esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Three-Dimensional Displacement of the Landslide  
Landslide movement is a three-dimension deformation process. Determining a movement situation in a single direction is difficult to grasp accurately with InSAR monitoring. The three-dimensional deformation field can derive from different directions of InSAR measurements [23,28]. The estimated 3-D displacements (d\(u\), d\(e\), and d\(n\)) and their variances (\(\sigma_{du}^{2}\), \(\sigma_{de}^{2}\) and \(\sigma_{dn}^{2}\)) were determined from three direction measurements (d\({}_{los,1}\), d\({}_{los,2}\) and d\({}_{los,3}\)) and their errors (\(\sigma_{dlos,1}^{2}\), \(\sigma_{dlos,2}^{2}\) and \(\sigma_{dlos,3}^{2}\)), as follows:  
\[\begin{bmatrix}\text{d}u\\ \text{d}e\\ \text{d}n\end{bmatrix}=\Gamma.\begin{bmatrix}\text{d}_{los,1}\\ \text{d}_{los,2}\\ \text{d}_{los,3}\end{bmatrix} \tag{1}\]  
\[\Gamma=\begin{bmatrix}\Gamma_{1}\\ \Gamma_{2}\\ \Gamma_{3}\end{bmatrix}=\begin{bmatrix}a_{1}\ b_{1}\ C_{1}\\ a_{2}\ b_{2}\ C_{2}\\ a_{3}\ b_{3}\ C_{3}\end{bmatrix}^{-1} \tag{2}\]  
\[a_{i}=cos\theta_{inc,i},i=1,2,3;\ b_{i}=-sint\theta_{inc,i}\sin\biggl{(} \alpha_{az,i}-\frac{3\pi}{2}\biggr{)};\ C_{i}=-sint\theta_{inc,i}\cos\left( \alpha_{az,i}-\frac{3\pi}{2}\right) \tag{3}\]  
\[\begin{bmatrix}\sigma_{du}^{2}\\ \sigma_{de}^{2}\\ \sigma_{dn}^{2}.\end{bmatrix}=\begin{bmatrix}\Gamma_{1}\ \Sigma\ \Gamma_{1}^{T}\\ \Gamma_{2}\ \Sigma\ \Gamma_{2}^{T}\\ \Gamma_{3}\ \Sigma\ \Gamma_{3}^{T}\end{bmatrix} \tag{4}\]  
\[\Sigma=\begin{bmatrix}\sigma_{dlos,1}^{2}\ \sigma_{dlos,1}\ \underline{d_{ los,2}}\ \sigma_{dlos,1}\ \underline{d_{los,3}}\\ \sigma_{dlos,1}\ \underline{d_{los,2}}\ \sigma_{dlos,2}^{2}\ \sigma_{dlos,2}\ \underline{d_{los,3}}\\ \sigma_{dlos,1}\ \underline{d_{los,3}}\ \underline{d_{los,2}}\ \underline{d_{los,3}}\ \sigma_{dlos,3}^{2}\ \end{bmatrix} \tag{5}\]  
where \(\theta_{inc,i}\) and \(\alpha_{az,i}\) are the orbit azimuth angle and incidence angle, respectively; d\(u\), d\(e\), and d\(n\) are the displacement in three different directions; \(\Gamma\) is the conversion coefficient; \(d_{los,1,2,3}\) are the LOS displacement in three different directions [18].  
Figure 3: Baselines of the three orbits used in this study. (**a**) Baseline of Sentinel-1 ascending images; (**b**) baseline of Sentinel-1 descending images; (**c**) baseline of PALSAR-2 ascending images.  
Although there is a 7-day difference in acquisition dates between adjacent ascending and descending Sentinel-1 SAR data, the deformation distortion error due to the sampling time interval is almost negligible. The time difference between PALSAR-2 and Sentinel-1 data is about one month, so we assumed that the landslide was in uniform motion during this period. We applied the time-domain interpolation method to eliminate the sampling time interval between the three orbits. It is reasonable to derive 3D time-series movement from three independent direction measurements [29].
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Qualitative Evaluation
#### 3.1.2 Qualitative Evaluation Based on Causality  
Causal relationships between the system dynamic variables, which may limit the design of the residual observer, can impact diagnosability.  
