esa-sceva/satcom-chunk-collection huggingface dataset:
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 5 Data Integrity Algorithms
### ECDSA-P256-Aes-128 Cmac  
The ECDSA-CMAC algorithm is an ECDSA algorithm that generates its message digest using the CMAC algorithm instead of the standard hash function. The ECDSA-CMAC algorithm is explained in Figure 6, where d is the ECDSA private key, Q is the ECDSA public key, G is the selected curve base point, n is the selected curve prime number, kCMAC is the CMAC private key, and m is the software to be signed. The output signature is the combination of the calculated values r and s, which are integers that range between 1 and (n \(-\) 1). Any signature value out of range or non-integer is rejected by the signature verification process.  
The ECDSA-CMAC algorithm cannot be used as a digital signature generation and verification algorithm because it requires a private CMAC key in the verification process. This contradicts the objective of digital signature algorithms, which is to enable other parties to verify a specific party's signature without the need for confidential information. This contradiction cannot be solved by making the CMAC key public because it will make the CMAC algorithm more vulnerable. But in the case of secure booting, the ECDSA-CMAC algorithm can be used since secure boot processes require data integrity only and do not involve other parties' authentication. This means that the generated signature is verified by the same party, which is the measurement component of the secure boot process. So, the CMAC key can remain private and is not required to be shared with other parties.  
The ECDSA-P256--AES-128 CMAC algorithm uses a 256-bit private ECDSA key, a 128-bit private CMAC key, and a 512-bit public ECDSA key to generate a 512-bit signature. To the best of our knowledge, the usage of CMAC as the underlying algorithm for the ECDSA signature scheme is a unique approach that has not been investigated for applications other than those mentioned in this manuscript.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 4 Evaluation
### Privacy Analysis Compared with Onion-Routing-Based Scheme  
In this section, we compare our proposed mix network scheme with the possible use of an onion-routing (OR) scheme. For this purpose, we introduced the concept of attack range that denotes the communication area that the attacker can access. The attacker can gain access to all messages  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mix Nodes** & **20\%** & **40\%** & **60\%** & **80\%** & **100\%** \\ \hline \multicolumn{5}{c}{**Cambridge**} \\ \hline  
\ \hline \hline \end{tabular}
\end{table}
Table 5: Average routing-step path length with centrality mix nodes.  
exchanged in this area. As we previously mentioned, one of the drawbacks of common OppNet scenarios is that it might be relatively easy to monitor the entire communication network. In general, we assumed that, if the attacker can monitor the communication of the entire onion-routing circuit, then the attacker could break the circuit. This means that the attacker could monitor the whole circuit or communication path, violating the privacy of the circuit from sender and receiver nodes.  
For example, suppose that a user sends a message from source node \(V_{s}\) to destination node \(V_{d}\) using three onion routers \(OR_{1},OR_{2},OR_{3}\), \(V_{s}\to OR_{1}\to OR_{2}\to OR_{3}\to V_{d}\). In order to go from one node to the other, the message might follow an epidemic routing path through different network nodes as described in Section 3.2. That is, we have something similar to the scenario described in Figure 1, but using onion routers instead of mix nodes. In this case, if all nodes are inside attack range, then the attacker can trace communication \(V_{s}\to V_{d}\). Depending on the size of the attack range, the attacker could trace a given number of communications.  
In order to show the case in our OppNet scenarios, we simulated the attack range with the random walk mode in the ONE simulator. The entire simulation movement area was \(800\times 800\). The number of ORs is three (i.e., this is the common number of onion router used in Tor). Table 6 indicates the percentage of onion-routing circuits that an attacker could break in terms of the size of the attack range, which is expressed as a percentage of the total geographical area of the network.  
From the table, we can see that the bigger the attack range is, the smaller the general privacy is achieved using onion routing. With regard to our mix network scheme, it is impossible to connect all packets with the whole circuit even if the attack range is 100%. This is due to the nature of the mixers, which prevents the correlation of source and destination even if the attacker can monitor the whole network. So, the privacy of our mix network scheme remains the same no matter the range that the attacker can access.
