esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Countermeasure Readability on Security Intentions
## 4 Methodology  
### Questionnaire  
Based on the literature discussed in previous sections, a structured questionnaire was drawn up. It started with a brief overview, including the goal of the study, what was expected of the participants in the online questionnaire, and an informed consent form. After respondents gave consent, they were introduced with a case description. This is also known as a vignette and its usage is recommended by [53]. Participants were asked to project themselves into this hypothetical situation to minimize faulty data that could result from respondents feeling disassociated from the context. Applied to our context, this could mean that cybersecurity threats were not being felt or seen. This is included, as the application of PMT requires participants to recognize being exposed to a threat [23]. A feeling of indifference about the statements, for example, could arise otherwise, which would yield distorted data. It also provides for more alignment between the participants, concerning how they approach the questions and statements. Lastly, it straightens out possible differing associations participants may have with the statements. In the questionnaire, participants are shown four cyber threat definitions and countermeasures against them. The selection of threats (Appendix A) and corresponding controls are based on the most common cyber threats as recognized by Cisco Systems [54]. Respondents were asked to respond to statements about both threats and controls related to all constructs found in the conceptual framework (Section 3). Statements corresponding to the constructs within PMT are based on work by [PERSON] and [PERSON] [10], [PERSON] et al. [12], and [PERSON] and [PERSON] [55]. All statements were to be rated using a 7-point Likert scale, ranging from'strongly disagree' to'strongly agree' to make the questions uniform across the measured constructs. This was in line with [PERSON]'s [56] proposed usage of Likert scales. what is important to highlight are the constructs SE, SI, and CR, as these are the focus of this study. SE was operationalized through two statements per countermeasure. The first is \"I feel confident carrying out these countermeasures\". The statement is a derivative of [PERSON] and [PERSON]'s [10] operationalization and is based on SE's definition worked out in Section 2.1. \"Carrying out these countermeasures is easy\" is the second questionnaire item used for measuring SE. This one is based on the application of PMT for cybercrime in the work of [PERSON] and [PERSON] [55], which is also used by [PERSON] et al. [12]. Work by [PERSON] et al. [57] could also be used to support this operationalization, as they conclude that perceived difficulty is a dimension of SE. SI was inquired through one statement per countermeasure, e.g. : \"I will carry out the countermeasures against malware\".
## 4 Results and Discussion
### Estimation Accuracy--Side Gusts
Increasing the time constant to 1.5 s leads to underestimation. Detailed computational results are provided in Table 3.  
Figure 41: Comparison of the gust value with the estimation results—level flight through a gust area; entire simulation (complementary filter, (**A**) T\({}_{\text{cv}}\) = 0.5 s, (**B**) T\({}_{\text{cv}}\) = 0.8 s, (**C**) T\({}_{\text{cv}}\) = 1.0 s, (**D**) T\({}_{\text{cv}}\) = 1.5 s). Figure 42: Comparison of the gust value with the estimation results—level flight through a gust area; series of selected gusts (complementary filter, (**A**) T\({}_{\text{cv}}\) = 0.5 s, (**B**) T\({}_{\text{cv}}\) = 0.8 s, (**C**) T\({}_{\text{cv}}\) = 1.0 s, (**D**) T\({}_{\text{cv}}\) = 1.5 s). The side gust estimation results were evaluated by comparing them to the low-frequency signal in reference to the recorded ideal gusts measurements. Analysis of the graphs in the subection 4.2 indicates, that the applied estimation method allows for a more accurate representation of the gusts' shape, and therefore their velocity, particularly in their extreme values, which pose the greatest threat to the aircraft. The estimation provides a more precise depiction of violent gusts where significant increases and decreases in gust speed occur over a short period of time, whereas the low-frequency signal tends to overestimate their magnitude. The undoubted advantage of the algorithm is its consistent performance across all evaluated flight conditions. Both the low-frequency signal and the estimation are delayed relative to the recorded side gusts. The best estimation is delayed by an average of 0.65 s. This delay is approximately 0.1 s longer than that observed in the estimation of head-on gusts. The low-frequency signal, although less accurate, reacted faster to the side gust--the delay in relation to the reference gusts was within 0.4-0.5 s.  
