equation & \(q_{t}=\) & \(\alpha\), Elovich constant (mg/g min) \\  & \(\frac{1}{\beta}\ln(\alpha\beta)+\frac{1}{\beta}\ln t\) & \(\beta\), Elovich exponent (g/mg) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mathematical expressions of kinetic models.
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 3 Results
### Analysis and Observations
#### 3.4.1 ABAC Support  
The primary goal of our experiment was to examine the ability to support fine-grained ABAC. The results indicate that this is feasible, but this requires significant software configurations, including writing scripts to connect the metadata to the policy enforcement program. There was a high risk of incorrect configuration.  
ABAC could be achieved by using policies of Apache Ranger that bring together attributes from three sources, as depicted in Figure 11. Such user information as group assignments could be provided by the directory service over LDAP by using Apache Ranger's User Sync. Ranger plugins for the HDFS and YARN were used to enable the configuring of policies on file permissions and job scheduling (e.g., capacity scheduler queues). The Tag Sync capability of Atlas allowed the metadata for the HDFS and YARN to be used as a basis for these security policies. Thus, for example, data could be tagged or classified as PII or HIPAA in Atlas, and these tags were then used as the basis for security policies that allowed certain users or groups to access the files. YARN policies could likewise be configured to permit or deny users access to submit jobs to queues based on tags from Atlas. This same strategy could be used with Apache Hive and other components. The classification tags from Atlas and policies created in Ranger provided the capabilities to achieve ABAC, but there were limitations associated with flexibility in assigning attributes to users. Users could be managed locally in Ranger or externally through a directory service and assigned to groups that could be mapped to the concept of attributes for policy-related decisions. Assigning users to multiple groups and then using these groups as part of the process of policy decision provided the concept of attribute-based control, but this might not have provided the flexibility of key-value pair attribute-based controls described in the reference implementations [55].  
Figure 11: Implementation of ABAC using the HDFS, LDAP, YARN, Ranger, and Atlas.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 4 Machine-Learning-Based Models
### Recurrent Neural Network Model  
The recurrent neural network based on the long-short-term memory layer is a type of ANN where the links between nodes form a directed graph along a temporal sequence. This makes it exhibit temporal dynamic behavior. Derived from feed-forward neural networks, RNNs can use their internal state (memory) to process variable-length sequences of inputs [88], which makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. While the ANN output layers depend on the previous layer output to train the neural network, the RNN requires both the previous layer output and the internal state of the neural network. The internal state is defined as the output of each hidden neuron when processing the previous input observations. They are thus well-suited to process time series of data and capture their time dependencies. On the other hand, it considers the current input and the output that it has learned from the previous input for making a decision. The proposed RNN architecture is expected to extract feature representations that encode some aspects of the path loss. This new way of learning gives the RNN-LSTM model significant performance on several applications, which motivated us to select the RNN-LSTM for our work. The architecture of the proposed model is the same as the original version of RNN-LSTM; the only changes are on the hyperparameters setup.  
The proposed RNN-LSTM model involves the hyperparameters of two hidden layers of internal state (LSTM) and one feed-forward neural network layer. The first and last LSTM hidden layers have 128 and 32 neurons, respectively. The feed-forward neural network layer contains 48 neurons. Each hidden layer is followed by the ReLU activation function. The model uses a learning rate of 0.0001 to train the model. The input for the network accepts four features from the preprocessed data. The output layer is a single neuron with a linear activation function as the transfer function. The output result is a real value representing the path loss. The cost function is used to optimize the mean square error. Figure 5 shows the architecture of the proposed RNN-LSTM model. It should be noted that the RNN-LSTM model is more complex compared to the ANN model and has more parameters to train [89; 90].  
Figure 4: The architecture of the proposed ANN model.  
Figure 5: The architecture of the proposed RNN-LSTM model.
# Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation
## 2 Background and Literature Review
Since the infrastructure is evolving continuously, the remote driving is likely to become a reality in the near future. Actually, ref [28] investigates whether tele-operated driving is possible with contemporary mobile networks. Their results show that the high variance of the network parametersinfluences the performance of remote driving. This result clearly indicates that better network infrastructure is needed for safe remote driving. In autonomous driving, AI plays an important role. However, the computation requirement of AI is quite large, so where the computation should be done is very important. Ref. [29] discusses the autonomous driving Cognitive Internet of Vehicles. To be specific, they focus on what to compute, where to compute, and how to compute for the AI of the autonomous vehicles. Since the remote driving can evolve to a remote driving by AI, the suggestions of this paper that considers the computation and communication together can be applicable to our proposed system, too. Each task of the autonomous driving may require a specific algorithm. For example, ref [30] presents a lane changing algorithm by using deep deterministic policy gradient learning algorithm. This algorithm is run at the vehicle. So depending on the specific task, where to compute should be determined. Actually, the two objectives of this paper are similar to clustering problems or node placement problems in wireless networks [31; 32; 33]. For example, ref [34] tries to deploy access points in an area to cover as many wireless mesh clients as possible. In this paper, we try to select the locations of remote driving facilities given the locations of base stations. However, in this paper, we only consider the base stations along the routes of the vehicles. Thus, we exploit this specific characteristics of remote driving to achieve better performance rather than just applying other general clustering algorithms or node placement algorithms. To date, we have discussed a number of existing works related to remote and autonomous driving. We summarize the above technologies by categorizing them depending on the research area in Table 1. These technologies deal with various aspect of autonomous and remote driving. However, in this paper, we focus on remote driving location selection problem, which is not addressed by the related works.
# Investigations of the Energy Transfer
## 3 Results
### Ultrafast Dynamics in Phyococyanin  
#### 3.1.1 Time-Resolved Fluorescence  
The fluorescence kinetics of PC was studied by exciting the sample at 580 nm, and the emission was monitored over the spectral region 640 to 680 nm. The spectra showed a fluorescence maximum around 650 nm. We probed several exponential reconvolution models and found out that a satisfactorily fit was obtained with a bi-exponential decay model with one component at 1.34 ns and one in the time scale of our IRF (Figure 3). The resulted EAFS were similar to the fluorescence spectrum of PC, with maxima around 650 nm. The EAFS associated to the long component exhibited higher amplitude and a broader spectrum, indicating higher fluorescence yield. We assign the longer kinetic component of 1.34 ns to the fluorescence lifetime of the terminal emitting chromophores in PC. However, the lifetime of the shorter kinetic component was limited by the temporal resolution of our system.  
Figure 3: (**a**) Time-correlated single photon counting (TCSPC) data showing the three-dimensional data carpet obtained from phyocyanin (PC) when pumped at 580 nm. The white line represents the steady-state fluorescence spectrum; (**b**) Evolution- associated fluorescence spectra obtained using global analysis according to the scheme given in the bottom right figure corner; (**c**) Normalized kinetic traces recorded at 655 and 661 nm with fits.  
Figure 2: Ground-state absorption and fluorescence emission (excitation 580 nm) spectra characteristic to the phyocobilisome (**a**) and to the isolated phyocyanin and allophyocyanin, respectively (**b**).
## 4 Conclusions  
Differences in GPP values and dynamics between sites are due to different crops and their interaction with the meteorological variables in each climate. GPP depends mainly on temperature especially in irrigated crops. In terms of energy fluxes, latent heat is more coupled to GPP in rainfed crops and the sensible heat depends especially on ground cover in both crops. There is a clear feedback between GPP, meteorological variables and energy fluxes in both type of crops that we have quantify by Granger causality tests. In this sense, GPP causes temperature and latent heat, especially in rainfed crops.  
Conceptualization, V.C., J.L., V.S.-G., A.P.-O.; methodology, V.C., J.L., V.S.-G., L.R., A.P.-O.; software, V.C., J.L.; formal analysis, V.C., J.L., A.P.-O.; investigation, V.C., J.L., V.S.-G., L.R., C.S., A.P.-O.; resources, J.L., A.P.-O.; writing--original draft preparation, V.C., J.L., V.S.-G., L.R., C.S., A.P.-O.; writing--review and editing, V.C., J.L., V.S.-G., L.R., C.S., A.P.-O.; supervision, A.P.-O.; funding acquisition, A.P.-O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Universidad Politecnica de Madrid within the project RP2013210028: \"Genercion de un observatorio de dinamica de la vegetacion a distintas escalas a partir de series de tiempo de imagenes de teledetecion\".  
We would like to thank the FLUXNET2015 data set and the European Fluxes Database Cluster for the assignment and permission to download the data of Gebesee and El Saler-Sueca, respectively.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 1: Granger causality test between gross primary production GPP_T (g C m\({}^{-2}\) day\({}^{-1}\)) and (**a**) sensible heat H (W m\({}^{-2}\)) and (**b**) latent heat LE (W m\({}^{-2}\)) in the two croplands (Left: Germany (DE), Right: Spain (ES)).