esa-sceva/satcom-chunk-collection huggingface dataset:
# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Electro-Optical
The actual video stream was used in real-time, and a differential method was employed for analyzing and investigating UAV detection and tracking. The differential method finds the differences in sequenced frames in a video stream. In the case of hovering UAVs or some axially moving and revealing objects near the frames, the contrast was selected only for the displaced part of the object to process the video streams using the DIS algorithm. The electro-optical detection method needs to consider the following factors: size and movement in 3D, speed of detected airborne objects, the maximal distance of detected objects from the camera position, optical lens descriptions, and linear object image resolution. Since these factors directly relate to image processing methods, detecting distance and outputs of detections get affected negatively if one of the aforementioned factors contains faulty or inaccurate information. Detecting moving objects at a maximal distance from a camera in real-time is the main objective of the method. A single dynamic vision sensing (DVS) camera, a base station, UAV, and a blinking marker are used in [45] to detect and locate mobile UAVs. During video streaming, the differences among the captured frames are computed and filtered to detect UAVs in the background image using a temporal-filtering algorithm. The triangulation algorithm was also used to help capture UAVs or drones by extracting spatial localization parameters and providing details about the physical size of the detected object. Similarly, in [43], [PERSON] et al. developed an algorithm to detect drones from a video stream. The system overview for the process is illustrated in Figure 6. The input frames are passed into a moving object detector algorithm. The authors relied on background subtraction followed by threshold filtering and morphological operations for detecting moving objects. The background subtraction method describes a model background image that is subtracted from all frames to extract the foreground. This method heavily depends on the background remaining static throughout the operation. A CNN-based classifier is used on the detections to distinguish drones from other objects such as birds. The limited detection range can pose a challenge while employing the electro-optic technique. The detection performance can be enhanced by incorporating other supporting algorithms. The summary of reviewed studies of this method is depicted in Table 5.  
The performance of vision-based solutions becomes poor with no LOS (angle of camera), bad quality of lenses, in foggy, dark, and dusty environments (weather conditions), and background temperature [9; 10; 21; 63]. The aforementioned limitations could be addressed to some extent by using an IR camera, i.e., detection based on drone component heat, but that increases the system cost significantly and limits the detection range and environment due to the sensibility of the sensors that measure the thermal difference between the drone and the background [14; 63].
## 5 Conclusions  
This study proposes a dynamic reliability assessment method for a pantograph system based on a multistate T-S fault tree, dynamic Bayesian and fuzzy theory, involving its time dependence and causality uncertainty. The system reliability of pantographs during operation is analyzed, and the following conclusions are obtained.  
1. The system reliability of pantographs without and with maintenance during 720 days of operation is analyzed, and the system reliability decreases continuously during this period. At day 720, the system reliability decreases to 0.881 for the no-maintenance case and 0.952 for the with-maintenance case. Maintenance during operation can effectively improve the system reliability of pantographs and reduce the system affected by equipment failure escalation; the sensitivity of the no-maintenance pantograph system to equipment failure rate is greater than that of the with-maintenance pantograph system;
2. According to the equipment sensitivity analysis, the order of concern for equipment in the pantograph system should be: Drop bow indicator > Electrical control box > Lifting bow motor > Valve box > Solenoid Valve > Lifting bow cylinder > Under-frame insulator > Lifting bow spring assembly > Sheep's horn > Spring Box > Upper and lower frames > Bracket > Bearing > Damper. The weak equipment is the drop bow indicator, the electrical control box, and the lifting bow motor;
3. The impact of fault escalation on the pantograph system reliability is analyzed and verified that the use of a preventive maintenance strategy can further improve the pantograph system reliability and gradually reach the steady-state reliability of about 0.9968.  
The above analysis results verify that the system reliability curve obtained based on the DBN model is mainly affected by the failure rate and maintenance rate; therefore, the system reliability can be improved from these two aspects. The weak links in the system can be designed with redundancy and derating in the early design stage of the system to  
Figure 12: Pantograph reliability with preventive maintenance.  
reduce the failure rate; the overhauling cycle of these components can also be shortened, or real-time status monitoring can be carried out to improve the maintenance rate, thus realizing the goal of improving the reliability of a pantograph system.  
Conceptualization, Y.C. and J.W.; methodology, Y.C.; software, Y.C.; validation, Y.C., J.W. and Y.T.; formal analysis, Y.C.; investigation, Y.C.; resources, J.W.; data curation, J.W.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., J.W., Y.T., S.Z., Q.Z. and X.C.; visualization, Y.C.; supervision, J.W., S.Z., Q.Z. and X.C.; project administration, J.W. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
Supported by National Natural Science Foundation of China (Grant No. 51975347).  
