esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
For each classification model taken into consideration, the findings of the performance metrics are displayed. The modeling for each classification model uses the data divided into both the training and testing sets. The training set is used to model, while the test data are used for the forecast using the constructed models. The model fitness for the XGBoost and ANN models are displayed in Tables 4 and 5, respectively.  
Table 4 shows the model fitness for the XGBoost model. The table shows the accuracy, sensitivity, specificity, and AUC values of 77.78%, 80.32%, 60.71%, and 78.64% at 70% training set; 77.83%, 80.40%, 61.67%, and 77.73% at 80% training set; and 77.90%, 89.61%, 60.61%, and 79.27% at 90% training set, respectively. The table also shows that the XGBoost model produced MSE, MAE, and RMSE values of 0.15417, 0.3086, and 0.3931 at 70% training set; 0.15381, 0.30497, and 0.39219 at 80% training set; and 0.14940, 0.29790, and 0.38650 at 90% training set. This implies that the XGBoost model performs best at the training set of 90% with the lowest MSE, MAE, and RMSE values.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{**Data Splitting**} \\ \hline
**Measures** & **70\%** & **80\%** & **90\%** \\ \hline Accuracy & 0.7778 & 0.7783 & 0.7790 \\ \hline
95\% C1 & (0.7486, 0.8051) & (0.7379, 0.8078) & (0.7216, 0.8213) \\ \hline Sensitivity & 0.8032 & 0.8040 & 0.8961 \\ \hline Specificity & 0.6071 & 0.6167 & 0.6061 \\ \hline AUC & 0.7864 & 0.7773 & 0.7927 \\ \hline MSE & 0.15417 & 0.15381 & 0.14940 \\ \hline MAE & 0.3086 & 0.30497 & 0.29790 \\ \hline RMSE & 0.3931 & 0.39219 & 0.38650 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model fitness for XGBoost model.  
Table 5 above shows the model fitness for the ANN model. The table shows the accuracy, sensitivity, specificity, and the AUC values of 76.39%, 78.59%, 53.70%, and 75.6% at 70% training set; 76.93%, 79%, 54.08%, and 77.29% at 80% training set; and 77.43%, 79.49%, 57.14%, and 79.14% at 90% training set, respectively. The table also shows that the ANN model produced MSE, MAE, and RMSE values of 0.1585, 0.3236, and 0.3981 at 70% training set; 0.15504, 0.31208, and 0.39376 at 80% training set; and 0.1511, 0.3046, and 0.3887 at 90% training set. This implies that the ANN model performs best at the training set of 90% with the lowest MSE, MAE, and RMSE values.  
Figures 1-3 show the area under curve (AUC) plots for the XGBoost model, while Figures 4-6 show the area under curve (AUC) plots for the ANN model.  
Figure 4: AUC plot for ANN model at 70% training set.  
Figure 5: AUC plot for ANN model at 80% training set.  
Figure 3: AUC plot for XGBoost model at 90% training set.
## 3 Stable Source Identification Algorithm
### Mathematical Model  
In this work, we consider a circular region \(\Omega\) as a conductive medium composed of two homogeneous media, as illustrated in Figure 1. Specifically, we consider that \(\Omega\) consists of two concentric circles centered at the origin with radii \(R_{1}\) and \(R_{2}\). \(\Omega_{1}\) is the circle with radius \(R_{1}\), and \(\Omega_{2}\) corresponds to the difference between the circle with radius \(R_{2}\) and the circle with radius \(R_{1}\), i.e., \(\Omega=\Omega_{1}\cup S_{1}\cup\Omega_{2}\), where \(S_{1}\) is the interface that separates regions \(\Omega_{1}\) and \(\Omega_{2}\). The conductivities of regions \(\Omega_{1}\) and \(\Omega_{2}\) are denoted by \(\sigma_{1}\) and \(\sigma_{2}\), respectively.  