# Network Visualization of Cooperative Games
## 4 Network Visualization of a Cooperative Game
_Links with positive sign in_ \(R\) _are assigned a value of zero if their absolute value does not exceed_ \(\beta^{+}\)_._
4. _Links with negative sign in_ \(R\) _are assigned a value of zero if their absolute value does not exceed_ \(\beta^{-}\)_._  
_Formally, we define the Filtered Players' relevance representation as follows:_  
\[\psi_{i,\alpha^{+},\alpha^{-}}=\left\{ \begin{array}{ll}\psi_{i}&\mbox{if $\psi_{i}>0$ and $|\psi_{i}|>\alpha^{+}$}\\ \psi_{i}&\mbox{if $\psi_{i}<0$ and $|\psi_{i}|>\alpha^{-}$}\\ 0&\mbox{otherwise}\end{array} \right. \tag{6}\]  
\[r_{i,j,\beta^{+},\beta^{-}}=\left\{ \begin{array}{ll}r_{i,j}&\mbox{if $r_{i,j}>0$ and $|r_{i,j}|>\beta^{+}$}\\ r_{i,j}&\mbox{if $r_{i,j}<0$ and $|r_{i,j}|>\beta^{-}$}\\ 0&\mbox{otherwise}\end{array} \right. \tag{7}\]  
The four threshold parameters \(\alpha^{+},\alpha^{-}\), \(\beta^{+}\), and \(\beta^{-}\) help filter out nodes and links that contribute little to the network. Choosing these values typically involves a data-driven approach that highlights only the most relevant nodes and interactions. One way to achieve this is by examining the distribution of player relevance scores (\(\Psi\)) and interaction values (\(R\)), and then setting thresholds based on a specific quantile (e.g., above the first or second quartile). If the thresholds are too low, they will not filter out insignificant elements, making them ineffective. On the other hand, well-chosen thresholds create a clearer, more focused view of the network, making it easier to identify the most impactful elements in the game.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 4 Discussion  
### Machine Learning Model Performance  
Among all evaluated models, XGBoost delivered the best overall predictive performance, with an AUC of 0.917 \(\pm\) 0.026, sensitivity of 0.792 \(\pm\) 0.075, and specificity of \(0.812\pm 0.033\), aligning closely with findings reported by [PERSON] et al. [42]. The model's ability to maintain a favorable balance between sensitivity and specificity stems from its integrated regularization mechanisms and capacity to capture complex nonlinear feature interactions, enabling effective discrimination between landslide-triggering and non-triggering events. Relative to other machine learning approaches, XGBoost exhibited superior identification of the minority (landslide) class while preserving overall accuracy.  
Although Random Forest and LightGBM also demonstrated strong predictive abilities, as reflected in their high AUC values (0.916 \(\pm\) 0.026 for Random Forest and 0.917 \(\pm\) 0.026 for LightGBM), both exhibited a noticeable bias towards the majority (non-landslide) class. This tendency was evident in their high specificity but lower sensitivity, indicating reduced capabilities in detecting minority class events. Such outcomes are likely attributable to the inherent algorithmic characteristics and training mechanisms of these models, which tend to prioritize overall discrimination performance in the context of imbalanced datasets, often at the expense of minority class detection.  
A relatively high AUC (0.903 \(\pm\) 0.032) and perfect sensitivity (1.000 \(\pm\) 0.000) were observed for the E-D curve threshold method, highlighting its exceptional ability to identify all potential landslide events. However, this came with a substantial reduction in specificity (0.219 \(\pm\) 0.034) and the F1 score (0.565 \(\pm\) 0.012). This trade-off is consistent with the primary aim of the E-D curve, which is to maximize the detection of possible landslides even at the risk of increasing false positives. The method's high sensitivity underscores its value in early warning systems, especially in contexts where missing a potential landslide poses a greater risk than issuing unnecessary alerts.  
