esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principle of SAR Imaging Simulation
### Ocean Surface Simulation  
The accuracy of the constructed ocean surface directly determines the accuracy of the ocean surface backscatter coefficient. In this paper, the Monte Carlo method was used to complete the simulation of the 2D ocean surface. The fast Fourier transform (FFT) expression equation of the ocean surface height fluctuation function is as follows [25]:  
\[F\Big{(}k_{m_{k}},\;k_{n_{k}}\Big{)}= \tag{5}\] \[\times\Bigg{\{}\begin{array}{cc}\frac{[N(0,\;1)+iN(0,\;1)]}{ \sqrt{2}},&m_{k}\
eq 0,\;M/2\;\text{and}\;n_{k}\
eq 0,\;N/2\\ N(0,\;1),&m_{k}=0,\;M/2\;\text{or}\;n_{k}=0,\;N/2\end{array}\]  
where \(L_{x}\) and \(L_{y}\) represent the length of the 2D ocean surface in the range direction and azimuth direction, respectively; M and N represent the sampling points of the ocean surface in the range and azimuth directions, respectively; \(m_{k}\) and \(n_{k}\) represent the range of the sampling points; \(S\Big{(}k_{m_{k}},k_{n_{k}}\Big{)}\) is the 2D PM wavenumber spectrum; \(k_{m_{k}}=2\pi m_{k}/L_{x}\) and \(k_{n_{k}}=2\pi n_{k}/L_{y}\) are the range and azimuth wavenumbers, respectively; and N(0, 1) represents a random number obeying a Gaussian distribution with a mean of 0 and a variance of 1. By performing the inverse Fourier transform (IFFT), the sea surface height fluctuation function \(f(x_{m},y_{n})\) can be simulated. The expression equation is as follows [25]:  
\[f(x_{m},\;y_{n})=\frac{1}{L_{x}L_{y}}\sum_{m_{k}=-M/2+1}^{M/2}\sum_{n_{k}=-N/2 +1}^{N/2}F\Big{(}k_{m_{k}},k_{n_{k}}\Big{)}\exp\Big{[}i\Big{(}k_{m_{k}}x_{m}+ k_{n_{k}}y_{n}\Big{)}\Big{]} \tag{6}\]  
where \((x_{m},y_{n})\) indicates the coordinates of the sea surface and the other variables have the same meaning as in Equation (5).
## 9 Results and Analysis  
According to the findings of this research study, there are several potential opportunities for enhancing and improving the current state of threat detection and prediction systems. One of the opportunities is to explore DL algorithms, which can potentially provide better performance than traditional ML algorithms like SVM and LR. Another opportunity is to adopt a semantic-based approach for feature extraction, which may enhance the accuracy and performance of the system. The integration of sentiment extraction for security breach identification is also recommended. The study suggests substituting LR with NN for sentiment extraction, which can potentially improve the accuracy and effectiveness of threat detection systems. Additionally, organizations can explore implementing DeepNN by [118] for system implementation and enhancement, as it has high ratings across most criteria. Further improvements can be made by substituting LR with NN for sentiment extraction and incorporating security breach identification. The study also recommends exploring different techniques and algorithms for feature extraction, such as a semantic-based approach, and using DL algorithms for classification. By combining the best performing techniques across all factors, there is an opportunity to develop a comprehensive threat detection system that can effectively detect and alert potential security breaches.  
Overall, these potential opportunities can lead to the development of more accurate, efficient, and effective threat detection and prediction systems, which can help organizations to better protect their assets and systems from cyber threats.
