esa-sceva/satcom-chunk-collection huggingface dataset:
# Human-Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery
## 2 Robot Motion Constraint Model Based on Virtual Fixture
### Motion Constraint Requirements Analysis  
Generally, the trajectory of the endoscope at the end of the robot in endoscopic sinus surgery is shown in Figure 1.  
Figure 1: Trajectory of the endoscope at the end of the robot during endoscopic sinus surgery (taking the right maxillary sinus as an example).  
It can be seen from Figure 1 that the movement of the endoscope can be mainly divided into four stages:  
The first stage is from point A to point B: The endoscope moves from a safe area away from the patient (point A) to the vicinity of the patient's nasal inlet (point B). In this stage, since the endoscope is far away from the patient, it is not easy to collide with the patient. Therefore, it is only necessary to construct a plane-based FRVF to prevent the endoscope from colliding with the patient.  
The second stage is from point B to point C: When the endoscope moves further from point B to point C, the endoscope gradually approaches point C at the patient's nasal inlet. To prevent the endoscope from colliding with the patient during this stage, a single-leaf hyperboloid based FRVF is constructed to guide and constrain the movement of the endoscope to prevent it from colliding with the patient's nasal inlet.  
The third stage is from point C to point D: When the endoscope moves from point C at the inlet of the nose to point D in the surgical area, the endoscope movement path is in the nasal cavity of the patient. Due to the narrow and complex anatomy of the nasal cavity, the endoscope easily collides with the nasal tissue during surgery; thus, a VF based on a spatial curve is constructed to guide the motion of the robot.  
The fourth stage: After the endoscope reaches the surgical area, the surgeon constantly adjusts the endoscope posture to obtain a suitable surgical vision field according to the needs of the operation. To prevent collision and interference between the endoscope and nasal tissue, an FRVF based on the anatomical structure of the nasal cavity is constructed to constrain the motion of the endoscope.
## 3 Materials and Methods
### Data Preparation
#### 3.3.2 Feature Selection
Hence, we applied both scaling techniques to minimize the accuracy loss and decrease the computing cost as the data values varied significantly across multiple orders of magnitude. There are two scaling methods. One is min-max normalization, which scales the values between 0 and 1 for each feature in the feature vectors. The other is standardization, which is a different approach to scaling, in which the numbers or qualities are centered around the mean (\(\mu\)), and a unit standard deviation (\(\sigma\)) is used [71]. The feature vectors were scaled and normalized for this analysis and calculated using (3) and (4), where \(X_{min}\) and \(X_{max}\) are the minimum and maximum values of \(X\) feature in normalization; on the other hand, \(\mu_{X}\) and \(\sigma_{X}\) are the mean and standard deviation for the calculation of the standard scaler. \[X_{norm}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{3}\]  
\[X_{std}=\frac{X-\mu_{X}}{\sigma_{X}} \tag{4}\]  
**Data Augmentation:** The consensus is that DL methods achieve good accuracy on massive datasets (i.e., a number of samples in the millions). However, if the data are modest or relatively small (i.e., a sample number less than one thousand), then there is a possibility  
Figure 5: Visualization of five types of correlation. of overfitting. Data augmentation is often carried out when there are insufficient data for a DL model. During this procedure, the data are artificially enhanced with the use of image alteration or enhancement operations such as flipping, rotation, cropping, shifting, scaling, blurring, noise, and so on [72; 73]. Data augmentation was crucial here as the amount of existing data or the number of observations was modest (i.e., several hundreds). As a result, we chose several augmentation methods to enrich our data.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Modulation
#### 2.2.4 Chirp Slope Keying  
A fast way to modulate the binary sequence is to have a simple decision block, that will output a chirp sequence of either upward or downward slope according to the binary value at the input (see Algorithm A5). The segments of the output sequence \(y_{\text{tx}}\) are assembled by filling each interval of length \(N_{\text{ref}}\) with the normalized sum of the superposed channels' sequences to  
\[y_{\text{tx}}[n]=\frac{1}{N_{\text{lo}}}\sum_{l}^{N_{\text{lo}}}y_{\text{ref}} [n,\,l,\,d_{\text{mux}}[k,\,l]]. \tag{14}\]  
The output sequence is transferred into an analog signal \(s_{\text{tx}}\), e.g., by a DAC, amplified by the PA and transmitted. Alternatively, this modulation can be calculated by zero-padding the multiplexed bit sequences \(d_{\text{mux}}\) by length \(N_{\text{ref}}\) and convolving the resulting sequence with the reference signal \(y_{\text{ref}}\), but this approach is neither efficient in memory usage, nor the calculation steps required [49].