The boundary value problem to study the identification problem is given by [7]:  
\[-\sigma_{1}\Delta u_{1}=0,\qquad\text{in}\quad\Omega_{1}, \tag{1}\] \[-\sigma_{2}\Delta u_{2}=0,\qquad\text{in}\quad\Omega_{2},\] (2) \[u_{1}=u_{2},\qquad\text{on}\quad S_{1},\] (3) \[\sigma_{1}\frac{\partial u_{1}}{\partial n_{1}}=\sigma_{2}\frac{ \partial u_{2}}{\partial n_{1}}+g,\qquad\text{on}\quad S_{1},\] (4) \[\sigma_{2}\frac{\partial u_{2}}{\partial n_{2}}=0,\qquad\text{on} \quad S_{2}, \tag{5}\]  
where \(g\) is the source, \(u_{i}=\left.u\right|_{\Omega_{i}}\), \(i=1,2\), and \(u\) represents the electric potential in \(\Omega\). The symbol \(\Delta\) represents the Laplace operator, which is also denoted as \(\
abla^{2}\). Boundary condition (3) corresponds to the conductivity of the potential, and boundary condition (4) is associated with the jump in current flow due to the presence of the source. The conductivity of \(\Omega^{c}\) is assumed to be zero, which leads to boundary condition (5). Using [PERSON]'s formulas, we obtain the following compatibility condition:  
\[\int_{S_{1}}gds=0. \tag{6}\]  
Problem (1)-(5) is known as the superficial boundary value problem [7]. This problem has been used to study the inverse source problem in electroencephalography for cortical sources [2]. The following definitions are related to problem (1)-(5).  
Figure 1: A circular conductive region composed of two homogeneous media. The source is located on the closed curve \(S_{1}\), which separates the regions \(\Omega_{1}\) and \(\Omega_{2}\) with constant conductivities \(\sigma_{1}\) and \(\sigma_{2}\), respectively.  
Given \(g\) defined on \(S_{1}\), the forward problem involves finding a measurement \(V=u|_{S_{2}}\), where \(u\) is the solution to problem (1)-(5).  
Given a function \(V\) defined on \(S_{2}\), the inverse problem involves determining a source \(g\) defined on \(S_{1}\) such that the solution \(u\) to the forward problem corresponding to \(g\) satisfies that \(u|_{S_{2}}=V\).
## 3 Proposed Security Protocol for Initial Attach in LTE
### Initial Attach with GUTI
#### 3.2.3 Case 3: MME Changed and IMSI Needed  
The third case is that the MME connected during the initial connection has been changed to a new MME, and there is no information about the UE in the MME connected during the initial connection.  
In a re-access, the initial attach process carries out authentication using GUTI to protect the IMSI. The process of transmitting the GUTI is the same as the Section 3.1. IMSI transmission process.  
Figure 7: Case 2: MME changed.  
When the MME is changed, the new MME requests the old MME for the information about the UE according to the information, such as GUTI, NAS-MAC and NAS Seq. No. At this time, if there is no information about the relevant UE in the old MME, the new MME requests the UE for the IMSI.  
In this process, to safely transmit the IMSI, this proposed protocol transmits the GUTI, encrypts IMSI and transmits it using the generated series of values. For the encryption, the UE hashes KGUTIand RN\({}_{UE\_2}\) (RN, random number) to generate a key and encrypts the IMSI using the generated key, KIMSI, to transmit to the MME. After the transmission of the IMSI, the \"authentication\", \"NAS security setup\", \"location update\" and \"EPS session establishment\" processes are carried out. Security protocol for the third case is shown in Figure 8.  
Figure 8: Case 3: MME changed and IMSI needed.
# Recurrence Quantification Analysis (RQA) of Toroidal End Tool Milling Process
## 3 Results and Analysis
### Statistical Analysis of Cutting Force Components  
Statistical analysis was performed for three components of the cutting force: F\({}_{\mathrm{f}}\), F\({}_{\mathrm{EN}}\), and F\({}_{\mathrm{a}}\). Four statistical parameters were determined for each force component, i.e., the mean value (MEAN), standard deviation (STD), skewness (SKEW), and kurtosis (KURT). Figures 5-7 illustrate the variation in these force component parameters as a function of the milling parameters. Very similar changes in the values of the different parameters were observed for each cutting force component. Trends in statistical parameters, particularly the mean value and standard deviation, are comparable for all components of the cutting force. Both parameters increase as the feed rate and depth of cut increase, with the increase being nearly proportional. This indicates that the primary influence on the STD and MEAN values is the size of the cross-sectional area of the cutting layer. Other factors, such as the variation in the specific cutting force with the feed rate or the change in the entering angle with the depth of cut, have a minimal effect on the MEAN and STD parameters.  