## Materials and Methods
### Methods
Multi-looking techniques of interferograms and adaptive phase filtering were applied to improve the signal-to-noise ratio (SNR) [54]. The multi-looking factor was chosen as 2 by 2; hence, the pixel spacing of the resampled interferogram doubled to 1.82 m and 3.80 m in the range and azimuth directions, respectively. Coherence was a quantitative value showing the amount of correlation between the two SAR observations. To evaluate the quality of the InSAR pair, we conducted a coherence analysis using a 5- \(\times\) 5-pixel window. Using two-pass differential interferometric SAR (DInSAR) processing with the GIMP DEM, the topographic phase was removed. It was assumed that the calculated differential interferometric phase contained only the surface displacement between the two observations and possible topographic errors. The Minimum Cost Flow (MCF) algorithm was used for phase unwrapping of the differential interferometric phase, as in Reference [48]. The 0.9 of coherence threshold for masking decorrelated phases was used before the phase unwrapping process. The unwrapped phases were added to the simulated topographic phases from the GIMP DEM. To estimate the interferometric baseline, ground control points (GCPs) with terrain height information are required. Since it is very difficult to select the GCPs over moving glacial surfaces or snow/ice-covered areas, we utilized the GIMP DEM to select the GCPs. Before we selected the GCPs from the GIMP DEM, the GIMP DEM was converted into range-Doppler coordinates. The unwrapped phase was converted into a height map using the baseline geometry in the slant range geometry. Then, the final DEM using TanDEM-X Science Phase was constructed using a geocoding process. The processing scheme is summarized as shown in Figure 2.
###### Abstract  
Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GEANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.  
Keywords:software defined network; computing first network; computing routing; computing resource measurement; reinforcement learning +
Footnote †: journal: _Sensors_
## 5 DC PD Measurements on Cable with Surface and Internal Defects  
DC PD measurements were also performed on an MV cable with surface and internal defects, separately, by means of the experimental setup depicted in Figure 7, which consists of a typical HV amplifier plus signal generator setup to generate a high-voltage DC supply and HFCT to sense PD. In case of the surface defect, the same configuration shown in Figure 4 was considered. As regards the internal defect, a configuration similar to that shown in Figure 5 but with a different cavity dimension was created. The details related to the manufacturing of the cavity and the simulated electric field profile, which was matched with the corresponding internal PDIE in case of DC, are reported in [25].  
The automatic PD detection and analytics software used to acquire and process the DC PD pulses follows the same SRI algorithm, but it is enhanced by means of additional software noise filters (to reduce repetition rate and magnitude of noise) [24]. \(\text{PDIV}_{\text{DC}}\) is generally indicated by the automatic DC PD software through the sentence \"Noise plus possibility of PD\", which is activated when even few PD pulses are collected together with overwhelming noise. Steady-state DC PD measurements were performed in a time window of 2 to 10 min to achieve robust estimates of \(\text{PDIV}_{\text{DC}}\). Figure 12 depicts the screenshot of PD analytics corresponding to the cable with surface defect at \(\text{PDIV}_{\text{DC}}\), where the presence of PD was detected even through there was only one PD pulse present along with large number of noise pulses. Five measurements were carried out for each defect to detect \(\text{PDIV}_{\text{DC}}\). The mean value of the measured \(\text{PDIV}_{\text{DC}}\) in the case of the surface defect and the internal defect were determined as 10.9 and 7.8 kV, respectively, which were noticed to be very close to the modelled/estimated PDIV values of 11.1 and 7.7 kV. This confirms that the designed surface and internal defects are valid in the case of DC along with AC.  
Figure 11: Typical screenshot from analytics software that illustrates how SRI works when both internal and surface PD are present; (**a**) global pattern, (**b**) separation map, (**c**) sub-pattern and cluster corresponding to noise, (**d**) sub-pattern and cluster corresponding to PD, and (**e**) recognition (PD or noise) and identification (type of defect generating PD).
# Licence to Simulate: When Agent-Based Models Are More Fiction than Function +
## 4 Guidelines for the Interpretation of the Classification Method
### Nature of Inter-Agent Interactions
#### 4.2.3 Indirect Interactions  
An indirect interaction occurs when the action of one agent influences the decision making of another without explicit communication or targeted behaviour. In these cases, agents may be unaware of each other's presence, yet their decisions are shaped by shared environmental state variables, emergent collective dynamics, or feedback mechanisms operating at the system level. Therefore, the influence is mediated through the environment, rather than occurring via deliberate inter-agent messaging or mutual recognition. In terms of SC modelling, these interactions are particularly relevant where agents respond to emergent global signals, such as price shifts, congestion, stock levels, or regional scarcity. To sum up, the defining feature of indirect interactions can be captured by the following question: \"_Is there an indirect flow of material or information between two or more different agents, based on their decision rules or utility function, that is conveyed by a third (mostly collective) entity, such as external environmental conditions or system-level variables?_\" This is exemplified in Figure 3, which extends the scenario depicted in Figure 2. Agents within each node have already interacted directly, and this has led to the emergence of a predominant behavioural pattern (e.g., red or green). Once established, this dominance triggers a system-level propagation effect (represented by colour-coded concentric ripples) that influences the surrounding nodes, without requiring further direct inter-agent communication.  