Not applicable.  
The data presented in this study are available on re-quest from the corresponding author. The data are not publicly available due to data is internal information of the cooperation organization.  
The authors declare no conflict of interest.
# The Application of Intelligent Data Models for Dementia Classification
## 2 Methods  
The primary variables used to derive the intelligent models in the methodology are based on the CDR method. CDR is used to rate the severity of AD in a subject, by assessing the patient's signs and symptoms in six cognitive areas. Typically, the CDR will be completed by a clinician or other specialist and follows an initial interview with the subject. During the process, the subject is scored between 0-3 across the different cognitive domains. Scores of 1, 2, and 3 indicate mild, moderate, and severe dementia, respectively, whilst 0.5 may indicate very mild dementia ([PERSON] et al., 2015) [14]. Subsequently, the scores can be combined as the sum of each of the domains is considered with equal weightings.  
This method is termed the CDR-Sum of Boxes (or CDR-SB) and assumes equal weighting among the cognitive areas ([PERSON], 2015) [15].  
In our study, we applied the data process described in Figure 1. The ADNI dataset was chosen as it contains both CDR scores and other neuropsychological features; additionally, its data collection was performed under a standard set of procedures to eliminate inconsistencies. Initially, a descriptive analysis was conducted to obtain more detailed statistics and information. Then, data pre-processing was performed to retain instances and variables that align within the research scope. We excluded any instance without a class (DX) and ignored any instance with missing class. We only kept the first visit data for the cases and controls.  
To elaborate on Figure 1, we filtered the initial ADNI dataset to retain only relevant features related to CDR and demographic variables based on the scope we have defined and our research questions. In this case, all features have been ignored other than the CDR-SB scores, age, education level, gender, ethnicity, race, exam date, and the exam site; thus, we have a dataset with 10 features (including the class, 'dx'), which is further described later. Once the dataset was prepared, we produced models using different classification algorithms. We measured the performance of our models by comparing metrics, specificity, sensitivity, precision, F1-score, and ROC area, to determine the highest performing models. Section 5 gives further details.
## 4 Results and Discussions
### Results of Ensemble Model  
It is expected that ensemble classifiers will improve the classification performance. This study analyzed the most prevalent ensemble strategies, including voting, boosting, and the proposed DESGM. A variety of ensemble classifiers were evaluated, encompassing homogeneous classifiers such as boosting and heterogeneous classifiers such as voting. Not all ensemble classifiers exhibit an enhancement in their ability to predict mortality. In Figure 3, the voting classifiers showed improved results in the overall physical activity,  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **IFEA Algorithm** \\ \hline
**Feature Subset** & **No of Features** \\ \hline Socio-Life Style & 9 \\ General & 10 \\ Pain & 13 \\ Physical Health Status & 7 \\ Physical Activity & 10 \\ Food Behaviour & 4 \\ Depression Screener & 9 \\ Overall & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Features and subset functionality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **No. of Features** & **DESGM Accuracy** \\ \hline IFEA & & **0.9251** \\ GA & 110 & 0.8547 \\ MRMR & & 0.8214 \\ \hline IFEA & & **0.9136** \\ GA & 95 & 0.8765 \\ MRMR & & 0.8355 \\ \hline IFEA & & **0.9495** \\ GA & 80 & 0.9125 \\ MRMR & & 0.8674 \\ \hline IFEA & & **0.9554** \\ GA & 70 & 0.9210 \\ MRMR & & 0.8956 \\ \hline IFEA & & **0.9687** \\ GA & 62 & 0.9286 \\ MRMR & & 0.9015 \\ \hline IFEA & & **0.9512** \\ GA & 55 & 0.9124 \\ MRMR & & 0.8967 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature selection based on a different algorithm.  
general, and depression screener datasets. The boosting classifier achieved its highest performance when applied to the general and comprehensive datasets, resulting in an F1 score of 0.9612. The overall performance gained through boosting did not surpass that of a single classifier. This can be explained by the fact that boosting employs a random selection of feature subsets at each branch, which might need more capability to impact the predictive abilities of the models significantly.