Figure 44: Comparison of the gust value with the estimation results—climb crossing a gust area; entire simulation (complementary filter, (**A**) T\({}_{\text{cv}}\) = 0.5 s, (**B**) T\({}_{\text{cv}}\) = 0.8 s, (**C**) T\({}_{\text{cv}}\) = 1.0 s, (**D**) T\({}_{\text{cv}}\) = 1.5 s). Figure 43: Comparison of the gust value with the estimation results—level flight through a gust area; single selected gust (complementary filter, (**A**) T\({}_{\text{cv}}\) = 0.5 s, (**B**) T\({}_{\text{cv}}\) = 0.8 s, (**C**) T\({}_{\text{cv}}\) = 1.0 s, (**D**) T\({}_{\text{cv}}\) = 1.5 s). This is a faster response of the low-frequency signal compared to the estimation of head-on gusts. Furthermore, a higher value of the complementary filter time constant leads to a larger increase in the estimation delay in contrast to the head-on gust estimation algorithm. It would be necessary to define criteria for situations in which a shorter algorithm response time is preferable, even at the cost of underestimated peak values. Table 3 summarizes the computational comparisons of the selected cases discussed above (the most favorable results are in bold). Negative values in the difference columns mean underestimation, and positive values mean overestimation.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 3 Decomposed Optical Network for STN Edge Computing
The on-board resource of the processor cubes is the CPU, and a small memory chunk (local memory) is still placed inside the processor cube to run the operating system and cache core data. Both the CPU and local memory are interconnected to the Memory Management Unit (MMU), which is the resource management module of the processor cube. The MMU is responsible to the translation of virtual data addresses to physical data addresses. The network interconnect module consists of a flow controller, Network Interface (NI), and transmitters (TX and RX in Figure 2a,b). The flow controller is for sending optical packets to corresponding TX and processing packets from RX, while NI can package the instructions to network packets. All the TRXs are divided into two parts: some in the packet plane for sending payloads and others in the control plane for sending optical tags. The functional diagram of the memory cube is shown in Figure 2b. The on-board resource of the memory cubes is based on the Double Data Rate 4 (DDR4) memory or Hybrid Memory Cube (HMC), connected to the memory controller (resource management module of memory cube). The memory cubes have the same network interconnect module as processor cubes, processing packets of reading/writing data from processor cubes and packets from other memory cubes for the direct memory access. Same as current server-centric architectures, the CPU first accesses the fast cache when processing application data [28]. Once data are missing in the fast cache, the instruction and logical data address are sent to the MMU. If the physical address of the target data locates in the local memory, CPU accesses the target data within the processor cube. Otherwise, the instruction and physical data address are forwarded to the flow controller. Based on the hardware cube address containing the target data address, the instruction is packaged in NI, and then sent to the corresponding TX and forwarded to destination memory cube.
## 3 Results and Discussion
### Optimisation of Extraction Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design Point** & \multicolumn{2}{c}{**Independent Variables**} & \multicolumn{2}{c}{**Response (Y\({}_{\text{TYP}}\), mg GAE g\({}^{-1}\) dm)**} \\ \hline  & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) (R\({}_{\text{L/S}}\), \% & \(\mathbf{X_{3}}\) (R\({}_{\text{L/S}}\), \(\mathbf{rpm}\)) & \(\mathbf{X_{3}}\) (S\({}_{\text{S}}\), \(\mathbf{rpm}\)) & \multicolumn{2}{c}{**Without UP**} & \multicolumn{2}{c}{**With UP**} \\  & \(\mathbf{\boldsymbol{w}}\)(\%) & & & & **Measured** & **Predicted** & **Measured** & **Predicted** \\ \hline
1 & \(-1\) (55) & \(-1\) (20) & \(0\) (500) & 62.82 & 62.18 & 66.09 & 66.18 \\
2 & \(-1\) (55) & \(1\) (100) & \(0\) (500) & 72.96 & 71.08 & 72.58 & 71.91 \\
3 & \(1\) (85) & \(-1\) (20) & \(0\) (500) & 76.00 & 77.88 & 57.48 & 58.15 \\
4 & \(1\) (85) & \(1\) (100) & \(0\) (500) & 70.56 & 71.20 & 72.50 & 72.41 \\
5 & \(0\) (70) & \(-1\) (20) & \(-1\) (200) & 68.73 & 67.21 & 67.03 & 67.72 \\
