esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Verify the SIF Anomaly Index
#### 4.4.2 Correlation Analyses between Drought Index and Areas Covered by Drought  
The areas affected by drought serve as a direct manifestation in the field of drought. The areas affected by drought are displayed by statistical data which can characterize the extent of the impact of drought. The drought-affected crop area is more appropriate to assess the drought effect in Henan Province. This study utilized data on drought-affected areas in Henan Province, obtained from the National Bureau of Statistics. Due to the unavailability of data for certain years, the study selected data from two periods: 2001 to 2014 and 2014 to 2019. The average SIF anomaly index values with 1 km spatial resolution were obtained by averaging the pixel values of the whole image. Figure 9 illustrates the correlation between the average SIF anomaly index value and areas affected by drought. The results indicated that the index had negative correlations with areas affected by drought, with a correlation coefficient of \(-0.58\). This negative correlation between the drought index and the affected areas demonstrates that the SIF anomaly index is an excellent tool for monitoring drought.  
Figure 8: The correlations among the value of the SIF anomaly index and crop yield (black line is fitting line). (**a**) SIF index and wheat yield; (**b**) SIF index and maize yield.  
Figure 7: The yield changes in wheat and maize in Henan Province.
## 4 Experiments and Results
### Dataset Profiling  
Once the ITR for each transaction studied has been obtained, we can perform a comparative analysis to evaluate the ITR distribution. To achieve this, we proceed to perform Kendall correlation analysis of all the sequences in pairs. As a result, we obtain, for each dataset, a collection of Kendall correlations, of which distribution is presented in the histogram form, as shown in Figure 10. Since there is a discrete number of possible combinations, the Kendall correlation reaches corresponding discrete values that may eventually correspond to datasets that share similar characteristics. In Figure 10, it can be seen for case (a) corresponding to the synthetic dataset, how a clear bimodality is visible in the values 1 and 0.6. This bimodality is repeated in the PaySim case at 0.85 and 1, although in the case of the German Dataset, the population model is closer to a Gaussian distribution.  
Under a consolidated perspective, Table 6 reports the average of all \(\tau\) correlations for each of the three datasets. As can be seen, a greater similarity can be seen in terms of the informative features and their contribution to the model in the case of PaySim, followed by the synthetic dataset, and, with lower values, in the case of the German Dataset. From this perspective, this parameter provides information regarding the dispersion in terms of the number of different models necessary to be able to characterize the entire dataset under study, and therefore it can be understood in absolute value as the inverse of the level of complexity necessary to approximate, by linear means, the underlying reality.  
Figure 9: Different groups of ITR in PaySim Dataset. Column (**a**) represents the ITR. In column (**b**) we can see all the transaction with the same ITR with the class predicted with our methodology. Column (**c**) is similar to (**b**) but it only shows the mismatch. Finally, (**d**) shows the confusion matrix for all the transactions with the same ITR.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
###### Abstract  
In complex terrain, ground-based lidar wind speed measurements sometimes show noticeable differences compared to measurements made with in-situ sensors mounted on meteorological masts. These differences are mostly caused by the inhomogeneities of the flow field and the applied reconstruction methods. This study investigates three different methods to optimize the reconstruction algorithm in order to improve the agreement between lidar measurements and data from sensors on meteorological masts. The methods include a typical velocity azimuth display (VAD) method, a leave-one-out cross-validation method, and a linear model which takes into account the gradients of the wind velocity components. In addition, further aspects such as the influence of the half opening angle of the scanning cone and the scan duration are considered. The measurements were carried out with two different lidar systems, that measured simultaneously. The reference was a 100 m high meteorological mast. The measurements took place in complex terrain characterized by a 150 m high escarment. The results from the individual methods are quantitatively compared with the measurements of the cup anemometer mounted on the meteorological mast by means of the three parameters of a linear regression (slope, offset, \(R^{2}\)) and the width of the 5 th-95 th quantile. The results show that expanding the half angle of the scanning cone from \(20^{\circ}\) to \(55^{\circ}\) reduces the offset by a factor of 14.9, but reducing the scan duration does not have an observable benefit. The linear method has the lowest uncertainty and the best agreement with the reference data (i.e., lowest offset and scatter) of all of the methods that were investigated.  