###### Abstract  
The present work investigates the uptake of selected trace elements (Cu, Sb, As, Pb, Cd, Zn, Cr, Mn, Ni, V, and Co) from soil and their accumulation in the biomass samples (leaves and flowers) of three selected native plants (namely _Oenothene picensis_, OP; _Sphaeralca velutina_, SV; and _Arggenome subfiliformis_, AS) around an industrial area (Puchuncavi-Ventanas) located in the Puchuncavi valley, in the central region of Chile. Primary emission sources in the area come from a copper refinery, coal-fired power plants, and a set of 14 other different industrial facilities. Trace element measurements in the native plants of this area and the ability to transfer of these pollutants from soil to plants (transfer factor) have been assessed in order to identify the potential use of these plant species for phytoremediation. Preliminary results showed a high concentration of trace elements in the OP, SV, and AS samples. The concentration of these elements in the plants was found to be inversely correlated to the distance of the primary emission sources. Moreover, the high concentrations of trace elements such as Cu, As, Cr and V, upon the toxic limits in the native plant species, suggest the need for continuous monitoring of the region. The OP species was identified as the plant with the highest capacity for trace elements accumulation, which also showed higher accumulation potential in whole aerial parts than in leaves. Transfer factor values suggested that these native plants had phytoremediation potential for the elements Cu, Pb, As, Ni, and Cr. This study provides preliminary baseline information on the trace element compositions of important native plants and soil in the Puchuncavi-Ventanas area for phytoremediation purposes.  
plant species; industrial pollution; trace elements; transfer factor; phytoremediation 2020  
10 January 2021
## 3 Basic Definitions and System Framework
### System Framework  
Our reference architecture is based on our funding project SEANET Technology Standardization Research System Development [10], but we believe that our scheme can be adapted to any SNR resolution-based and named network objects on self-certifying ICN architecture such as DONA or NetInf. The system framework design is as follows. Three main parts corresponding to the system are _Users_, _SNR nodes_, and _Audit Institutions_, as shown in Figure 1.  
**Users** refers to the content publishers and consumers in ICN. Publishers advertise an information item they possess by publishing information about the item's identifier to an SNR node. Consumers request access to an information item by sending a subscription message to the SNR node, which manages a one-to-one or one-to-many mapping relationship on the item's identifier and its network address (NA). A user can be a publisher or a consumer at the same time. Generally, every legitimate user in the system has the following properties:  
* Independently generate public and private key, user ID.
* Initiate content query request, initiate name registration request, content registration request.
* Initiate public key or content revocation events, reply the corresponding voting request.  
**SNR nodes** mean the resolution nodes of Standalone Name Resolution (SNR). The SNR domain means each network user in the domain will have the same logical resolution handler (SNR node). In the SNR approach, the name resolution process is decoupled with message routing, and it usually uses flat names to look up content's locators (e.g., IP); then, the content is routed by the locators. The resolution is essentially a lookup service that maps information requests to information advertisements. In addition to normal resolution services, SNR nodes also have the following functions:  
* Verify public key and user ID, complete the registration and query request.
* Store PKBL (Public Key Revocation Blacklist), update PKBL, and synchronize malicious PKBL to other SNR nodes.
* Handle error content or key pairs revocation reports in the resolution domain, store and update the SWL (Security Weight List) period.  
To the existing SNR nodes of ICN architecture, the resolution node only needs to add the storage and update two lists, PKBL and SWL, without changing the system architecture design, which is easy to deploy and implement.  
**Audit Institutions** are trusted third parties to verify and store the corresponding identity of the users. It is an institution that is responsible for reviewing identity information, issuing the user's initial security weight, and issuing public key certificates. It is a trusted authority confirmation organization by default, such as a distributed blockchain structure or a centralized third-party organization. SNR nodes and user nodes can query the public key registration real identity information of the users from the Audit Institutions when needed.