However, the dynamics of the cutting process depend not only on the cross-sectional size of the cutting layer but also on other factors. The STD and MEAN parameters are particularly effective for evaluating changes in the cutting layer's cross-section independently of these additional variables. Furthermore, the same analysis technique can be applied to any cutting force component. Therefore, to monitor changes in the cutting layer's cross-sectional area, it is sufficient to record and analyze the variation of just one cutting force component, regardless of which component is selected.  
Figure 5: _Cont._  
Figure 5: Changes in statistical parameters of F\({}_{\rm F}\) cutting force component: (**a**) Mean Value; (**b**) Standard Deviation; (**c**) Skewness; (**d**) Kurtosis.  
Figure 6: Changes in statistical parameters of F\({}_{\rm N}\) cutting force component: (**a**) Mean Value; (**b**) Standard Deviation; (**c**) Skewness; (**d**) Kurtosis.  
Figure 7: _Cont._  
The remaining two statistical parameters, SKEW and KURT, showed no significant relationship with the technological parameters. Their values fluctuated randomly within the range of feed and depth of cut tested. This suggests that skewness and kurtosis are not influenced by the cross-sectional area of the cutting layer and do not reflect the dynamics of the milling process. These findings are consistent with those of [PERSON] et al., who demonstrated that skewness and kurtosis are more suitable for monitoring the stability of the milling process [10]. Consequently, the utility of these parameters differs from that of the MEAN and STD.
## 2 Design and Fabrication
### Fabrication Process  
All the resistors were fabricated using a 4-mask/4-layer process. All resistors were fabricated on B-doped Si wafers by Ultraviolet (UV)-photolithography processes. All the wafers were first cleaned with organic solvents to remove grease and metallic impurities. The bottom metal layer consisting 10 nm Ti and 100 nm Au was deposited using e-beam evaporation. The unwanted material was removed by lift-off process in hot acetone (~80 \({}^{\circ}\)C). Next, 100 nm thick Silicon Nitride (Si\({}_{3}\)N\({}_{4}\)) layer was grown on top of bottom Au contact pad using plasma enhanced chemical vapor deposition (PECVD). For vertical resistors, Reactive Ion Etching (RIE) was performed to pattern the via holes in the Si\({}_{3}\)N\({}_{4}\) layer. A high vacuum RF sputtering of GeTe (99.999% purity) target was done to deposit GeTe thin films of desired thickness. Finally, e-beam metal evaporation and lift-off processes were used to deposit and pattern the top contact pads consisting of 10 nm Ti and either 250 nm or 400 nm Au, depending on the GeTe film thickness.
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 5 Results and Discussion
### Benchmark Against State-of-the-Art Adaptation Techniques  
We recognize the importance of benchmarking against recent adaptive metaschedulers, such as the discrete particle swarm optimization (DPSO)-based method mentioned in [36] and the genetic algorithm (GA)-based metascheduler in [37]. However, a direct quantitative comparison is currently not feasible due to the unavailability of their source code, which is not publicly accessible. Additionally, these implementations are specifically tailored to niche domains, namely wireless IoT-based wireless sensor networks (IoT-WSNs) and network-on-chip (NoC)-based multi-processor system-on-chips (MPSoCs), each with unique architectural assumptions, fault models, and evaluation metrics. Despite this limitation, a qualitative comparison reveals several key differences. The metascheduler proposed in [36] concentrates on fault recovery in the event of single and double component failures, utilizing DPSO to precompute schedule graphs. While effective, this method treats adaptation and context synchronization as distinct layers and relies on periodic task re-allocation. Conversely, our approach seamlessly integrates timing-aware context communication into the metascheduling process. This integration allows for proactive and selective synchronization that minimizes communication overhead and preserves system-wide determinism. Similarly, the approach in [37] constructs multi-schedule graphs using a GA and implements path reconvergence to address state-space explosion. However, it does not consider context propagation within the scheduling loop. In contrast, our algorithm jointly optimizes both schedule construction and the selective transfer of context information, creating a communication-efficient adaptation framework. Although we present a simplified \"No Context Exchange\" scenario to illustrate a theoretical lower bound, the results demonstrate that our approach offers structural and functional advantages over those in [36; 37], thereby supporting our primary claims of efficiency and predictability.