6 & \(0\) (70) & \(-1\) (20) & \(1\) (800) & 76.86 & 77.15 & 70.65 & 69.19 \\
7 & \(0\) (70) & \(1\) (100) & \(-1\) (200) & 67.74 & 67.45 & 81.00 & 82.46 \\
8 & \(0\) (70) & \(1\) (100) & \(1\) (800) & 77.61 & 79.13 & 75.14 & 74.45 \\
9 & \(-1\) (55) & \(0\) (60) & \(-1\) (200) & 56.94 & 59.11 & 69.62 & 68.83 \\
10 & \(1\) (85) & \(0\) (60) & \(-1\) (200) & 70.31 & 69.96 & 70.69 & 69.33 \\
11 & \(-1\) (55) & \(0\) (60) & \(1\) (800) & 72.50 & 72.85 & 68.46 & 69.82 \\
12 & \(1\) (85) & \(0\) (60) & \(1\) (800) & 80.00 & 77.83 & 61.01 & 61.80 \\
13 & \(0\) (70) & \(0\) (60) & \(0\) (500) & 72.94 & 72.98 & 66.07 & 68.02 \\
14 & \(0\) (70) & \(0\) (60) & \(0\) (500) & 73.97 & 72.98 & 68.47 & 68.02 \\
15 & \(0\) (70) & \(0\) (60) & \(0\) (500) & 72.04 & 72.98 & 69.52 & 68.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Points of the experimental design considered, and measured and predicted values of the response.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
###### Abstract  
This study proposes a hybrid blockchain system for secure and transparent data management in multinational space missions. By combining public and private blockchains, the model enables open access to non-sensitive data while protecting confidential mission records. Data integrity is ensured through cryptographic proofs without exposing the underlying content, and a cross-chain protocol enables real-time synchronization between chains without relying on centralized intermediaries. The system was implemented using Ethereum and Hyperledger Fabric and tested with real extrarehicular activity data. Results show that it effectively detects data tampering, enforces access control, and synchronizes records with low latency. Compared to traditional centralized systems, this approach offers improved resilience, auditability, and trust across organizations. It provides a practical foundation for future space data infrastructures requiring both transparency and confidentiality.  
s +
Footnote †: journal: Applied sciences  
article
## 4 Solution Method  
The solution method typically depends on the characteristics of the optimization model. The commonly used solution methods for cascaded hydropower plants fall into three categories: Mathematical programming, dynamic programming and its improved algorithms, and artificial intelligence algorithms. The aforesaid optimization problem, including Equation (19), Equation (21), Equation (22), and other nonlinear constraints, is a typical nonlinear problem. Especially, the proposed model cannot meet the Markovian character, because the market-to-market energy ratio constraint, i.e., constraint (6), is included. Consequently, the dynamic programming and similar algorithms are not applicable. Moreover, the intelligent algorithms have problems, such as long computing time, easily trapped into local optimization, and unstable calculation results. Therefore, nonlinear programming (NLP) in mathematical programming is chosen in this paper.  
In this paper, the solution procedure involves two main steps. At step 1, the combined MCP scenarios are generated and reduced by using MATLAB software; at step 2, based on the reduced scenarios, the NLP is implemented by the Global Solver, available in LINGO 17.0 ([[http://www.lindo.com/](http://www.lindo.com/)]([http://www.lindo.com/](http://www.lindo.com/))), to solve the proposed model. LINGO is a special software for solving mathematical programming problems, such as linear programming, non-linear programming, quadratic programming, and integer programming problems. It has the advantages of easy operation, fast computation, and stable calculation results. The basic computational principle and detailed computational procedure of the Global Solver for solving NLP problems have been given in literatures [29; 30]. It mainly includes three steps: (1) With the branch-and-bound algorithm, the entire feasible solution spaces are segmented into series sub-problems; (2) a slack problem for each sub-problem can be obtained by convexity and linearization transformation and solved by the linear programming method; and (3) the global optimal solution is obtained by traversing all the sub-problems (i.e., the whole feasible space). Figure 1 shows the detailed calculating process. To set the parameters and calibrate the Global Solver, read the manual provided in LINDO.
## 3 The CART-Based Methods
### CART-Based Integrated Algorithms  
#### 3.1.1 CART Algorithm  
The decision tree is a tree structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category [19]. The most famous CART algorithm assumes that the decision tree is a binary tree and uses discretization methods to discretize continuous variables so that the model can be used for both classification and regression problems. For regression problems, the regression tree selects the minimum square error criterion as the index for evaluating split attributes. Suppose training data \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{n},y_{n})\}\), where \(x\) is the input instance, \(y\) is the output variable, then divide the input into M regions (\(R_{1}\), \(R_{2}\), \(R_{m}\)), the output value of each area is \(c_{1}\), \(c_{2}\),..., \(c_{m}\). It is important to find the optimal regression tree model.  
\[f(x)=\sum_{m=1}^{M}c_{m}I(x\in R_{m}) \tag{1}\]  
The squared error is as follows:  
\[\sum_{x_{i}\in R_{m}}(y-f(x_{i}))^{2} \tag{2}\]  
Find the optimal cut-off point for each area to minimize the model's square error. A single decision tree selects feature variables only once at each node, lacking an iterative feedback process. This can lead to overfitting and low generalization ability on the test set. Additionally, a decision tree's structure changes drastically with changes in the sample. To address these inherent flaws of a single model, the current popular approach involves using bagging or boosting methods to create an ensemble learning model. Among the most representative algorithms are the random forest (RF) and AdaBoost algorithms. The schematic diagram is shown in Figure 4.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 3 Network Model and Mathematical Foundations
### Problem Description  
It makes sense that efficient network slicing maximises the use of physical network resources while simultaneously lowering the cost of slice provisioning [27]. With these limitations in mind, we may formulate the issue of minimising the cost of slice provisioning as an integer linear programming model, as illustrated below.  
\[\begin{split} min,\sum_{n^{k}\in N^{NSR}}\sum_{n^{l}\in N^{PI}}x_ {i}^{k}(1+SL(n^{i}))CPU(n^{k})\\ +\sum_{e^{m}\in E^{NSR}}\sum_{e^{n}\in E^{PI}}a_{n}^{m}BW(e^{m}) \\ \text{subject to},\sum_{k}x_{i}^{k}=1,\ \forall i\in N^{NSR} \\ \sum_{i}x_{i}^{k}\leq 1,\ \forall i\in N^{PI}\\ x_{i}^{k}CPU(n^{k})\leq CPU(n^{i}),\forall i\in N^{PI},\forall k \in N^{NSR}\end{split} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref. No.** & **Methodology Adopted** & **Components** & **Performance Metrics** & **Considered Attributes** \\ \hline Proposed & \begin{tabular}{c} Machine Learning for Slice \\ Creation, PROKTHTHETRERE for \\ Size Isolation and Resource \\ Transfer for Slice Management \\ \end{tabular} & \
\begin{tabular}{c} Slice creation: Precision, Recall, and F1 \\ Score \\ Size isolation and management \\ Resource Efficiency, Acceptance Rate \\ \end{tabular} &
\begin{tabular}{c} CPU capacity, link capacity, bandwidth, jitter, delay rate, and closeness centrality \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
\[x_{i}^{k}SR(n^{k})\leq SL(n^{i}),\forall i\in N^{PI},\forall k\in N^{NSR} \tag{5}\]  
\[\sum_{e^{m}\in E^{NSR}}a_{n}^{m}BW(e^{m})\leq BW(e^{n}),\forall e^{n}\in E^{PI} \tag{6}\]  
\(x_{i}^{k}\) is a binary variable. If the k-th node of NSR is served onto the i-th node of PI, then \(x_{i}^{k}=1\); otherwise, \(x_{i}^{k}=0\). \(a_{n}^{m}\) is a binary variable. If the m-th link of NSR is served to the n-th link of PI, then \(a_{n}^{m}=1\); otherwise, \(a_{n}^{m}=0\). In order to guarantee the integrity of the nodes, network providers employ various additional measures. To enhance resource allocation and slice generation accuracy at nodes, we additionally take into account Link Capacity, Delay Rate, and Jitter in addition to CPU Capacity and bandwidth. Constraint (2) specifies that each request node must be associated with a physical node. The third constraint guarantees that a given physical node will only ever host a single node from the same request. Limitation (4) shows the CPU Capacity. The security constraints of all nodes are guaranteed by the fifth constraint. Constraint (6) ensures that the bandwidth requested by services delivered over a physical link does not exceed the available bandwidth.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 5 Discussion and Data Analysis
