esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Long-Range Underground Monitoring
### Technology Description
#### 7.2.5 IQRF  
IQRF is a LPWAN wireless mesh technology developed by the IQRF Technology company. The technology operates in 433, 787, 868, 915 and 920 Sub-GHz ISM bands and allows for ranges of tens of meters inside and hundreds of meters in free spaces with a data rate of 19.8 kbps. The coverage can be extended by forming a mesh network with up to 240 devices [10; 111; 112].  
The network consists of communication nodes which can form a mesh network and communicate with each other. Use Cases for IQRF LPWAN technology range from lighting and temperature control, heat and CO\({}_{2}\) monitoring or wireless torque wrenches in car production processes [10; 111; 112].  
In [113], link performance in line-of-sight (LOS) and non-LOS scenarios in an urban environment were investigated. Signal investigations were undertaken around a house block, and non-LOS scenarios were performed on the adjacent (one turn) or opposite side (two turns) of the block. Measurements and path loss model-based analysis resulted in estimated non-LOS ranges of 660 m (one turn) and 90 m (two turns) when an external antenna in the transceiver was used.  
To the best knowledge of the authors, there are no use cases and studies for IQRF usage in underground mining environments. The possibility of forming a mesh network might be useful for usage in dynamic underground mining environments to expand the network dynamically. Further research can evaluate the technologies' performance for mining.
## 2 Literature Review
### Summary and Identification of Gaps  
The reviewed studies emphasize various approaches to optimizing hinterland connectivity, such as enhancing transportation routes, integrating intermodal systems, and applying advanced optimization techniques. For instance, optimization frameworks that combine Markov processes, max-flow problems, and traffic flow models offer a comprehensive approach to improving connectivity and managing transportation challenges. Markov processes address operational uncertainties and delays, max-flow problems optimize cargo throughput through the network, and traffic flow models simulate vehicle congestion and travel times. This tri-stage framework highlights the benefits of a holistic approach, integrating uncertainty modeling, network optimization, and traffic simulation to improve port-hinterland connectivity.  
Despite these advancements, current research often falls short of integrating these models into a cohesive framework that addresses the full complexity of transportation systems. Many studies tend to focus on isolated aspects of connectivity, such as specific network components or operational strategies, without accounting for the dynamic and stochastic nature of real-world scenarios. For instance, while max-flow problems effectively optimize cargo flow, they may not fully consider the impact of random delays or equipment failures modeled by Markov processes. Similarly, traffic flow models provide valuable insights into congestion but may not integrate seamlessly with network optimization models to offer a comprehensive solution.  
There is a notable gap in developing integrated frameworks that combine probabilistic analysis, real-time traffic simulation, and network flow optimization. Expanding research to include these elements in a unified model could address the challenges posed [13] by unpredictable events and operational uncertainties. By bridging these gaps, future studiescan provide more robust and adaptable solutions, enhancing the resilience and efficiency of transportation systems. This approach would better align with the dynamic nature of modern logistics, leading to more effective management of port-hinterland connectivity.
###### Abstract  
In order to obtain a highly accurate profile of a measured three-dimensional (3D) free-form surface, a scanning measuring device has to produce extremely dense point cloud data with a great sampling rate. Bottlenecks are created owing to inefficiencies in manipulating, storing and transferring these data, and parametric modelling from them is quite time-consuming work. In order to effectively compress the dense point cloud data obtained from a 3D free-form surface during the real-time scanning measuring process, this paper presents an innovative methodology of an on-line point cloud data compression algorithm for 3D free-form surface scanning measurement. It has the ability to identify and eliminate data redundancy caused by geometric feature similarity between adjacent scanning layers. At first, the new algorithm adopts the bi-Akima method to compress the initial point cloud data; next, the data redundancy existing in the compressed point cloud is further identified and eliminated; then, we can get the final compressed point cloud data. Finally, the experiment is conducted, and the results demonstrate that the proposed algorithm is capable of obtaining high-quality data compression results with higher data compression ratios than other existing on-line point cloud data compression/reduction methods.  