complex terrain; complex flow; lidar; VAD; remote sensing; wind energy +
Footnote †: journal: Accepted: 10 September 2018; Published: 13 September 2018
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 6 Performance Evaluation
### Network Cost  
The service cost across all the network is proportional to the aggregated delays across the paths, i.e., nodes and links in the request path occupy resources that add to the total cost. Namely, the cost for a single request \(r\) of \(Path_{r}\) is gauged as,  
\[cost(r)\ =\ \sum_{n_{i}\in path_{r}}\sum_{f_{t}\in path_{r}}\Lambda(n_{i})a _{r}+\sum_{c\in path_{r}}\sum_{v_{c}\in path_{r}}\Lambda(e)b_{r}, \tag{13}\]  
where the variable \(\Lambda(n_{i})\) represents cost of the node per usage unit for the cached and uncached NFs requested by the request. Further, the variable \(\Lambda(e)\) denotes the cost of the usage unit for the link that is proportional to the link bandwidth demands \(b_{r}\).  
For further analysis on cost, the work in [35] presents a comprehensive cost model to \"minimize application deployment cost and maximize the cloud owner's revenue in terms  
Figure 3: Service Delay for Various MS Requests.  
of the requested traffic, the backhaul capacity, and the number of MEC servers\". The model incorporates the leasing cost of the MEC and cloud resources by end-users considering latency requirements. It also includes the deployment cost of servers (e.g., software running cost), the number of users using the server, and the size of their traffic.  
Figure 4 shows that the cost for the two proposed methods is approximately similar with a slight increase for the clustered approach. This is attributed to the high utilization of the secondary node in each cluster during the entire service time, along with the cost of the primary nodes and links that route the requests to the secondary nodes in Layer III. For example, the cost approaches 78 units at 80 requests. The distributed method requires multiple primary nodes to cache the various NFs, which also increases the cost. However, the reduction in cost here is due to the unoccupancy on the secondary nodes, particularly at low traffic volumes. However, at higher traffic, the distributed method is compelled to map uncached NFs on the secondary nodes to accommodate the high demand, in particular after 80 requests.  
The uncached provisioning methods demand a higher number of nodes in the request path, given the redundant mapping of all incoming NFs, regardless of the traffic popularity; this is mostly shown in the clustered approach that favors nodes in the upper fog layer that features more available resources, at the detriment of increased cost. For example, the cost is increased by 10% for the clustered method as compared to the cached counterpart at 200 requests, and 35% increase for the distributed uncached method.
## 2 Materials and Methods
### Description of Experimental Setup  
The experimental tests were carried out using a Glorystar GS4020 (Glorystar Laser Technology Co., Ltd., Dongguan, China) fiber laser cutting machine with a maximum laser power of 1.5 kW. This machine ensures high cutting precision and stability and is therefore suitable for processing various types of metal sheets.  
The material used in this study was cold-rolled low-carbon steel of grade EN 10130, which is widely used in various industries due to its excellent surface finish and dimensional stability. The sheet thickness used for the experiments was 5 mm. During the laser process, the cutting speed, laser power, and gas pressure were varied. The laser power was set within a certain percentage of the maximum machine power (1.5 kW), while the cutting speed was selected to ensure efficient material separation and minimal thermal deformation. Oxygen was used as an auxiliary gas. The selected cutting modes are listed in Table 1. Parameters and their levels during laser cutting.  
An illustration of a steel sheet cutting experiment is presented in Figure 2. In all experiments, various laser cutting parameters were kept constant to ensure process stability  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **A** & **B** & **C** \\ \hline Cutting speed & & & \\ [m/min] & 0.9 & 1.1 & 1.3 \\ Laser power [\%] & 80 & 84 & 88 \\ Gas pressure [bar] & 1 & 1.1 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and their levels during laser cutting.  
Figure 1: Workflow of RSM and machine learning integration for surface roughness prediction.  
and repeatability. Oxygen was used as the auxiliary gas, and a constant nozzle configuration with a double nozzle of 1.5 mm diameter was used. The cutting height was maintained at 0.7 mm, while the laser frequency was set to 5000 Hz. The laser focus position was set at \(-3.8\) mm and the focus setting of the device was kept at +2.6. These parameters were chosen based on manufacturer recommendations to ensure consistent cutting conditions across all trials.  
The surface roughness was evaluated with the average roughness parameter Ra, which was measured with a Mitutoyo Surffest SJ-210 (Mitutoyo Corporation, Kawasaki, Japan). The laser-cut surface usually consists of three different zones: the initial entry zone that occurs when the laser beam penetrates the material, a smooth zone that results from a stable cut, and a rough zone at the bottom, where the roughness is most pronounced. In this study, the width of the rough zone was used as a key indicator of cut quality, as it is the most sensitive to irregularities during processing. The roughness measurements were taken in the middle of this rough zone (Figure 3).
