esa-sceva/satcom-chunk-collection huggingface dataset:
# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 2 Methodology of 1D CNN Seismic Damage Assessment
### 1D CNN Architecture
In one training iteration, a batch of GMRs from the training dataset was fed into the CNN model, and all trainable parameters in \(\left(\theta,\mathrm{\tilde{\theta}}\right)\) were updated based on the loss CE through the gradient descent optimization algorithm. This training strategy is also known as batch training [43]. A training epoch was reached when all the samples in the training dataset were fed into the model once. A basic gradient descent optimizer is given in Equation (3):  
Figure 2: The architecture of 1D CNNs with the feature extracting (convolutional and max-pooling) layers and the fully connected layers (the FNN classifier). \[\left(\theta,\,\widetilde{\theta}\right)^{\text{new}}=\left(\theta,\, \widetilde{\theta}\right)^{\text{old}}-\eta\frac{\partial\text{CE}}{\partial \left(\theta,\,\widetilde{\theta}\right)} \tag{3}\]  
where \(\eta\) is the learning rate and \(\partial\) is the partial differential operator. Additional modified advanced optimizers based on Equation (3) were referred to in [44], where the [PERSON] optimizer was adopted in the study due to its superior performance [45]. Finally, the optimal \(\left(\theta,\,\widetilde{\theta}\right)^{*}\) was acquired to achieve the lowest CE on a separate validation dataset. The trained 1D CNN model with the optimal parameters was saved and could be used to rapidly predict the resultant damage states of structures by future GMRs.
## 3 Results and Discussion
### Behavior Study of the MSAMT
#### 3.3.2 Effect of Matching Probability on Multi-Tasking Optimization  
Exploration and exploitation capabilities determine how well the optimization algorithm performs. In the G-MFEA, the matching probability \(rmp\) is used to achieve the balance between exploration and development. When \(rmp\) is closer to 0, the probability of cross-task mating becomes smaller, thereby enhancing the exploitation ability. However, this may result in the algorithm converging on a local optimum. Conversely, when \(rmp\) is closer to 1, the probability of cross-Zzztask mating increases, and it is able to search the decision space extensively, thus escaping from local optimality.  
Figure 3 plots the mean fitness values of the MSAMT with different \(rmp\) values on problems F2, F3, F5, and F9 with 20D. As Figure 3 shows, there was a considerable variation in the performance of MSAMT with different \(rmp\) values. Without considering the red outliers, the MSAMT could obtain the best overall performance when the matching probability \(rmp\) was equal to 0.4. Therefore, \(rmp\) was set to 0.4 in this work.  
Figure 3: Performances of the MSAMT with different \(rmp\) values on 20D F2, F3, F5, and F9 (From (**a**) to (**d**) are the box plots of F1, F3, F5, and F9, respectively).
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 3 Results and Discussion
### Application on Data Sets  
#### 3.1.1 Thruster 1  
The data set for model identification with 207 samples included shaft speed in the range 143.32-602.53 rpm. The number of accepted samples in quality control was 201 and the selected shaft speed range was 249.86-353.47 rpm, which consisted of 141 samples (see Figure 7). The derived parameter values and covariance matrices of the identified models are presented in Appendix A.  
The application of the models is demonstrated in Figure 11 where the first 141 samples were used to train the models. The samples 142-344 are new independent samples inside the selected shaft speed range and they correspond to a 364-day period, indicating there were various days in which accepted samples were missing in the selected shaft speed range.  
Figure 11: Monitoring of thruster 1 by using three different sets of features during a period spanning over 515 days.  
Two periods with the same length are indicated in the plots to represent negative class and positive class. The data used for these samples were used for classification in Section 3.2 as well. The defects were confirmed based on the videoscope inspection after sample no. 196.  
Figure 11 indicates that the squared Mahalanobis distance values had an increasing trend after the training period indicating that the operation began to diverge from the operation in the training period. Perhaps the moving median gives a clearer indication of this progress. The bearing model especially gave distinct indications of change around samples no. 142-195, which corresponds to a 102-day period before the videoscope inspection. In addition, the time domain model showed some changes after the 141 first samples and the gear model showed deviations more regularly at the end of the complete period shown. In conclusion, the models together suggest that the condition changed in the thruster based on the observations in the monitored speed range.
