esa-sceva/satcom-chunk-collection huggingface dataset:
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 4 Optical Transmission: Key Challenges and Proposed Directions
### Capacity Increase Employing a Greater Number of Channels
#### 4.3.5 Towards the Ultimate Capacity Limits of Optical Transmission
Predictions on the attainable capacity times length product can be also performed using the Optical Moor's Law (OML) formula introduced in [138] for submarine networks as follows:  
\[C\cdot B_{[Pb/s\cdot km]}=10\cdot 2^{(Year-2000)/F} \tag{2}\]  
where the parameter _Year_ denotes the year selected to estimate the predicted \(C\cdot B\) product and \(F\) is a parameter that characterizes how fast in time the \(C\cdot B\) product is scaled. In the original work of [138], \(F\) is equal to 3.75, which means doubling every 3.75 years or every 45 months. In this work, we exploit this formula, and based on recent experimental results for ultra-long haul and/or submarine transmission (taken from Figure 23), we slightly modify the value of \(F\). In particular, from Figure 33 we can observe that by setting an \(F\) value equal to 3.125 in Equation (2), the fitting error is smaller. This is translated into doubling of the \(C\cdot B\) product approximately every 37.5 months, which designates faster scaling compared to the value of \(F\) equal to 3.75. According to our opinion, this can be mainly attributed to the unlocking of new amplification bands, which allowed an increase in the transported capacity \(C\). Figure 31: Illustration of the three ways to increase the number of transmitted data in an optical system: by increasing the number of spatial elements, by migrating to a higher cardinality modulation format, and by transmitting a higher number of channels. Figure 32: Prediction of transportation capacity (in Tb/s) based on currently deployed and planned submarine cables [16].
## 1 Introduction
Their results showed that awareness and the dimension of public sectors positively and significantly affected the probability of adopting GPP practices. Moreover, [PERSON] and [PERSON] [9] checked factors that determine the adoption of green procurement, especially involving the Kenya Pipeline Company, focusing on its green capacity, green incentives and pressure, cost of green products, and green supply capacity. Their analysis found that the internal green capacity, incentives and pressures mainly determined the adoption of GPP, based on semi-structured questionnaire results. [PERSON] and [PERSON] [10] investigated challenges of GPP implementation in the Australian public healthcare sector. They found that the most critical challenges are insufficient legislation and financial support, lack of senior management support, and lack of government incentives. With structural equation modeling, [PERSON] [11] investigated the conditions under which the organizations were more willing to prioritize sustainable procurement implementations, especially the leadership style of the leader surveying in US public agencies. Transformation leadership, innovativeness, and stakeholders' expectations were found to be critical. GPP is a widely debated and highly expected policy used to address environmental problem. The potential of green public procurement as a strategic decarburization instrument has been acknowledged by major intergovernmental organizations including the EU, UN, and OECD. GPP considering carbon footprint of services and products in public tenders can help to reduce the emissions from government consumption [6]. Therefore, investigating what factors can relate with this policy is of significant value, while not being a sufficiently discussed topic. This research is a supplement to the existing literature. We examine public procurement features, i.e., procedures, volume, and award criteria of procurement per se, and the characteristics of purchasers, suppliers, and procurement objects from an empirical standpoint in a bid to find out the influential factors involved in green contracting. We employ a Probit and fixed effect regression model to analyze 2018 public contract awards available on Tenders Electronic Daily [12]--an official platform for publishing EU public contracts. In so doing, we aim to shed light on what and to what extent certain factors influence the application of green award criteria in public contracts. The results of this analysis can help to inform policy-makers wishing to increase the use GPP to allocate resources more effectively. The examined factors are different from those in the literature, and we cross-tested using logit regression, Probit regression without robustness check, and regress on subsamples of products, services, and works. The dataset employed in this paper encompasses 33 European nations and a wide variety of industry sectors. This paper is organized as follows. The next section provides details on the regression model and limitations. Results of the analysis are then presented, followed by an interpretation of the results and general conclusions.