Returning to the example of waste disposal, the external or \"third entity\" that influences citizens' behaviour can either be the surrounding environment (e.g., overflowing bins, a general perception of neglect and decay, lack of cleanliness, etc.) or regulatory authorities (such as municipalities or regional governments), which may introduce incentives or penalties informed by the historical average behaviour of citizens.  
Figure 2: An example of direct interactions, based on peer dialogue and internal rules.
## 4 Engineering Cases
### Establishment of Finite Element Calculation Model
#### 4.1.2 Computational Parameters for Structural Analysis  
1. Material and cross-section properties:  
The concrete of the main beam is C55, the elastic modulus of concrete is 3.45 \(\times\) 10\({}^{4}\) MPa, the longitudinal prestress of the main bridge is 15\(\phi\)s15.2 with a 19\(\phi\)s15.2 steel hinge harness, and the control stress under the tension anchor of the steel harness is 1395 MPa. In the calculation of prestress loss, the deviation coefficient of the passage is K = 0.002, the friction coefficient of the pipeline is \(\mu\) = 0.25, and the retraction of the anchor at one end is \(\Delta\) = 6 mm.
2. Bridge deck pavement and load value setting:  
The weight of the load hanging basket is 80 t. The concrete bulk density is 26 \(\mathrm{KN/m^{3}}\). The second phase of the pavement structure contains an 8 cm concrete leveling layer + 10 cm asphalt concrete surface layer, and the second phase's dead load is 65 \(\mathrm{KN/m}\). The loading age of each cantilever cast-in-place beam segment is 10 days. Concrete shrinkage and creep coefficients are adopted according to the standard, and their calculation considers the effects of local temperature differences in the structure, considering the actual loading age of the concrete, according to the standard.
# Sigma Routing Metric for RPL Protocol
## 6 Results
### Latency of the Network  
OF0 is presented in relation to ETX, MRHOF, PH-ETX, and SIGMA-ETX, the latter of which has a significantly higher latency percentage. According to Figure 11, it can be seen that SIGMA-ETX is superior in performance to OF0 and MRHOF. As for PH-ETX, the performance difference with the proposed metric is not as significant as with OF0 and MRHOF, but it is still noteworthy. To accomplish that, SIGMA-ETX makes an optimal route selection, preferring paths with fewer long hops, and thus making it superior to the other three metrics. In terms of SIGMA-ETX, detailing the results at the 15 th hop, significant differences can be noted against OF0 (23.33%), MRHOF (56.45%) and PH-ETX (83.33%).  
Based on the Figure 12, the network latency does not change much between the four methods for the 20 nodes scenario, due to the low number of nodes. The 40 nodes scenario does not show much difference, but it becomes more apparent in the 50 and 100 nodes scenarios. In terms of percentages, SIGMA-ETX comes first among the metrics, with a better latency performance, beating the next best metric by 3% in the 100 nodes scenario. While this might seem of little significance, it suggests a better quality  
Figure 11: Latency of the network.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Performance Metrics  
A set of common evaluation metrics is adopted to evaluate the performance of deep-learning models according to their accuracy in detecting floods in SAR images. We calculate these measures to investigate how well the model distinguishes between the flood and non-flood pixels. The performance measures used in this study are:  
Accuracy measures the proportion of correctly classified pixels, including flood and non-flood categories, compared to the total pixel count in the dataset. It is defined as:  
\[Accuracy=\left(TP+TN\right)/\left(TP+TN+FP+FN\right) \tag{9}\]  
where:  
* _TP_ = True Positives (correctly detected flood pixels)
* _TN_ = True Negatives (correctly detected non-flood pixels)
* _FP_ = False Positives (non-flood pixels classified as flood)
* _FN_ = False Negatives (flood pixels classified as non-flood)  
[PERSON]'s Kappa coefficient evaluates the alignment between predicted and actual classifications, factoring in chance agreement. Its calculation is as follows:  
\[\kappa=\left(P_{o}-P_{e}\right)/\left(1-P_{e}\right) \tag{10}\]  
where:  
* \(P_{o}\) = Observed agreement (the actual accuracy of the model)
* \(P_{e}\) = Expected agreement by chance (the agreement expected if the model were to classify randomly)  
A higher Kappa value signifies improved agreement beyond what would be anticipated by chance.  