# Long-Term Occurrence and Fate of Microlastics in WWTPs: A Case Study in Southwest Europe
## 3 Results and Discussion
### Occurrence and Evolution of MPs
Regarding the tertiary treatment applied in this WWTP, that consists of a coagulation-flocculation, a lamellar settler, a RSF and an UV disinfection, the removal efficiency of MPs of around 41% was achieved, which increased the overall removal efficiency until values of around 93% and entails an emission of 1.13 MPs/L in the effluent. In those effluent samples, during the warmest months (April to September) the MP concentration was higher compared to the coldest ones (January to March) with ranges of 0.77-1.58 MPs/L (1.21 \(\pm\) 0.31) and 0.59-1.31 MPs/L (0.87 \(\pm\) 0.38), respectively. These results are in accordance with those reported by [PERSON] et al. [53]. Although coagulation-flocculation is a typical process found in drinking water treatment plants (DWTPs) [54; 55], it is also commonly employed in WWTPs. For example, [PERSON] and [PERSON] [49] reported removal efficiencies of MPs between 50-82% by means of a coagulation-flocculation process. The effect of RSF in the MP elimination has been analysed in previous works with a wide variety of results. For example, in a WWTP located in Finland, MPs were reduced from 0.7 to 0.02 MPs/L, which means an efficiency of 97% [30]. In another study carried out in two German WWTPs, the use of a sand filter achieved a noteworthy MP removal (above 99%) [56], whereas [PERSON] et al. [19] described a MP elimination by a RSF of only 50%. The overall MP removal efficiency of the WWTP analysed in this work was between 89% and 95%, with an average value of 92.9 \(\pm\) 2.1% and it is remarkable that no noticeable variation between months was detected, so rainfall and temperature does not seem to affect MP elimination. The removal efficiencies found in the facility analysed in the present work were within the range reported in different European WWTPs (72-98%) [3, 57, 58, 59, 60]. A wide variation can be found depending on the treatment technology used and the operating conditions in the WWTP [61], the origin and type of wastewater [20], as well as the sampling and identification methods used in the process, population density and regional development [40].
## 2 Materials and Methods
### Procedures
#### 2.2.2 Landing Mechanics  
The participants performed three trials of a single leg counter movement jump (one practice and two analyzed trials), with a 1-min rest between the trials. The average value of the two trials was used in a subsequent analysis [43]. The participants were instructed to take two steps forward; immediately jump as high as possible off one leg (preferred), imagining that they were reaching for a ball above their head; and land on two feet. This stop-jump task is considered more game-specific compared with the previously used drop-jump task off a box and is a convenient testing tool particularly for female athletes and increases the ecological validity of testing [44; 45; 46]. Furthermore, the practice of jumping from a box in the context of the LESS has encountered criticism, with concerns raised about its sport-specificity, its efficacy in detecting landing biomechanics linked to lower limb injuries, and its utilization in predicting ACL injuries [47; 48; 49]. Recordings from two high-definition video cameras, SONY HXR-MC2000 and SONY HXR-NXSE (SONY Corporation, Tokyo, Japan; frequency 25 Hz), were used for the purpose of a two-dimensional biomechanical landing analysis. The cameras were positioned on tripods 3.5 m from the marked landing area in the frontal and sagittal plane. For the evaluation of images, the ImageJ software 1.50i (National Institute of Health, Bethesda, MD, USA) was used. The videos of the participants were scored retrospectively by the same experienced rater [50]. The LESS, a 17-item scale devised by [PERSON] et al. [40], was applied to evaluate the landing mechanics of the participants. The initial set of observable criteria pertains to the alignment of the lower limbs and the trunk during the moment of the first contact with the ground (Items 1-6). The second set of criteria aims to evaluate inaccuracies in foot positioning (Items 7-11). The third set of criteria is dedicated to examining the motion of the lower extremities and trunk from the point of initial ground contact to the instance of either maximum knee flexion angle (Items 12-14) or maximum knee valgus angle (Item 15). The final two \"comprehensive\" criteria encompass the overall movement in the sagittal plane and the rater's general assessment of landing quality (Items 16 and 17). The LESS is a comprehensive, valid, and reliable procedure which includes a comprehensive assessment of multiplanar biomechanics which could be used to identify individuals at a risk of sustaining a non-contact ACL injury [31; 43], where the landing is evaluated by analyzing the records of landing in the sagittal and frontal planes. The scoring is based on the presence or absence of kinematic characteristics, using a standardized checklist where a higher LESS score suggests poor landing technique, while a lower score is indicative of a proficient jump-landing technique [40]. The overview of the LESS specific errors is presented in Table 2 (adopted from [PERSON] and [PERSON] [51], with permission of authors).
