esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stability Analysis for Upper Limit
### Root Loci
Therefore, three types of DPLL systems are identified in terms of system stability while varying \(BT\) as follows (see Table 4):  
1. Type A--The poles drift outside the unit circle in the \(z\)-domain as \(BT\) increases. The system is stable only when \(BT\leq BT_{\text{osc}}\), otherwise it is unstable;
2. Type B--All of the poles are within the unit circle but approach \(\mid\!\!z\!\mid\) = 1 as \(BT\) increases. The instability of the system increases as \(BT\) increases;
3. Type C--All of the poles of the system are located inside the unit circle. The system is unconditionally stable, even for large \(BT\) values. Figure 4 presents the same plots as Figure 3, but the unit delay is included in the NCO transfer function as (8) for the assumption of the computational delay (i.e., \(t_{\text{D}}\) = \(T\) assumed) in this case. Unlike the zero computational delay case, regardless of the used digital integration method, all the root loci magnitude exceed \(\mid\!\!z\!\mid\) = 1 as the \(BT\) increases. For instance, as can be seen in Figure 4b, the II and BL of the first-order DPLL, which are stable for all \(BT\)s with zero computational delays, are now stable only at \(BT\leq\) 0.51 (i.e., \(BT_{\text{osc}}\) = 0.51) because of the computational delay. Similar results can be observed for the second- and third-order DPLLs. Therefore, it is viewed that the computational delay in the NCO degrades the stability of the DPLLs. Consequently, such DPLLs are conditionally stable. Table 5 summarizes the stability conditions of the \(BT\) for different integration methods in the loop filter and the NCO. For the zero computational delays, the II method plays an important role in making the system unconditionally stable (Type C) and the SI method forces the system to be conditionally stable (i.e., Type A). However, as discussed above, if the unit computational delay is included in the stability analysis, all the methods are conditionally stable (i.e., Type A) regardless of the filter order or integration method. Furthermore, all the \(BT_{\text{osc}}\) for \(t_{\text{D}}\) = \(T\) are located within 0.25 and 0.75 which does not exceed 1, so the results are matched with the well-known rule-of-thumb \(BT\) threshold (\(BT\approx\) 1) for the stable operation of the DPLL. \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} **Remarks** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Three types of DPLL systems regarding stability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} A \\ C \\ B \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Marginal stability conditions of the \(BT\) for different integration methods in the loop filter and NCO. Figure 4: _Cont._  
* Values for \(BT\) should be equal to or less than this value for stability. \begin{table}
\begin{tabular}{c} 0.75 \\ 0.55 \\ 0.75 \\
## 3 Establishment of the Slope Model Using UAV Photogrammetry Technology  
### UAV Measurement System  
The field investigation shows that the exposed rock mass discontinuities on the slope surface (e.g., Figure 2) are highly developed, and the rock mass has a high degree of fragmentation. To analyze the stability of the rock slope, information on discontinuities should be recorded in detail and their distribution characteristics should be obtained. Also, to accurately calibrate and obtain the spatial coordinates of the rock mass discontinuities in the study area (Figure 2), a DJI Phantom 4 Pro UAV (Dajiang Innovation Technology Co., Ltd., Shenzhen, China) was selected to collect high-resolution digital images of the studied slope surface (Figure 3). The used UAV was equipped with 360\({}^{\circ}\) Flight Autonomy technology, adding rear visual sensors and infrared sensors on both sides of the fuselage, giving the Phantom 4 Pro five-directional environmental awareness and four-directional obstacle avoidance capabilities. Moreover, the used UAV consists of six visual sensors, one high-definition camera, two sets of infrared sensors, one set of ultrasonic sensors, a GPS/GLONASS dual-mode satellite positioning system, compass dual redundancy sensors, and a powerful computational core. Real-time images and positioning can be utilized to construct a 3D map around the aircraft based on the above sensors. The abovementioned UAV system is widely used in geological exploration and dangerous rock mass investigations, which meets the requirements of aerial image coverage and overlap rate and effectively ensures the accuracy and effectiveness of the established 3D model reconstruction. The details of the used UAV are shown in Table 1.  
Figure 2: Cross-section photo of the freshly exposed local slope surface.
