esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
## 5 Discussion
### Connectivity and Domain Knowledge
However, geographical information is not the only type of contextual information that M-SOCs can utilize. Research by [75; 76] developed a vessel surveillance anomaly detection framework. In it, they defined the two main categories of anomalies, namely dynamic kinematic and dynamic non-kinematic anomalies. Dynamic kinematic anomalies captured were the vessel course, speed, maneuver, reporting, and location. Dynamic non-kinematic anomalies included the ports of call (departure and arrival), cargo list, ship signature, crew list, and passengers. While not comprehensive, these are additional data points that M-SOCs can consider using to help refine their incoming alerts. Relevance for Future ROC  
Having up-to-date and actionable knowledge of the environment that is to be monitored is crucial for the successful operation of an ROC, just as it is for M-SOCs. Achieving this is dependent on having an effective communication network. The level of communication required to support autonomous ships would need to be bidirectional, scalable, secure, and supported by multiple systems to ensure there is redundancy and minimal risk. The connectivity solution has to guarantee sufficient communication link capacity for sensor monitoring and remote control [60]. A review of the literature highlights more effort being dedicated to resolving autonomous vessel connectivity challenges. In the literature review, we highlighted research looking into spectrum requirements and mitigation of communication delays [56; 57]. Ref. [58] built a network optimizer based on their ship-to-shore communication framework (SSCF), which was a proof of concept on how a future ROCand SOC would be able to optimize their data flows for efficient operation. The concept of a connectivity manager to help manage data flows was also a standout feature in the literature on how to improve connectivity for autonomous vessel operation [59; 60]. We are therefore of the opinion that much of the research on how to optimize connectivity for autonomous vessel operation is applicable and can be carried over to M-SOC operation. When it comes to the use of contextual information, we saw that geographical information is the most commonly utilized for M-SOCs. Research utilizing the concept of operational envelopes, which aims to enhance situational awareness for ROC operators, has similarly used geographical information as part of the contextual data [61; 62; 63]. It has, however, been enriched with both environmental (traffic density, wind, temperature) and system data (sensors, engine state, ship stability). There are clear overlaps in the type of contextual data utilized by both ROCs and M-SOCs further reinforcing how both entities can closely collaborate in the efficient execution of their operations.
# Precision and Reliability of a Dynamometer for Trunk Extension Strength and Steadiness Assessment
###### Abstract  
Low back pain is a major cause of disability worldwide, often associated with deficits in trunk extensor strength control. Accurate assessment of trunk extension strength control is crucial for diagnosing impairments and monitoring interventions. This study evaluated the reliability of a dynamometry-based protocol for isometric trunk extension strength control assessment. Twenty-eight healthy volunteers (9 females, 19 males) completed two sessions, seven days apart. A single-point load cell system, encapsulated within a 3D-printed structure and connected to a Delsys system(r) at a sampling frequency of 2000 Hz, was used for data acquisition. Participants performed maximal voluntary contractions (MVC) and submaximal isometric contractions (SMVC) guided by trapezoidal visual feedback. Key outcome variables included peak force, mean force, and force steadiness. Calibration demonstrated high accuracy (R\({}^{2}\) = 1) with a low root mean square error (0.55 N). Test-retest analysis showed excellent reliability for peak force (ICC = 0.81, SEM = 0.50, MDC = 1.39), mean force (ICC = 0.93, SEM = 0.17, MDC = 1.08), and steadiness (ICC = 0.87, SEM = 0.85, MDC = 2.36), with no significant intersession differences (\(p\) > 0.05). This study demonstrates the high reliability of using dynamometry to assess trunk extension strength during MVC and SMVC, endorsing the dynamometer as a tool for functional assessment and the development of personalized rehabilitation and training strategies.  