[PERSON] published three papers to address in detail the impact of causality on the qualitative evaluation of diagnosability [33]. Specifically, [PERSON] analyzed the implications of integral causality, derivative causality, and mixed causality on the qualitative evaluation of diagnosability, respectively. [PERSON] obtained the following conclusions:  
1. In structural analysis, causality does not affect the results of the qualitative evaluation of diagnosability if the dynamic variables and the corresponding derivatives are considered the same variables [34];
2. In qualitative evaluation, the preconditions for the equivalence of integral causality and derivative causality are [35]:  
* The system is free from factors such as noise interference;
* The initial values of the dynamic variables are known;
* The dynamic variables are derivable, and the number of differentiations is not limited.
3. In the fault diagnosis process, differential causality should be minimized.  
Based on the structural model, [PERSON] gave sufficient conditions for diagnosability under integral, differential, and mixed causality conditions. He analyzed the requirements for determining fault isolability under different conditions. In addition, the definition of the differential causality index was given, which related it to the difficulty of residual construction. According to the difficulty of integral causality calculation, a qualitative analysis method of diagnosability based on integral causality was given based on the principle of a low differential index. He obtained the result that the isolability under integral causality conditions was equal to the fault isolability of MSOs with a low differential index.  
In addition, two residual generation principles based on the structurally overdetermined system were introduced: the sequence residual generation principle and the observer residual generation principle. Based on the principle of selecting differential indicators forlow MSOs, the MSOs would be divided. The residuals could be obtained only through integral operations, avoiding the appearance of differentials in the computation process.
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 3 Problem Description
### Objective Function
#### 3.3.3 Uncertainty Modeling of the Wind Generation  
The reserve or penalty cost for wind energy conversion system can be obtained by assuming or finding some known probability distribution function for the wind speed [48]. The characterization of the wind speed is needed for economic dispatch with the Wind Energy Conversion System (WECS). The existing literature on uncertainty modeling of wind generation shows that the wind speed profile follows a Weibull distribution over time. The probability distribution function for the Weibull distribution is denoted as  
\[f_{u}(v)=(k_{1}/a)(v/a)^{(k_{1^{\prime}}-1)}*(\exp)[-(v/a)^{k_{1}}] \tag{15}\]  
where \(f_{u}(v)=\) Weibull distribution for wind speed; \(v=\) wind speed; a = scale for wind speed at given location; \(k_{1}=\) shape factor.  
After characterizing the uncertain nature of the wind as a random variable, the output power of the WECS can also be modeled as a random variable by transforming from the wind speed to the output power.  
\[P_{w}(v)=\{0\hskip 42.679134 pt\text{for }v_{0}<v<v_{i} \tag{16}\]  
\[P_{rated}*(v-v_{i})/(v_{r}-v_{i})\text{for }v_{i}\leq v\leq v_{r} \tag{17}\]  
\[P_{rated}\text{ for }\begin{array}{l}v_{r}\leq v\leq v_{co}\\ 0\text{ }for\text{ }v_{co}\leq v\end{array} \tag{18}\]  
where \(v_{i},v_{r},v_{co}=\) cut-in speed, rated speed, cut-off speed. \(P_{w}=\) output power of the WECS during state w.  
3.3.4. Risk Factor in Solar and Wind Generation  
Solar generation and wind generation are not reliable power supplies. There is the risk of changes in electricity generation due to a lack of sunshine or snow covering solar panels for long periods of time. Similarly, the wind power generations purely depend on the velocity of the wind. In order to overcome these issues and to provide a reliable power supply to the consumer, the new concept of the virtual power plant is introduced. A virtual power plant works remotely to combine a number of independent renewable energy resources from disparate locations into a network that provides reliable power 24/7. Hence the uncertainty in the nature of solar and wind power generations can be solved using the VPP. The consumer will receive a reliable, good quality of power supply.
