esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Material and Methods
### Classifiers  
The DTC, RFC, ETC, and XGBC classifiers used in [4] and \(k\)NNwere employed for the experimentation. While the former are all tree-based ensemble methods, \(k\)NN was included as a non-tree-based, instance-based learning algorithm to provide a comparative perspective and evaluate its performance under the same feature selection strategies.  
\(k\)NN (\(k\)-Nearest Neighbors) is a nonparametric instance-based learning algorithm used for classification and regression tasks [12; 13]. It classifies a new instance by considering the K closest training examples in the feature space, typically measured using Euclidean distance or other distance metrics. The predicted class is determined by a majority vote among the nearest neighbors.  
DTC (Decision Tree Classifier) is a nonparametric supervised learning algorithm, which is used for both classification and regression tasks [14]. It has a hierarchical tree structure, consisting of a root node, branches, internal nodes and leaf nodes.  
RFC (Random Forest Classifier) is a classifier that relies on combining a large number of uncorrelated and weak decision trees to arrive at a single result [15].  
ETC (Extra Trees Classifier) is a classification algorithm based on decision trees, similar to RFC, but with more randomization [16]. Instead of searching for the best splits at each node, it randomly selects features and cutoff values. This makes it faster and less prone to overfitting. It works by creating a set of decision trees and makes predictions by majority vote.  
XGBC (Extreme Gradient Boosting Classifier) is a machine learning algorithm based on boosting, which builds sequential decision trees to correct errors in previous trees [17]. It is efficient, fast, and avoids overfitting thanks to its regularization. It uses gradient descent to optimize the model and is known for its high classification accuracy. However, it can be complex to fit and has less interpretability than other models.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 2 System Model
### Capture Effect  
When the superimposed packets are received with significantly different powers, the so-called capture effect may take place, i.e., the strongest packet may be decoded by the GW despite a collision [26]. The signal-to-interference-plus-noise ratio (SINR) for the \(j\)th signal is defined as  
\[\gamma_{j}=\frac{P_{r}(d_{j})}{\sum_{m\
eq j}P_{r}(d_{m})+N_{0}}. \tag{3}\]  
We assume that a signal \(j\) may be \"captured\" by the GW and survive the collision if and only if \(\gamma_{j}\geq\tau\), with \(\tau>0\) representing the so-called capture threshold of the system [26]. The capture threshold \(\tau\) is a system parameter, whose value depends upon the sensitivity of the radio receiver. For instance, [PERSON] et al. [27] showed a SINR of 6 dB was required for a Chipcon radio to capture packets, while [PERSON] et al. [28] found that just 1 dB was enough for packet capture when the captured packet arrived before the interfering packet with an Atheros WiFi card.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### RSSI Based Solutions  
The estimation of the expected connection quality is provided by the Received Signal Strength Indicator (RSSI) of the received packets. The following solutions measure the average RSSI value for selecting a preferred parent. The use of RSSI in the following proposed solutions helps in resolving multiple mobility issues faced by RPL, which are discussed further.