Therefore, in practical landslide early warning applications, the balance between sensitivity and specificity should be carefully tailored to the specific context. For instance, prioritizing sensitivity in densely populated areas can help minimize missed detections  
Figure 10: Model-predicted rainfall threshold distribution maps for landslide triggering in Italy at (**a**) 50%, (**b**) 70%, and (**c**) 90% probability.  
and enhance public safety, whereas emphasizing specificity in low-risk regions may be preferable to reduce unnecessary false alarms. Ultimately, such context-specific optimization ensures that early warning systems are both effective and reliable in diverse real-world scenarios.
# Sustainable Removal of Cr(VI) by Line Peel and Pineapple Core Wastes
## 3 Results and Discussion
### Box-Behnken Analysis
#### 3.4.2 Total Cr removal  
The tested model for the Cr adsorption was highly significant (Table 3), as evidenced by a really small probability value (_p-values_\(<\)0.0001). For this response, pH (\(x_{1}\)), PP dosage (\(x_{2}\)), temperature (\(x_{3}\)), pH-PP dosage (\(x_{1}x_{2}\)), pH-temperature (\(x_{1}x_{3}\)), PP dosage-temperature (\(x_{2}x_{3}\)) and the square of PP dosage (\(x_{2}{}^{2}\)) are significant model terms. The correlation coefficient value (R\({}^{2}\) 0.9876) was lower than  
Figure 4: Response surface methodology (RSM) plot: (**a**) Cr(VI) removal to assess the pH and PP dosage with the temperature held constant at central level; (**b**) Cr adsorption to estimate the temperature and PP dosage with the pH held unchanging at the central level.  
Figure 3: Pareto analysis of (**a**) Cr(VI) removal and (**b**) total Cr removal.  
Cr(VI) removal response, however, it is an acceptable value and the Pred R\({}^{2}\) is in good concordance with the Adj R\({}^{2}\).  
Similarly to previous section, the expression relating the response, total Cr removal, with the selected variables was obtained (Equation (8)):  
\[\begin{split}\text{Total Cr removal (\%)}&=25.47-3.01\times x_{1}+7.38 \times x_{2}+3.51\times x_{3}-1.82\times x_{1}x_{2}+2.26\times x_{1}x_{3}\\ &+4.36\times x_{2}x_{3}-7.5\cdot 10^{-4}\times x_{1}{}^{2}-3.62 \times x_{2}{}^{2}-1.03\times x_{3}{}^{2}\end{split} \tag{8}\]  
The total Cr removal response was analyzed by Pareto (Equation (8)) (Figure 3b). This analysis evidences biosorbent dosage (52.7%) has the biggest influence in the total Cr removal, followed by the interaction between PP dosage and temperature. This is in accord with [PERSON] et al. [32] who found out that adsorbent dosage is the governing factor of the Cr(VI) removal.  
Figure 4b shows the response surface plot for total Cr removal in the function of temperature and PP dosage keeping pH stable at the central level (3.5). According to the response surface, the total Cr removal grows when the PP dosage and temperature increase. These outcomes confirm that the Cr(VI) removal and the Cr adsorption took place by different mechanisms in the studied biosorbent.