l 2025 15, 4081
# Hybrid Graph Models for Traffic Prediction
## 6 Experimental Results
### Ablation Studies
#### 6.2.3 Impact of Gated Function  
In order to verify the effectiveness of the proposed method to dynamically fuse information from different branches, we conducted experiments on the PeMS-BAY and METR-LA datasets. Figure 9 records the experimental results under different weights. From the experimental results, it can be seen that assigning different weights to different  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**MAE**} & \multicolumn{2}{c}{**15 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**60 min**} \\  & & & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{3}{*}{PeMS} & HGM-V6 & 1.30 & 2.73 & 2.68\% & 1.62 & 3.68 & 3.62\% & 1.93 & 4.49 & 4.60\% \\  & HGM-V7 & 1.30 & 2.74 & 2.67\% & 1.62 & 3.69 & 3.60\% & 1.90 & 4.41 & 4.48\% \\  & HGM & 1.25 & 2.66 & 2.64\% & 1.54 & 3.57 & 3.47\% & 1.85 & 4.31 & 4.29\% \\ \hline \multirow{3}{*}{Data} & \multirow{3}{*}{Method} & \multicolumn{3}{c}{15 min} & \multicolumn{3}{c}{30 min} & \multicolumn{3}{c}{60 min} \\  & & MAE & RMSE & MAPE & MAE & RMSE & MAPE & MAE & RMSE & MAPE \\ \hline \multirow{3}{*}{METR-LA} & HGM-V6 & 2.68 & 5.18 & 6.87\% & 3.06 & 6.21 & 8.42\% & 3.53 & 7.44 & 10.43\% \\  & HGM-V7 & 2.67 & 5.17 & 6.84\% & 3.10 & 6.30 & 8.57\% & 3.57 & 7.49 & 10.54\% \\ \cline{1-1}  & HGM & 2.58 & 5.06 & 6.60\% & 3.04 & 6.10 & 8.19\% & 3.35 & 7.03 & 9.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction results of different variants of DGCN. The HGM-V6 uses temporal attention to model temporal information in DGCN and the HGM-V7 removes the DGCN module.  
branches affects the prediction performance, which verifies the effectiveness of the proposed gated function. It is not easy to obtain the optimal performance of the model by artificially specifying fixed weights. Therefore, in the HGM model, the method calculates the corresponding weights according to the branch results to realize the dynamic fusion of different components in the experiment.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 2 Theoretically Background
### Data Transformation  
Data formats can be transformed to meet the premises of a statistical inference procedure or to improve interoperability [27]. Data normalization is the process of transforming raw data values into another format with properties better suited for modeling and analysis. For a variable X represented by a vector {\(x_{1}\), \(x_{2}\), \(x_{3}\),..., \(x_{n}\)}, we can use Z-score normalization (also known as standardization).  
Z-score normalization is a normalization method that transforms not only the magnitude of the data, but also the dispersion. It will convert a variable range with some mathematical heuristics, allowing all variables to have the same range. This patterning of values is not essential in machine learning algorithms, but it can make patterns in the data more visible [23]. Z-score normalization overcomes the problem of variables with different units, as it transforms the variables; therefore, they are centered on 0 with a standard deviation of 1 [21]. Each attribute can be transformed using Equation (1):  
\[x^{\prime}_{i}=\frac{x_{i}-\overline{X}}{std_{dev}(X)} \tag{1}\]  
where  
* \(x_{i}\) is the Z-score value of \(x\);
* \(\overline{x}\) is the row mean of \(x\);
* \(std\) is the standard deviation given by  
\[std_{dev}(\overline{x})=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(x_{i}-\overline{x} )^{2}} \tag{2}\]
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach  
In this section, the proposed framework is described step by step. A light field stereo image pair (.raw) denotes the input data. Each light field raw image can be decoded into 4D light fields \(L(u,v,x,y)\), with the resolution of sub-aperture images being \((x,y,3)\), where 3 represents the RGB channels.
