esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Post-Treatment Using CW
#### 3.2.1 Effect of HRT  
The pilot-scale CWs were operated under two hydraulic residence times (HRTs) of 4 and 2 days. These two HRTs were selected based on previous experiments with other agro-industrial wastewaters [65; 67], containing similar COD concentrations as the WW. Under 4-day HRT, mean d-COD removal rates were 91% for CW-P and 78% for CW-U, while under 2-day HRT, the mean d-COD removal rates were 92% and 82% for CW-P and CW-U, respectively. The effect of HRT was statistically assessed by applying one-way ANOVA to the data obtained under 4- and 2-day HRT operation. To exclude the temperature effect, temperature values from these two different operational periods were also tested using one-way ANOVA. In both cases, no statistically significant differences were found (p > 0.05). ANOVA results showed that COD removal rates were not significantly affected by HRT values in either unit (p > 0.05 for both units), thus confirming previous studies [65; 67], which also indicate an HRT of 2 days as sufficient for organic matter removal.
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 5 Results and Discussion
### First Scenario: Without Considering MAI Effects  
In order to focus on the other system limitations (i.e., nonlinear effects, increasing of bit rate and increasing of fiber length), we consider the system performance with a single user and without considering the MAI effects.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Loss Function  
We adopt binary cross-entropy loss for building localization loss \(L_{loc}\), which is defined as  
\[L_{loc}=-[y_{loc}\log P_{loc}+(1-y_{loc})\log(1-p_{loc})] \tag{18}\]  
where \(P_{loc}\) and \(y_{loc}\) are the probability of building location and the reference label. The damage classification outputs a mask of five channels, including one channel of localization and four of damage levels. In order to alleviate the imbalance of samples on damage levels, a weighted mixed loss function which consists of focal loss and dice loss is used for damage classification loss \(L_{cls}\), which is formulated as:  
\[L_{cls}=\sum_{n=0}^{4}w_{n}\times[c_{1}\times\text{Focal}_{n}\big{(}m_{p},m_{t }\big{)}+c_{2}\times\text{Dice}_{n}\big{(}m_{p},m_{t}\big{)}] \tag{19}\]  
where \(m_{p}\) and \(m_{t}\) are the predicted mask and true mask for channel \(n\), respectively. \(c_{1}\) and \(c_{2}\) are the weights for focal loss and dice loss, respectively. \(w_{n}\) is the weight for channel \(n\). Larger weights are set for minor damaged and major damaged, which are uncommon classes (\(c=2,3\)). Accordingly, a smaller weight is set for localization weight (\(c=0\)).
# Optimal Analysis of 40 Gbps Dispersion Compensated Optical Fiber System +
## 2 Methodology  
This work proposes an optimal analysis of maximum data rate of 40 Gbps using distances up to 100 km. The simulation was performed using software OPTISYSTEM 7. An optical channel was made using different devices to work on an optical link with least dispersion and high Q factor. The technique used for simulation is based on the NRZ modulation format. The apodization profile of FBG is tanh, Gauss, and uniform is used to make the optical link. According to this scheme, the simulations are carried out for a continuous wave CW laser with different values of power inputs. EDFA optical amplifiers are used to transmit high bit data in the optical communication link.  
As shown in Figure 1, the optical communication link was designed using OPTISYSTEM 7 software. The optical system in the figure contains a bit sequence generator from which we can change the data rate. This defined bit sequence is fed into the pulse generator, where NRZ modulation format is used to compare the Q factor. The generated electrical signal is the input of Mach Zehnder modulator that combines the modulated pulse with the CW laser. Apodization parameters tanh, Gauss, and uniform are used for finding the optimal Q factor. The resulting signal of the MZ modulator then travels from two single-mode fibers (SMFs) of 50 km separated with optical amplifiers of EDFA with a gain of 20 dB. Forty gigabytes per second of bit data, after travelling 100 km, are fed into fiber Bragg grating, which reduces the unwanted dispersion in the signal. A low-pass Bessel filter is used to limit the noise power. The results are measured using a BER and eye diagram analyzer.
# Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
## 2 Methods
### Classification Workflow Phase  
For our project, we use the literature available in the entire UniProtKB database to determine whether experimental evidence of protein function exists. The UniProtKB/Swiss-Prot database currently lists about 560,000 manually curated protein sequences. Many of these sequences have publications tagged in the Function category, which ensures that experimental evidence of their function exists, and these do not require use of our smart program. On the other hand, the UniProtKB/TrEMBL database has more than 200 million unreviewed entries. This unreviewed database clearly contains the majority of sequences with publications to use with our smart program. In addition, while it is not the objective of our project, it also seems clear that our smart program could be of use in the curation of unreviewed sequences in the UnitProtKB database.  
We periodically query the UniProtKB database for protein sequence accession numbers using the UniProt Java application program interface. For each query, we consider only entries with two or more associated journal publications; this is a proxy indicating that sufficient research exists for a protein. We then extract the publication titles for the queried sequences and perform the same pre-processing steps used during the training phase, including tokenization, conversion of uppercase characters to lowercase, removal of punctuation and non-alphabetic characters, and stop-word filtering. For the logistic regression and SVM with linear kernel models, we convert the titles into 700-dimensional biomedical sentence embeddings using BioSentVec [21]. For the RCNN, we use BioWordVec embeddings [16] to convert each word in the title into a numeric feature vector and incorporate context information into the bidirectional recurrent structure. Again, as with our training phase, after linear transformation and max pooling operations have been performed, titles of different lengths are converted into a numeric vector of a fixed length, and this vector is used for classification by the softmax layer.  
During ensemble learning, each trained model classifies the input title independently, and the final decision on whether the query protein sequence has experimental confirmation or not is determined by a majority vote.  
If at least one publication associated with a protein entry in UniProtKB is classified as positive, the publication title and the protein accession number are submitted to a human curator for manual review. All publications classified as negative are ignored. After manual confirmation, the protein annotation is marked by the curator as having been verified.
## 5 Experimental Results
### Robustness Evaluation
**Overwriting attack:** Assuming that the attacker knows the watermarking technique, they may attempt to damage the original watermark by embedding a new watermark in the watermarked model. In practice, attackers lack knowledge about watermarked layer locations. However, considering the worst-case scenario, we assume that the attacker knows the watermark location but not the original watermark or projection function. To perform the overwriting attack, the attacker follows the protocol described in Section 4 to embed a new watermark of the same size (256 bits) or larger size (512 bits) using different or identical latent spaces. Table 5 presents the results against this attack for all four benchmarks. DICTION demonstrates robustness against overwriting attacks and successfully detects the original embedded watermark in overwritten models, while the DeepSigns and ResEncrypt watermarks are perturbed and sometimes completely erased (achieving a BER close to 50%), especially with shallower models such as BM1 and BM4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Benchmark ID**} & \multicolumn{2}{c}{**DICTION**} & \multicolumn{2}{c}{**DeepSigns**} & \multicolumn{2}{c}{**ResEncrypt**} \\ \cline{2-7}  & **256 bits** & **512 bits** & **256 bits** & **512 bits** & **256 bits** & **512 bits** \\ \hline
1 & **0** & **0** & 0.199 & 0.277 & 0.527 & 0.527 \\ \hline
2 & **0** & **0** & 0.367 & 0.418 & **0** & **0** \\ \hline
3 & **0** & **0** & 0.391 & 0.438 & **0** & **0** \\ \hline
4 & **0** & **0** & 0.484 & 0.484 & 0.406 & 0.418 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: BER of watermarking schemes for the four benchmarks after an overwriting attack with 256-bit or 512-bit watermarks using different keys. Best results are highlighted in bold. Figure 3: Robustness against pruning attack for (**a–c**) across the four benchmark models (BM1-BM4). Solid lines show model accuracy, and dashed lines show BER as functions of pruning percentage \(\alpha\). DICTION demonstrates superior robustness, maintaining low BER, even at high pruning percentages.