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## 4 Discussion
### GMDSS Education and Training
They imagine one of the most digitalized areas of the ship with very sophisticated (high-bandwidth) data communication possibilities with land and other ships. They are convinced that DISTRESS communications will be something simple, whether they are senders or receivers of those messages. And the reality is quite discouraging for them during the immersive experience within a radio communications simulator and onboard ship. Communication procedures are as important as the handling of equipment. There is a maintenance protocol and a radio log that must be filled out. There is responsibility regarding the misuse of the GMDSS. During the officer cadet period, other problems arise: (i) lack of planning, supervision, and oversight of deck cadets; and (ii) lack of instructions and procedures to guide the officer-in-charge in tests/checks, and maintenance of equipment, GMDSS installation, and reserve power supply. The reality of today is that obtaining the GMDSS general operator certificate is only the first step that requires a commitment to continuous training. According to the current IMO's Model Course 1.25, the training period for obtaining GOC is about a total of 108 h, accounting for theory, practices  
Figure 5: Time series showing the variation in the 18 years of records of the year-over-year of the 4 types of most frequent radio deficiencies 5118, 5114, 5105, and 5110, causing the detention of the ship, represented by relative frequency distribution (percentages). with real equipment, simulators, and exams. Whether this is sufficient is the subject of study in another article [67], where a questionnaire is answered by 90 OOWs. One of the results is precisely that the officers themselves recognize unsatisfactory knowledge about communications in dangerous situations. Memory deficiencies when trying to reproduce the things learned during the courses joined to the exceptional use of these procedures, make 70% of those surveyed estimate the need for mandatory and regular refreshing training. It seems to follow from all this that the minimum requirements for updating GOC certificates should be harmonized to guarantee continued familiarization and ship-specific training of GMDSS operators on board ships. Another challenge is the necessary training of OOWs on the new equipment and procedures that are necessary to guarantee the correct operation of the updated GMDSS equipment.
## 3 Test with simulation
### Influences of image pattern size  
The followed tests are conducted with synthetic image pattern groups of 17 different basic diameters, i.e. from \(d_{\theta}\)=2 to 10 with a step of 0.5 pixels. The position error distributions on the image pattern basic diameter (\(d_{0}\)) for four different tracking freedom limits are given in Fig. 7. Fig. 7 shows that the position errors of maximal tracking freedom (\(L\)=1) are significantly larger than those in other cases for most image pattern sizes, especially for small image patterns. The reason for the overall large bias distribution is that the bias errors are added from frame to frame to finally obtain a large accumulated bias. The position error of the fixed image pattern (\(L\)=600) is small at small image pattern, but it increases with increasing image pattern size because the effects of the image pattern rotation and asymmetric shrink become stronger when the image pattern size increases. The minimal position error has a relatively flat distribution and raises a little at large image patterns. The position error distribution of the minimal tracking bias is close to the minimal position error distribution.  
Figure 8: Displacement error distributions on image pattern size for different tracking freedom limits.  
Figure 7: Position error distributions on image pattern size for different tracking freedom limits.  
The displacement error distributions on the image pattern basic diameter (\(d_{0}\)) for the four different tracking freedom limits are given in Fig. 8. It is shown in Fig. 8 that the displacement error of the fixed image pattern (\(L\)=600) is much larger than those of the other three cases for all tested image pattern sizes, and it has a minimum around \(d_{\theta}\)=3 pixels and increases almost linearly with increasing image pattern size. The displacement errors of the other three cases are large with the smallest image pattern, reduce quickly with increasing image pattern to a large enough size (\(d_{\theta}\)=5), and then converge to a constant level. No obvious differences can be identified between the displacement error distributions of the other three cases in Fig. 8.  
According to the above discussions, an optimal image tracking freedom limit can be determined with the minimal accumulate tracking bias before the first high peak. With the optimal freedom limit determined with the minimal accumulated tracking bias, an image pattern tracking result of position and displacement errors close to the minima can be obtained.  
Figure 10: Time histories of the termite head position in a 33-ms head-banging period.  
Figure 9: Tracking bias distribution on the freedom limit for termite head-banging case at the 225\({}^{\text{th}}\) frame.
## 5 Conclusions  
To avoid the problem that traditional RSS fingerprint is vulnerable to multipath propagation, we proposed a multivariable fingerprinting-base indoor localization algorithm in 5G system. We combine SS-RSRP, SS-RSRQ, SS-RSSI and SS-SINR to construct 5G physical layer multivariable fingerprints. We use the sliding window to transform the origin observation matrix into the observation images and achieve data enhancement. MVF are used to train CNN location classification model for indoor positioning. A hybrid positioning model combining CNN with path-loss model is proposed to optimize the overall positioning. Experimental results show that KNN, MLP and CNN with MVF achieve accuracy improvement in both two experimental scenarios. CNN achieves best performance among three positioning algorithms and shows 36.22% and 46.12% accuracy improvement with MVF in two experimental environments, respectively. The positioning accuracy of CNN is 29.59% and 6.22% higher than that of KNN and MLP, respectively, in room A. The positioning accuracy of CNN is 34.23% and 9.68% higher than that of KNN and MLP, respectively, in room B. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.  
In future work, we will build more appropriate path-loss model in indoor environment. Additionally, we will try to construct more robust fingerprints and find a better method to combine CNN model and path-loss model to further improve positioning accuracy.  