### State-of-the-Art Comparison Discussion  
It is quite difficult to compare our framework to other state-of-the-art solutions due to its multiple different outputs. However, there are some solutions that provide parts similar to some of our outcomes. These are listed in Table 10. SDNDASH [25, 26] focuses on QoE requirements to build an SDN-based management and resource allocation system to maximize the quality perceived; However, their solutions do not consider the network data as a straight feature contribution for direct prediction. Our approach focuses on using network data to automatically predict the QoE of a real user. With multiple network configurations, our predicted features and QoE reached new heights on known classifiers. SDNDASH focuses on a single user and recommends a certain bit rate and buffer levels, where our framework comes with pre-designed adjustable network features and shows the developers how each feature affects the QoE on a human level, thus eliminating testing with users. Another solution [27] proposed a network application controller called service manager, which reads video traffic and allocates resources fairly among competing congestion. Our solution has elements of this step and shows the user the best segments with the most optimal resolution for their network configuration. The higher the configuration, the better the quality perceived and shown on the classifiers will be. A similar state-of-the-art solution [25] offers a caching technique where users are informed of the cache's content as well as a short-term prediction of the bottleneck bandwidth, whereas our framework shows prediction for the entire duration of the segmented perceived file. QFF [28] proposed an open-flow-assisted QoE framework with optimizing QoE among HAS clients with heterogeneous device requirements. This is ideal because the device information can limit the allocation to certain devices and save the rest for devices that require more bandwidth. Our test bed is missing this functionality, and it can be useful to adapt it. Our framework can produce training-ready data; thus, this can be an addition in future work to enhance the test bed. However, it will not affect the prediction due to the fact that the prediction is based purely on network features.  
Figure 11: Framework resolution prediction results.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 4 Experimental Environment  
This section describes the experimental environments that have been set to evaluate the performance of MQTT, CoAP, OPC UA.  
A first experimental environment used is a local, constrained environment illustrated in Figure 2 ([[http://copelabs.ulusofona.pt/index.php/research/projects/324-iotlab](http://copelabs.ulusofona.pt/index.php/research/projects/324-iotlab)]([http://copelabs.ulusofona.pt/index.php/research/projects/324-iotlab](http://copelabs.ulusofona.pt/index.php/research/projects/324-iotlab)) (accessed on 15 May 2021)). The experiments run in this testbed have as aim to evaluate the performance of the different protocols in terms of _packet loss_ and _time-to-completion_ of requests.  
A second experimental environment used has been the large-scale FIT-IoT testbed [30] ([[https://www.iot-lab.info/](https://www.iot-lab.info/)]([https://www.iot-lab.info/](https://www.iot-lab.info/)) (accessed on 15 May 2021)).  
The protocols evaluated on FIT-IoT were MQTT and CoAP, as the support for OPC UA in FIT-IoT was not, at the time when we have run the experiments, out-of-the-box. We have tried to install the required libraries to run OPC UA. However, the quota provided to users was not sufficient to enable the installation of an open version of OPC UA.  
For both experiments and regardless of the protocol _time-to-completion_ means the time spent sending the message through the sender and receiving the message by the receiver, considering the time spent by the broker/server. However, the definition of _packet loss_ has differences due to the different characteristics of the protocols. For MQTT, which has the characteristic of forming queues for the delivery of messages at the Broker, the packet loss occurs when a message packet sent is no longer received. For CoAP and OPC UA that relies on REST to increasing its interoperability, the _packet loss_ is the failure of a GET or PUT command sent to the server.  
Figure 2: The IoT Testbed at COPELABS.