## 3 Proposed DNN Based UC in NOMA
### Power Allocation for Predicted UC  
Once the prediction of UC is performed by DNN-UC, the next step is to allocate the power to the predicted UC. To perform power allocation, UC needs to satisfy the necessary power constraint of the NOMA system. Intuitively, the power constraint for performing successful SIC of \(N\) number of users present in downlink NOMA clusters on subcarrier \(m\) can be generalized as follows [16]:  
\[p_{n,m}g_{n-1,m}-\sum_{c=1}^{n-1}p_{c,m}g_{n-1,m}\geq p_{min},n=2,\;3,\ldots.\;N \tag{15}\]  
where \(p_{min}\) is the minimum power level required to differentiate between decoded and non-decoded signals. With respect to SIC constraint, the maximum power allocated to high channel gain user \(n\) on subcarrier \(m\) has to be  
\[p_{n,m}<\frac{p_{c}-\Delta}{2^{N-1}},n=2,\;3,\ldots,\;N \tag{16}\]  
where \(p_{c}\) is the maximum downlink transmission power for a cluster and \(\Delta\) is the minimum required power variance to perform SIC and can be expressed as  
\[\Delta=\frac{p_{min}}{g_{w}} \tag{17}\]  
Therefore, the power allocation for an \(N\)-user cluster can be generalized as:  
\[p_{n}\leq\frac{p_{c}-\Delta}{2^{N-1}}-\frac{\Delta}{2^{N-2}}-\cdots-\frac{ \Delta}{2}\approx\frac{p_{c}}{2^{N-1}} \tag{18}\]  
With the cluster formation obtained from the DNN-UC and the power allocated based on (18), the throughput of a cluster can be computed using (5) and the sum throughput of the NOMA system can be obtained via (6).
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 3 Results  
### Cluster Characterization and Result Analysis  
Using the optimized clustering parameters obtained from parameter tuning--eps = 3.94 and MinPts = 70--the radar displacement point cloud data were clustered. Based on the clustering results, several clusters of monitoring points were identified according to their spatiotemporal features. Each cluster was labeled in a different color to facilitate a detailed analysis of the clusters with the highest mean cumulative displacement and the largest area. Figure 8 shows the clustering results of radar monitoring data from the slope. As seen in the figure, six clusters were identified from the Bayan Obo Mine southern slope radar monitoring point cloud, with Cluster \(-1\) being classified as noise. Except for Clusters 0 and 4, the remaining clusters have relatively small surface areas and limited impact on overall slope stability.  
By observing the clustering cloud map, it can be seen that there are clusters with a larger number of points and greater cumulative displacement between the 1650 m and  
Figure 8: Clustering effect diagram of slope radar monitoring data.  
1570 m platforms. Among them, Cluster 4 is located between 1570 m and 1626 m. The displacement time series curve of this cluster is shown in Figure 9.  
It is relatively clear from the above figure that the southern slope experienced a slow-rapid-accelerated deformation phase between 27 August and 29 August. All six clusters showed relatively small cumulative displacements in the early stage. Around 18:00 on 27 August, the displacement change of Cluster 0 began to lag behind the other five clusters. From 12:00 on 28 August onward, the cumulative displacement of Cluster 4 began to increase significantly, reaching over 60 mm on 29 August--the maximum displacement before the landslide occurred. According to the resistivity profile and geological cross-section along the survey line, the area where Cluster 4 is located contains a fault fracture zone and an adverse-dip structural plane, with a high degree of weathering. Under the combined influence of these factors, the slope in this area became unstable, which explains why Cluster 4 exhibited the greatest displacement.  
These results indicate that the proposed method leverages the spatiotemporal continuity of radar remote sensing data from the slope surface, effectively avoiding false alarms caused by monitoring data from small areas or isolated points. It also allows for more accurate monitoring and early warning of landslide scale and deformation stages.  
As shown in the figure, Cluster 4 exhibits the largest mean cumulative displacement, and its displacement curve shows non-stationary characteristics. In the subsequent analysis, VMD will be applied to decompose the displacement curve of Cluster 4. The low- and mid-frequency components after decomposition will be predicted using the GRU and LSTM neural networks in order to reveal the characteristics of their displacement evolution.
## 3 Results
### Example 6. The Two-Dimensional Bolza Optimal Control Problem with an Exact Solution  
The dynamical system is described by two difference equations:  
\[x_{1}(t+1)=x_{2}(t);\ \ \ x_{2}(t+1)=2x_{2}(t)-x_{1}(t)+\tfrac{1}{N^{2}}u(t),\]  
where \(x\in\mathbb{R}^{2}\), \(t=0,1,\ldots,N-1\), and \(u\in\mathbb{R}\).  