## 1 Introduction
### The Need for XAI  
The adoption of AI techniques cannot disregard the fundamental value of trustworthiness, which, along with inclusiveness and sustainability, represents the three core values of the European Union Flagship Hexa-X (www.hexa-x.eu (accessed on 16 August 2022)) vision for the upcoming 6G era [1]. Trustworthiness has become paramount for both users and government entities, as witnessed by the \"right to explanation\" described in the General Data Protection Regulation (GDPR) and by the European Commission's (EC) Technical Report on \"Ethics guidelines for trustworthy AI\" [4]. According to these, explainability represents a key requirement towards trustworthiness. Thus, industry and academia are placing increasing attention on XAI, that is, an AI \"that produces details or reasons to make its functioning clear or easy to understand\" [5].  
In this context, two strategies for achieving explainability can be identified [5]: the adoption of post-hoc explainability techniques (i.e., the \"explaining black-box\" strategy) and the design of inherently interpretable models (i.e., \"transparent box design\" strategy). In this article, we focus on this latter class of approaches, noting that certain applications may tolerate a limited performance degradation to achieve fully trustworthy operation. In fact, performance and transparency are typically considered conflicting objectives [5; 6]. However, this trade-off holds as long as the target task entails a certain complexity and the data available are many and high quality. In this case, complex models, such as Deep Neural Networks (DNNs), which are hard to interpret due to their huge number of parameters and non-linear modelling, have proven to achieve high levels of accuracy; conversely, decision trees and rule-based models may feature lower modelling capability but are typically considered \"highly interpretable\".  
The importance of explainability has been recently highlighted in the context of Secure Smart Vehicles [7]: on one hand, explanation is crucial in safety-critical AI-based algorithms, designed to extend some widely available capabilities (e.g., lane-keeping and braking assistants) towards fully automated driving; on the other hand, explainability is needed at the design stage to perform model debugging and knowledge discovery, thus positively impacting system security by reducing model vulnerabilities against external attacks. Explainability of AI models will be crucial for 6G-enabled V2X systems. A prime example is an AI service consumer requesting in-advance notifications on QoS predictions, as studied in Hexa-X [1] and the 5G Automotive Association (5 GAA) [8]. Accurate and timely predictions should support very demanding use cases, with a horizon ranging from extremely short to longer time windows. Better explainability of such predictions and any consequent decision will provide benefits not only for technology and service providers (see Section 4), but also for end-customers, who will become more receptive to AI-based solutions.
## 3 Results
### Optimisation of the Sensing Material Production Process via the MORIME Algorithm  
The multi-objective optimisation technique aims to achieve material ratios and MTs that align with superior hysteresis and sensitivity characteristics. The examination of the experimental data presented in Table 1 indicates that the hysteresis and sensitivity of the composite sensing material exhibit a mutually exclusive relationship. To reconcile the two optimisation objectives of hysteresis and sensitivity, the HKOA-LSTM model was formulated as the objective function of the MORIME algorithm, with the concentrations of the three types of nanofillers and the MT serving as constraints. The resultant findings are illustrated in Figure 8, depicting the fabrication process of the sensing material. The factors are interrelated and exhibit a competitive dynamic; specifically, the hysteresis progressively intensifies as sensitivity increases. The Pareto-optimal solution O was identified utilising the CRITIC approach, yielding model simulation findings of hysteresis = 3.279% and sensitivity = 1.382 kPa\({}^{-1}\). The related model parameters were Fe\({}_{3}\)O\({}_{4}\): 0.665 g, CNTs: 1.098 g, MLGs: 0.99 g, and MT: 67 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{l l l l l l} \hline  & **Metrics** & **RSM** & **RF** & **LSTM** & **HKOA-LSTM** \\ \hline \multirow{3}{*}{Hysteresis} & RMSE & 0.0865 & 0.1354 & 0.0521 & 0.0225 \\  & MAE & 0.0189 & 0.0852 & 0.0079 & 0.0094 \\ \cline{1-1}  & RMSE & 0.2820 & 0.3600 & 0.1865 & 0.1285 \\ \cline{1-1}  & MAE & 0.1030 & 0.2100 & 0.0994 & 0.0547 \\ \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of RMSE and MAE for four predictive models regarding hysteresis and sensitivity.  
Figure 7: Graph of predicted versus observed values for the optimal regression model: (**a**) hysteresis; (**b**) sensitivity.
## 3 Results and Discussion
### Wall Height  
This section investigates the influence of varying the overall height of precast concrete walls on their shear capacity. Unlike the previous sections, which focused on the height of the section, this analysis considers adjustments to the entire vertical dimension of the wall, with the section height hc fixed at 2 m, thickness of the wall equal to 0.2 m, fck of 30 MPa, and v = 0.4. The numerical models involve systematically adjusting the wall height from 1.5 m to 3.5 m, with increments of 0.25 m, while keeping other parameters constant.  
The results, presented in Figure 9, indicate a clear inverse relationship between wall height and shear capacity. As the height increases, the shear capacity generally decreases. For instance, at a wall height of 1.5 m, the maximum shear capacity is 1878 kN, which gradually declines to 1386 kN at 2.0 m and further reduces to 1088 kN at 2.5 m. The lowest shear capacity observed is 760 kN at the height of 3.0 m, representing a significant reduction from the initial value. Notably, an increase in shear capacity is observed at 3.25 m, where the value rises to 820 kN before falling again to 760 kN at 3.5 m. This nonlinear behaviour suggests a complex interaction between the wall's height and its ability to resist shear forces, particularly at taller heights.  
Figure 8: Influence of the section height: Shear Increment-Normalised axial force curves (a) hc = 2.00 m (b) hc = 2.25 m (c) hc = 2.50 m (d) hc = 2.75 m (e) hc = 3.00 m.  
This fluctuation aligns with well-established structural principles, where taller walls tend to have reduced lateral stiffness and an increased vulnerability to lateral forces, leading to lower shear capacity. As the wall height increases, the moment arm of the applied forces also increases, resulting in a higher demand for lateral resistance, which the wall may not be able to provide adequately. The slight increase in shear capacity at 3.25 m could be attributed to localised effects or specific interactions between the geometry and load distribution that momentarily enhance performance. However, the overall downward trend reinforces the importance of considering wall height as a key factor in the design of precast concrete walls, especially when optimising for shear resistance.
## 3 Near-Infrared Silicon Absorption Physics
### Photoconductivity and Linear Absorption
#### 3.1.2 Surface-State Absorption (SSA)
As example of optical absorption due to surface states the results obtained by [PERSON] _et al._[27] are illustrated in Figure 2: the presence of electronic surface states, localized in the forbidden energy gap of a semiconductor, causes optical absorption extending to energies lower than the bandgap energy. Different processes could be useful to explain the results in Figure 2: (i) optical transitions between two bands of surface states localized in the gap, (ii) optical transitions from the valence band to an empty band of surface states located in the gap and (iii) optical transitions from a filled band of surface states in the gap to the empty levels of the conduction band.
## 3 Experiments
### Datasets and Preprocessing
#### 3.1.2 UC_Merced  
To pre-train the model, this paper utilized the UC_Merced dataset, with a total of 2100 images selected from aerial orthophotos, each with a size of 256 \(\times\) 256 pixels. The original dataset has 21 categories, and each category consists of 100 images. In this paper, each scene is randomly selected as training and validation sets with a ratio of 7:3.
## 4 Influencing Factors Analysis
### Concrete Tensile Strength  
The change of the ultimate shear capacity \(\mathbf{V_{u}}\) along with concrete tensile strength \(\mathbf{f_{t}}\) is shown in Figure 11. Obviously, as concrete tensile strength \(\mathbf{f_{t}}\) increasing, the shear bearing capacity \(\mathbf{V_{u}}\) was increased approximately proportionally. When the concrete was changed from C35 to C65, corresponding tensile strength \(\mathbf{f_{t}}\) increased from 2.20 MPa to 4.01 MPa, \(\mathbf{V_{u}}\) is increased from 61.21 \(k\)N to 105.86 \(k\)N with an increasement up to 72.9%.
## 2 Study Area and Data Sources
### Overview of the Dataset  
This study utilized single-look complex imagery data from the C-band of Sentinel-1A, covering a five-year period from January 2017 to January 2022. The imagery had a ground resolution of 5 m by 20 m and consistently employed VV polarization. Precise orbital data and a 30-meter resolution digital elevation model (DEM) sourced from the NASA Space Shuttle Radar Topography Mission (SRTM) were used for satellite orbit refinement and terrain phase removal. The relevant parameters of the imagery data are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **Orbit** & **Start Date** & **End Date** & **Polarization** & **Number** \\ \hline S1-A & 47785 & 19 March 2017 & 21 January 2022 & VV & 53 \\ S1-A & 47887 & 14 March 2017 & 4 January 2022 & VV & 57 \\ S1-A & 6835 & 13 March 2017 & 10 January 2022 & VV & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Sentinel-1A image data.  
Figure 1: Route map of the Ming Great Wall in Shanxi Province.
## 3 System Model and Problem Formulation
### ILP Model
#### 3.4.2 Delay Constraint  
We use \(d_{i}\) to denote the delay of IoT-SR\({}_{i}\):  
\[d_{i}=\sum_{c\in\mathcal{C}}\sum_{p\in\mathcal{F}_{i}}x_{pc}d_{c}+\sum_{uv\in \mathcal{L}}x_{i}^{uv}d_{uv} \tag{4}\]  
where the first part is the processing delay of VNFs type of \(p\) on cloudlets (\(c\)), which is computed in Equation (5), and the second part is the propagation delay of the links (\(uv\)).  
\[d_{c}=\frac{1-\omega_{c}^{cpu}}{\omega_{c}^{cpu}}t_{c}^{pro},\forall c\in \mathcal{C} \tag{5}\]  
where \(t_{c}^{pro}\) indicates the processing time required by a cloudlet to handle a unit packet (\(c\)) [39].  
If an IoT-SR\({}_{i}\) is admitted, its total end-to-end delay in a path cannot exceed the maximum tolerated delay. We use a binary variable (\(\delta_{i}\)) to show whether IoT-SR\({}_{i}\) is accepted or not:  
\[\delta_{i}=\left\{\begin{array}{ll}1&d_{i}\leq R_{i}^{delay},\\ 0&\text{otherwise}.\end{array}\right. \tag{6}\]
## 2 Materials and Methods
### Study Area and Surveying Methods
For these, the UAV data were collected with a DJI Phantom 4 RTK photogrammetric drone [36] flown at an average altitude of 25 m, resulting in a ground sample distance of 0.68 cm. The flight was conducted with a 90% longitudinal overlap and 80% lateral overlap at a speed of 2 m/s. The drone's positions during data acquisition were obtained via the GPSUmbra network of permanent stations. The UAV survey data were processed using Agisoft Metashape Professional v.1.7.5 [37], obtaining the three point clouds shown in Figure 7.  
The extracted UAV point clouds were utilized to validate the tree heights estimated by the proposed model, which is described in the following sections. In addition to the validation conducted with the drone survey, for Areas 2 and 3, a Terrestrial Laser Scanning (TLS) survey was available for a small number of trees. The TLS point cloud, with a higher level of detail compared to the UAV survey (about 7 mm at a distance of 10 m), was used to validate the experimental results, accounting for the influence of terrain slope and aspect on height estimation. A comparison between UAV and TLS surveys was performed in a previous study [38], which analyzed the geometric features extracted from the respective point clouds.
## 2 Material and Methods
### Signal Extraction  
The input data of the algorithm are a sound signal sampled at a frequency of 44,100 Hz and converted into a two-dimensional 2 xN matrix, where N denotes the length of the signal as a number of samples and 2 results from the microphone recording 2 audio channels. However, more than one audio channel is unnecessary for this algorithm; therefore, the additional channel has been removed.  
After the extraction, the signal was divided into fragments of 1 s length (L = 44,100 [samples]). The fragments did not overlap, which provided a lower computational load and due to the low speed of the robot, such fragmentation procedure gives reliable results.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 4 Analysis of the Feature Compatibility Problem
### KubeletConfiguration Manifest  
The Kubelet is the local agent of Kubernetes on every node of the cluster. It is responsible for integrating the container runtime and networking plugin. Moreover, it also configures the container runtime to enforce resource isolation for CPU, memory and ephemeral storage as well as isolation of file system and network isolation between colocated containers. The kubelet command in the reference manual of Kubernetes has gradually evolved from accepting a long list of parameters to taking a single configuration manifest file that supports imperative configuration management. However, all vendors only allow setting some fields of the KubeletConfiguration manifest via a proprietary customization interface [37; 38; 39], yielding restricted customization without vendor-agnostic reconfiguration support. The affected K8s features include [5; 35]:  
1. Supported container runtimes. The main container runtimes are containerd and cri-o (docker has been deprecated). Various libraries and settings must be installed on the worker nodes themselves in order to make the selected container runtime work;
2. Supported authentication and authorization schemes of the Kubelet with respect to securing the Kubelet API and authenticating the Kubelet to the API server;
3. Various logging features such as container log rotation;
4. Container image garbage collection;
5. CPU management policies for reserving CPUs to specific Pods.  
Section 5.2 presents the vendor-agnostic reconfiguration strategy for full access to the KubeletConfiguration manifest and illustrates it for the CPU management policy.
## 4 Results
### Model Evaluation
#### 4.1.2 Random Forest  
The number of trees generated by the random forest and the maximum depth of the trees were chosen as parameters to be adjusted. The scoring results of the classification indicators for each parameter value were converted into bar charts to interpret their trends, as shown in Figure 7. The accuracy score started to exceed 0.8 at a depth of 3, and then slowly increased. If the number of trees generated was 10, a maximum accuracy of 0.828 was achieved at a depth of 14. If the number of trees generated was 100, a maximum accuracy of 0.832 was achieved at a depth of 18. Thereafter, the accuracy slowly decreased until it remained higher than 0.8 at a depth of 20. The indicator trends confirmed that the accuracy and F1 scores were best when the number of trees generated was 100 and the depth was 18. The comprehensive evaluation results showed that the parameters were optimal in this case. The optimal parameters were brought into the model test for target prediction, and the prediction results were presented using a confusion matrix and ROC curves (Figure 8) to verify that the model was optimal. The model validation process was as follows:  
**Grid search** The parameters were optimal when the number of generated trees was 100 and the depth was 18. In this case, the model accuracy was 0.832.  
**Confusion matrix** The validation results of the indicators for each power consumption level are presented in Table 5. The indicators of low and high energy consumption scored higher, showing that the analytical model had better classification effects in terms of low and high energy consumption.  
**ROC curve** For medium energy consumption, the AUC was 0.87, showing that the model had good discrimination. For both low and high energy consumption, AUC was greater than 0.9, confirming that the model had excellent discrimination. Overall, the model had clear discrimination for all power consumption levels.
## 3 Experiments and Results  
### Data Sets  
To evaluate the performance of AEUNet++, the data sets we used in the experiments were two open building data sets, i.e., the Massachusetts buildings dataset and WHU aerial image data set. In addition, as shown in Figures 6 and 7, we can see that the two data sets cover various building characteristics, such as size, shape, and spatial resolution and distribution, Thus, the two kinds of data sets can be used to evaluate the generalization ability of the proposed model.