## 3 Methodology  
In this section, the methods for LULC classification based on fusion feature image are described in detail. Firstly, the features were extracted from Landsat 8 OLI and ALOS 2 SAR satellite images, and redundancy was removed from high-dimensional features. Then, we retained the features with low correlation and eliminated other redundant features. Features level fusion was conducted based on a certain weight. An adaptive multi-scale segmentation model was used to create image objects and optimize the scale parameters, and then an improved SVM classifier was used to conduct LULC classification experiments in the study area. The concept of the novel feature-level fusion framework and the detailed features processing is demonstrated in Section 3.1. Section 3.2 describes the basic concept of several classification system and LULC categories selected in this article. To the conduct experiment and assess the performance of the applied methods, the improved SVM classifier and its characters are described in Section 3.3.  
Figure 5: Terrain correction of ALOS-2 data. (**a**) Before terrain correction; (**b**) After terrain correction.
## 3 Proposed Framework (Fed-DTB)
### Implementation of Vehicle Selection Mechanism  
The vehicle selection mechanism, described in Section 2.3.2, selects the most trustworthy vehicles using trust scores and IDS alerts to maximise the total trust score while excluding those with low scores or IDS alerts. This process solves the optimisation problem defined in Equation (5), subject to the constraints in Equations (6)-(9). This optimisation problem is solved using a threshold approach, Equation (10), as presented in Algorithm 2.  
Formulating the selection as an optimisation problem ensures that the chosen subset of vehicles reflects the current environment, balancing factors such as network stability, energy efficiency, and compliance.  
```
0: Set of vehicles \(V\), trust scores \(\{\tau_{i}(t)\}\), IDS alert scores \(\{\tau_{li}(t)\}\), maximum number of selected vehicles \(K\), minimum trust score threshold \(\tau_{\text{min}}\)
0: Set of selected vehicles \(V_{s}\)
1:for each vehicle \(v_{i}\in V\)do
2:if\(\tau_{li}(t)=0\)then
3: Set \(\tau_{i}(t)=0\)
4:endif
5:endfor
6: Sort vehicles in descending order of \(\tau_{i}(t)\)
7:\(V_{s}=\text{Top }K\) vehicles with \(\tau_{i}(t)\geq\tau_{\text{min}}\)
8:return\(V_{s}\)
```  
**Algorithm 2** Vehicle Selection Mechanism  
Figure 3: Trust Score Model Architecture.
## 3 Case Study
### Model Application to Thermal Energy Networks  
Among the strategies to improve the operational management of DH networks, the adoption of distributed thermal storage systems at the building level is gaining interest as a measure to shave peak demands [23]. Therefore, the first case study considered in this article is the reduction of the peak thermal request from a substation of the DH network of Torino (Italy), which is characterized by a climate zone E. This substation belongs to a building known as the Energy Center, which is a research center owned by Politecnico di Torino. Thanks to its monitoring system, the building energy data are available with a frequency of 15 min. For this case study, a representative day of January 2020 is taken as a reference. As can be seen in Figure 2, the building heat demand has a common shape characterized by a morning peak (from 6:30 to 9 am) and a rather steady thermal request during the remainder of the day. During the peak hours, the amount of heat requested to the DH network is 3100 MJ, which represents the 27.1% of the overall heat demand within the same day.  
Figure 2: Building heat demand (15-min average thermal power).  