It is required to find such a pair of trajectory and control \((x^{*}(\cdot),\ u^{*}(\cdot))\) that the value of the performance index \(I\) is minimal:  
\[I=-x_{1}(N)+\frac{1}{2N}\sum_{t=0}^{N-1}u^{2}(t).\]  
In this case, it is possible to solve this problem analytically:  
\[u^{*}(t)=\tfrac{N-t-1}{N};\ \ \ \min I=-\tfrac{1}{3}+\tfrac{3N-1}{6N^{2}}+ \tfrac{1}{2N^{3}}\sum_{t=0}^{N-1}t^{2}.\]  
In this problem, the initial condition vector is known: \(x(0)=(0;0)^{T}\), and the constraints on the control are also given: \(0\leq u\leq 100\). For this task, the number of stages is set: \(N=10\).; the exact value of the performance index is \(\min I=-0.1425\).  
When solving this problem, the approximate value of the performance index obtained by the TFO algorithm was \(I_{TFO}^{*}=-0.142499964879453\) and the runtime was \(0.13\) s. The relative error of the obtained value of the performance index was \(2.5\times 10^{-7}\). The same problem was solved by Perch School Search algorithm [7]. The runtime was \(7.39\) s and the value of the performance index was \(I_{PSS}^{*}=-0.142499999999796\). The new algorithm makes it possible to obtain solutions much faster and with almost the same accuracy.  
To solve this problem, the TFO algorithm was used with the parameters shown in Table 9. Figure 11 shows the obtained pair \((x(\cdot),\ u(\cdot))\). Figure 12 shows deviations of approximate control values from exact ones at different moments of the dynamic system operation, i.e., \(\Delta u(t)=|u(t)-u*(t)|
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cancer Type** & **Analytical** & **Biomarker’s Candidates/** & **Prediction Model** & **Validation Characteristics** & **Reference** \\  & **Approach** & **Findings** & Urine & & & \\ \hline \hline \multirow{4}{*}{Pancreatic} & TD-CC-TOF-MS & 2.6-Dimethyl-Science, nonanal, & Repeated & & & \\  & GC-TMS & 4-ethyl-1,2-dimethyl-benzene, & 10-Fold & NA & [16] \\  & GC-TMS & 2-pentanone & CV & & & \\ \hline \multirow{2}{*}{Bladder, prostate} & \multirow{2}{*}{CC-TOF-MS and GC-TMS} & \multirow{2}{*}{35 VOMs} & \multirow{2}{*}{ROC, Repeated 10-Fold CV} & GC-TMS & \multirow{2}{*}{[17]} \\  & & & & & Sens 87\% \\  & & & & Spec 92\% & \\  & & & & AUC: 0.95 & \\ \hline \multirow{4}{*}{Prostate} & Urine HS conditioning, followed by e-nose analysis & The e-nose detected alterations in the urine volatilome associated with PCa & & & Sens 85\% \\  & & & & urine volatilome associated with PCa & & 79\% \\  & & & AUC: 0.82 & & \\  & Urine HS conditioning, followed by e-nose analysis & The e-nose discriminated the urine smell prints of patients with PCa & PCA & & Sens 83\% \\  & & (Cyranose C320) & from healthy controls & & & \\  & & & & & AUC: 0.90 & \\ \hline \multirow{4}{*}{Prostate} & Urine HS conditioning, followed by e-nose analysis & The e-nose discriminated patients with PCa from healthy controls & & & Sens 82\% \\  & & & & & & Spec 87\% \\  & & & & & AUC: NA & \\ \hline \multirow{4}{*}{Pancreatic ductal} & HiSort probes coupled & 2-Pentanone, heasnal, & & & & \\  & & & & & AUC: 0.82 & \\  & & & 3-hexanone, p-cyrene & & & & \\ \hline \multirow{4}{*}{Breast} & GC-MS analysis of the urine & The e-nose software discriminated & & & & Sens 100\% \\  & HS. Sample’s smell print by & between early stage breast cancer & & & & Spec 50\% \\  & the e-nose prototype & & healthy controls & & & Classification rate: 75\% \\ \hline \multirow{4}{*}{Bladder} & \multirow{4}{*}{HS-SPME/GCGC TOF-MS} & Butylrolactone, 2-methoxy, & & & \\  & & 3-methoxy-5-methylphenol, & & & \\  & & 1-(2.6-6,trimethyl-cyclohex-1,3-dien-1,y-)-2-butanol-ione, nodkatone, & & & NA & [23] \\  & & 1-(2,6,6-trimethyl-1-cyclohexenyl) & & & & \\  & & 2-butten-ione & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent studies on volatile organic metabolites for the identification of cancer biomarkers found in urine, exhaled breath, and saliva.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Carboxylic Groups