The \(F1\) Score represents the harmonic mean of precision and recall. This metric is particularly valuable for handling imbalanced datasets because it balances the trade-off between false positives and false negatives:  
\[F1=\left(2\times Precision\times Recall\right)/\left(Precision+Recall\right)= \left(2 TP\right)/\left(2 TP+FP+FN\right) \tag{11}\]  
where:  
* Precision is the proportion of detected flood pixels that are correctly classified as flood
* Recall is the proportion of actual flood pixels that are correctly detected  
The mean Intersection-over-Union (mIoU) metric measures the overlap between predicted and actual flood areas. It represents the average Intersection over Union for both flood and non-flood classes:  
\[\text{mIoU}=0.5\,*\,\left(\left(\left(TP/\left(TP+FP+FN\right)\right)+\left( \left(TN/\left(TN+FN+FP\right)\right.\right)\right)\right) \tag{12}\]  
This metric evaluates the model's ability to correctly distinguish between flood and non-flood areas by determining the ratio of the intersection of predicted and actual regions to their union.
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 2 Method
### Deep-Reinforcement-Learning Algorithm Combined with Manipulator  
This paper models the path-tracking problem of manipulator as a Markov decision process (MDP), which is represented by \(<S,\ A,\ R,\ T,\ \gamma>\), where \(s_{t}\in S\) represents the observations of the agent. The policy \(\Pi:S\to A\) maps the current environmental state \(s_{t}\) to the control input \(a_{t}\in A\) of each joint of the manipulator, \(T(s_{t+1}|s_{t},\ a_{t})\) represents the dynamic characteristics of the robotic arm, that is, the probability that the system transitions from state \(s_{t}\) to \(s_{t+1}\) under the control of \(a_{t}\). The expected path \(P^{*}\in\mathbb{R}^{N\times 3}\) of the manipulator can be generated by traditional path-planning methods, where N is the number of points on the path. The instantaneous reward obtained by the agent at time \(t\) is represented by \(r_{t}\in R\), which is related to the tracking accuracy of the robot arm on the desired path and the energy consumed. The policy continuously interacts with the manipulator system to obtain the sampling trajectory \(\tau=\{s_{0},a_{0},\cdots,s_{t},a_{t},\ \cdots,s_{T},a_{T}\}\). The objective of reinforcement learning is to maximize the expected cumulative reward that the agent receives, as illustrated in Equation (12).  
\[\text{maximize}_{\pi\in\prod}E_{\tau\sim p(\tau)}\left[\sum_{t=0}^{T}\gamma^{t }r(s_{t},a_{t})\right] \tag{12}\]  
where  
\[p(\tau)=p(s_{0})\prod_{t=0}^{T}\pi(a_{t}|s_{t})T(s_{t+1}|s_{t},a_{t}) \tag{13}\]  
Figure 1 depicts the framework of the deep-reinforcement-learning-based robot-arm path-tracking model. The framework is made up of the manipulator body, the desired path, the control strategy, and the feedback controller. The tracking error of the desired path minus the actual path of the manipulator is the input signal. The input signal used by the control strategy to determine the anticipated position and speed of each joint at the following instant serves as the reference signal for the feedback controller. The feedback controller combines each joint's current position and velocity data and produces the necessary joint torque to move the manipulator's end point, thereby performing the path-tracking function.
# Elastica of Non-Prismatic and Nonlinear Elastic Cantilever Beams under Combined Loading
## 2 Geometry of the Non-Prismatic Beam  
In general, the configuration of a non-prismatic member is arbitrary. In this study, the geometry of a cantilever beam with a span length of \(l\) (Figure 1) was selected as a representative non-prismatic member. The beam cross-section is solid and rectangular with a width and height that vary linearly in the \(z\) and \(y\) directions, respectively, along the axial length \(s\). The width and height along \(s\) are denoted as \(B\) and \(H\), respectively. Those at the clamped end \(c\) (\(s=0\)) are denoted as \(B_{c}\) and \(H_{c}\), respectively. Those at the free tip \(f\) (\(s=l\)) are denoted as \(B_{f}\) and \(H_{f}\), respectively. It should be noted that the \(z\) axis is the bending axis.  