## 4 Experiments
### Implementation Details  
CodeTransFix focuses on Java single-block bug fixes and therefore uses the dataset provided by [PERSON] et al. [10] for model training. The dataset is divided 90% into a training dataset and 10% into a validation dataset. For CodeBERT models, the pre-trained model hyperparameters are directly followed. We implemented our framework using Python 3.8 and PyTorch 1.10, leveraging the HuggingFace Transformers library (v4.24.0) to load the pre-trained CodeBERT model along with its dedicated tokenizer. Based on the fact that CodeBERT only acts as an encoder, an additional Transformer decoder was added to allow the model to learn the context vector of the buggy code. The chosen Transformer decoder consists of 12 attention heads, 6 layers, and 768-dimensional hidden states. The following ranges were used in a Tree-structured Parzen Estimator (TPE) search to fine-tune the APR model's hyperparameters: learning rate (\(10^{-5}\)-\(10^{-3}\)), dropout (0-0.5), number of attention heads (2-8), hidden layer dimension (128-768), and number of encoder and decoder layers (6-8). In addition, the Adam optimizer was employed to update the parameters of the model. In the inference mode, a beam search with a beam width of 1000 was used. Model training and evaluation were performed on a server with a 20-core Intel Xeon Platinum 8457C CPU and 100 GB of RAM running the Ubuntu 20.04 LTS operating system with an NVIDIA L20 GPU using CUDA 11.3 drivers. The single-block defects were fixed by applying a 5 h end-to-end time constraint to all tests, which is consistent with previous methods [3, 11, 16].
## 3 Results  
### Antiwetting Behavior of the Stationary Model  
#### 3.1.1 Droplets Impacting on Static Hydrophilic Model  
The adhesive behavior of water is evident on the hydrophilic-model surface (Figure 3). It can be seen from the image of the partially enlarged area that, upon initialization (\(t=0.00\) ms), the surface of the model was in a dry state. A droplet spread rapidly after impacting the upper surface of the wing, and it reached the maximum spread at \(t=52.33\) ms. The large droplet size (D\({}_{0}=4.2\) mm), high impact velocity (v\({}_{0}=1.9\) m/s), and hydrophilic surface together led to the higher viscous force and lower retraction force of the droplet. After a period of time (\(t=1125.63\) ms), low-thickness liquid films appeared on the hydrophilic surface. The deposition of water on the hydrophilic surface was observed, and the droplet impingement caused the typical \"crown splash\" phenomenon [5; 37] (\(t=1758.95\) ms), which showed that the liquid film here was remarkably thick. Over time, as more raindrops impinged on the surface of the model, these liquid films became markedly thicker and  
Figure 2: High-speed-camera system: (**a**) Settings for zones of simulated precipitation. The model is placed in a simulated-precipitation area, and the instantaneous flowmeter controls the size of the droplets to be consistent; the droplets fall vertically and hit the model. (**b**) Configuration of stepper motors. The model, cantilever beam, and linear stepping motor are connected; the model moving speed is 0.3 m/s. (**c**) Attachment of cantilever for resonant-speaker testing. The computer controls the vibration, driving selected frequencies through the power amplifier to the resonant speaker (see “S1. Devices and Analysis” and “S2. Vibration Control” in the Supplementary Material).  
more continuous and asymmetrical (see more details in \"Supporting Movie S1\" in the Supplementary Material).
## 2 System Model
### Random Channel Access Model
Indeed, due to node activation failures in a specific frame, the actual number of nodes contending the channel is lower than \(K\), and thus a frame set to \(K\) slots would increase the number of idle slots. Similarly, capture effect also impacts on the optimal frame duration. Specifically, multiple transmission slots does not necessarily result in collisions, and thus the frame length could be set to a value lower than \(K\) to achieve optimal performance. Albeit a formal derivation of the optimal performance is beyond the scope of our analysis, we include a discussion on optimal frame length in Section 4.
## 5 Empirical Case Study: Courier Company in Bangkok
### Experimental Results and Analysis
#### 5.3.2 Effects of Assigning Customers in Ascending Order of Travel Time  
To demonstrate the importance of assigning a customer in ascending order of travel time to the nearest non-violating vehicle as described in the route adjustment algorithm (Algorithm 2), we modified  
Figure 5: Pre- and post-route adjustment obtained before and after applying Algorithm 2 for problems in Scenario A at time instances between 9 a.m. and 7 p.m. The black dashed line displays the working time limit of 480 min.  