## 6 Protocol Comparison and Security Analysis
### Impersonation Attacks  
An attacker may attempt to impersonate a user to send an authentication request to a satellite. However, without the user's private key, the attacker cannot give the correct signature. When the attacker impersonates a user's request to the satellite with his own public key, the request cannot pass the verification of the satellite. Since only the addresses of legitimate users are stored in the blockchain, there is no address corresponding to the attacker's public key. At the same time, the attacker cannot obtain the correct session key from the message encrypted with the public key and cannot use the service. If an attacker impersonates a satellite by sending a message to the user, since the attacker does not have the private key of the satellite, the signature cannot be verified by the user, and the user will discard the message.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 5 Proof of Identity
### Proposed SRUP Implementation
#### 5.2.2 Autonomous System  
The second use-case is one where the device requesting the join is an autonomous entity and so is operating without direct human intervention. In a system of this type, the join request could, for example, be made as a part of the process for an autonomous device entering a specific region of control.  
The general pattern of operation in this more complex example would be for the server to generate encrypted data in the same manner as before, but then request that another (already connected and trusted) device make the observation check via a point-to-point connection outside of the protocol. For example, if the autonomous platform making the join request recognises that it has entered into the  
Figure 1: The sequence of a human-in-the-loop join operation.  
region subject to C2 by the server by virtue of establishing a point-to-point connection with some kind of beacon device, it could use the identity of this beacon as a part of its join request. The server could then send the beacon a message containing the value that it should expect to receive from the device. Once the joining device has received the server's request for confirmation, it would then transmit the confirmation data via the point-to-point link to the beacon, which would be responsible for checking that the two values matched and signalling back to the server.  
Under SRUP, this would be accomplished by having the device send an SRUP_MESSAGE_TYPE_OBS_JOIN_REQ message containing its own identity and the identity of the observing node and signed with the requesting device's key.  
The server would then generate a 128-bit UUID value and an SRUP_MESSAGE_TYPE_OBSERVE_REQ message to the observing node, which would contain an encrypted version of the value (encrypted with the observing node's public key) and a signature.  
The server would also send a SRUP_MESSAGE_TYPE_OBS_JOIN_RESP message back to the joining device, using the same scheme, consisting of an encrypted version of the confirmation value, this time using the joining node's public key. The joining node would then be required to prove its identity to the observing node outside of SRUP (for example, via a short-range point-to-point link); and then, finally, the observing node would send a regular SRUP response message to the server, with a status value to signal whether the proof of Identity has been successful or not.  
Figure 2 shows a sequence diagram depicting the protocol message flows for an externally-observed join; together with both a device-initiated and a server-initiated withdrawal operation.  
Figure 2: A sequence diagram showing the message flows for autonomous join and withdrawal operations. C2: command and control.
## 4 Experiments
### Evaluation Index
#### 4.2.1 Accuracy and F1 Score  
Accuracy based on correct answers is often used in cloze tests and multiple choice evaluations. Given a problem set with m problems \(Q=\{Q_{1},Q_{2},\ldots D_{m}\}\) if the model accurately predicts the answers to n questions.  
Exact matching (\(EM\)) is a variant of accuracy often used in span-extraction MRC. Specifically, an exact match indicates whether the predicted answer exactly matches the standard answer. The \(EM\) value is 1 if the predicted answer matches the standard answer and 0 otherwise.  