## 4 Comparison with Experimental Results
### Ultimate Strength
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Model**} & \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{**b/R (rad.)**} & \multicolumn{2}{c}{**Ultimate Strength (MPa)**} & \multirow{2}{*}{**FEA/Exp.**} \\ \cline{3-3} \cline{5-6}  & & & **Experiment** & & **FEA** \\ \hline F01 & & 0.00 & 218.98 & 190.97 & 0.8720 \\ C01 & & 0.20 & 218.65 & 265.84 & 1.2158 \\ C02 & & 0.10 & 203.46 & 243.53 & 1.1969 \\ C03 & & 0.05 & 209.63 & 222.90 & 1.0633 \\ \hline F02 & & 0.00 & 249.63 & 234.26 & 0.9384 \\ C04 & & 0.20 & 279.29 & 299.71 & 1.0731 \\ C05 & & 0.10 & 233.11 & 278.64 & 1.1953 \\ C06 & & 0.05 & 218.15 & 249.21 & 1.1424 \\ \hline F03 & & 0.00 & 226.42 & 257.06 & 1.1352 \\ C07 & & 0.20 & 255.18 & 319.30 & 1.2513 \\ C08 & 2.01 & 0.10 & 206.13 & 298.95 & 1.4503 \\ C09 & & 0.05 & 232.34 & 274.48 & 1.1813 \\ \hline F04 & & 0.00 & 240.68 & 205.32 & 0.8530 \\ C10 & & 0.20 & 240.28 & 271.73 & 1.1309 \\ C11 & & 0.10 & 217.47 & 253.84 & 1.1673 \\ C12 & & 0.05 & 207.13 & 201.67 & 0.9736 \\ \hline F05 & & 0.00 & 243.40 & 216.51 & 0.8895 \\ C13 & & 0.20 & 289.76 & 294.15 & 1.0151 \\ C14 & & 0.10 & 281.00 & 293.15 & 1.0433 \\ C15 & & 0.05 & 273.79 & 233.82 & 0.8540 \\ \hline F06 & & 0.00 & 266.54 & 240.20 & 0.9011 \\ C16 & & 0.20 & 251.66 & 315.99 & 1.2556 \\ C17 & & 0.10 & 234.66 & 322.47 & 1.3742 \\ C18 & & 0.05 & 254.34 & 259.92 & 1.0219 \\ \hline F07 & & 0.00 & 217.27 & 228.90 & 1.0535 \\ C19 & & 0.20 & 248.25 & 267.43 & 1.0772 \\ C20 & & 0.10 & 242.86 & 254.98 & 1.0499 \\ C21 & & 0.05 & 202.86 & 234.73 & 1.1571 \\ \hline F08 & & 0.00 & 243.87 & 234.26 & 0.9606 \\ C22 & & 0.20 & 275.95 & 303.82 & 1.1010 \\ C23 & & 0.10 & 235.86 & 283.76 & 1.2031 \\ C24 & & 0.05 & 225.30 & 267.77 & 1.1885 \\ \hline F09 & & 0.00 & 276.15 & 257.06 & 0.9308 \\ C25 & & 0.20 & 244.03 & 321.62 & 1.3180 \\ C26 & & 0.10 & 231.83 & 303.46 & 1.3090 \\ C27 & & 0.05 & 218.75 & 291.36 & 1.3319 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental and FE analysis results. Figure 14: Comparison of FE analysis and experimental results: (**a**) effect of flank angle and (**b**) slenderness ratio.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Initial Access
#### 2.3.2 Beam Management in 5G  
In NR, when operating in the beamforming mode, the UE must detect and choose the optimal beam for the RACH process. The Synchronization Signal Block (SSB) is closely related to beam management. SSBs are bursts of signals that contain essential information for the UE to perform initial cell search and synchronization with the gNB. In the context of beam management, SSBs are used to establish the initial beam-pair link between the UE and the gNB where the gNB transmits multiple SSBs, each on a different beam. The UE detects the best beam among them and sends PRACH to the location, which is mapped to a specific SSB beam ID. Beam management procedures are applied for both DL and UL transmission and reception [61, 62]. These procedures include the following:  
* Beam sweeping and Beam Alignment (BA) process: This involves broadcasting a series of beams across a defined spatial zone with predetermined timing and directional patterns. Each SS block corresponds to a specific beam.