## 1 Introduction
#### Current Advances in Related Research
The aforementioned system exhibited a high degree of redundancy, facilitated by the extensive freedom of movement provided by the robotic arms. The successful integration and cooperation of the UAV and the mobile landing pad resulted in a symbiotic relationship. The UAV's wide field of view and aerial capabilities complemented the wheeled vehicle's high payload capacity, potentially serving as a charging station for the aerial robot. This effective collaboration maximized the utilization of both platforms, unlocking their full potential in various applications. The work of [PERSON] et al. [24] provided valuable insights into the vision-system-based tracking of a landing pad. Another notable study [25] proposed the use of a tethered UAV. However, the main objective of the tethered UAV was not to serve as a power supply but rather to ascertain the position of the aerial units in relation to ground units. The specific configuration of the loose tether was determined by observing different mechanical components, which in turn provided valuable insights into the positions of the robots in their respective local coordinate systems. In contrast, a rather unconventional approach was presented in [26], introducing an unmanned surface vehicle (USV) utilizing solar power, which was designed specifically for recovering waterproof UAVs that were intentionally splashed down into the water. It is important to note that, while this concept demonstrated effectiveness for such scenarios, its applicability to large-scale ocean vessels is limited. These various research endeavors have contributed to the exploration and development of different techniques and platforms for visual tracking, relative position determination, and the recovery of UAVs, showcasing the diverse strategies employed to address specific challenges in maritime operations.
## 3 Condition Monitoring of the Impedance Bonds
### Aspects about Monitoring and Possible Faults of the Impedance Bonds
In conclusion, monitoring and diagnosis must allow good knowledge of the impedance bonds' technical conditions to retain their behavior in the most varied situations (i.e., temperature in the contact areas, the current imbalance through the two railway tracks, the interruption of the connecting \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters/Characteristic** & **Techniques and Devices** & \begin{tabular}{c} **Monitoring** \\ **Possibility** \\ \end{tabular} \\ \hline \multirow{2}{*}{Contact resistance} & \multicolumn{2}{c}{Measurement of the resistance by the} \\  & volt-ampere method. (Static contact resistance) & online/offline \\ \hline \multirow{4}{*}{Contact temperature} & \multicolumn{2}{c}{Infrared techniques. Temperature} \\  & \multicolumn{2}{c}{measurement at one point with the} \\  & \multicolumn{2}{c}{thermocouple, thermistor, optical sensor,} \\  & \multicolumn{2}{c}{infrared sensor, etc.} \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Impedance bond** \\ **monitoring** \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Current through the \\ impedance bond \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Measuring the current with current \\ transformers with ferromagnetic core, with air \\ core (Rogowski coils), hall effect transducers, \\ optical current sensor. \\ \end{tabular} } & \multirow{4}{*}{online} \\  & \multicolumn{2}{c}{Partial discharges} & \multirow{4}{*}{
\begin{tabular}{c} Ultrasonic and acoustic vibrations. Ultrasonic \\ sensors, online acoustic emission technique \\ \end{tabular} } & \multirow{4}{*}{online} \\ \hline \multirow{2}{*}{Oil level} & \multicolumn{2}{c}{Electronic, optical or mechanical level} \\  & \multicolumn{2}{c}{indicator online} \\ \hline \multirow{2}{*}{Oil quality} & \multicolumn{2}{c}{Determination of moisture content, acidity,} \\  & \multicolumn{2}{c}{dissolved gas analysis, power factor} \\  & \multicolumn{2}{c}{measurement} \\ \hline \multirow{2}{*}{Coil impedance} & \multicolumn{2}{c}{Measurement by direct methods with special} \\  & \multicolumn{2}{c}{devices, respectively indirect methods} \\  & \multicolumn{2}{c}{(volt-ampere method)} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters and techniques for monitoring and diagnosing of the impedance bond. conductors between the impedance bond terminals and railway tracks, etc. ), and compare it with a database of other impedance bonds of the same type, but exposed under similar conditions. This fact is of great importance because the data obtained by impedance bond monitoring will prevent the occurrence of electrical traction installations malfunctions.