\(F1\) value is a critical evaluation index in the classification task. In MRC, \(F1\) is often used in span extraction tasks; it can be calculated using Equation (2).  
\[F1=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{2}\]  
Suppose \(S\) is the model's predicted answer and \(T\) is the standard answer. \(|S|\) shows the text length of the predicted answers, and \(|T|\) shows the length of the accurate answers. \(|S\bigcap T|\) shows the length \(S\) overlapped with \(T\). For Equation (2), the calculation of the _Precision_ and _Recall_ are shown in Equations (3) and (4).  
\[Precision=\frac{|S\bigcap T|}{|S|} \tag{3}\]  
Figure 9: Structure of information improved by contrastive learning.  
\[Recall=\frac{|S\bigcap T|}{|T|} \tag{4}\]  
Compared to the \(EM\) value, the \(F1\) value is used to measure the degree of textual overlap between the model's predicted answer and the standard answer. The \(F1\) value of all QA pairs in the dataset is averaged, which is the \(F1\) value of the whole dataset.
## 2 Materials and Methods
### Development of the Cox-PH Model  
To identify significant features, univariate and multivariate Cox regression analyses were performed. Variables with \(p\)-value \(<\) 0.05 in univariate Cox regression analysis were included in multivariate Cox regression analysis. The Cox proportional hazard models were constructed using R software (version 4.3.1). The used packages were caret, survival, plyr, MASS, rms, ggplot2, riskRegression, ggDCA, ggprism, and forestplot.
## 3 Results
### Tests in Motion Simulator  
In this section, different results from the afore-named motion simulator will be presented. Following the steps described in Section 2.2.2, the validation of the smart sensor and the real-time PPP algorithm in the three tests (movement around A, B, and A and B axes simultaneously) will be discussed.  
The results achieved with the first test that was carried out performing periodic movements around the B axis (\(\pm 180^{\circ}\)) are exhibited as ENU components in Figure 13. The results, as can be observed, represent, approximately, a half sinusoidal movement accordingly to the plan. The small peaks that appear in the east and north components occur because the axis in the rotation table is relative to its reference position and not to the ENU components. The red squares in the figure indicate where the solution values exceed the programmed movements. As can be seen, after some steps, the solution converges, and the results are within the average.  
The other test conducted in the motion simulator was relative to the rotation around the A axis. As previously mentioned, due to space problems inside the radome, these movements were limited to 0-14\({}^{\circ}\). The results obtained in this test are not obvious as those tested before. Ideally, only the up component must change (about 17 cm as shown in Figure 14) with the movements, and the other two should remain constant. However, these last two components also show small variations but always within the established margin of error (20 cm). These small errors may be caused by the vibrations produced in the movement of the platform and by the error of the real-time PPP solution itself.  
Figure 13: Solution rotating around B axis.  
The last test in the rotary table was the movement combination of both axes to simulate the 3D platform motion. A capture of the results of this test that was taken around 18 h is represented in Figure 15. Observing Figure 15a, it can be noticed that the system is able to maintain the same dynamics during a long data capture. It is important to remark that in the case of losing convergence, the algorithm is able to recover it quickly, as can be distinguished around the 7200 and 54,000 epochs. To show these movements more clearly, in Figure 15b, one hour of the calculated solution is displayed. The movement's periodicity in the east and north components is distinctly noted. The up component, however, shows small variation because of the high sensibility of this component with the geometry of the satellites.  
Figure 14: Solution rotating around A axis.
## 3 Problem Formulation  
Energy network matrix modeling is presented. This modeling is performed in several steps, from the lowest energy level (final energy consumption) to the highest energy level (primary energy carriers).  
Final energy consumption based on different energy carriers is specified as:  
\[E_{2}=\ T_{1,2}\,\times\,E_{1} \tag{3}\]  
where \(E_{2}\) is the final energy consumption based on different energy carriers and \(T_{1,2}\) is the transform matrix of different sectors to different energy carriers. The values of \(T_{1,2}\) are determined based on the simulation of experimental and practical data.  
Energy loss is modeled using (4).  
\[E_{3}=\ T_{2,3}\,\times\,E_{2} \tag{4}\]  
where \(E_{3}\) is the final energy consumption based on different energy carriers considering losses and \(T_{2,3}\) is the efficiency matrix. The values of \(T_{2,3}\) are determined based on the simulation of experimental and practical data.  