Figure 6: Pre- and post-route adjustment obtained before and after applying Algorithm 2 for problems in Scenario B at time instances between 9 a.m. and 7 p.m. The black dashed line displays the working time limit of 480 min.  
Step 4 of the algorithm by randomly assigning a customer from \(\mathcal{V}\). The results are shown in Figure 7. The red plot shows distribution of Phase I's vehicle violation time. The blue and black plots correspond to results obtained after going through Phase II with ascending order and random assignment, respectively.  
We can see clearly that Phase II with ascending order assignment performed significantly better than the random one. While there were only two problems for ascending order assignment exiting with unsuccessful termination status (Problem B--11 a.m., and Problem B--12 p.m.), eight problems with the random assignment exited with unsuccessful termination status (2 problems in Scenario A, and 6 problems in Scenario B). In addition, the algorithm also had more remaining working time left with the ascending order assignment than that of the random version.
# Software-Defined Networking: Categories, Analysis, and Future Directions
### Application Programming Interfaces
It resolves the scalability problem by distributing load to the forwarding devices. NetConf [28] uses a remote procedure call paradigm to manage and configure network devices remotely. It was already present before the emergence of SDN and offers a very simple API to send and receive full or partial configuration datasets. * OpenFlow-Based SBIs Emerging Technology: Sensor OpenFlow (SOF) [29] is upgraded as per the specifications of low-capacity sensor nodes. It is based on OpenFlow and aimed to address the challenges of flow and congestion control. It offers the ability to redefine flow tables as per the specific addressing schemes of wireless sensor networks (WSN) to install flow rules on sensor network devices. Software-defined wireless networks (SDWN) [30] aimed to decrease energy consumption in WSNs with the help of duty cycles and in-network data aggregation. Duty energy minimizes consumption by turning radio off in case of idle periods, and in-network data aggregation is also helpful in this regard. Its protocol architecture utilizes both generic and sink nodes. In addition, it supports elastic flow rules due to its nature, unlike traditional OpenFlow. SDN for wireless sensors (SDN WISE) [31], implemented in OMNet++, aimed at reducing sensor nodes' communication with the controller, in addition to making sensor nodes programmable. SOF and SDWN utilize a central controller to provide theoretical details; however, SDN-WISE is based on a distributed controller paradigm, using an ONOS controller [32] to provide services based on practical implementations. 2. Northbound API: Control and management planes use NBI to provide programmability to application developers. NBI is very important with respect to the ability of SDN adoption to support variety of SDN applications. A wide range of NBIs are offered by current controllers and programming languages due to the lack of standardization. In addition, some programmers and many controller platforms use the REST API as NBI. One of the initiatives of ONF is the Northbound Interface Work Group (NBIWG) [33], which was formed with the intention of standardizing NBI. 3. East/Westbound API: Inter-controller communication of SDN domains is established using eastbound API. Westbound API is responsible for communication from the legacy domain to SDN domain. Central network control is the key feature of SDN; however, the single controller can handle only a limited number of forwarding devices. To accommodate the exponential increase in forwarding devices and for large-scale networks, distributed controllers have become a requirement. Eastbound APIs are used to import/export information among distributed controllers [34; 35; 36], and westbound APIs enable communication between legacy network devices and the controllers [37; 38; 39].
Another valuable contribution of the current work is that the coexistence of all potential geohazards (standard and complex) has been successfully taken into account. Specifically, the effectiveness and reliability of the upgraded version of the computational tool have been validated through its application in a real case study in northern Greece. In this particular area, the aforementioned earthquake-related geohazards coexist within a few square kilometers, thus making the process of optimal routing demanding and challenging. The optimal route proposed by the tool has been compared with the route of an existing high-pressure gas pipeline, where the coexistence and presence of more complex earthquake-related geohazards (i.e., regarding secondary faults potential impact) might not have been adequately evaluated during its routing design phase. Various scenarios corresponding to designer preferences have been considered by assigning different weight values to the adopted criteria. The proposed tool offers significant flexibility and efficiency to designers involved with energy lifelines since it can assist operators in the decision-making process when selecting the optimal route of CI in such critical areas. At this point, it is important to clarify that the term \"optimal CI routing\" usually refers to a length-minimized route, which in turn is directly related to a cost-minimized route. Therefore, the optimal CI routing will not only be the shortest in length but also the most cost-effective. Nonetheless, if the proposed routing has to cross geohazardous areas, it is crucial to assess the predicted strain levels on the pipeline and compare them against contemporary international standards and guidelines. In addition, a holistic life-cycle cost analysis should include other aspects, such as construction and maintenance issues in remote areas with steeper slopes, but these are beyond the scope of the current study. Following this introductory section, Section 2 provides a detailed description of the examined problem and the proposed decision-support tool. Section 3 presents the application of the proposed tool, and in Section 4, the obtained results are discussed. Lastly, Section 5 concludes with the key findings of the study, as well as the advantages and limitations of the decision-support tool.