Overall, an LHTS system composed of 300 units (791 MJ) is needed in order to achieve an energy demand reduction of 25.5% during the peak. However, due to the high power ceased or absorbed by each LHTS unit at the beginning of its operation, the overall storage system is subdivided into six bundles made of 50 units (tube + PCM) as depicted in Figure 3. These bundles are sequentially activated with a delay of few minutes both during charge (in the night) and discharge (in the morning peak). Moreover, each bundle follows the subsequent duty cycle: it is charged up to \(SOC\) = 0.97 and discharged down to \(SOC\) = 0.02. Further partial charge and discharge cycles are not considered for this case study. This is done since the building heat demand is rather constant for the rest of the day.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 3 Link Quality Measurement and the Metric for Evaluating
### Burstiness Distribution Metric
#### 3.2.2 Calculate Burstiness Distribution Metric  
To calculate the Bdist metric, we consider the possible loss ratio for each link, and determine the Bdist value from the BDL. We assume that a target PRR is considered. For example, to reach a PRR target of 99% for the data transmission period, the loss ratio should be lower than 1% for each link. If the route has h hops, then every link that the route goes through has a loss ratio lower than \(\sqrt[]{0.01}\). First, based on the hop count of the current node and target PRR, the end-to-end loss ratio (e2 eloss ratio) that we accepted to reach the PRR target is calculated by Equation (1):  
\[\text{e2e}_{\text{loss ratio}}=1-\sqrt[]{\text{PRR}_{\text{target}}} \tag{1}\]  
Then, we can calculate the number of packet loss threshold (denoted Nloss_threshold) corresponding to the number of probes (denoted Nprobes) with Equation (2):  
\[\text{N}_{\text{loss\_threshold}}=\text{e2e}_{\text{loss ratio}}\times\text {N}_{\text{probes}} \tag{2}\]  
The number of current packet loss (denoted Ncurrent_loss) is calculated by multiply pair of {burstiness_value, burstiness_time_count} at ith in BDL as follows:  
\[\text{N}_{\text{current\_loss}}=\text{i}\;\text{*}\;\text{BDL[i]};\]  
in which, i is burstiness value, BDL[i] is burstiness_time_count of i.  
The Bdist metric is set equal to the burstiness value, such that the total of \(\text{N}_{\text{current\_loss}}\) lower than, or equal to, the \(\text{N}_{\text{loss\_threshold}}\). The detailed calculation of Bdist is in Algorithm 2 and the flowchart for Algorithm 2 is in Figure 4.  
```
Input : Ascending sorted burstiness distribution list (BDL),  number of probes (\(\text{N}_{\text{probes}}\)), target PRR (PRRarget), hop count (h) Output : Burstiness distribution metric (Bdist)
1 : Calculate end-to-end loss ratio by using Equation (1)
2 : Calculate \(\text{N}_{\text{loss\_threshold}}\) by using Equation (2)
3 : Initialize the \(\text{N}_{\text{current\_loss}}=\text{Bmax}^{*}\text{BDL}[\text{Bmax}]\)
4 : For loop in BDL from i = Bmax to 0
5 : If(\(\text{N}_{\text{current\_loss}}\leq\text{N}_{\text{loss\_threshold}}\))
6 : \(\text{N}_{\text{current\_loss}}\) += i * BDL[i];
7 : Else
8 : Bdist = i + 1;
9 : Break;
10 : End if
11 : End for
12 : Return Bdist
```  
**Algorithm 2**Calculate Burstiness Distribution Metric  
Figure 3: The flowchart of the main procedures in Algorithm 1.  
For example, a one-hop link has a target PRR of 99%, and the number of probes is 1000 in order to measure the link quality, which means that 1% of 1000 packets can be lost. The number of packets that can be lost during data transmission is 10 packets. In the BDL in Figure 2, we can calculate the Bdist value for successful packet transmission as 2 since the consecutive loss of two times transmission in the link at a PRR of 90% was 10 packets, equal to the allowed packet losses.
