esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Significance of Input Features  
The significance of input features for the problem considered is quantified using the feature importance scores obtained from the RFR, ABR and GBR models. The feature importance scores are calculated based on how much the base decision-tree nodes that use an input feature reduce uncertainty in the data. The normalized feature importance scores of each input feature are presented in Figure 9 for three DTR-based ensemble ML models after the initial training phase. Figure 9 clearly shows that \(a_{max}\) has the highest normalized feature importance score (about 40% to 50%) in the predictions of AAR, followed by \(C_{r}\) (about 20%). This is consistent with the close relationship of AAR with \(a_{max}\) and \(C_{r}\) presented in Figure 3 and indicates that the AAR is more sensitive to \(a_{max}\) and \(C_{r}\) than the other parameters. The other three input features (\(A/A_{c}\), \(h/B\) and \(I_{a}\)) have approximately 10% of feature importance scores each. These observations are consistent for all three DTR-based ensemble models and  
Figure 8: Comparisons of three DTR-based ML model predictions with experimental results for the acceleration amplification ratio (AAR) during the initial testing phase of the models: (**a**) RFR, (**b**) ABR and (**c**) GBR. Note: the dashed lines represent 1:1 lines.  
Figure 7: Comparisons of three ML model predictions with experimental results for the acceleration amplification ratio (AAR) during the initial testing phase of models: (**a**) MLR, (**b**) KNN and (**c**) SVR. Note: the dashed lines represent 1:1 lines.  
confirm that none of the input features considered in this study are redundant. It should be noted that when the type of soil is included as an input feature to ML models, it results in feature importance scores of less than 5%, consistently for all three DTR-based models. In addition, it does not make any significant difference in ML model predictions when the type of soil is included as an input feature, and hence the type of soil is not included as an input feature in this study. However, the effect of soil type on rocking response of foundations is indirectly included in \(A/A_{c}\) and \(C_{r}\) through shear strength and the bearing capacity of the soil.
## 6 Evaluation  
To evaluate the overhead introduced into the dataflow, due to the homomorphic encryption, we evaluated three scenarios: without homomorphic encryption, with partially homomorphic encryption and with fully homomorphic encryption in order to compare their runtimes. Currently, the encryption service utilized open source libraries, providing wrapper interfaces to their low-level functions and integrating the encryption seamlessly into the VICINITY architecture. For the fully homomorphic encryption, we utilized the HElib Library [39]. The implemented encryption scheme of HElib is based on the Brakerski-Gentry-Vaikuntanathan (BGV) scheme, yet implementing numerous optimizations for a more efficient runtime behavior. For the partially homomorphic encryption, we integrated the libhcs [40] library into our encryption service. This library implements \"a number of partially homomorphic encryption schemes\" [40], yet we focused on the implementation of the Paillier encryption scheme for additive homomorphic encryption, which is necessary for our given use case. Finally, a simple Python script based on the Python Flask framework [41] was implemented to mimic the endpoints of the encryption service, yet operating on plain text input data. However, the simulation framework presented in this paper is not limited to either the given encryption service or any of the given encryption schemes. In further research, more encryption schemes will be tested and evaluated.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 3 System Configurations
### Offshore Electrolyzer Scenario
#### 3.1.1 Centralized Electrolyzer  
In a centralized electrolyzer system, the individual installation of the wind turbines is the same as a typical offshore wind farm, with turbines in strategic places to minimize losses by the wake effect. The power produced by each individual turbine is transmitted to a central platform through regular underwater cables; while voltages can differ, newer and higher power turbines, such as the Haliade-X 13 MW [25], operate at 66 kV.  
Once the electrical power reaches the central platform, most of it can be rectified to DC; the other part is used to power the seawater pumps and hydrogen compressor in AC. The DC power is used mainly to produce hydrogen but also to power the backup power source and the supporting systems. The produced hydrogen exits the electrolyzer at high purity and with a pressure of 30 bar, so the next step is compressing it to the desired pipeline input pressure. After being compressed, the hydrogen is fed into the export pipeline, where it is transmitted to the shore.
## 3 Results and Discussion
### Fluorescence Spectroscopy  
We evaluated the energy and the number of fluorescence photons coming from Xe clusters for the spectra shown in Figure 2b,e,h. Under conditions where the intensity of the incident beam is not extremely high, the number of fluorescence photons from a cluster, \(N_{flu}\), can be assumed to be proportional to the product of \(J_{0}\) and the cluster volume, i.e., the number of atoms in the cluster \(N\) (\(\propto r_{0}^{3}\)). On the other hand, the number of scattered photons, \(N_{scat}\), is proportional to the product of \(J_{0}\) and \(N^{4/3}\) (\(\propto r_{0}^{4}\)) considering spherical shaped clusters. Therefore, one can expect a relation between \(N_{flu}\) and \(N_{scat}\) as \(N_{flu}\times r_{0}\propto N_{scat}\). We extracted the number of fluorescence photons from xenon clusters by subtracting the background signals in the fluorescence spectra. We also estimated \(N_{scat}\) from SAXS data, and checked the correlation between \(N_{flu}\times r_{0}\) and \(N_{scat}\), as shown in Figure 4a. In Figure 4a, there is evidence of a linear correlation between \(N_{scat}\) and \(N_{flu}\times r_{0}\), indicating that the observed fluorescence and scattering photons came from the same targets for each XFEL shot. We also plotted the position of the fluorescence photon peak in Figure 4b. A broad peak was found to be located at 4 keV; the position of this peak was not changed by the FEL fluence. The observed peak position corresponds to the fluorescence energies of the Xe atomic \(L_{\alpha}\) and \(L_{\beta}\) lines. Therefore, these photons are a result of the radiative decay of inner-core excited Xe atoms in the cluster. These results show that fluorescence spectroscopy monitoring of the fluence of the incident pulses offers the possibility of studying the degree of inner ionization of samples under an intense laser field [5; 6; 28; 46].  
Figure 4: Correlation maps between the observables obtained from fluorescence spectrum and small-angle X-ray scattering (SAXS). (**a**) Correlation between the number of scattered photons detected by the octal MPCCD and fluorescence photons detected by one of the two single MPCCD sensors. The black dotted line is a guide representing \(N_{flu}\times r_{0}\propto N_{scat}\). (**b**) Correlation between the X-ray dose (=cluster size \(\times\) XFEL fluence) and the photon energy of fluorescence. The positions of the photon energies of Xe atomic \(L_{\alpha}\) and \(L_{\beta}\) fluorescence are shown to the right side of (**b**). The error bars shown in both (**a**,**b**) are composed of the statistical errors of the number of fluorescence photons.  
Figure 3: (**a**) Characteristic radial profiles of diffraction images. The radii of the Xe clusters giving the top and bottom and the profiles are 81 nm and 39 nm, respectively. The formula of the fitting curves (black dotted lines) is described in [39]. (**b**) The histogram of radii of the observed Xe clusters. The black dotted line is a fitting curve based on the log-normal distribution of cluster size and gives the average radius of 80 nm.
###### Abstract  
In this study, we developed a system to collect and analyze log data related to truck travel times in underground mines using Bluetooth beacons and tablet computers. When a signal from beacons installed at a major underground mine is received by a truck-mounted tablet computer, the beacon information is collected and uploaded to a cloud server. A data processing program integrates the uploaded log data files into a single file, calculating the statistical values for each section of the transport route. The developed system was applied to a limestone underground mine located in Jeongseon, Korea, to diagnose and analyze the transport routes in the study area. As a result of this analysis, it was possible to select sections in which the truck transport time was stable and sections in which it was unstable. Consequently, the transport route could be classified into four types based on the distribution and fluctuations in the truck transport time data. Moreover, it was possible to analyze the causes of the stable and unstable sections through production logs and field staff interviews. The developed system could be used as a tool to improve transport operations by diagnosing and analyzing the truck transport routes of a mine.  
underground mine; transport time; Bluetooth beacon; tablet computer; smart mining +
Footnote †: journal: Applied Sciences
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 6 The Novel ML-AKA Protocol for the 5G-NSA Based C-IoT Network
### Security Assessment  
This section highlights the methodology and parameters required to analyze the proposed protocol. To analyze and validate our proposed ML-AKA protocol, we have used some formal simulation and validation tools. The objective details of the analysis are mentioned below:  
* _Mutual authentication among the devices present in the network:_ The ML-AKA protocol follows the security framework of EPS architecture and inherits the security features for ML-AKA device-to-device communication and the mutual authentication between the devices and APs. The ML-AKA protocol uses HMAC functions and some secret authentication keys like \(R_{AP}\) and \(K_{ps}\) to perform authentication between devices. In order to compute the common secret key \(K_{cm}\) and achieve the MAC verification, the shared secret \(R_{AP}\) and \(K_{ps}\) are used. The common secret \(K_{cm}\) is used to generate \(MAC_{1}\) and \(MAC_{2}\) for \(H_{1}\) and \(H_{2}\), respectively, and is also used to authenticate the identity of devices which are communicating with each other.
* _Manage session with the use of session keys:_ Devices communicate with each other for a particular session prior to the exchange of actual information. This session is created by generating and exchanging some session keys between the devices. In ML-AKA protocols, the generated session keys are exchanged between entities that are present across various levels.
* _Enhance session key privacy:_ In cellular-based IoT communication, heterogeneous types of devices are connected to each other by sharing secret keys through eNodeB and the core network. The primary objective of core network entities like MME and HSS is to maintain this privacy and preserve the identity of the shared secret keys. However, in some cases, if the LTE core network entities are compromised, data exchanged between the IoT devices can be easily evaluated by the third party. To avoid this, the ML-AKA protocol disallows outside attacks, meaning that attackers are unable to access the plaintext.
* _Reduce the probability of security attacks at the network junction:_ In the traditional C-IoT network, the channels between the APs are secured with the help of a standard EPS-AKA protocol under the 3 GPP standard. But communication channels between APs and IoT devices are secured by the standard Wi-Fi Protected Access (WPA) protocol under the IEEE 802.11 standard. Due to this difference in the protocols and authentication mechanism, unauthorized malicious attackers may target the transition points. To reduce the likelihood of this occurring, the ML-AKA protocol uses a common key that can be used interoperatively in all the forms of communication passing through the C-IoT architecture.
## 3 The Main Parameters and Indices of MOT
### Conventional Performance Indices
#### 3.2.2 Atomic Density  
The atomic density in the MOT is limited primarily by two effects: the fluorescence self-trapping effect and the double-body collision effect. The fluorescence self-trapping effect creates a repulsive force between atoms in the potential trap, whereas the double-body collision effect increases the rate at which atoms escape.  
The atomic density can be calculated by determining the ratio of the number of atoms to the volume of the cold atomic group. However, the distribution of atoms in the cold atomic group is not always uniform, so the atomic density obtained via this method may not be sufficiently accurate. To determine the atomic density more precisely, the weak light absorption method can be employed. This method operates on the same principles as fluorescence detection and involves measuring the laser intensity and the length of the cold atomic group to obtain the atomic density via a specific formula [45].  
\[n=\frac{\ln\left(\frac{I_{i}}{I_{o}}\right)}{\sigma L(1-2P)} \tag{8}\]  
where \(\sigma\) represents the resonance absorption cross-section of the atom, \(P\) represents the probability of the atom being in the excited state, \(I_{i}\) represents the incident laser intensity, \(I_{o}\) represents the outgoing laser intensity, and \(L\) represents the length of the cold atomic group.
## 4 Discussion
### Effects of Landscape Composition on Surface Water Quality  
Landscape composition reflects the non-spatial characteristics of the landscape. The results showed that when the areas of some landscape composition types increase, surface water quality improves. In contrast, for some other landscape composition types, the result is the inverse.  
With the change in the radius of buffer zones, different landscape composition types have different correlations with surface water quality indicators. As a'sink' landscape composition type, forest land can effectively improve the quality of surface water flow by absorbing pollutants [17; 25; 26]. In this study, when forest land was near water bodies, its landscape compositional changed: The area, patch numbers, or the largest patch area increased, which decreased concentrations of COD and TP. This conclusion confirms the findings of previous studies [27; 21; 28; 29]. The soil and plants in forest land can effectively reduce storm run-off, water loss, and soil erosion, as well as absorb pollutants, which prevent organic matter and nutrients from penetrating into water bodies through surface water flow.  