## 2 Proposed Scheme
### Secret Embedding and Encryption Stage
In the same example, \(p^{\prime}(i,j)\) can be constructed as 11100100, and the first 3 MSBs \(w_{1}^{\prime}w_{2}^{\prime}w_{3}^{\prime}\) as 111; the second part for embedding \(payload(i,j)\) bits of secret data (e.g., obtain three bits of the to-be hidden data (001 in this example), owing to the \(w_{1}w_{2}w_{3}\) equal to 101 in Table 1); and the third part as the last (i.e., \(5-3=2\)) bits of the original pixel (i.e., the last two bits of original pixel \(p(2,2)\) are 00). If \(payload(i,j)\) is less than 0, no data are hidden in this situation, and the 4 th MSB onward to the last bit of \(p(i,j)\) is equal to that of \(p^{\prime}(i,j)\). For clarity, the \(k\) position of the pixel \(p^{\prime}(i,j)\) is represented by \(p^{rk}(i,j)\). In the image encryption stage, the key \(s_{2}\) generates a random number \(r(i,j)\), where the \(k\) bit of \(r(i,j)\) is represented as \(r^{k}(i,j)\). Finally, \(p^{\prime}(i,j)\) can be encrypted via Equation (9) with key \(s_{2}\). For example, if \(r^{k}(2,2)\) is 01011001, the encrypted pixel \(p^{*}(2,2)\) will be \((1011101)_{2}=189\). \[m= \begin{bmatrix}(w_{1}^{*}&w_{2}^{*}&p^{5}(i,j)&w_{3}^{*}&p^{6}(i,j)&p^{7}(i,j)&p^{8 }(i,j)\end{bmatrix}  \tag{6}\]  
\[\text{Let }w_{1}w_{2}w_{3}=mC\text{ mod 2, where }C= \begin{bmatrix}1&0&1\\ 1&1&0\\ 1&1&1\\ 0&1&1\\ 1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}  \tag{7}\]  
\[w_{1}^{\prime}w_{2}^{\prime}w_{3}^{\prime}=w_{1}^{*}w_{2}^{*}w_{3}^{*}\oplus r _{1}r_{2}r_{3} \tag{8}\]  
\[p^{*k}(i,j)=p^{\prime k}(i,j)\oplus r^{k}(i,j),\text{for }k=1\text{ to }8 \tag{9}\]
## 2 System Description
### Inverter Design  
The topology of the single-phase inverter is shown in Figure 2. It connects mainly a DC link, power stage (inverter IGBT), and filters. The DC link consists of two 10 mF capacitors connected in series that links the DC power to the inverter system. Its high capacitance helps stabilize the input voltage for the invertor. The equivalent input capacitor can be determined with the following equation [14]:  
\[C_{ech}=\frac{S}{2\omega_{g}V_{DC}\Delta V_{DC}} \tag{1}\]  
where \(S\) is the apparent power, \(\omega_{g}\) is the grid frequency, \(V_{DC}\) is the nominal input voltage and \(\Delta V_{DC}\) is the input voltage ripple [14]. The inverter is connected to the grid through an RL load, made of a filter inductor of 2 mH and a resistor of 20 m\(\Omega\). The filter inductor reduces the high-frequency harmonic components injected into the grid system. The PWM logic signals G1, G2, G3, and G4 for the inverter switches are rendered by the PWM generator based on both inverter output parameters and the strategy of the control algorithm [14].  
Figure 1: The block diagram of the photovoltaic system.  
The transistors of bridge diagonals are controlled simultaneously. In this way, PWM control is used with bipolar voltage to switch on these transistors. Thus, when the pair IGBT1 and IGBT4 are commanded for opening, the IGBT2 and IGBT3 are locked and vice versa. Consequently, the four power transistors require two additional PWM width-modulated control signals with dead time [14].
## Appendix A  
In this section, we provide mathematical development of IRHSF preprocessing and histogram specification, which are used in the \"Macula Localization module\".  