To express \(B\) and \(H\) along \(s\) functionally, the two following non-dimensional section ratios are defined:  
\[r_{B}=\frac{B_{f}}{B_{c}},\;r_{H}=\frac{H_{f}}{H_{c}}. \tag{1}\]  
Considering \(r_{B}\) and \(r_{H}\) as defined above, \(B\) and \(H\) are expressed as linear functions of \(s\) as follows:  
\[B=B_{c}\Big{[}(r_{B}-1)\frac{s}{l}+1\Big{]}=B_{c}\Big{(}\alpha_{B}\frac{s}{l}+ 1\Big{)};\;\alpha_{B}=r_{B}-1, \tag{2}\]  
\[H=H_{c}\Big{[}(r_{H}-1)\frac{s}{l}+1\Big{]}=H_{c}\Big{(}\alpha_{B}\frac{s}{l}+ 1\Big{)};\;\alpha_{H}=r_{H}-1. \tag{3}\]The geometry of other types of non-prismatic beams other than the aforementioned linearly varying rectangular cross-section can be easily formed in a similar manner.
## 4 Conclusions  
Through orthogonal experiments combined with numerical simulation, the effects of room and opening parameters on T-wall performance in Tibetan dwellings in Sichuan-Tibet alpine valley areas are studied. The research conclusions are as follows:  
(1) For the study of a single room in a Tibetan dwelling, after applying a T-wall, the indoor air temperature range of the room in January, which is the coldest month, is 2.2 \({}^{\circ}\)C\(\sim\)24.5 \({}^{\circ}\)C, and the range of the average indoor air temperature range was from 9.1 \({}^{\circ}\)C to 12.2 \({}^{\circ}\)C. The average indoor temperature is much higher than the average indoor temperature of the existing Tibetan dwelling test room (\(-\)0.94 \({}^{\circ}\)C). The room's energy consumption per square meter in January is between 4.0 and 8.0 kW\(\cdot\)h/m\({}^{2}\).  
(2) The room configuration has a significant impact on the performance of a T-wall. The four influencing factors identified in the study have a highly significant effect on room temperature and energy consumption. The rankings were consistent for their effects on both room temperature and energy consumption, as Factor A (the length of the room) > Factor B (the width of the room) > Factor C (the width of the opening on the north wall of the room) > Factor D (the distance from the lower edge of the opening to the floor).  
Figure 10: The configuration parameters of the room corresponding to the optimal combination.  
Figure 9: Room energy consumption per square meter, in January, of the 27 schemes.  
(3) The optimal combination for room temperature, determined by range analysis, is S26, and that for energy consumption is S27. Through further simulation analysis between S26 and S27, S26 was determined to be the best combination with the highest indoor air temperature and low energy consumption. Factors (A, B, C, D) in S26 are, respectively (12,300, 5100, 1500, 0) mm.
## 2 Methodology
### Data Collection
#### 2.2.3 NAO Robot in Schools  
Once both systems were ready for demonstration, in the subsequent schools, the teachers interacted with an actual NAO robot. The demonstration had two parts: (1) the teacher conversing with NAO and (2) the teacher observing pupils being taught by NAO. In the first part, teachers had an average of 20 min of conversation with NAO, where they were encouraged to try out different conversational topics, from academic to general knowledge. Before starting, they could choose their language of preference between English and Swahili. This was because English is a medium of instruction in most private primary schools.  
In the second part, teachers observed the NAO robot teaching pupils, where it introduced the topic, provided examples, gave exercises for assessment, and provided appropriate personalised feedback (Figure 2). Each teacher got to observe at least six pupils having such an interaction with the NAO robot, with each interaction lasting approximately 15-20 min. Moreover, the teachers were asked to note down key observations during the entire experience.  
Thereafter, we administered semi-structured interviews about the entire experience and their opinions on using a NAO robot as a teacher in school. Moreover, using interviews enabled us to probe for more information, which added clarity and depth to our findings. Additionally, as the team was physically at the schools, observation was used to check the validity of some of the responses and determine the ground truth.