Keywords:data compression; data reduction; free-form surface; point cloud; scanning measurement; redundancy identifying; redundancy eliminating; geometric feature similarity +
Footnote †: journal: Applied
## 2 Planar Ultra-Wideband FSS Design
The transmission phase curves have only minor differences for reason that the tiny change in dielectric constant value of the ultrathin FSS has little effect on the phase. A profound study on the periodic unit transmission performance is related to the transmission characteristics in the complex dielectric waveguide and some other theories and is not the main content of this paper. The results of the transverse magnetic (TM) mode and other incident angles are comparable to Figure 3 and are omitted. The conventional FSS unit has a dimension about half of the resonant wavelength, and the bandwidth could not be very wide. Under this circumstance, the mode of the incident wave obviously affects the FSS transmission coefficients when the incident angle increases. However, the PM FSS structure proposed in this paper possesses a TE/TM insensitivity trait, which primarily comes from the unit periodicity, and is about only 1/10 wavelength of the lower passband. In such a small dimension, the FSS structure have negligible influence on the TE and TM incident wave, even when the incident angle increases. In the frequency range higher than 24 GHz, the transmission coefficients have distortions, the TE and TM mode transmission coefficients have significant changes. However, in this paper, the passband is approximately 7 to 17 GHz, where the TE/TM insensitivity trait of the PM FSS is obvious. In this way, the high transmissivity of an FSS, which has a 7 to 17 GHz ultra-wideband, is fulfilled. Depending on the validity of the high-end simulation software, the results above can certify the applicability of this PM FSS and can be applied to radome calculations. Figure 3: The transmission coefficients and transmission coefficients phase comparisons of different media and incident transverse electric (TE) waves: (**a**) Vertical incident normalized transmission coefficients; (**b**) vertical incident transmission coefficients phase; (**c**) 30\({}^{\circ}\) incident normalized transmission coefficients and (**d**) 30\({}^{\circ}\) incident transmission coefficients phase.
## 4 Experimental Scenario for CO2 Emission Prediction Through the GreenNav Application
### Scenario 1: Comparison of Predicted vs. Actual CO2 Emissions  
In the first scenario, [PERSON], a user of the GreenNav application, seeks to evaluate the accuracy of our CNN-LSTM prediction model by comparing predicted CO2 emissions with actual values obtained from our baseline estimation algorithm. For this experiment, he selects Boulevard Saint-Germain in Paris, specifies 6 January 2023 as the date, and chooses 01:00 p.m. as a reference time for focused analysis (see Figure 15). To gain a more comprehensive overview, he also decides to retrieve emission data for all hours of the day, enabling a full 24-hour evaluation.  
The GreenNav application queries the prediction model and retrieves both actual and predicted CO2 emission values. These results are displayed through a comparative table (see Table 3) and an interactive graph (see Figure 16), facilitating a clear and comprehensive performance analysis. Key variables such as traffic flow (q), occupancy rate (k), and traffic state are also presented to provide additional context on emission-driving factors.  
Figure 16: Accuracy of CNN-LSTM Model Predictions vs. Actual CO2 Emissions.  
Figure 15: Component: CO2 Emission Predictions for a Street.  
An analysis of the results reveals that the predicted values closely follow actual emissions throughout the day. For example, at 10:00 a.m., the actual CO\({}_{2}\) emission rate is 41,121.14 g/h, while the predicted value is 41,050.67 g/h, with a minimal error of 70.47 g/h, indicating a prediction accuracy of 99.83%. Similarly, at 03:00 a.m., the actual emission rate is 4472.72 g/h compared to a predicted rate of 4450.34 g/h, resulting in a difference of only 22.38 g/h, reflecting an accuracy of 99.50%.  
The analysis further highlights a direct correlation between traffic flow and CO\({}_{2}\) emissions. For example, as traffic flow increases from 517 vehicles at midnight to 1151 vehicles at 03:00 p.m., CO\({}_{2}\) emissions rise accordingly. This strong relationship demonstrates the model's ability to capture traffic dynamics and their impact on emissions accurately.
# HVdc Circuit Breakers: Prospects and Challenges
## 6 More Recent Activities and HVdc Breaker Related Technologies  
### Combined Optimization of HVdc Breaker's Topology and Control  
Most studies on M-HVdc system topologies and HVdc breakers only focus on a specific distinct aspect. Sometimes, it is assumed that the dc breaker is known, and the network controller is designed, while the system control activities are set; other times, as per the requirements of the DCCB, and the breaker is designed. No comprehensive combined efforts are reported in the literature to optimize the entire system. It is advised to regulate the control structure to ease the needs of the dc breakers. The rise of the dc short circuit current can be reduced by adding additional inductance to the VSC station's dc side, but it would slow down the system's normal operation. On the other hand, the dc short circuit current can also be limited by employing high grounding impedance for a line-to-ground fault. Focusing on just one aspect is most probable, not the best solution as only a collective optimization can only achieve the globally optimal solution. Multi-modular converters are an example of combined optimization [115; 116; 117]. The voltage level inside the valve is a fragment of the total dc-link voltage, and, in principle, it can be interrupted at these levels. Thus, the corresponding dc breaker has a lesser rated voltage, but the valve scheme needs to be remodeled to include a dc circuit breaker. Thus, the satisfying results may be achieved by the dc breaker and valve design's joint efforts.
## 6 Methodology
### Description of the Experiments
#### Scenario 1  
In this case, Node 0 sent a NONARQ message every 20 s. Each message contained a unique ID and a timestamp. The receiver nodes (from 1 to 13) had a passive role, and they only tracked record of all the error-free messages they received. In this scenario, 16 different retransmission configurations were evaluated (from 0 to 15). All the configurations were run independently but simultaneously during enough time to generate a sufficient number of packets. In this case, the simulation was carried out during 25 days, generating more than 110.000 messages.  
Transmitter (Node 0) and receiver nodes (from 1 to 13) saved to log files the generated or received messages, containing the aforementioned unique ID for each packet. Each ID generated on Node 0 found on the i-th receiver node incremented a counter, easing the calculation of the PLR as the ratio between found IDs and generated IDs. Finally, thanks to the topology design of this scenario, the PLR of each node corresponded to a link range of interest.
## 2 Methodology
Setting Model Evaluation Strategy: Evaluate the model using the root mean square error (RMSE) evaluation metric. The formula for RMSE is: \[\text{RMSE}= \sqrt{\frac{1}{N}\sum_{i=1}^{N}\big{(}\text{TEC}_{i}^{\prime}-\text{ TEC}_{i}\big{)}^{2}},\] (5)where TEC\({}^{\prime}\) represents the model's predictions, TEC represents the observed values, and N is the total amount of data considered.
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology
### Label-Setting KSSP Algorithm
The maximum number of labels that can be created for a destination node \(t\) depends on the number of simple paths available to it from \(s\). As the worst case, consider a complete graph where every node is directly connected to every other node. The number of paths with no intermediate nodes between origin \(s\) and destination \(t\) is \(1=\frac{(n-2)!}{(n-2)! }\) (i.e., the path \(s\to t\)). The number of paths with 1 intermediate node is \(n-2=\frac{(n-2)!}{(n-3)! }\) (i.e., \(n-2\) choices for the intermediate node other than \(s\) and \(t\)). The number of paths with 2 intermediate nodes is \(\frac{(n-2)!}{(n-4)! }\) and so on. Finally, the number of paths with a maximum of \(n-2\) intermediate nodes is \(\frac{(n-2)!}{1!}\). Therefore, the total number of simple paths available between \(s\) and \(t\) is:  
\[\frac{(n-2)!}{(n-2)!}+\frac{(n-2)!}{(n-3)!}+\cdots+\frac{(n-2)!}{1!}=\sum_{i=1 }^{n-2}\left((n-2)!\right)\left(\frac{1}{i!}\right)<(n-2)!e=O((n-2)!).\]  
\(\square\)  
Theorem 1.: _The proposed label-setting algorithm produces an optimal solution to the KSSP problem at termination, if it exists._  
Proof. : The correctness of the label-setting algorithm is proved through the following hypotheses. \(\square\)  
**Hypothesis 1**. : _Selecting the smallest temporary label \(d_{u}^{p_{u}+1}\) from \(T\) and making it permanent will ensure optimality._  
**Hypothesis 2**. : _The temporary label \(d_{u}^{p_{u}+1}\) to node \(u\) in \(T\) represents the smallest label of all simple paths made up of subpaths belonging only to \(P\)._  
First, we prove Hypothesis 1, assuming Hypothesis 2 holds. Let \(d_{u}^{p_{u}+1}\) be the smallest temporary label in \(T\). This implies \(d_{u,3}^{p_{u}+1}\leq d_{i,3}^{p_{i}+1}yd_{i}^{p_{i}+1}\in\mathrm{T}\). Also, it corresponds to a simple path ensured by the cycle check performed in line 21. Suppose there is a better \(p_{u+1}\)th simple shortest path from the origin \(s\) to node \(u\) composed of some label \(d_{i}^{p_{i}+1}\in T\). Then, \(d_{u,3}^{p_{u}+1,new}=d_{i,3}^{p_{i}+1}+C_{i,u}\). \(C_{i,u}\) is non-negative and \(d_{i,3}^{p_{i}+1}\geq d_{u,3}^{p_{u}+1}\). Therefore, \(d_{u,3}^{p_{u}+1,new}\geq d_{u,3}^{p_{u}+1}\) and is not better than \(d_{u,3}^{p_{u}+1}\). So, \(d_{u}^{p_{u}+1}\) must be optimal. Hence, the \(p_{u+1}\)th simple shortest path to \(u\) need not be composed of any other label in \(T\). By Hypothesis 2, the current smallest label \(d_{u}^{p_{u}+1}\) in \(T\) is optimal among paths made up of labels only in \(P\). Thus, selecting the smallest temporary label \(d_{u}^{p_{u}+1}\) from \(T\) and making it permanent will ensure optimality.