IRHSF preprocessing is used to reduce the illumination variation of an image (intra-image variation). It considers an image as a product of two components: illumination \(I_{s}\) which can be regarded as the background, and \(I_{r}\), the fundus image without illumination, as shown in (A1).  
\[I(x,y,\lambda)=I_{s}(x,y,\lambda)\times I_{r}(x,y,\lambda)\] (A1)  
where \((x,y)\) are the image coordinates and \(  
To obtain \(P\) considering the anatomical structures of the retina, such as blood vessels and optic disc, a diagonal matrix \(W\) of size, NMxNM with the value 1, whose pixel positions belong to anatomical structures, is considered. Multiplying \(S^{T}W\) in (A2), we obtain \(S^{T}W{I_{L}}=S^{T}WSP\) and solving with respect to the coefficient vector \(P\), we obtain (A4).  
\[P=\Big{(}S^{T}WS\Big{)}^{-1}\Big{(}S^{T}W{I_{L}}\Big{)}\] (A4)  
Once \(P\) is obtained, the background illumination image without anatomical elements \(I_{s}(x,y,\lambda)\) and the retina image without considering background illumination variation \(I_{r}(x,y,\lambda)\) can be obtained in (A5).  
\[I_{s}(x,y,\lambda)=\exp(SP)\] (A5) \[I_{r}(x,y,\lambda)=\exp(I_{L}(x,y,\ \lambda)-SP)\]  
The histogram specification process can be described as shown in (A6).  
\[M(i)=min\big{\{}j\big{|}G_{target}(j)\geq G_{input}(i)\big{\}}\] (A6)  
where \(G_{input}(i)\) represents the Cumulative Distribution Function (CDF) calculated using the input image histogram, \(G_{target}(j)\) is the CDF calculated using the target image histogram, and \(M(i)\) is the mapping function that maps intensity levels in the input image to intensity levels in the target histogram. This is typically performed by matching the CDF values of the input image to the closest CDF values of the target histogram.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 3 Reinforcement Learning Environment Modeling and Design
### State Space Design  
In the optimization design of wellbore trajectory, the state space is the basis for the reinforcement learning model to perceive the environment, which directly determines the effectiveness of strategy learning and the responsiveness of trajectory adjustment. In order to ensure that the model can fully capture the spatial pose changes and geological conditions during the drilling process, the state space designed in this paper not only includes traditional drilling parameters, but also introduces multi-source LWD data to realize dynamic perception and real-time feedback of geological environment changes:  
\[s_{t}=[\theta_{t},\phi_{t},N_{t},E_{t},H_{t},GR_{t},DEN_{t},CNL_{t},RT_{t},DT_{ t},pL,obs] \tag{12}\]  
Figure 5: Three-dimensional geological model generated by the WGAN model.  
where \(\theta_{t}\) is the current deviation angle, indicating the angle between the direction of the vertical section bit and the depth direction, which is used to control the deflection degree of the bit relative to the vertical direction, and the range is \([0,\pi/2]\); \(\phi_{t}\) is the current azimuth, which represents the angle between the direction of the drill bit on the horizontal plane and the North direction, and represents the orientation of the wellbore on the horizontal plane, and the range is \([-\pi,\pi]\); \(N_{t}\) denotes the current northerly position in meters; \(E_{t}\) represents the current position of East in meters; \(H_{t}\) represents the current vertical depth position in meters; \(GR_{t}\) is the current position gamma logging value; \(DEN_{t}\) is the density logging value of the current position; \(CNL_{t}\) is the neutron logging value at the current position; \(RT_{t}\) is the current position resistivity; \(DT_{t}\) is the acoustic time difference logging value at the current position; \(pL\) is the preset wellbore trajectory information; and \(obs\) is the obstacle information.  
After introducing the self-attention mechanism, the input of the state is a state sequence \([s_{t-k+1},s_{t-k+2},\cdots,s_{t-1},s_{t}]\), which can capture the long-term dependence, where \(k\) is the timing length.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Photothermal Therapy (PTT)  
PTT using magnetic nanoparticles involves the use of nanoparticles that possess both photothermal and magnetic properties. These nanoparticles can be designed to absorb light and convert it into heat, as well as respond to an external magnetic field. This combination allows for a targeted and controlled approach for treating diseases, particularly in the field of cancer therapy. PTT is a minimally invasive treatment that utilizes MNPs to effectively address cancer. This technique involves subjecting diseased tissues containing nanoparticles to near-infrared radiation (NIR), resulting in the production of heat and subsequent eradication of malignant cells. This method of localized heating is highly efficient in specifically targeting and treating cancers [146; 147; 148].  