**Author Contributions:** The authors confirm contribution to the paper as follows: Conceptualization, Y.W. and K.Z.; data curation, S.H.; methodology, Y.W.; resources, K.Z. and Z.Z.; software, Y.W. and W.J.; supervision, K.Z. and Z.Z.; validation, Y.W. and D.M.; visualization, W.J.; writing--original draft, Y.W.; writing--review and editing, K.Z., S.H. and D.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 61771197) and Science and Technology Commission of Shanghai Municipality (Grant no. 18 DZ2270800).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **System** & **Signal Source** & **Measure-Ments** & **Positioning Area** & **Positioning Error** \\ \hline Ref. [11] & 5G & Single base station & RSSI & 3 m \(\times\) 4 m & average 1.16 m \\ Ref. [18] & 5G & Single base station & multivariable & 3 m \(\times\) 4 m & CDF = 80\% 1.18 m \\ Proposed method & 5G & Single base station & multivariable & 7 m \(\times\) 6 m & average 1.47 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of the proposed method and other references.
## 3 Results
### Traditional Machine Learning Techniques
Recently, a light gradient boosting machine (LightGBM)-based model was developed to identify AD patients from OCTA images [24]. Some 170 images taken from both eyes of 48 healthy and 37 AD individuals were used for training and testing. The geometric characteristics of the foveal avascular zone such as area and eccentricity along with medical record data including age and gender were used for the model. The test set achieved a sensitivity of 54.4%, a specificity of 83.7%, and an AUC of 72%. While this approach is quite novel, its performance has room for improvement before it can be suitable for clinical application. In another study, OCT images were utilized to identify AD patients using the XGBoost algorithm [20]. Some 299 healthy and 159 AD subjects were recruited for this study. Retinal characteristics that have statistical differences between two groups were used as features. The test set achieved an F1 score of 70% and an AUC of 69%. Furthermore, [PERSON] et al. [25] developed a linear discriminant analysis (LDA) model to identify AD patients by using hyperspectral \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref** & **Year** & **Modality** & **Algorithm** & **Features** & **Type of Study** & **Age Range** \\ \hline
[24] & 2024 & OCTA & LightGBM & Geometric characteristics of FAZ zone, patient data & Human & 65.70 (7.90) \\
[20] & 2022 & OCT & XGBoost & Retinal layer thicknesses, macular volume & Human & 63.03 (9.06) \\
[34] & 2022 & OCT & Support vector machine & Texture features & Mice & — \\
[19] & 2021 & Fundus photography & Support vector machine & Pixel intensities & Human & 65.17 (4.16) \\
[25] & 2020 & Hyperspectral imaging, OCT & Linear discriminant analysis & Reflectance values, RNFL thickness & Human & 55-85 \\
[26] & 2019 & Hyperspectral imaging & Support vector machine & Vascaluture characteristics, texture features & Human & 60–85 \\
[27] & 2019 & OCT & Support vector machine & Texture features & Human & 53–77 \\
[28] & 2017 & OCT & Support vector machine & Texture features & Mice & 4–8 months \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of traditional machine learning-based methods for AD detection. and OCT images. The dataset consisted of 22 subjects with normal cognitive function and 17 subjects with AD. A 74% AUC was achieved in the validation set.
# Smart Low-Cost Control System for Fish Farm Facilities
## 5 Smart Control Algorithm for Aquaculture
### Example 2: Control Algorithm Operation in RAS
20 NTUs, the minimum DO is 7.87 mg/L, and the aeration system operates for 7 min at an average ratio of 100%. Thus, the use of the smart control algorithm supposes the following improvements in the performance of the aquaculture control system:  
* An increase in fish welfare, since the water quality conditions in the tank in the first example are much better than in the second case. The reduced DO and high turbidity cause stress and alterations in the swimming patterns of fish. The turbidity in the first case represents 12.5% of the turbidity in the second case. The DO in the first case dropped by less than 2%. Meanwhile, the DO reduction is supposed to be 8% in the second case. * An improvement in energy use, since the use of aeration, is more efficient in the first case. Even though aeration is activated for more time, 19 min in the first case compared with 7 min in the second case, the power consumption of the aeration system is much higher in the second case. The energy is reduced by 35% when the AI of the control system controls the situation compared with the predefined control algorithms. * Finally, the fast action of workers due to the alarm helped to avoid further problems in the fish farm facilities in the first case. In the second case, no activation of the  
Figure 21: Scenario 2 without connection to the Smart Control System. (**a**) Portrays the water quality conditions in the tanks. (**b**) Shows the operation of the water flow pump and aeration system. The high turbidity and use of upper and lower limits of dissolved oxygen for the activation of the aeration system produce unstable water quality conditions in the tank which affect the fish welfare. alarm is triggered by the AI. Thus, the turbidity reaches different parts of the water conditioning steps and the tanks. This supposes more maintenance tasks and can even cause the necessity of replacing elements of the facilities. Adequate maintenance based on the prediction of failures, thanks to the Smart Control System, reduces the time workers must dedicate to maintenance tasks. In this case, the use of the control system facilitates that the solid filters are fixed in the first minutes by simple action with no need to replace elements. This supposes economic savings for the company and better working conditions for operators.