When the buffer zones have a larger radius, a greater proportion is in the urban area, and the urban area has considerable effects on surface water quality. When the urban area increases, concentrations of COD and TP also grow. The largest urban patch area can affect concentrations of NH\({}_{3}\)-N and TP, which are positively correlated. This indicates that a greater proportion of the urban area could cause an increase in surface water pollution because the impervious surface in urban areas results in pollutants draining into water bodies through surface water flow [30; 31; 3].  
Farmland is the landscape composition type that experiences frequent human disturbance. The effects of farmland on the surface water quality are quite complex. Fertilization results in nutrients flowing into water bodies. However, the plants in farmland and the paddy field can stop and absorb some water pollutants. In this study, when the area of farmland increased, the concentration of TP decreased, which aligns with the results of existing studies [5; 23].
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 3 Understanding Large Language Models  
### Fundamentals of Large Language Models  
Large Language Models (LLMs) are a class of artificial intelligence models designed to understand, generate, and manipulate human language on a scale and with a level of nuance previously unattainable. Fundamentally, LLMs are built upon deep learning architectures, particularly neural networks known as transformers, which enable them to process sequential data and capture complex patterns within language [58].  
At the core of LLMs is the ability to model the probability distribution of sequences of words in a language. They achieve this by being trained on massive datasets comprising text from books, articles, websites, and other written materials. During training, the models learn to predict the likelihood of a word given the context of preceding words, effectively learning grammar [59; 60; 61; 62], syntax [63; 64; 65], semantics [66; 67; 68], and even some aspects of world knowledge embedded within the text.  
Key features that differentiate LLMs from traditional language models include their scale and the use of self-attention mechanisms. Traditional models often rely on fixed-size context windows and have limited capacity to capture long-range dependencies within text. In contrast, LLMs utilize self-attention [69] to weigh the importance of different words in a sentence relative to each other, regardless of their positions. This allows them to maintain context over longer passages and understand subtle nuances in language.  
Another distinguishing aspect is the unsupervised [70] or self-supervised [71; 72] nature of their training. LLMs do not require labeled datasets; instead, they learn from raw text by predicting masked or subsequent words, enabling them to acquire a broad understanding of language without task-specific annotations. This generality allows LLMs to be fine-tuned for various downstream tasks with relatively small amounts of task-specific data.
## 4 Empirical Study
### Results with LGPIF (H = 1679)
the conditional error variance terms--\(V(\mathbf{x}^{*}|\mathbf{x})\) and \(V(\mathbf{x}^{*}|\mathbf{z})\)--is largely captured in this window of \(\zeta\) as reflected by the prior knowledge: \(\overline{\mathbf{x}}_{j}^{2}=(1-\zeta)\overline{\lambda}_{j}^{2}\) in Equation (34). \begin{table}
\begin{tabular}{c|c|c} \hline \hline
**Model** & **Scale** & **Parameter Estimates \(\sigma_{j}^{2}\)** \\ \hline  & \(\sigma_{j-1}^{2}\) & 3.35 with 95\% Credible Interval: \(\{1.04\leq\sigma_{j-1}^{2}\leq 9.85\}\) \\ Model (A) & \(\sigma_{j-2}^{2}\) & 3.44 with 95\% Credible Interval: \(\{1.05\leq\sigma_{j-2}^{2}\leq 10.01\}\) \\ Gold & \(\sigma_{j-3}^{2}\) & 3.22 with 95\% Credible Interval: \(\{1.02\leq\sigma_{j-2}^{2}\leq 9.48\}\) \\ standard & \(\sigma_{j-4}^{2}\) & 3.40 with 95\% Credible Interval: \(\{1.04\leq\sigma_{j-2}^{2}\leq 9.78\}\) \\  & \(\sigma_{j-5}^{2}\) & 3.40 with 95\% Credible Interval: \(\{1.23\leq\sigma_{j-2}^{2}\leq 10.13\}\) \\  & \(\sigma_{j-6}^{2}\) & 3.23 with 95\% Credible Interval: \(\{1.01\leq\sigma_{j-6}^{2}\leq 9.65\}\) \\ \hline  & Error rate \(R_{c_{i}}\) in \(\mathbf{x}^{*}\): 0.01 & Error rate \(R_{c_{i}}\) in \(\mathbf{x}^{*}\): 0.10 & Error rate \(R_{c_{i}}\) in \(\mathbf{x}^{*}\): 0.40 \\ \hline  & \(\sigma_{j-1}^{2}\) & 3.36 & 3.38 & 3.59 \\ Model (B) & \(\sigma_{j-2}^{2}\) & 3.44 & 3.51 & 3.65 \\ Before & \(\sigma_{j-3}^{2}\) & 3.26 & 3.38 & 3.46 \\ correction & \(\sigma_{j-4}^{2}\) & 3.43 & 3.58 & 3.67 \\  & \(\sigma_{j-5}^{2}\) & 3.44 & 3.57 & 3.65 \\  & \(\sigma_{j-6}^{2}\) & 3.28 & 3.45 & 3.51 \\ \hline  & \(\zeta:0.5\) & \(\zeta:0.6\) & \(\zeta:0.7\) & \(\zeta:0.5\) & \(\zeta:0.6\) & \(\zeta:0.7\) \\ \hline  & \(\sigma_{j-1}^{2}\) & 3.28 & 3.30 & 3.33 & 3.22 & 3.36 & 3.36 & 3.34 & 3.31 \\  & \(\sigma_{j-2}^{2}\) & 3.37 & 3.38 & 3.41 & 3.29 & 3.46 & 3.48 & 3.45 & 3.33 \\ Model (C) & \(\sigma_{j-3}^{2}\) & 3.19 & 3.22 & 3.34 & 3.26 & 3.26 & 3.57 & 3.24 & 3.23 & 3.21 \\ After & \(\sigma_{j-4}^{2}\) & 3.27 & 3.31 & 3.48 & 3.31 & 3.35 & 3.61 & 3.41 & 3.38 & 3.36 \\ correction & \(\sigma_{j-5}^{2}\) & 3.10 & 3.29 & 3.49 & 3.25 & 3.35 & 3.57 & 3.42 & 3.38 & 3.35 \\  & \(\sigma_{j-6}^{2}\) & 3.11 & 3.29 & 3.33 & 3.39 & 3.32 & 3.57 & 3.26 & 3.25 & 3.19 \\ \hline LPPD (\(\times 10^{5}\)) & \(-16.79\) & \(-16.77\) & \(-16.78\) & \(-16.41\) & \(-16.39\) & \(-16.41\) & \(-16.19\) & \(-16.20\) & \(-16.21\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the scale parameter \(\sigma_{j}^{2}\) estimates from the hierarchical log-normal GLMs in Model (A), -(B), and -(C) across risk clusters \(j=1,\ldots,6\). The largest LPPD values are highlighted in bold.
## 5 Case-Study: Resource Fitting
### Experiment 3: Performance of ML Backends under Constrained Resources  
In previous experiments, we assessed the performance of FL deployments on fully allocated \"server-grade\" machines. In such a configuration, the containerized FL clients can use all of the CPU resources available on the node where the container is running. However, this scenario is uncommon at the edge, where devices have limited resources or share them among multiple applications. To understand how resource allocation can affect FL performance, we now focus on investigating FL deployments under different CPU allocation policies.  
While resource management (reservation or limitation) can apply to other resources like memory and network, there are some distinctions. Memory management requires a static definition of the desired capacity, as dynamically shrinking or expanding memory can lead to application crashes (due to invalid memory addresses causing segmentation faults) or frequent swapping to disk, which harms overall performance. Network bandwidth can be limited, but end-to-end reservation is impossible without complete access to the network's switches and routers. Therefore, our investigation focuses solely on CPU adjustments, which can be made without affecting the application's correctness. For our FL deployment, we specifically consider the PyTorch CNN model applied to the MNIST dataset, and study the effects CPU policies may have on the training performance and the utilization of memory and network resources.