* Beam measurement: This describes the assessment of signal reception quality at either the gNB or the UE, utilizing metrics like Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference and Noise Ratio (SINR), or Signal to Noise Ratio (SNR).
* Beam determination: This pertains to choosing the most appropriate beam or set of beams at the gNB or UE based on the data acquired from beam measurement activities.
* Beam reporting: This is a procedure during which the UE is communicating information about beam quality. The UE sends out the PRACH preamble that matches the SS block linked to the optimal beam. This direct correlation between the incoming SS block and the outgoing RACH preamble serves as the UE's method of indicating the best beam choice to the gNB.
* Beam recovery: This is a process that detects beam failure and searches for another candidate beam with good quality. When the number of beam failures reaches the limit in RRC, the UE triggers the beam failure recovery process with the candidate beam using PRACH identified by a preamble index [63].
## 2 Materials and Methods
### Final Model Evaluation  
The final results from the model prediction and probabilities were saved in a.csv file (detection_results.csv) for future analysis. Also, additional metrics, such as the total correct probabilities for argumentative and non-argumentative detections, were displayed and plotted. This included the probability for each separate detection, total F1 score, Accuracy, precision, recall, confusion matrices, ROC curves/AUC and positive and negative predictive values (NPV and PPV). These metrics provide a fine-grained understanding of the model's classification capacity.
## 3 Results
### A Pattern of Seismic Activity at the Globe after the Magnetic Storms on 17 March 2013 and 2015
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Data on**} & \multicolumn{2}{c}{**Data on Strong (M \(\geq\) 7.5) Earthquakes**} & \multicolumn{2}{c}{**The Time Delay between**} & **Magnetic Local Time (MLT)** \\ \# & \multicolumn{2}{c}{**(Date, Time of Onset (UT); Maximal Positive and Negative Values of the SYM/H Index (nT)**} & \multicolumn{2}{c}{**Following the Geomagnetic Storm.**} & **Geomagnetic Storm Onset** \\ \# & \multicolumn{2}{c}{**Maximal Positive and Negative Values of the SYM/H Index (nT)**} & \multicolumn{2}{c}{**(Region, Date, Coordinates, Depth-h km; Magnitude)**} & **and Earthquake Occurrence** & **Epicenter in a Moment of** \\ \hline
**1** & **2** & **3** & **4** & **5** \\ \hline
**1**. & 17 March 2013; 06:04; SYM/H: +33; -132 & Iran, 2013-04-16; 10:4420; 28.033”N, 6.9196”E; h = 80; M7.7 & 30 & 10.48 \\ \cline{3-6}  & & Okhotski Sea, Russia, 2013-05-24; 05:44:48; 54.892”N, 153.221”E; h = 598; M8.3 & 68 & 15.66 \\ \hline  & & Nepal, 2015-04-25; 06:11:25; 28.231”N, 84.731”E; h = 82; M7.8 & 39 & 10.54 \\ \cline{3-6}  & & Japan, 2015-05-30; 11:23:02; 27.839”N, 140.493”E; h = 664; M7.8 & 74 & 13.82 \\ \cline{3-6}
2. & 17 March 2015, 04:48; SYM/H: +67; -234 & Papua New Guinea, 2015-03-29; 23:48-31; 4.729”S, 152.562”E; h = 41; M7.5 & 12 & 15.63 \\ \cline{3-6}  & & Papua New Guinea, 2015-05-05; 01:44:06; 5.462”S, 151.875”E; h = 85; M7.5 & 49 & 15.57 \\ \hline
3. & 22 June 2015, 18:37; SYM/H: +88; -208 & Chile, 2015-09-16; 22:54:32; 31.573”S, 71.674”W; h = 22.4; M8.3 & 86 & 14.12 \\ \hline  & & Peru, 2015-11-24; 22:45:38; 24 October 2015, 19:00; SYM/H: +39 & Peru, 2015-11-24; 22:50:54; 10.060”S, 7.018”W; h = 620.6; M7.6 & 31 & 14.52 \\ \cline{3-6}
4. & 31 October 2012; 16:52; SYM/H: +35; -68 & Alaska, 2013-01-05; 08:58:14; 55.228”N, 134.859”W; h = 8.7; M7.5 & 66 & 7.15 \\ \hline
5. & 17 January 2013, 03:00; SYM/H: +55; -57 & Solomon Islands, 2013-02-06; 01:12:25; 10.799”S, 165.114”E; h = 24; M8.0 & 20 & 14.88 \\ \hline
6. & 4 August 2013, 06:11; SYM/H: +24; -56 & Pakistan, 2013-09-24; 11:29:47; 51 & 10.79 \\ \cline{3-6}
7. & 27 August 2013, 16:00; SYM/H: +13; -68 & 26.951”N, 8.6501”E; h = 30; M7.7 & 82 & 12.72 \\ \hline
8. & 20 September 2015, 06:18; SYM/H: +3; -84 & Afghanistan, 2015-10-26; 09:09-42; 23.652”N, 70.368”E; h = 231; M7.5 & 36 & 11.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data on geomagnetic storms, strong (M \(\geq\) 7.5) earthquakes following the storms, the time delay between storm onset and earthquake occurrence, and magnetic local time (MLT) at the areas of future epicenters in times of geomagnetic storm onset.
# Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique
## 3 Methods
### Object Identification and Vehicle Tracking
#### 3.3.2 Object Tracking by Drawing Virtual Lines and Hot Zones  
In this study, we used DeepSORT to track the objects (vehicles) identified by YOLO v4. In brief, DeepSORT first uses the Kalman filter to predict the trajectories of objects from previous frames. Then, it uses the Hungarian algorithm to match the predicted trajectories with the detected objects in the current frame (using cascade matching and IOU matching). Thereafter, it updates the Kalman filter for further predictions [13]. By executing DeepSORT, we were able to calculate the similarities between the apparent features and/or motion features of objects in adjacent frames, link the same objects together, assign different IDs to different objects, and track the objects throughout the video.  
In order to calculate lane-specific traffic flows, we drew a virtual line in each direction of the highway (Figure 4) in the images and counted the number of vehicles (as defined by the centers of their bounding boxes) passing the lines in each lane. As a result, a vehicle that changed lanes in the video would only be counted in one of the lanes, as determined by its location while passing through the virtual line. There are six lanes on the sections of National Freeway No.1 in which the videos were taken (Figure 5). The input video's resolution was 1920 \(\times\) 1080 pixels, with 30 frames per second.  
During object tracking, the system sometimes lost track of an object for a few frames and then recaptured it; sometimes it was identified as a different object and reassigned with a different ID. Such object tracking inconsistencies can cause the system to become unreliable, as a result of repeated counting of vehicles and confusion in speed calculations (in Section 3.3.3). Therefore, we modified the algorithm by adding hot zones in the vicinity of virtual lines (Figure 4), and only performed object tracking when the center was within the hot zones. The introduction of hot zones improved the accuracy of vehicle tracking and the subsequent calculations.  
Figure 4: Virtual lines and hot zones of the inner lanes of both directions (conceptualization).  
Figure 5: Sample image of the video taken on National Freeway No.1.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Edge Inference: Goal Value and Effectiveness
#### 3.2.2 Computation Delay of GO User  
Uplink transmission is just the first of the (at least two) phases of wireless edge inference, whose second step is remote processing through an ML model (e.g., a DNN), running in an MEH. The processing phase constitutes another source of delay, which is, typically, non-negligible, especially for demanding inferencing tasks served by MEHs of limited computing capabilities, as compared to the ones at the distant cloud. The computation delay is not, in general, a deterministic quantity, as it depends on CPU loading, execution of background processes, access to memory, etc. Obviously, it strongly depends on the employed ML model [54]. In this paper, we assume that the MEH, irrespective of wireless and computing resource availability, always employs the same model (assumed already trained), and we leave GO model selection criteria for future investigations. As such, we denote by \(D_{\text{comp},t}\) a random variable (whose statistics are possibly unknown) representing the computation time at the MEH. At each time slot, the computing time is possibly different, and its estimation is assumed to be available at the MEH, making it able to optimize connect-compute resources.  
Finally, the total inference time, neglecting downlink transmission of small size results (typically a few bits for binary or multi-class classification), is given by  
\[D_{\text{tot},t}=D_{\text{b},t}+D_{\text{comp},t}. \tag{11}\]  
Part of the goal is to retrieve inference results earlier than a predefined deadline \(D_{\text{max}}\), as clarified in the remainder of this section. However, retrieving incorrect inference results within the deadline can be extremely harmful for a special purpose functionality. For this reason, we need to define a measure of inference confidence to be incorporated into the overall definition of goal effectiveness.