## 2 Methods
### Terrestrial NPP--\"Ground Based\" Approach
#### 2.2.4 Stand Density Calculation  
Forest management reduces stand density, stand density affects individual tree growth and thus the \"ground-based\" terrestrial NPP predictions [3]. For characterizing the stand density variation on the inventory plots we select the following two commonly used competition measures: _CCF_ the crown competition factor [27] with Equation (11) and _SDI_ the stand density index [28] with Equation (14):  
\[CCF=\frac{\sum PCA_{i}\times nrep_{i}}{A}\times 100\% \tag{11}\]  
Where _PCAi_ the species-specific potential crown area [m\({}^{2}\)] according to Equations (12) and (13)  
\[PCA_{i}=(\frac{CW_{i}}{2})^{2}\times\pi \tag{12}\]  
\[CW_{i}=e^{e_{i}+a_{i}\times\mathrm{ln}(dbh)} \tag{13}\]  
with _nrepi_ as the representative stem number [ha\({}^{-1}\)] and \(A\) the observed area. \(CW_{i}\) is the potential crown width [m] and _dbh_ the diameter at breast height [cm]. The _PCAi_ defines the crown area of a tree assuming open-grown conditions (the tree never experienced competition). Coefficients _co_ and _c1_ in Equation (13) are derived from open-grown tree dimensions [29] and are given in Table 14 in the Appendix.  
The Stand Density Index (_SDI_) is calculated according to Reinecke [28]:  
\[SDI=N\bigg{(}\frac{dg}{25}\bigg{)}^{1.605} \tag{14}\]  
where \(N\) is the number of trees per unit area [ha\({}^{-1}\)], _dg_ is the quadratic mean stand diameter at breast height [cm], 25 is a reference _dg_ and 1.605 is the slope parameter for the maximum carrying capacity. The SDI has been proven to be site and age independent and SDImax defines an estimate for the carrying capacity of a given forest type [30].
# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 3 Area of Study
### FSC Algorithm  
We developed our FSC algorithm using the Python 3 programming language. The installation of three modules was required: OpenCV, Matplotlib, and URLLIB.  
First, the script we developed asks the user to enter the URL of the PhenoCam image to be processed, which allows the user to select the best image each day for FSC estimation. Then, both images, the RGB and MoNIR, are downloaded, and the processing starts. Each band is isolated, and the average DN is calculated. The averages of the Red, Green, and Blue images are also calculated.  
The Blue and MoNIR bands were normalized, and a Gaussian filter with an intensity of five is applied to their histograms. The Gaussian filter smooths the histogram curve by averaging pixels (\(5\times 5\)); this process helps to correct high or low values considered as noise (Figure 8).  
The Blue and MoNIR bands are used to calculate the shadow part of the image. The values between \(-80\) and \(20\) are considered shadow. The next steps are based on decisions that the algorithm takes using knowledge from validation of the scenarios described. The workflow, also shown in Figure 9, is:  
Images with a MoNIR average (M) greater than \(135\) are classified using the MoNIR band. The threshold value is the lowest between RGB average, M and \(100\).  
The Red band has higher values when there is no snow and the lowest values with snow presence, and this is inverse with the Blue band. A ratio between B/R is calculated. When the value is lower than one and B is lower than \(90\), a threshold of \(127\) is applied to the Blue band.  
When i and ii are false, a threshold is applied to the Blue band, whose value is the lowest among RGB average, Blue and \(100\).  
Steps i-iii produce an image with values of \(0\) (no snow) or \(1\) (snow). The new image is masked with the region of interest (ROI), and the FSC is calculated.  
Sometimes, grassland has reflectance values as high as snow reflectance values. When the FSC value is lower than \(10\), the FSC is recalculated using a higher threshold value of \(165\).  
Images with FSC between \(10\) and \(60\) do not have to pass to any other classification.  
The values calculated with the shadow formula are added to the images with FSC higher than \(60\), which is the final FSC value.  
Finally, the FSC is saved with the name of the image as a text file.  
Figure 8: (a) Image with noise, (b) pixel values before and (c) after the Gaussian Filter.  
Figure 9: Image processing workflow.
## 3 Results
### Discharge and Sediment Sampling Measurement  
The results of the discharge and sediment sampling measurements for the establishment of a relationship between turbidities and the total suspended load can be seen in Table 2. The measurements were performed by LDVWD and UPS FWS with standard methods (pump sampling and ADCP discharge measurement) in the relatively low water period of the years 2021-2022, when only a few medium-sized flood waves happened (Figure 7).  
In order to provide a picture of the water regime of the Danube River, the hydrograph of the Baja gauging station for the investigated period is provided, with indication of the measurements (Figure 8).  
The suspended sediment yield rating curve (suspended sediment yield as the function of the discharge) based on the data above is shown in Figure 8. The fit of the regression to calculate the suspended sediment yield based on the discharge is medium. The correlation coefficient squared is R\({}^{2}\) = 0.59 for the exponential curve with Equation (5).  
\[\text{Suspended sediment yield}=11.149*\text{e}^{0.0009*D\text{ discharge}} \tag{5}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Water Level** & **Discharge** & **Sediment Yield** & **Hydrological** & **Performer** \\  & **[cm]** & **[m3/s]** & **[kg/s]** & **Condition** & **UPS FWS** \\ \hline
11 February 2020 & 546 & 3722 & 445 & Falling & LDVWD \\ \hline
13 May 2020 & 171 & 1633 & 35 & Stagnating & LDVWD \\ \hline
12 June 2020 & 219 & 1914 & 30 & Rising & LDVWD \\ \hline
23 June 2020 & 490 & 3500 & 161 & Rising & LDVWD \\ \hline
29 June 2020 & 422 & 2620 & 194 & Falling & UPS FWS \\ \hline
8 July 2020 & 444 & 3098 & 113 & Falling & LDVWD \\ \hline
10 August 2020 & 580 & 3979 & 345 & Falling & LDVWD \\ \hline
4 February 2021 & 513 & 3713 & 265 & Falling & LDVWD \\ \hline
6 May 2021 & 278 & 2223 & 62 & Rising & LDVWD \\ \hline
25 May 2021 & 523 & 3587 & 136 & Falling & LDVWD \\ \hline
7 September 2021 & 442 & 2900 & 300 & Falling & UPS FWS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discharge and sediment yield.  
Figure 8: Hydrograph of the Baja station in the investigated period. Water level time series is marked with blue and measurement days are indicated as purple points.  
As a comparison, the suspended sediment yield rating curve based on the old data (1951-1965) is also shown in Figure 9. The correlation coefficient squared is R\({}^{2}\) = 0.76, which indicates a good relationship between the discharge and the suspended sediment yield.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Cover Timing Patterns
#### 4.3.3 (Artificial) Retransmission (PT12)  
Pattern  
The hidden information is encoded by intentionally retransmitting a message or packet that was already transferred beforehand. Within the PDCP, the sequence number can be exploited by the covert sender to utilize this pattern for covertly exchanging data. The covert sender can transmit the covert bits through a single or a duplicate PDU transmission. The covert receiver can extract the covert information by monitoring and sequentially comparing the sequence numbers of the incoming PDCP PDUs. However, packets with sequence numbers that were already received are eventually discarded by the overt receiver.