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 2 Materials and Methods
### Modified Algorithm
```
1:functionModifiedLandingStripDetection(image)
2:Input: image, a georeferenced satellite image with dimensions \(width\times height\)
3:Output: A set of geographical coordinates of detected landing strips
4:Step 1: Patch Extraction and Processing
5: Patch size: \(352\times 352\)
6: Step size for patch movement: \((88,88)\) (75% overlap)
7: Initialize list of detected centroids: \(C=[]\)
8:for\(x=1\) to width with step size 88 do
9:for\(y=1\) to height with step size 88 do
10: Extract patch \(p(x,y)\) from image with top-left corner at \((x,y)\)
11:Step 2: Classification
12: Apply the classification function \(f_{\text{class}}(p(x,y))\)
13:if\(f_{\text{class}}(p(x,y))=0\) (Non-Landing Strip) then
14: Discard the patch
15:else
16:Step 3: Segmentation
17: Generate the segment \(S(x,y)=f_{\text{seg}}(p(x,y))\)
18:Step 4: Enhanced Validation
19: Calculate the NDWI for the segment pixels
20:if any pixel in segment represents water bodies then
21: Discard the patch
22:endif
23: Calculate the minimum bounding box \(B(x,y)\) for the segment \(S(x,y)\)
24: Calculate the Circularity Ratio \(CR(B(x,y))\)
25:if\(CR(B(x,y))>0.1\)then
26: Discard the patch
27:endif
28: Calculate the centroid \(C(x,y)=\frac{1}{n}\sum_{j=1}^{n}x_{j}\) of the segment
29:if distance to any federal or state highway \(<50\) meters then
30: Discard the location
31:else
32: Append the centroid \(C(x,y)\) to the list \(C\)
33:endif
34:endif
35:endfor
36:endfor
37:Step 5: Clustering of Detected Points
38: Perform clustering on centroids \(C\) using hierarchical clustering
39: Merge points within a distance threshold of 1250 meters
40:Step 6: Enhanced Classification
41:for each centroid \(C_{i}\) in \(C\) do
42: Obtain images of dimensions \(2\times 2\) km around \(C_{i}\)
43: Apply new classification function \(f_{\text{new\_class}}(image_{2x2})\)
44:if\(f_{\text{new\_class}}(image_{2x2})\
eq 0\) (Landing Strip) then
45: Add \(C_{i}\) to the final list of predicted landing strips
46:endif
47:endfor
48: Return the final set of predicted landing strips
49:endfunction
```  
**Algorithm 2** Modified Landing Strip Detection Algorithm
## 1 Introduction
It was shown that the non-parametric models (NP-KD, NP-JW) generally outperformed the parametric models (AG, Weibull, Rayleigh, JW-TNW, JW-FMN) and showed a more robust performance in fitting the joint speed and direction distributions. [PERSON] et al. (2019) used the multiplication theorem and the AL model to model the joint probability distribution of wind speed and direction, respectively. The results show that the joint probability density function of wind speed and direction derived from the AL model is better than that based on the multiplication theorem, and that ignoring the effects of wind direction significantly improves estimates of extreme wind speeds. [PERSON] et al. (2019); [PERSON] et al. (2020) proposed a new method for establishing a joint distribution model, based on a wind rose diagram using a continuous AL joint distribution model, drawing a new wind speed and direction distribution diagram. The results show that the statistical model has high reliability and a strong correlation with the original data distribution. Previous researchers have mainly focused on the modeling of wind speed distribution or joint distribution of wind speed and direction, i.e., one or two-dimensional joint distribution models; however, for wind power in small geographic areas, in areas with complex tall buildings ([PERSON] et al., 2019; [PERSON] et al., 2020) and so on, it is necessary to take three-dimensional joint distribution into consideration. Specifically, the spatial correlation of wind power is concerned when clusters of wind generators are spread over small geographic areas, and a suitable three-dimensional joint distribution model can describe the correlation suitably. For flexible tall buildings, wind-induced dynamic responses are three-dimensional, so the three-dimensional wind load should be clearly described. For the third dimension, the height direction, the change in wind speed is usually considered by exponential law and logarithmic law; however, some studies ([PERSON] et al., 2019; [PERSON] et al., 2020) show that these laws do not describe the variation law of wind speed in some areas. Therefore, the three-dimensional modeling of wind speed probability distribution is necessary. Based on the AL (angular-linear) model and the copula function, this paper proposes a modeling method to describe the three-dimensional distribution of wind speed.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Beaconless Opportunistic Routing Protocol