At this stage, the electrical energy is proportioned to the energy carriers. In fact, at this point, the UC problem must be resolved. The electrical energy of different power plants is determined as shown in (5):  
\[E_{u}=\ T_{u}\,\times\,E_{e} \tag{5}\]  
where \(E_{u}\) is the electrical energy of different power plants, \(T_{u}\) is the separation matrix of electricity generated by various power plants that is calculated based on solving UC, and \(E_{e}\) is the total electricity demand.  
Input fuel for different power plants is determined by (6):  
\[E_{e_{1}}=\ T_{u,f}\,\times\,E_{u} \tag{6}\]where \(E_{e_{1}}\) is the input fuel for different power plants and \(T_{u,f}\) is the power plant efficiency matrix.  
The source energy carrier for electricity generation is determined using (7).  
\[E_{e2}=\ T_{f,c}\ \times\ E_{e_{1}} \tag{7}\]  
where \(E_{e_{2}}\) is the source energy carrier for electricity generation and \(T_{f,c}\) is the conversion matrix of input fuel to energy carriers.  
After the calculation of electrical energy, the final energy consumption is calculated using (8).  
\[E_{4}=\ E_{3}+E_{e_{2}}-E_{e} \tag{8}\]  
where \(E_{4}\) is the final energy consumption after converting electrical energy to input carriers to units.  
At this stage, the process of refining crude oil is simulated using (9).  
\[E_{p_{1}}=\ T_{p}\ \times\ E_{p} \tag{9}\]  
where \(E_{p_{1}}\) represents the energy carriers produced by refining, \(T_{p}\) is the separation matrix of products created from refining crude oil, and \(E_{p}\) is the maximum capacity of refineries.  
After simulation of the refining crude oil process, the final energy consumption is calculated using (10).  
\[E_{5}=\ E_{4}+E_{p}-E_{p_{1}} \tag{10}\]  
where \(E_{5}\) is the final energy consumption after refining crude oil. Actually, \(E_{5}\) determines the energy carriers that supply the energy demand.  
Import and export of energy carriers are determined by (11):  
\[E_{6}=\ E_{5}-P \tag{11}\]  
where \(P\) is the domestic production of energy carriers, and \(E_{6}\) is the import and/or export of energy carriers. In \(E_{6}\), the positive sign means import and negative sign means export of energy carriers.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 2 Data and Methods  
### Study Area and Data Sources  
#### 2.1.1 Study Area  
The Qaidam Basin is located in the northeastern edge of the Tibetan Plateau. The geographical coordinates are 34\({}^{\circ}\)41\({}^{\prime}\)-39\({}^{\circ}\)20\({}^{\prime}\) N and 87\({}^{\circ}\)48\({}^{\prime}\)-99\({}^{\circ}\)18\({}^{\prime}\) E, spanning the Gansu Province, Qinghai Province and Xinjiang Uygur Autonomous Region. The vast majority of the Qaidam Basin is in the Qinghai Province with an area of 234.14 thousand km\({}^{2}\). The area in Xinjiang Uygur Autonomous Region is 17.42 thousand km\({}^{2}\) and that in the Gansu Province is 17.89 thousand km\({}^{2}\). The northwest, northeast and south of the Qaidam Basin are surrounded by the Altun Mountains, Qilian Mountains and Kunlun Mountains, respectively, as shown in Figure 1. The Qaidam Basin is the only large plateau inland basin in the world and its elevation ranges from 2653 m to 6748 m. The basin is deep in the mainland and surrounded by mountains. It is hard for the warm and humid airflow from the southwest to reach the basin, forming the typical cold-dry continental climate. Affected by the topography and latitude, the temperature of the basin is high in the central portion, but low all around. The lowest temperature occurs in January with \(-\)9.8--16.1 \({}^{\circ}\)C in the basin area and \(-\)14.7--17.2 \({}^{\circ}\)C in the mountainous area. The highest temperature is in July with 13.5-19.2 \({}^{\circ}\)C in the basin area and 5.6-10.4 \({}^{\circ}\)C in the mountainous area. The annual sunshine duration is generally above 3100 h.