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 4 DNN Architecture, Implementation, Performance Characterization and Optimization
### Cn^2 Net Architecture  
The Cn^2 Net architecture is illustrated in Figure 5, where we also included the major characteristics of convolutional, max-pooling and fully connected layers.  
The Cn^2 Net model has \(M_{FEB}=16\) feature extraction blocks (FEBs) with identical topology and trainable weights. The FEBs simultaneously receive \(M_{FEB}\) sequential grayscale (8-bit) normalized scintillation image frames (\(N_{c}\times N_{c}=\)128 \(\times\) 128 pixels) from a selected dataset (ATM or SIM). The number of FEBs represents a unique regularization parameter of the Cn^2 Net, which was chosen in a set of DNN topology optimization experiments.  
Each FEB in Figure 5 is composed of three convolutional and max-pooling layers that succeed each other, and a single 1D input layer (perceptron layer) composed of \(n_{FEB}=20\) neurons.  
In each convolutional and max-pooling layer Cn^2 Net utilizes \(N_{filter}=30\) trainable filters of sizes \(3\times 3\) (\(n_{c}=n_{p}=\)3) for the first two layers and \(2\times 2\) (\(n_{c}=n_{p}=2\)) for the last layer. After flattening, \(N_{filter}=30\) output low-resolution (\(6\times 6\)) feature maps are sent to the FEB's perceptron layers. Note that the weights of all the FEB's dense (perceptron) layers are shared across all \(M_{FEB}\) FEBs. To prevent DNN overfitting, each FEB's perceptron layer utilizes dropout regularization [26].  
The output feature vectors of all FEBs are merged into a single feature vector that inputs two fully connected layers comprised of \(n_{fc}=n_{FEB}\times M_{FEB}=360\) neurons with a 10 % dropout rate each, and an output layer that reduces the dimension of the resulting feature vector into a scalar. The total number of Cn^2 Net trainable parameters reaches 45,000.
# Elastic-Plastic Threshold and Rational Unloading Level of Rocks
###### Abstract  
During loading and unloading test, various rocks manifest different stress values of elastic-plastic transformation. This study proposes to include axial pressure increment ratio in the conventional triaxial compression test to evaluate different variables (nominal elastic modulus, nominal Poisson's ratio, strain, and energy). The relationships among various factors including variables, the stress level of initial confining stress and axial pressures, were analyzed by analyzing the stress-strain plot record obtained from testing various rocks. The extreme value point of the deformation parameter, also known as the elastic-plastic threshold, was analyzed. In addition, the elastic-plastic thresholds were later used as unloading points during the unloading tests. Under the same confining condition, different rocks demonstrated different unloading levels. Furthermore, a linear correlation was observed between unloading levels and changing confining pressures, and the gradient is mainly related to the types of rocks. During the unloading tests of rocks, the rational unloading level is recommended to be no higher than the stress level at the elastic-plastic threshold under the corresponding confining pressure.  
Keywords:triaxial compression test; unloading level; deformation parameter-axial pressure increment ratio; strain-axial pressure increment ratio; energy-axial pressure increment ratio; elastic-plastic threshold +
Footnote †: journal: Applied Sciences
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 6 Discussion  
* Drop in DC system efficiency at 4 th hour:  
The reason is that 'A' category loads operate at lower loading that causes connected inverters' efficiency to reduce. The effect can also be seen in the building's efficiency graph. The building's efficiency drops in this period and the effect is shifted to the system's efficiency as well.
* The DC curve remains almost constant from the 5 th to 10 th hour with fair DC efficiency values:  
The 'D' and 'A' category loads operate at an almost constant level in a combined fashion. While DC-DC converters operate at lower efficiency, DC-AC converters operated at higher efficiency and vice versa, keeping overall efficiency constant.
* Drop in the DC efficiency during the 11 th and 12 th hour:  
Again, due to the reduced loading of inverters, 'A' category loads operate at lower efficiency of converters and the effect is pronounced at the DC-DC converter transformer level and in turn the grid level, lowering the overall efficiency of the system
* DC efficiency improved at the 13 th hour and onwards:  
The reason is the same as that for the 4 th to 9 th hour. The loads operate in coordination, and the loads consuming more power with converters operating at higher efficiency balance the loads consuming less power with converters operating at lower efficiency.  
* Average efficiency  
Apparently, DC shows dominance in the comparative graph for the most part of the day. The average efficiencies of both the systems are separated by an amount of almost 2% with the DC system operating at 89.9% and AC system at 87.6% in Figure 4.