The success of PTT is highly influenced by the absorbance, radiative emission, stability, crystallinity, and size of the nanoparticle [149].  
The synergic action of plasmonic gold nanoparticles in combination with MNPs has proved successful in targeting and treating cancer [150].  
Iron oxide functionalized with alumina has shown therapeutic potential in treating nosocomial and antibiotic resistant bacterial infections [151]. The strategy was based on the magnetic properties of MNPs to form aggregates under an applied magnetic field and, with the help of alumina molecules attached at their surface, to be able to target bacterial species and then damage the formed bacterial population by means of NIR laser irradiation.  
Radiolabeled molecule-coated MNP multifunctional hybrid nanostructures have been also reported as efficient PTT agents [152].  
In light of the ideas presented above, PTT combined with MHT has offered promising results and a few more examples can be found in Table 1.
## 2 Methodology
### Fuzzy-TOPSIS Method  
The typical TOPSIS method aims to select solutions that are closest to the ideal positive solution and farthest from the ideal negative solution simultaneously. The ideal positive solutions maximize the project's benefit criteria while minimizing its cost criteria, whereas the ideal negative solutions do the opposite. The TOPSIS approach fully utilizes the available data and generates a numerical rating for the alternatives [71].  
Step 4: In this step, the participants who completed the initial questionnaire are provided with a follow-up questionnaire to rate the quality of education in their respective universities using a numeric scale of 1 to 9. These numerical ratings can be based on the descriptions provided in the pairwise comparison table of the AHP method. A non-fuzzy decision-making matrix is then compiled based on the questionnaire responses. In certain cases, data normalization techniques and the interpretation of linguistic variables may be necessary [49; 73; 74].  
Step 5: In this step, the normalized weighted fuzzy decision matrix is constructed using the fuzzy weight matrix of problem criteria from Step 2, along with the normalized fuzzy decision matrix obtained in Step 4 [49; 73; 74; 75].  
Step 6: The calculation of the fuzzy positive ideal solution (\(\overset{\sim}{A}^{*}\)) and fuzzy negative ideal solution (\(\overset{\sim}{A}^{-}\)).  
Step 7: The sum of the distances of the ith alternative from the positive ideal solution in the jth criteria, i.e., \(d_{i}^{*}\), and the sum of the distances of the ith alternative from the negative ideal solution in the jth criteria, i.e., \(d_{i}^{-}\), are calculated.  
Step 8. The relative closeness coefficient (\(CC_{i}\)), which represents the closeness to an ideal solution or the distances to the fuzzy positive-ideal solution (\(d_{i}^{*}\)) and the fuzzy negative-ideal solution (\(d_{i}^{-}\)) simultaneously by taking the relative closeness to the fuzzy positive-ideal solution, is calculated based on the following relationship [49; 50; 73; 74; 75].  
\[CC_{i}=d_{i}^{-}/\left(d_{i}^{-}+d_{i}^{*}\right)\ \ \ i=1,\ 2,\ \ldots,\ m, \tag{1}\]  
where \(CC_{i}\) has a value between 0 and 1 according to the above equation. In general, the value of relative closeness will be closer to one if an alternative is closer to the positive ideal solution. Furthermore, one choice with a higher \(CC_{i}\) value is superior to another.  
Figure 2 specifies the problem's purpose, qualitative educational criteria based on [PERSON]'s model, the alternatives or universities involved in the study, and the instruments required to address the problem using the FAT approach.
# A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS
###### Abstract  
The identification of risks associated with collision for vessels is an important element in maritime safety and management. A vessel collision avoidance system is a topic that has been deeply studied, and it is a specialization in navigation technology. The automatic identification system (AIS) has been used to support navigation, route estimation, collision prediction, and abnormal traffic detection. This article examined the main elements of ship collision, developed a mathematical model for the risk assessment, and simulated a collision assessment based on AIS information, thereby providing meaningful recommendations for crew training and a warning system, in conjunction with the AIS on board.  
collision avoidance; collision risk index; collision risk assessment; AIS +
Footnote †: journal:  
_Article_  
A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS