esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
## 3 The Cybersecurity Aspects of Microgrid
### Vulnerabilities and Threats in Microgrid
#### 3.2.3 Potential Threat Agents against Microgrid  
Several threat agents against microgrids have been identified:  
* **Hostile threat agents.** Companies or organisations may be correlated to offensive tactics. These companies usually have a high capability of intelligence in technology or human beings.
* **Cyber-criminals.** This category is a hostile threat by nature, and targets financial gain at a high level of skill. This criminal act can be coordinated at a national, local, or international level.
* **Threat agents from the inside, including employees and third party.** The employees of a microgrid include the operational staff as well as contractors. Other, third parties, also help at the power facilities. All of these agents can access the private system of the microgrid and expose it to attacks on sensitive assets.
* **Hacktivists.** This type of threat is created by individuals who protest against political or social agendas, and promote their cause by hacking intelligence agencies, corporations, websites, and military institutions.
* **Capabilities of offensive cyber in nation-states.** This attack is considered a cyber-weapon. Nation states have high skill and expertise in malware, and use them to attack adversaries.
* **Terrorists.** Their activities have been expanded to include cyberattacks targeting critical infrastructure, including public health agencies, energy production facilities, and telecommunication infrastructures. This type of threat may have a severe impact on the government and society.
* **Cyber-fighters.** This is an emerging threat agent. It is composed of a group of patiotically motivated citizens who have the potential to initiate cyberattacks. There may be a conflict between their activities and those of other groups (e.g., hacktivists).
* **Insider Threat.** A cyberattack occurs when intruders use false system information to deceive the operators. Such operations cause the power system to become unstable. This situation obtains because insiders have knowledge of the power grid, especially its vulnerabilities. The detection and prevention of attacks initiated by insiders is challenging.
## 5 Conclusions  
The main conclusions are as follows:  
1. CFD methods, based on physical models, accurately capture the dynamic characteristics of the ship airway along the glide path. High spatial resolution (3 m) ship airway from both steady RANS and time-averaged DES simulations consistently show complex ship airway distributions along the ideal glide path, particularly within 200 m behind the stern of the aircraft carrier. Significant crosswind shear and vertical upwash/downwash flows in the glide path and rear deck region pose substantial risks to the take-off and landing control of carrier-based aircraft/UAVs.
2. BP neural networks, driven by data, efficiently extract features from large-scale datasets and enable accurate and rapid predictions of the ship airway. By leveraging CFD sample data to train BP neural networks to learn ship airway characteristics, the BP prediction results for headwind, crosswind, and vertical wind along the glide path within 200 m behind the stern achieved correlation coefficients of 0.95, 0.91, and 0.82,respectively, with the testing samples. With high temporal resolution (3 Hz) wind speed and direction input from DWL, this model achieves precise predictions of the high spatiotemporal resolution (3 m, 3 Hz) 3D ship airwake in the glide path.  
Conceptualization, methodology, writing--review and editing, Q.L.; software, writing--original draft preparation, G.R.; data curation, visualization, Z.L.; supervision, project administration, D.Z. and B.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (grant numbers 42406184 and 12404374) and the Fundamental Research Funds for the Central Universities of China (grant number 24 CAFUC04034).  
Not applicable.  
The data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request.  
This work also received project support and technical guidance from [PERSON], [PERSON].  
The authors declare no conflicts of interest.
## 3 Results  
### Data Characteristics and Preprocessing  
Three data samples from two sources described in Section 2 were used in the survey. The data in every sample are divided into two parts: a training set and a validation set used to compare the estimated/trained model's performance. In regression models with regularisation and in ML models, data from the training set were further subdivided into a training set and a test set of varying contents. Two of the samples--the old design data (a 191-object training set and 10-object validation set) and the existing building data (48 training and 5 validation observations)--are the datasets used previously in [1]. The third one--containing new design data--consists of 245 training records followed by 25 validation records. Some characteristics of the data samples, including their respective ranges, are presented in Table 1, and the data distributions are depicted in Figure 1. The main difference between the old and the new design data is that in the former the validation set was added incrementally to an existing data, mimicking a real-life framework with new data being observed after modelling, while in the latter, the validation set was drawn from a larger group of observations, thus it better represents the respective distributions.  
In the distribution diagram (Figure 1), it can be seen that there is a considerable amount of variation in the data and that the validation data (dark points) generally correspond with the training data in terms of distribution. The validation set of old projects is the least fit to the distribution of the \(A_{U}\) variable, with buildings with small-to-medium usable area being underrepresented. This can cause difficulties in the proper final evaluation of the models.  
In order to preserve the best features of the algorithms used, data preprocessing was performed. All regularised regression models and all neural networks were trained using standardised features; i.e., all the variables (except for binary B and three-valued GP) were scaled according to the formula:  
\[u_{i}=\frac{x_{i}-\overline{x}_{i}}{S(x)} \tag{10}\]  
where \(x_{i}\) is the observed value of a variable \(X\), \(\overline{x}_{i}\) is the mean, and \(S(x)\)--the standard deviation of \(X\).
## 4 Experimental Procedure and Data Analysis  
In order to perform a characterization experiment of the copper rod based on monochromatic neutron imaging, we used the BOA beamline at the Paul Scherrer Institute, which receives a cold neutron spectrum from the moderator [15] and is equipped with a double crystal monochromator in order to perform monochromatic radiographies and tomographies. [15] The L/D collimation power was set at the minimum value of 140 to privilege the neutron flux, while the resolution of the double crystal monochromator device was \(\Delta\lambda/\lambda=1.4\%\).  
The detector used was a 16-bit IKON-M camera from Andor coupled with a 50 mm objective looking onto a 100-um thick \({}^{6}\)LiF-ZnS scintillator. The 1024 \(\times\) 1024 pixels chip was focused on a field of view 37 \(\times\) 37 mm and 80 um effective resolution, each pixel corresponding to 36 \(\upmu\)m on the scintillator.  
The wavelength scan in radiography mode was performed with the DCM in a wavelength range between 2.450 A and 4.300 A with a wavelength step of 0.025 A, resulting in a total of 75 monochromatic radiographies. Each radiography was acquired with an exposure time of 60 seconds and was repeated twice. Direct beam measurements for the transmission normalization were repeated three times with identical exposure time. The copper rod was measured in two specific relative orientations: the second configuration was rotated 90\({}^{\circ}\) around the rod axis with respect to the first one.  
Data reduction and normalization was performed by using ImageJ code. [16; 17]  
Since preliminary observation of the normalized wavelength scan showed the presence of dark areas with the aspect of branched structures within specific areas of the rod (similar to what can be expected by dendritic growth structures), we decided to perform two monochromatic tomographies at the wavelength values in which the contrast of such structures was maximum with respect to the rest of the rod body: 3.6 A and 3.75 A. The tomography measurements were performed by taking 181 projections with 1\({}^{\circ}\) angular step size and 60 s exposure time per projection for each of the selected wavelengths. The tomography reconstruction routine included a dedicated pre-processing algorithm for the correction of lines in the sinograms, to mitigate the presence of ring artefacts in the reconstructions, which deteriorates the segmentation of the investigated structures [18].  
The software used for the reconstruction was the ASTRA toolbox [19], which includes the SIRT iterative reconstruction technique, particularly efficient for limited projections and noisy data such as the ones obtained with wavelength selective techniques. We took into account that Bragg dips only appear at certain projection angles and their number is enough for the reconstruction technique to catch the 3D geometry. This is possible since the rest of the sample was composed by a single crystal.
## 5 Discussion
### Generalization  
The models trained on the DS3 dataset, when tested on DS1, achieved a less favorable average accuracy with the n-gram analysis (71.54%) compared to the models trained and tested on DS1 (75.27%). Conversely, by testing the models based on DS3 on the dataset DS1 with the analysis using a combined approach, better average results (78.01%) were obtained compared to training and testing the models on the DS1 data using the same processing technique (77.04%) (Tables 12 and 13). The same trend applies to the LIWC analysis, which also achieved better results. For model DS2, the analysis with LIWC or a combination of n-grams and LIWC generalizes better compared to an analysis exclusively with n-grams since it achieves better performance during training models on several different datasets from different domains.  
On the other hand, by testing the models obtained based on the DS3 dataset on the data DS2, an average drop in performance was recorded using all models and all data analyzing methods compared to both training and testing the models on the dataset DS2, but by combining the analysis with n-grams and LIWC, all models achieved higher maximum accuracies. The LIWC analysis on all models gives an average accuracy of 71.08%, which is 2.05% less than in the case of training the model on DS2 data. The analysis with n-grams recorded an average drop in the accuracy of predictions of 1.92%, and the smallest average drop was achieved with the analysis with a combined approach (0.43%) (Tables 12 and 13), which, on the other hand, for certain n-grams, achieved higher maxima in relation to the models trained on the DS2 dataset (Table 7). Although the average accuracy when training all models on the combined DS3 dataset slightly drops, it again follows that combining the analysis with n-grams and LIWC has somewhat greater generalization power compared to exclusively using the n-gram analysis. In addition, the analysis with the combined approach, unlike the analysis with n-grams, achieved better average results on almost all sets of n-grams during the testing of the models based on the DS3 dataset on the DS1 and DS2 data (Table 15). The biggest difference in the performance of the models obtained with the n-gram analysis and the combined approach of analysis is visible during the testing of models based on the DS3 on the DS2 data by analyzing trigrams where the combined approach achieves a 16.24% higher accuracy.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On/Off-Policy
#### 3.3.4 Path Planning  
In [87], the authors developed a technique for robot path planning in uncertain environments, including fixed and dynamic obstacles. The authors proposed an enhanced Dyna-Q algorithm by combining _\"heuristic search techniques, simulated annealing, and reactive navigation principles\"_[87] to improve robot path planning efficiency and resilience. This work helps the robot to learn and plan a near-optimal collision-free path rapidly in complex and uncertain scenarios.  
The learning process for the robot is based on an enhanced Dyna-Q algorithm which involves interactions with its environment, receiving rewards, and penalties for accidents. The simulation results show that the enhanced Dyna-Q algorithm performed better than traditional Q-learning and previous versions of the Dyna-Q algorithm in global search, convergence, and rapid learning in environments. In this work, an ROS facilitated communication between algorithms and robot hardware. It also facilitied sharing sensor data and controlling robot actions, as shown in Figure 18.  
The above-mentioned work demonstrate the effectiveness of RL algorithms in developing intelligent robotic systems. The enhanced Dyna-Q learning algorithm in dynamic contexts shows the ability to have the robot learn from experience, making it a strong contender for real-world applications. The success in diverse scenarios demonstrates its potential to revolutionize robotic applications, improving adaptability and decision making in complex environments. Exploring more recent RL algorithms such as PPO in place of Dyna-Q learning algorithms may lead to improved performance in robotic applications.  
Figure 18: ROS publisher–subscriber model. For more information, please refer to Table 4.
## 5 Illustrative Example Results and Discussion
### Numerical Example
As \(CI(\tilde{P}^{(0)})>\delta_{0}\), then we apply Step 5\({}^{\prime}\) in Algorithm 2 to adjust the consistency of HMPR \(\tilde{P}^{(0)}\), and one can obtain a new HMPR \(\tilde{P}^{(1)}\) as follows: \(\tilde{P}^{(1)}=\left( \begin{array}{cccc}\{1\}&\{0.1478,0.1672,0.1935\}&\{0. 9696,2.3351\}&\{0.1561,0.3023\}\\ \{5.1658,5.9809,6.7659\}&\{1\}&\{1.1823,2.4077\}&\{5.5718,6.8125\}\\ \{0.4282,1.0314\}&\{0.4153,0.7234\}&\{1\}&\{0.2802\}\\ \{3.3080,6.4061\}&\{0.1468,0.1795\}&\{3.5689\}&\{1\}\end{array} \right)\) Step 5\({}^{\prime}\). Using model (M-4), we get the optimal deviation values \(\widehat{d}^{+(1)}_{13}=0.3288,\widehat{d}^{-(1)}_{12}=0.1329,\widehat{d}^{+ (1)}_{14}=0.6202,\widehat{d}^{-(1)}_{23}=0.8904,\widehat{d}^{-(1)}_{24}=0.3359, \widehat{d}^{-(1)}_{23}=\widehat{d}^{+(1)}_{24}=\widehat{d}^{+(1)}_{12}=\widehat{ d}^{-(1)}_{13}=\widehat{d}^{-(1)}_{34}=0\). By using Equation (3), we have \(CI(\tilde{P}^{(1)})=0.5016>\delta_{0}\). Thus, by Step 5\({}^{\prime}\) in Algorithm 2, one can obtain \(\tilde{P}^{(2)}=\left( \begin{array}{cccc}\{1\}&\{0.1503,0.1702,0.1966\}&\{0. 9349,2.1205\}&\{0.1701,0.3141\}\\ \{5.0865,5.8754,6.6534\}&\{1\}&\{1.6102,2.6441\}&\{5.1098,6.1204\}\\ \{0.4716,1.0696\}&\{0.3768,0.6210\}&\{1\}&\{0.3112\}\\ \{3.1837,5.8789\}&\{0.1634,0.1957\}&\{3.2134\}&\{1\}\end{array} \right)\) Step 6\({}^{\prime}\). Using model (M-4), we determine the priority weight vector \(\tilde{w}^{(2)}=\left(0.1107,0.3910,0.2056,0.3027\right)^{\mathrm{T}}\). Step 7\({}^{\prime}\). By using Equation (3), we have \(CI(\tilde{P}^{(2)})=0.2855\). As \(CI(\tilde{P}^{(2)})<\delta_{0}\), then the iteration stops, and \(\tilde{P}^{(2)}\) is acceptable consistent HMPR. Step 8\({}^{\prime}\). Output \(\tilde{w}^{(2)}=\left(0.1107,0.3910,0.2056,0.3027\right)^{\mathrm{T}}\). Step 9\({}^{\prime}\). As \(\tilde{w}^{(2)}_{2}>\tilde{w}^{(2)}_{4}>\tilde{w}^{(2)}_{1}>\tilde{w}^{(2)}_{3}\), then we have \(x_{2}\succ x_{4}\succ x_{1}\succ x_{3}\), and the most important factor for \(\mathrm{fog
## 3 Results
### Sensitivity and Specificity  
Several studies have defined a A\(\beta\)42:P-tau ratio cutoff value to differentiate between patients developing AD and patients with stable MCI or with other dementias. For example, [PERSON] et al. (2016) used a cutoff value of A\(\beta\)42:P-tau < 6.16 and predicted the future development of AD in MCI patients within 9.2 years (range 4-12 years) with a sensitivity of 88% and a specificity of 90%. The same group used in an earlier study ([PERSON] et al., 2016) a cutoff value of A\(\beta\)42:P-tau < 6.5 for predicting the future development of AD in the same patients within 5.2 years (range 4-6.8 years). These cutoff values may be used as an example for calculating the specificity and sensitivity of our method. The implanted fluorescent tubes were divided according to their true Cy7.5:Cy7 concentration ratio, to two groups: over/under the ratio cutoff value. These two groups were created once with the lower cutoff value (<6.16), which is a cutoff value for early AD diagnosis (9.2 years) and with the second cutoff value (<6.5), which is the cutoff value for a less early AD diagnosis (5.2 years). An estimated concentration ratio was received for each fluorescence ratio measurement of each tube and compared to the cutoff value. A total of 41 estimated concentration ratios were compared to the cutoff value and used for dividing 41 data points to two groups--over/under the cutoff value. These 41 values were compared to their corresponding true values of the concentration ratio. The data points were divided to 4 groups: TN (true negative), FN (false negative), TP (true positive) and FP (false positive). The results according to each cutoff value are presented in Table 2. For both cutoff values the sensitivity was 100%, meaning that all simulated AD 'patients' were diagnosed successfully. The specificity for the higher cutoff value was 82% and for the lower cutoff value was 88%. This result is due to an underestimation in one of the abdomen tubes, which resulted in some FP cases. Obviously, there were more FP cases for the higher cutoff value than for the lower value, resulting in a better specificity in the lower cutoff value.  
Figure 2: (**a**) The mean error of estimated concentration ratio (circle symbols) with their 90% confidence interval, for the abdomen tubes (purple) and subcutaneous (blue); (**b**) the mean of absolute errors of estimated concentration ratios (circle symbols) with their 90% confidence interval, for the abdomen tubes (purple) and subcutaneous (blue).
## 3 Results and Discussion
### Classification of Population according to BMI  
According to the diagnostic from BMI, the participants were classified in two groups: normal weight (N) and with overweight and obesity (OO). Within the population who participated, 58% presented normal weight while 42% was classified with OO. The highest prevalence of overweight and obesity was observed in the School of Commerce and Business Administration ICEA (52%), while the lowest prevalence was observed for the population of the School of Health Sciences ICSA (29%). The participants of ICEA belong to the highest socioeconomic stratification (data not shown) allowing them to have a higher access to food, besides studying careers like Culinary Arts and Tourism. Since the population of ICSA is related with undergraduate programs of the health field (medicine, nursing, nutrition), it might be more aware of health care by eating healthier food. The observed results regarding the BMI are close to the data reported by the National Survey of Health and Nutrition [2] which showed a national prevalence of 72.5% of overweight and obesity for Mexican adults up to 20 years old.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Formulation**} & **Content of Total Phenolics** & **DPPH Method** & **FRAP Method** \\ \cline{2-4}  & **[mg GAE/100 mL]** & **[mg TE/100 mL]** & **[mg EFe2+/100 mL]** \\ \hline BR & 844.74 \({}^{\circ}\)\(\pm\) 3.29 & 464.64 \({}^{\mathrm{b}}\)\(\pm\) 3.96 & 426.12 \({}^{\mathrm{c}}\)\(\pm\) 1.04 \\ BB & 810.50 \({}^{\mathrm{a}}\)\(\pm\) 3.29 & 375.38 \({}^{\mathrm{a}}\)\(\pm\) 3.67 & 414.08 \({}^{\mathrm{b}}\)\(\pm\) 0.52 \\ BM & 831.43 \({}^{\mathrm{b}}\)\(\pm\) 5.70 & 547.49 \({}^{\mathrm{c}}\)\(\pm\) 3.98 & 434.86 \({}^{\mathrm{d}}\)\(\pm\) 2.57 \\ BC & 943.38 \({}^{\mathrm{d}}\)\(\pm\) 4.74 & 776.94 \({}^{\mathrm{d}}\)\(\pm\) 3.35 & 379.59 \({}^{\mathrm{a}}\)\(\pm\) 1.50 \\ \hline \hline \end{tabular} BR, BC, BB, BM indicate the developed beverages. Mean \(\pm\) Standard deviation; n = 3; different lowercase letters (\({}^{\mathrm{a}}\), \({}^{\mathrm{b}}\), \({}^{\mathrm{c}}\), \({}^{\mathrm{d}}\)) within columns indicate significant difference (\(p<\) 0.05).  
\end{table}
Table 3: Total phenolics and antioxidant capacity of the developed beverages.
# Adaptive Feature Fusion for Small Object Detection
## 2 Materials and Methods
### Methods
#### 2.2.2 MMF-PANet Feature Fusion Module
We let the initial feature \(f\in R^{C\times H\times W}\) first pass through the channel attention mechanism CAM, increasing the weight on the channel and reducing the features in other dimensions, so that the information on the channel can be focused during feature extraction. Then, the Hadamard product of the weights output by the CAM and the initial features is performed to obtain the intermediate features \(f_{c}\in R^{C\times H\times W}\), where the information is focused on the channel. Then, the intermediate features are input into the spatial attention mechanism SAM, and the intermediate features go through the Sigmoid function in space, and the weight coefficients about the spatial position are obtained based on the channel information, and the coefficients are between (0, 1). It is multiplied with the initial feature tensor elements one-to-one, and the feature information \(f_{s}\in R^{C\times H\times W}\) of the corresponding spatial position on the channel is obtained. Finally, \(f_{c}\) and \(f_{s}\) are added in the channel dimension to obtain the complementary information of the feature in the channel and space. The effective information is prepared in advance for the next convolution, and the final feature \(f_{cs}\in R^{C\times H\times W}\) is output. Unlike the original CBAM attention mechanism, we multiplied the initial features with the corresponding weights through the SAM attention mechanism. This was to make the network targeted when performing feature extraction, reducing the overhead of other dimensions, and speeding up the efficiency of the network operation. The structure of the improved attention mechanism is shown in Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attention Mechanism** & **mAP/\%** \\ \hline CA\_v1 & 32.63 \\ CBAM\_v1 & 32.95 \\ CA\_v2 & 33.10 \\ CBAM\_v2 & 33.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effects of different attention mechanisms on the network model performance. Figure 3 shows the overall architecture of the M-CBAM attention mechanism. The calculation process of the M-CBMA attention mechanism can be represented by Formula (9):  
\[f_{cs}=Add(f*CAM(f),f*SAM(f*CAM(f))) \tag{9}\]  
\(*\) represents the Hadamard product, that is, the corresponding multiplication of two matrix elements, and Add represents the addition of the feature information on the channel of the feature map. In order to verify the effectiveness of the improved attention mechanism, we added the basic network to the original attention mechanism CBAM and the improved attention mechanism M-CBAM to conduct the following comparative experiments. The experimental results are shown in Table 3.  
As can be seen from the table, compared to the mAP of the original CBAM, our improved M-CBAM increased by nearly 0.2%, which was 0.43% higher than the basic network, which showed that our improved attention mechanism is more helpful to the network.