## 4 Discussion  
In this study, evaluation of radiological dose from landfill disposal of CPs containing naturally occurring radioactive materials in South Korea was conducted using various ranges of AC below 1 Bq/g and above 1 Bq/g. Results for RESRAD-ONSITE for AC below 1 Bq/g show that the doses were below the ICRP recommended public dose limit of 1 mSv/y for both pathways, which makes the site safe for onsite dwellers. For AC above 1 Bq/g, the dose was far above the recommended public dose limit of 1 mSv/y for all pathways considered, which makes the landfill sites hazardous for onsite dwellers; hence, the competent authority needs to provide further protection actions to ensure that the public is safe from unjustified radiation exposure. Results from RESRAD-OFFSETITE show that dose from both categories of AC ranges were all below the public dose limits of 1 mSv/y, which makes it safe for offsite dwellers and visitors to live within the vicinity of a landfill site. The results of HotSpot code from incineration of NORM waste considering inhalation pathway to the offsite dwellers for AC below 1 Bq/g were all less than the public dose limit for all stability classes considered, while for AC above 1 Bq/g, the doses were all above the specified IRCP dose limit.  
Thus, people living within the vicinity of 10 km from the incinerator are prone to unjustified exposure from incineration of CPs containing naturally occurring radioactive materials. This is due to gaseous decay of NORMs incorporated in the incinerated waste. People living outside 10 km from the release point are safer from unjustified exposure from the incinerator. Therefore, the competent authority needs to apply a protection action for people living at a close distance to the incinerator. Comparison of results of RESRAD-ONSITE with the results of NORMALYZA code show that the dose for both categories of activity concentration are all above the public dose limit recommended by the ICRP. Therefore, it is not advisable for people to settle on a former landfill site without clearance from the competent authority. This is key to limit undue and unjustifiable radiation exposure to the general public. Comparison of results from all the codes used in this study show that landfill sites with activity concentration above 1 Bq/g are not safe for dwellers until further clearance is given from the competent authority. Moreover, evacuation is recommended for people living within the vicinity of the incinerator. This is due to the  
Figure 9: Comparison graph for the four computer codes.  
release of gaseous decay of NORMs that contaminate the atmosphere, giving rise to high inhalation dose.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 3 Novel Self-Adaptive VM Consolidation Strategy
### Dynamic Multi-Thresholds (DMT)
#### 3.1.3 Source PM Selection Based on DMT  
To select a proper source PM, the first step is to detect their workload based on the comparison of each parameter in DMT (Algorithm 1).  
```
1 Input:\(PM(j)\)
2 Output: load_status
3 foreach \(PM(j)\) in \(PM_{List}\)
4 if (some parameter * in \(PM(j)\) fit \(P_{j}^{*}>~{}T_{*}\))
5 load_status = 1
6 else if (all parameter * in \(PM(j)\) fit \(P_{j}^{*}<~{}T_{*}\) )
7 load_status = 2
8 else
9 load_status = 0
10 return load_status
```  
**Algorithm 1** Load Detection (LD)  
Here we take three parameters (CPU, RAM, Bandwidth) of the whole system to reflect its total resource utilization by each PM with different numbers of virtual machines in it (Algorithm 2). For each host, if one of its parameters is over the upper threshold or all of them are below the lower threshold, it will be selected as one of the target physical machines in \(Source_{PM}\) which contains the migration list of all target hosts (Table 1). At the same time, PMs with higher SLA violation than the threshold \(T_{SLA}\) will first be selected to \(Source_{PM}\), and will be set to higher priority.  
To avoid too much energy consumption caused by the frequent migration of VMs and the low QoS caused by the short shut down during each migration, we decided to prioritize the implementation order of VM consolidation. That is, first migrate some VMs from overload hosts to lower load hosts, and then set all VMs on hosts with low load to migrated out and switch these hosts to sleep mode or shut down.  
Thus, DMT has been set to adaptively fit the cloud environment of source PM selection until now, while the parameter of \(T_{m}\) in multi-thresholds will later be taken into consideration in Section 3.2 for helping the selection of target VM during VM consolidation. Once a PM is detected to be overloaded or low load by our defined dynamic multi-thresholds, the next step is to select proper VMs to migrate from this host and we will further discuss the adaptive VM consolidation strategies on the improvement of VM selection and placement based on the dynamic multi-thresholds in Sections 3.2 and 3.3.