## 6 Results and Discussion
The questions regarding whether there are any damage instances in the image being analyzed, how many buildings were affected, and how many damages can be identified were the main questions used in this study. Overall, the developed VQA algorithm combination of the CNN and BoW model obtained over 90% accuracy validation (Table 6). Specifically, the overall accuracy of this method considering any type of damage on Argo Hall was 92%, and considering damage occurrences on Martin Hall, it resulted in an overall accuracy of 93%. These results suggest that the developed approach is able to successfully understand and answer questions regarding damage detection. Please note that the high accuracy of this feasibility study is an indication of overfitting, because all images are taken from the UWF campus where the buildings look alike. The lack of diversity in images and the similarity of the training and test dataset inflated the accuracy of the proposed algorithm. However, we expect the accuracy to decrease nominally as we evaluate the method on a more comprehensive dataset. Figure 11: Examples 1-3: VQA Analysis of Post-disaster Footage. Figure 10: Precision-recall for Argo Hall using the CNN. According to the results, the approach that had a better performance when trying to detect damages is the VQA; the VQA had an accuracy of 92% and 93%, the CNN had accuracy of 85% and 88%, and the CC had 48% and 55% for the selected region of the test data for Argo Hall and Martin Hall, respectively. The main reason for the CC having a lower accuracy level when compared to other models was about the training process. By acquiring a larger dataset of positive and negative images, as well as increasing the training stages, it is likely that the final accuracy of this CC approach might also significantly increase. Furthermore, the CNN and VQA approaches had similar results. This is expected because the main reason why the CNN model did not have a relatively higher overall accuracy than VQA was that it failed to detect specific instances of damage due to the motion blur. However, most of this was mitigated in the VQA approach because of the specific questions being asked. Since the VQA model was asked mainly yes/no questions, or whether there were damaged buildings, any specific instance of a single damage being missed is harder, as it would just be counted as part of the whole building. Additionally, when comparing all approaches, both the CC and CNN provide an easier-to-follow damage detection result, which can prove to be useful in post-disaster scenarios. Both models simply point out all the detected damages. Due to this, when only looking for any instances of damage, these approaches would be better to utilize. However, when looking for more details inside an image, the VQA model can provide more information than the other models, due to the fact that it has a language model paired to the image classification. Analysis shows that the VQA approach has a lot more options when analyzing an image, which proves the feasibility of the use of VQA for post-disaster damage detection.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## 3 Results and Discussion
### Contact Angle Test Results  
The influence of SMA/SiO\({}_{2}\) on the wettability of the shale surface was measured, and the test results were shown in Figure 13. The original shale was strongly hydrophilic with a contact angle of 34.4\({}^{\circ}\). With the increase of SMA/SiO\({}_{2}\) concentration, the contact angle was gradually enlarged. After treatment with 2.0% SMA/SiO\({}_{2}\) solutions, the contact angle was increased to 72.7\({}^{\circ}\). Thus, a more hydrophobic shale surface would be formed after treatment with SMA/SiO\({}_{2}\). Less water intrusion into the shale due to the hydrophobic surface was more conducive to the shale stability [48].
## 3 System Implementation
### Solar Energy Harvester

Table 2: Detected voltage levels in voltage level detector. \(\mathrm{V_{bat}}\) is low, \(\mathrm{F_{osc}}\) is decreased to reduce the power consumption of digital circuits in the ADC. When the system is operating at a lower frequency, lower bias currents can be used to reduce the power consumption of analog circuits in the sensor interface circuit. By using these frequency-proportional bias currents, power consumption of the sensor can be scaled down dynamically by reducing \(\mathrm{F_{osc}}\). The digital control unit (DCU) activates the comparator and LD blocks in a timely manner, to check the charging status of battery. As neither \(\mathrm{V_{bat}}\) nor \(\mathrm{V_{pv}}\) change rapidly, these blocks are activated every few seconds to minimize their average power consumption. After activating each block, DCU generates the required control signals for related SC circuits. Standard complementary metal-oxide-semiconductor (CMOS) logic has been used to implement DCU. The power consumption of DCU is mainly determined by the low frequency 20-bits counter that activates the comparator and LD in a timely manner. Table 3 presents die area and power consumption of individual blocks, operating at 1 MHz as its highest clock frequency. As can be seen, the simulated total power consumption of the circuit is mainly determined by the clock generator, bias circuit and DCU blocks, which are always active. Although comparator and LD blocks consume considerable power during their active time, nevertheless, as they are activated every 10 seconds, their average power consumption is less than 1 nW. When \(\mathrm{V_{bat}}\) is low, the average power consumption of the clock generator and DCU blocks is reduced by operating at a lower clock frequency. By reducing the operating clock frequency, the simulated power consumption of clock generator and DCU is reduced roughly linearly, reaching to 23 nW and 13 nW, respectively, at 125 kHz. \begin{table}
\begin{tabular}{c c c c} \hline
**Block** & **Die Area (\(\mathrm{\SIUnitSymbolMicro m}\))** & **Active Power (\(\mathrm{\SIUnitSymbolMicro W}\))** & **Average Power (\(\mathrm{\SIUnitSymbolMicro W}\))** \\ \hline Clock generator & 14,400 & 165 & 165 \\ Digital control unit & 38,115 & 90 & 90 \\ Bias circuit (10 nA) & 2,832 & 35 & 35 \\ Comparator & 759 & 55 & \textless{}1 \\ Level detector & 31,960 & 520 & \textless{}1 \\ SCBM & 6,300 & 640 & \textless{}1 \\ \hline \end{tabular}
\end{table} 
Table 3: Die area and active power of main blocks in solar energy harvester circuit. Figure 6: **(a)** Reconfigurable current-starved ring oscillator; **(b)** switched-capacitor beta-multiplier (SCBM) current source.