## 3 Theoretical Background and Hypothesis Development
### Hypothesis Development  
3.2.1. Central Route: Privacy Fatigue and Privacy Paradox  
Privacy concern denotes the attitudinal measure of the user towards a series of privacy decision behaviors, such as privacy disclosure and privacy protection. It is the core of privacy research. The positive influence relationship between privacy concern and privacy protection behaviors has been fully documented by academics. For example, [PERSON] et al. discovered that when users of mobile social media believed personal information had been accessed by unknown means or used inappropriately, they would tend to choose to give up using social media [23]. Academics such as [PERSON] et al. also similarly confirmed that privacy concern would lower users' privacy disclosure intention [27]. [PERSON] et al. noted that privacy concerns would influence users' goods purchasing behaviors in the social business environment [37]. Research by [PERSON] et al. found that the privacy concerns of young and teenage users of social media led to a significant positive influence on their practice of privacy protection behaviors [38]. These studies demonstrated that privacy concern could clearly affect users' information security behavior intention. Stronger privacy concern usually implies fewer privacy disclosures and a greater willingness towards privacy protection behaviors. The positive influence between privacy concern and privacy protection behaviors is one of the prerequisite assumptions of the privacy paradox being valid. Our hypothesis is thus:  
**H1.**_There is a positive influence relationship between privacy concern and the intention towards privacy protection behaviors._  
\"Privacy fatigue\" is exhibited in people cutting their efforts or even directly abandoning the goal when the pressure they are facing is too big [39]. In response to privacy threats, the user reduced corresponding behaviors, resulting in slackening of daily security actions [9]. Individuals experiencing privacy fatigue often hope to reduce their efforts regarding privacy decisions as much as possible, rather than look for solutions to the issue. They may withdraw from actively tackling privacy threats [25]. Through interviews, the research by [PERSON] and [PERSON] concluded that the mental distancing of users from the privacy issue or an attitude of cynicism was the important reason leading to decoupling of privacy concern and privacy protection behaviors [26]. Previous research suggested that in such scenarios as social media and e-commence, users' great investment of time, money and cognitive efforts and deep involvement with social links with other users leads to a high sunk cost. In these scenarios, privacy fatigue, functioning as a coping mechanism, encourages disclosure behaviors [34]. Hence, our hypotheses are as below:  
**H2a.**_Cynicism would weaken the positive influence relationship between privacy concern and privacy protection behaviors._  
**H2b.**: _Emotional exhaustion would weaken the positive influence relationship between privacy concern and privacy protection behaviors._
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 4 Discussion
### Integration of Multi-Sensor Data  
Having seamless and comprehensive continental-wide maps of ice velocity is crucial for the scientific community to study the evolution of ice sheets in a warmer climate, but his task remains technically challenging especially when trying to make annual time series [1; 3; 19; 41]. This is the main reason why we have, so far, to rely on different sensors in order to obtain a comprehensive view of the dynamics of the ice sheets. Thus each sensor contributes uniquely to the final map [22]. The different advantages range from spatial coverage (e.g., South Pole covered by RADARSAT-2 only) to better correlation (L-band). In this way, the integration of Landsat-8 to the SAR sensors used previously [22] allows to provide a more complete coverage of the ice velocity in Greenland and Antarctica than achieved with single sensor. Indeed, combining the new datasets, RADARSAT-2, Sentinel-1 and Landsat-8, with the previous sensors (ERS, ENVISAT/ASAR, ALOS/PALSAR, RADARSAT-1) we cover 99.6% and 99.9% of the Antarctic (Figure 9) and Greenland (Figure 10) ice sheets, respectively versus our earlier mappings [2; 3] that covered 95.1% and 97.4%. These complete products will benefit glaciologists and ice sheet modelers by providing maps with no gap, continuous data, and improved precision.
## 2 Routing Protocols in Large-Scale WSNs
### Control Overhead Reduction Algorithms
2L-OFFIS includes two parts, which are formation of cluster and data forwarding. In the first phase, the algorithm inherits the feature of LEACH in grouping sensor nodes. That is the nodes choose themselves as the cluster heads based on a pre-defined probability and then the sensors pick up a CH to join the cluster based on the receiving signal strength from the CH. Time division multiple access (TDMA) is used in each cluster when transmitting sensed packets in order to power off the transceiver until the right assigned time slots. The only differentiation with LEACH is that in 2L-OFFIS the more distant nodes will get earlier slots and the closer nodes will get later slots. In the second phase, the sensed data will be first delivered to the corresponding CH and then transmitted to the sink node. During the delivery, either intra-cluster or inter-cluster, OFFIS is applied to select the next hop among its neighbors. It works as follows: the forwarding node utilizes its neighbors' location information to calculate the distance between the node and its neighbors and the distance between its neighbors, and the linear distance between the source and the destination is also required. Besides, the neighbor's battery usage and link usage are also combined to make a fuzzy inference used to select a neighbor node as the next hop. Generally, the nearest node from the source and from the shortest path, also with the most abundant resource will be selected as the next hop. As shown in Figure 4, blue nodes are the candidate nodes in the forwarding path, and yellow nodes are discarded. In this protocol, a GPS positioning service or some localization algorithms are assumed to be available. Therefore the routing protocol is more scalable than that without position awareness. The next hop during transmission is chosen independently without route request flooding in the whole network, and so there is no need to maintain the ID of each sensor node. Additionally, every sensor node just needs to maintain the neighbor information, and accordingly the storage costs to store the routing table are saved. In a word, the energy consumption will be reduced thanks to these advantages and the network lifespan will be prolonged. However, the assumption of a GPS positioning service will increase the monetary costs and the multi-hop routing increases the end-to-end delay with respect to the single hop routing used in LEACH.
## 7 Conclusions  
This study proposes a model for identifying the grade of surrounding rock based on TBM excavation parameters and Optuna-XGBoost algorithms. First, the RFECV algorithm is used for the global measurement of feature importance in the model's predictions; ultimately, the optimal feature subset was selected and compared with the complete feature set. Then, the Optuna method was used to optimize the XGBoost model's hyperparameters to improve the recognition model's accuracy. Finally, the accuracy, precision, recall, F1 score, and confusion matrix of the Optuna-XGBoost model were compared with those of other algorithms to verify its effectiveness. The main conclusions drawn are as follows:  
(1) Feature importance was measured using RF. The results show that T and N contributed the most to the model among the six input features, with contribution scores of 0.2798 and 0.2424, respectively. PR contributed the least to model performance, with a score of only 0.0762.  
(2) The use of the RF-RFECV feature selection algorithm to construct the XGBoost classification model was better than the full feature set in each evaluation criterion; the four evaluation indicators were increased by 8.26%, 8.03%, 7.67%, and 7.89%. This indicates that RF-RFECV can enhance the accuracy of identifying surrounding rock grades.  
(3) The Optuna-XGBoost algorithm was compared with the RF, GBDT, DT, PSO-XGBoost, and XGBoost algorithms, based on the TBM excavation parameter dataset of the KS Tunnel. The experimental results show that the accuracy of the Optuna-XGBoost model was 0.9833, which was much higher than that of the other algorithms. The superiority of the Optuna-XGBoost algorithm in dealing with transformer fault diagnosis problems is verified. A new method for predicting the grade of surrounding rock based on TBM excavation parameters is proposed.  
**Author Contributions:** Conceptualization, R.S. and K.S.; methodology, T.F.; resources, Z.L.; data curation, J.Z.; writing--original draft preparation, R.S.; writing--review and editing, K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** This study comes from a confidential project and data cannot be disclosed due to privacy concerns.  
**Acknowledgments:** Thanks to all the anonymous reviewers for their value comments on this paper, which improved quality of our paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment
## 4 Research Methodology
### Validation by Experts  
We ran three workshops with 9 cyber security and ICS experts (5 academics and 4 industry practitioners). The participants of these validation workshops were not involved in the mind mapping exercise at the initial stage of the project. The experts were presented with a printed version of the unified SCADA mind map (Figure 1) and asked to answer the following questions:  
1. Does the top-level mind map reflect your vision of a SCADA system?
2. Are there any irrelevant elements?
3. Are there any elements missing (completeness and coverage)?
4. Is the suggested hierarchy of elements consistent with your understanding of dependencies within a SCADA system?  
There were no new elements suggested during the workshops, and the expert group positively commented on the completeness of the unified mind map. There were suggestions made about the restructuring of elements under the key area \"External Dependencies\". All comments from the first workshop were addressed, and the final version of the mind map was presented at the second workshop when the expert group agreed on the structure. No further suggestions for change were received during the second workshop.  