## 2 Data and Methods
### Feature Analysis  
Feature importance is defined as the contribution of each variable to the model, with important variables showing a greater impact on the model evaluation results. In this study, we use both impurity-based and permutation-based methods to calculate the feature importance for RF and XGBoost algorithms, respectively. RF and XGBoost are all ensemble models based on the decision tree as the base estimator, the difference between them is that each decision tree is generated in a different way. In a single decision tree, the model uses the amount of each feature split point improvement performance measure to calculate feature importance, called the impurity-based importance measure. It is used to describe how useful each feature is in constructing the decision tree in the model. However, when the model is overfitting, some features that have less predictive effect may get high importance. The permutation importance is the decrease in a model score when a single feature value is not available, which is complementary to the impurity-based importance. In this study, the impurity-based predictive importance scores of features were obtained from the attribute of \"feature_importances_\" of the best trained RF or XGBoost models generated during the modeling development. The feature importance of the two models and different biomes will be output by the above processes. The permutation-based predictive importance scores used the method of \"permutation_importance\" to evaluate the training samples with a \"n_repeats\" value of 10.
## 6 Open Challenges, Future Prospects and Research Directions
### Supporting Demanding Applications  
The advancements in cooperative relaying are aimed at supporting increasingly demanding applications such as autonomous vehicles and IoT networks. Autonomous vehicles require ultra-reliable, low-latency communication to ensure safety and efficient operation. Similarly, IoT networks involve a vast number of connected devices that need robust and seamless connectivity. The future research directions in cooperative relaying will ensure that FSO systems can meet these demands, providing high-capacity, reliable, and efficient communication links.
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 2 Related Work
### Skeleton-Based HGR Methods  
Skeleton-based HGR methods leverage the structural and temporal information of hand landmarks, making them especially effective for dynamic gesture recognition. Deep learning has driven significant advances in skeleton-based HGR [6; 7; 8; 9; 10; 11], particularly with recurrent architectures such as Long Short-Term Memory (LSTM) networks [24]. LSTM  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Subjects** & **Gestures** & **Instances** & **Data Modality** \\ \hline DHG-14/28 [22] & 20 & 14/28 & 2800 & Depth \\ SHREC’17 [21] & 28 & 14/28 & 2800 & Depth \\ Briareo [23] & 40 & 12 & 1440 & Depth, IR \\ IPN HandS (ours) & 50 & 14 & 5177 & RGB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of publicly available skeleton-based HGR video datasets.  
exels at modeling temporal sequences, making it suitable for capturing the dynamics of actions and gestures over time, effectively capturing dynamic gesture patterns [25]. It has been traditionally used in HGR for classifying gesture sequences based on skeletal data.  
A different approach is based on Graph Convolutional Networks (GCNs) [26], which are designed for graph-structured data. GCNs emerged as a powerful alternative, treating hand skeletons as structured graphs where nodes (joints) and edges (bones) encode spatial dependencies [26]. GCN-based approaches [11; 27; 28] have demonstrated state-of-the-art performance in skeleton-based gesture recognition by jointly learning spatial and temporal dynamics.  
Transformers have also gained attention in skeleton-based HGR [7; 29]. Unlike RNNs and GCNs, Transformers rely on self-attention mechanisms to capture long-range dependencies in motion sequences, improving performance on complex gestures. Across all of these methods, high-quality annotations remain crucial, since noisy or inconsistent skeletons can significantly degrade model accuracy. By refining hand-landmark annotations, our approach enhances data reliability, directly benefiting HGR model performance.  
The recent literature also highlights the growing relevance of Artificial Intelligence-based Internet of Things (AIoT) gesture recognition and sim-to-real adaptive learning strategies in practical deployments. For example, [PERSON] et al. [30] provide a comprehensive review of human activity recognition in AIoT systems, categorizing techniques and applications that emphasize lightweight, adaptive, and resource-efficient solutions. While our current study focuses on accurate single-hand annotations from RGB data, future work could explore how such annotations support AIoT-specific constraints and generalization in diverse, real-world settings.