#### 4.1.2 Ugi Multicomponent Reaction  
The urge to explore the possibility of creating alginate hydrogel with varied cross linkers is massive. This variety of crosslinkers brought the opportunity to provide controlled mechanical features. The ester linkages, crosslinking with poly(ethylene glycol)-diamines, or crosslinking provided by adip dihydrazide and lysine, mainly establish crosslinking-density controlled mechanical properties [68; 69; 70; 55]. The crosslinking is formed from a protonated amine and a carboxylate, which reacts with an isocyanide, resulting in a bisamide (Figure 4) [71]. This method was conceived from the possibility of achieving more regulated hydrogel rheological features [72].  
[PERSON] et al. later adopted the Ugi multicomponent reaction to synthesize amphiphilic alginate [73]. This alginate acid ester displayed tunable organic solubility and thermal properties.  
Figure 3: The general mechanism of carbodiimide chemistry.
## 2 Materials and Methods
### Sequential Chemical Extraction  
Sequential chemical extraction was performed to determine the distribution of Cd among different geochemical fractions of the soil. Soil samples were collected from the pots after the 90-day growth period. The extraction method was based on the protocol described by [PERSON] et al. [37], with modifications made by [PERSON] et al. [38], and originally developed by [PERSON] et al. [39]. The process involved the use of the following reagents in a stepwise manner:  
* F1 (water-soluble and exchangeable): 0.001 M KNO\({}_{3}\) for 24 h at room temperature.
* F2 (exchangeable): 1 M NH\({}_{4}\)OAc at soil pH for 2 h at room temperature.
* F3 (specifically adsorbed): 1 M NH\({}_{4}\)OAc at pH 5 for 2 h at room temperature.
* F4 (oxide-bound): 0.04 M NH\({}_{2}\)OH-HCl in 25% HAc for 6 h at 80 \({}^{\circ}\)C.
* F5 (organic matter-bound): 30% H\({}_{2}\)O\({}_{2}\) at pH 2 for 5.5 h at 80 \({}^{\circ}\)C, followed by 3.2 M NH\({}_{4}\)OAc in 20% HNO\({}_{3}\) for 0.5 h at room temperature.
* F6 (irreversibly adsorbed): 7 M HNO\({}_{3}\) for 6 h at 80 \({}^{\circ}\)C.
* F7 (residual): the remaining solid fraction after previous extractions.  
After each extraction step, the samples were centrifuged, and the supernatant was filtered and stored for analysis. The solid fraction was washed with 10 mL of distilled water, centrifuged again, and the wash solution was combined with the previous extract for further analysis. Cd concentrations in each fraction were measured using ICP-OES (Perkin Elmer Optima 4300 DV). This procedure allowed for the quantification of Cd in the following soil fractions:  
* F1 and F2: physical sorption (reversible).
* F3: electrostatic adsorption (exchangeable).
* F4, F5, F6: chemisorption (irreversible).
* F7: residual Cd.  
The recovery percentage was calculated as the ratio of the sum of the six fractions (F1-F6) to the total amount of Cd initially added to each pot.