During the validation workshops, the following comments were received from the industry participants commenting on the usefulness of the SCADA DM:  
* An ISC manager at a manufacturing plant: _\"It is useful to have an extensive model on its own to look at. It is very difficult to sit down in a traditional workshop and cover every scenario: you always forget something, you always miss something. Getting the right people round the table, being able to manage those people to go though all the scenarios is very time consuming and expensive. Most people do not want to be there it becomes boring after a while for them. Certainly, if there is a predefined model that covers the majority of the scenarios, you miss less. To have a model which captures that wider experience saves us money, saves us time. Certainly, it is a useful tool\"._
* An ICT manager at a manufacturing plant: _\"There is not really anything we use in terms of modelling that helps us accurately look at probabilities. That is why a dependency model may be quite useful, because you do not want to spend a huge amount of money if there is a very low probability and because for everything we do we have to justify costs against benefits. Without understanding probabilities it is very difficult to do that\"._
* A SCADA specialist: _\"Never I could come up with over 400 elements in a model, I would have struggled. It is certainly useful as it is all about knowledge sharing\"._
## 4 Mesh Dependency and Data Filtering  
In order to obtain a mesh-independent solution and avoid the \"checkerboard\" instability, the raw sensitivities and/or design densities are processed with a blurring filter [6; 101]. This article adopted the filter scheme used in [17], which is a simplification of the scheme proposed by [PERSON] and [PERSON] [102], described as follows:  
\[\rho_{e}=\sum_{j}\left(\frac{w(r_{ej})}{\sum_{j}w(r_{ej})}\rho_{j}\right) \tag{60}\]  
where the value of \(w(r_{ej})\) is equal to the difference between the maximum range of the filter \((r_{max})\) and the actual distance between the central element \(e\) and the \(j\) elements in its neighborhood \((r_{ej})\), defined as follows:  
\[w(r_{ej})=max(0,r_{max}-r_{ej}). \tag{61}\]  
This parameter can be interpreted as a pondered measurement of how close the two elements are. Note that the code implementation provided allows the user to select if the blurring filter should be applied to the sensitivities (excluding the volume constraint sensitivities, or equivalent), the design densities, or both.  
It is relevant to note that, although not implemented in the methodology herein proposed, it is common to couple filtering techniques with projection methods [103] in order to obtain a better geometry definition in the final solution. Doing so may eliminate residual grey areas, which is particularly relevant in stress-constrained problems, where a smoother geometry may avoid the creation of stress concentration points.
## 2 Coexistence and Interference in WBASNs
### Inter-WBASN Coexistence and Interference
The frequency band is from 900 to 9000 MHz and is used by a UWB antenna. The simulation results show that the simplified model can be used to investigate and test the CR-WBASN. The most important task in a dynamic multiple-WBASN environment is to detect and predict the coexistence of WBASNs. As a learning-based method, the naive-Bayesian-supervised learning method [14] was applied to detect whether a WBASN moves or not. Coexistence can be categorized into four states: static, semi-dynamic, dynamic, and none (no interference). The average packet reception ratio (PRR) and SINR are used as variables in the learning-based method to detect interference by comparing the thresholds of PRR and SINR. Four types of interference cases were also considered in the experiment to generate the training data set. In a multiple-WBASN environment, a WBASN consists of one coordinator node and four sensor nodes. Interference occurs in a WBASN working in the ISM band. In addition, IEEE 802.15.6 UWB-based WBASNs can receive interference from other UWB devices. In [15], another interference study analyzes UWB-based WBASNs. The interference characterization is discussed with regard to the SINR, emitting power, and temporal model. The simulation results show that the BER performance of the UWB-based WBASNs depends on the power signal of the interferers working on IEEE 802.15.4a (piconets) or IEEE 802.15.4f (RFID) systems.
## 6 Summary Comparison: Functionality and Performance
### Functionality Comparison  
In this section, we present a detailed functional comparison of our protocol against four contemporary protocols [6; 7; 8; 35], based on the evaluation metrics outlined in Section 5.1. The outcomes of this comparison are summarized in Table 3, where the notation \(\blacklozenge\) means that the protocol demonstrates the property and \(\blacklozenge\) denotes that the protocol does not demonstrate the property. According to Table 3, the protocol by [PERSON] et al. does not meet criteria \(C_{5}\), \(C_{8}\), and \(C_{10}\). Despite incorporating only three chaotic map operations, the protocol in [35] is unable to ensure forward secrecy when the long-term key is compromised. Moreover, the protocol [35] by [PERSON] et al. stores critical authentication parameters on the smart card and transmits identities in plaintext over public channels, rendering it susceptible to eavesdropping and resulting in potential message desynchronization. The protocols outlined in [6; 7; 8] do not satisfy criterion \(C_{1}\), which concerns user anonymity and untraceability. For instance, in the protocol [8], the edge node (EN) transmits the real user identity (\(ID_{en}\)) to the access point (AP), thereby directly compromising the user's real identity. In the case of [6], while the resource device \(RD\) does not directly disclose its identity to the \(IC\), the \(IC\) is able to ascertain \(RD^{\prime}\)s identity via a static index, contradicting the anonymity and untraceability principle that mandates different labels for each authentication attempt. Similarly, the scheme in [7] compromises user anonymity as it requires user \(U_{i}\) to reveal his real identity \(ID_{i}\) to server \(S_{j}\) in every session, allowing for user tracking. Additionally, in the aspect of \(C_{4}\), both [7]'s and [PERSON] et al. 's [38] protocols exhibit vulnerability to DoS attacks. Consequently, both protocols also fall short of meeting \(C_{4}\), further indicating areas for significant improvement in their security apparatus. Overall, Table 3 indicates that our proposed protocol achieves the outlined security and usability objectives, proving robust against a spectrum of known attacks. While the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Notation** & **Description** & **Description** & **Description** \\ \hline \(C_{1}\) & Ensure user privacy and anonymity & \(C_{6}\) & Achieve secure key agreement \\  & Avoid using password & \(C_{7}\) & Ensure mutual authentication \\ \(C_{2}\) & Verifier tables & \(C_{8}\) & Operate without clock synchronization \\ \(C_{3}\) & Prevent password & \(C_{8}\) & Synchronization \\ \(C_{3}\) & leakage & \(C_{9}\) & Have system recovery capabilities \\ \(C_{4}\) & Defend against known threats & \(C_{9}\) & \(C_{9}\) \\ \(C_{5}\) & Protect smart card & \(C_{10}\) & Ensure forward secrecy of communications \\  & security & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Security evaluation criteria for AKA protocols.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation
### Experimental Setup  
Our experiments were implemented in Python, leveraging the PyTorch framework for deep learning model construction and GPU acceleration. We disabled PyTorch's automatic batching functionality to enforce the use of our custom collate_fn. For baseline results, all models without optimizations utilized the standard PyTorch data-loading pipeline for comparison.  
We evaluated our view-based batching optimization using two key performance metrics:  
Figure 7: Memory saving by view-based batching vs. traditional stacking-based batching. \(B\) is the batch size, \(S\) is the sequence length, and \(F\) is the number of features.  
* **Memory Usage:** We measured the peak GPU memory consumption during batching with special attention to memory behavior during the execution of collate_fn.
* **Runtime Performance:** We measured (i) the processing time of each batch and (ii) the full end-to-end runtime per training epoch, averaged across multiple iterations.
* **Forecasting Accuracy:** This was evaluated using standard mean squared error (MSE) and mean absolute error (MAE) metrics on the ETT benchmark datasets to confirm that our batching optimization does not affect model performance.