## 2 Problem Formulation
### Sensor Model  
In the 2D problem, the available sensing area of a specific camera could be identified as a circular, triangular, sector, or trapezoid. In this study, as seen in Figure 1, the sensor model is reduced to a sector. The area enclosed by the sector is the camera's field of view. Consequently, any target object inside the sector is regarded as covered by the camera.  
The camera is located at the vertex of the sector with coordinates \(\left(c_{x},c_{y}\right)\). \(dof\) (depth of field) and \(fov\) (field of view), respectively, denote the distance and angle range that a camera is capable of capturing. Azimuth angle \(\varphi\) of the camera is described by the angle between the angular bisector of angle \(fov\) in the sector area and the positive half of the \(x\) axis. Thus, ternary parameter \(C=\left(C^{p},\varphi\right)\) can be used to represent the state of a given type of camera, and \(C^{p}=\left(c_{x},c_{y}\right)\).  
axes, respectively, once the sampling interval is determined. It is shown in Figure 2 that \(\Delta_{b}=1\ m,\Delta_{m}=5\ m,s_{\varphi}=\mathbf{4}\). Therefore, the value ranges of \(C,W^{p}\) could be a finite set of discrete points, and the continuous problem is transformed into a discrete problem.
## 5 Discussion
### Burned Area Misclassification
The OBIA filtering procedure, introduced in this work and used to remove arable land pixels misclassified as burned pixels, is based on the assumption that each crop in agricultural areas (the object) covers a relatively small surface and has a low pixel value variability, due to the homogeneous vegetated cover through time resulting from agricultural practices. This procedure may reduce commission error but may also remove small natural homogeneous patches from the identified burned areas, increasing the omission error. Additionally, the procedure could remove arable pixels that have been affected by fire disturbance as a consequence of stubble burning practice. Since the commission error has only been marginally reduced by OBIA filtering (around 1%), new strategies should be adopted to reduce it, such as the identification of the soil line spectral signature [62]. In order to decrease commission errors in arable lands, the synthetic aperture radar (SAR) backscatter temporal difference between pre-fire and post-fire satellite observations has been integrated with optical spectral indices to reduce confusion between burned areas and low albedo surfaces [24; 58; 60]. Even if the integration of SAR and optical data allows one to reduce local commission errors, it exhibits low separability values for bare soil and sparse vegetation classes [24]. In fact, Sentinel-1 SAR has been found to detect changes related to wildfires in areas where the vegetation structure was more affected [58]. Moreover, the occurrence of precipitation events, and consequently the change in soil moisture, can significantly affect the backscattering signature [24], requiring a higher threshold for the detection of burned areas [58]. Even the use of fully polarimetric SAR data with large time differences between SAR data acquisitions increases the probability of the removal of vegetation due to causes other than burns, such as harvesting or deforestation, resulting in a misclassification of burned areas [63].
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 3 Uncertainty Analysis of Renewable Energy
### The PV Data  
Gansu Province was taken as an example; the longitude and latitude of this area are 103\({}^{\circ}\)52\({}^{\prime}\) E and 34\({}^{\circ}\)26\({}^{\prime}\) N. All solar radiation data were from NASA's Surface Solar Energy Data Set. The average solar radiation intensity of this area is 4.505 kWh/m\({}^{2}\)/day. The monthly solar radiation intensity and mean clearness index in Gansu are shown in Figure 2. If we know either solar radiation intensity or clearness index, HOMER can predict the time series of solar radiation intensity for a whole year. Figure 3 shows the result of HOMER, i.e., the time sequence of solar radiation intensity in a year.