## 5 Results and Discussion
### RQ1: What Cyber-Attacks in the MSCN Have Been Reported in the Academic Literature?
#### 5.2.2 Practices Towards an Information Security Policy

Table 5: Practices for managing information security. on training personnel, developing new techniques to mitigate risks, and creating tools to detect vulnerabilities [21; 22; 57; 75]. Establishing a certification authority for maritime mobile service identity (MMSI) pseudonyms: To protect the identities of ships, there is a proposal to use pseudonyms for MMSI, overseen by a certification authority. This approach would enhance privacy and security by preventing unauthorized access to sensitive information about ship movements and cargo, which have been previously targeted by hackers. The certification authority is responsible for the distribution of public keys to guarantee secure communication and verify identities [52; 130].
# Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning
## 3 Servo System Control Strategy Based on Reinforcement Learning
### Implementation Process of the Adaptive PID Algorithm Based on DDPG Algorithm  
In this paper, two types of agents are designed with the same neural network structure. Agent 1 searches for the optimal PID parameters within 0 to 100 range, in order to realize the adaptive tuning of the PID parameters of the speed servo system. The adaptive compensation of PID output current is realized by Agent 2, which further reduces the steady state error.  
Agent 1 is designed on the basis of the actor-critic network structure in 3.3. The critic \(Q\big{(}s_{t},\mu(s_{t}|\theta^{\mu})|\theta^{Q}\big{)}\) and actor \(\mu(s_{t}|\theta^{\mu})\), with weights of \(\theta^{Q}\) and \(\theta^{\mu}\), respectively, are randomly initialized. The target networks \(Q^{-}\) and \(\mu^{-}\) with weights of \(\theta^{Q^{-}}\gets\theta^{Q}\) and \(\theta^{\mu^{-}}\leftarrow\theta^{\mu}\), respectively, are randomly initialized. A memory library \(M_{1}\) with capacity \(C_{1}\) is built. The current state \(s_{t}\) is stored. Action \(\mu_{t}\) is taken. The reward \(r_{t}\) and the next moment state \(s_{t+1}\) are acquired. The first state \(s_{1}=[0,0,0,0,0,0]\) is initialized. The action \(a_{t}=\big{[}K_{p},K_{i}\big{]}=\mu(s_{t}|\theta^{\mu})+\text{Noise}\) is selected on the basis of the actor evaluation network. The action \(a_{t}\) is performed in the PID servo controller to obtain the return \(r_{t}\) and the next state \(s_{t+1}\). The transition \((s_{t},a_{t},r_{t},s_{t+1})\) is stored in \(M_{1}\). A random minibatch of \(N\) transitions \((s_{i},a_{i},r_{i},s_{i+1})\) from \(M_{1}\) is sampled. \(y_{i}=r_{i}+\gamma Q^{-}\left(s_{i+1},\mu\big{(}s_{i+1}|\theta^{\mu}\big{)} \big{|}\theta^{Q^{-}}\right)\) is computed, and the critic is updated by minimizing the loss \(L=\frac{1}{N}\sum_{i}\big{(}y_{i}-Q\big{(}s_{i},a_{i}|\theta^{Q}\big{)}\big{)}^ {2}\) to integrate into Equation (8). The actor network is updated with \(\
abla_{\theta^{\mu}}J\approx\frac{1}{N}\sum\limits_{i}\
abla_{a}Q\big{(}s,a| \theta^{Q}\big{)}|_{s=s_{i},\mu=\mu(s_{i})}\
abla_{\theta^{\mu}}h(s|\theta^{ \mu})|_{s_{i}}\) on the basis of Equation (12). The target network is updated with \(\theta^{Q^{-}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{-}}\), \(\theta^{\mu^{-}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{-}}\).  
Agent 2 is designed on the same neural network structure and state set as agent 1. By changing the action space of agent 1, the current compensation of the PID controller is realized and the control performance is further improved. The action \(a_{t}=[I_{t}]=\mu(s_{t}|\theta^{\mu})+\text{Noise}\) is selected on the basis of the actor evaluation network. The same DDPG algorithm with agent 1 is adopted to train the agent 2.
# Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
## 2 Landslide Overview  
The Huangtupo landslide is in the south band of the Yangtze River in Badong County (Figure 1a) and is an ancient large-scale landslide, which seriously threatens life and property in its vicinity [27]. Given the instability of the deformation, the migrant relocation project of Huangtupo landslide dated from February 2009 and was finished in May 2017, altogether 16,000 people relocated from the landslide (Figure 1b). Because a highway on the landslide is connected with the provincial highway, there are always cars and wagons using the road throughout the year, which generates very low-frequency traffic cyclic loading on the shear-zone of the landslide (Figure 1c).  
The Huangtupo landslide consist of mudstone, sandstone, and limestone strata of the Badong Formation of the Triassic period (T\({}_{3}\)_b_) and is a multi-stage landslide accumulation. The landslide is made up of No. 1 and No. 2 sliding accumulation near the Yangtze River. At the end of 2021, the China University of Geosciences (Wuhan) built the Badong National Observation and Research Station of Geohazards on the No. 1 riverside sliding accumulation to study the deformation evolution of the landslide under fluctuations of the reservoir level.  
According to the monitoring data from the ground, slip body, and slip bed, there is fatigue loading on the shear-zone soil because of the variation in reservoir level (145 m-175 m), as well as dynamic stress by earthquakes. Therefore, it is quite important to reveal the stress-strain relationship under various vertical loads, cycle periods, and cycle numbers. A flowchart for the whole work process is provided in Figure 2.  
Figure 1: Maps of the Huangtupo landslide (**a**) the location of the Huangtupo landslide; (**b**) the scope of landslide and relocation project; and (**c**) shear-zone of 3\({}^{\text{\#}}\) test tunnel.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 4 Experimental Results and Discussion  
Due to the geographic restriction of field experiments in polar areas, the performance of the system was tested using a hardware simulator and semiphysical experiments to validate the necessity and advantages of the proposed system. In this section, the experiment descriptions will be introduced firstly. Then, the experiment results will be analyzed and discussed.
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
## 6 Conclusions and Discussion  
1. From the sparse characteristic of the vibration impact signal, we put forward a new type of vibration impact sparse recognition method, based on the MC penalty function sparse decomposition impact vibration signal identification method; this method can not only separate the impact of the components of bearing fault signal, and effectively restrain noise interference with sparse decomposition process, but also has a good noise reduction effect. At the same time, the high amplitude components are better retained.
2. The improved primal-dual interior point method improves the iterative convergence speed of the model, and the convergence process is gentle. Compared with the original method, the new method shows good robustness, and the training process is more stable.
3. The simulation signals and the test analysis results from the data collected from the motorized spindle test bed and a gas turbine test bed demonstrate that this method can effectively identify the vibration and impact components in the rotor system, and provide an effective method for rotor fault diagnosis to identify the impact fault.  
Figure 17: Time-domain/envelope results of sparse decomposition simulation. (**a**) MC penalty function sparse decomposition time domain graph, and (**b**) MC penalty function sparse decomposition envelope spectrogram.  
**Author Contributions:** Conceptualization, H.W.; Data curation, Z.W.; Methodology, X.Z. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by National Natural Science Foundation of China (No.51975058).  
**Conflicts of Interest:** The authors declare no conflict of interest.