#### 4.4.3 Discussion and Analysis  
To summarize the two protocols above, the information of QoS parameters is compared in Table 5. In order to improve the robust of routing protocols, each node will send the data to all its neighbors, and the information of its neighbors is needed. Therefore, the indexes of link quality and geographic location are considered during the design of routing protocols. Due to that all the neighbors of a sender must receive, save, and forward the relayed data, thus the transmission overhead will be very high. However, this index is not considered. The authors of [52] considered the buffer state; however, this index can not accurately represent the level of network overhead directly. The authors of [51; 52] aim to transmit large video streams in FANET by creating reliable and consistent multi-hop paths in network. In [51], a set of cross-layer parameters is combined to calculate the DFD, including queue length, link quality, geographic location, and residual energy. However, the high mobility of the UAVs results in the deterioration of video transmission quality, since this protocol does not guarantee reliable communication links. To solve this problem, the author of [52] adds position prediction scheme in design of routing, considering the nodes' velocity and 3D position information, which can improve the reliability of communication paths. Compared to XLinGO in article [51], the CABR [52] improves the transmission quality of video streams, reduces transmission delay and provides persistent routing in FANET.  
Some traditional routing protocols that rely on end-to-end routing (such as DSR, AODV, or OLSR) have many disadvantages, because when nodes move or wireless channels change, end-to-end routing may be frequently interrupted or not always exist, such as in FANET. In this case, using the principle of opportunistic routing can improve mission success rate. However, some unresolved issues have also been noted, which are explained below.  
(1) How to reduce the impact of UAV mobility to prevent communication defects, delays, and void space, thus reducing packet loss in video transmission?  
(2) How to ensure continuous network connection during video transmission?  
Therefore, we need to study the cross-layer structure in FANET to improve the accuracy of video transmission service and better relay placement mechanism to ensure the link connection between UAVs.
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 5 Experiments and Evaluation
### Data Set  
We selected the data set collected by a national key research and development project in the live network environment as the training set, and part of it as the test set. The continuous collection period was 30 days. The data set includes behavioral events such as network traffic, host process, and HTTP request. The event contains 21 fields, such as timestamp, host ID, event ID, the request method, process status, and the name of the user who initiated the event, involving 323,263 events. The primary characteristic data contained in the data set are shown in Table 5.  
**Data Preprocessing.** Since the intrusion behavior in the live network is less than the expected behavior and the sparsity is high, the method in Figure 5 is used to resample the intrusion behavior and normal behavior to generate a labeled normal and abnormal event sequence. In the end, we screened out 1505 normal host nodes from the data set, including five host nodes with sparse behavior and 301 abnormal host nodes.  
According to the resampled data, the event association graph generated by the experiment is shown in Figure 6. The actual _local_bound_size_ = 10 in experiments; the larger the point in the graph, the larger the degree of the point.  
Figure 5: Schematic representation of the data resampling method: The interval between normal events and abnormal events is (Vague) 10 min. The abnormal events (Attacking) time length is 20 min. The resampling window length is 10 min, and the sliding step is 1 min.  
Figure 6: Generated event association graph: The dots in the graph are visual representations of events in the association graph, and the larger the dots, the more events they are associated with. The event node information in the graph includes the event itself, domain, and time information, and the edges are the relationships between events.  
\begin{table}
\begin{tabular}{c c c} \hline
**Timespan** & **Type** & **Content** \\ \hline
30 days & Network Traffics & timestamp, src\_ip, src\_port, dst\_ip, dst\_port, etc. \\
30 days & Process & pid, user\_id, session\_id, etc. \\
30 days & Service Requests & timestamp, src\_host, dst\_host, request mode, request path, etc. \\
30 days & Entities & host, user, service, application, etc. \\ \hline \end{tabular}
\end{table}
Table 5: Examples of events data collected by a national key R&D plan.