esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 4 System Model
### Dataset Choice  
As 6G network traffic is not yet widely available, we chose an open-source dataset [12] that analyses the RSS values received by a Raspberry Pi 3 which is subject to periodic and constant jamming attacks.  
The dataset in [12] was created using a software-defined radio (SDR) connected to a laptop that was programmed to transmit a jamming signal using the open-source software _GNU Radio_. A second SDR radio was connected to a Raspberry Pi 3, designated to receive the jamming signal. The jamming radio operates at a frequency of 2.412 GHz with a bandwidth of 40 MHz, while the Raspberry Pi 3 was programmed to sample the RSS values with a frequency of 32 K samples per second.  
The dataset contains three different _.txt_ files that store the RSS values sampled by the Raspberry Pi 3. The first file contains the RSS values sampled during a constant jamming attack, the second file contains the RSS values sampled during a periodic jamming attack and the third file contains the RSS values sampled during normal network operation. The samples are stored in a single column, with each row representing a single sampled RSS value, expressed in _dBm_ (_decibel-milliwatts_).  
Figures 2 and 3 show, respectively, plots of the RSS values sampled during a constant jamming attack and a periodic jamming attack, while Figure 4 shows the RSS values sampled during normal network operation.  
\begin{table}
\begin{tabular}{c c} \hline
**Classification** & **Description** \\ \hline Active & The attacker is actively trying to disrupt network operation \\  & by transmitting a jamming signal. \\ External & The attack takes place at level 1 of the OSI model, meaning \\  & that the attacker is not part of the network. \\ Local & The attack is local, as it is targeted at a specific drone or \\  & drone cluster and not at the entire network. \\ Malicious & Jamming attacks are considered malicious as their main \\  & goal is to disrupt correct network operation. \\ \hline \end{tabular}
\end{table}
Table 2: Attack classification details.  
Figure 1: Proposed jamming scenario. The drone communicates with the 6G BS and receives information about how to behave. The fixed jammer (**1**) and the mobile jammer (**2**) target the communication between the drone and the Base Station. The drone has an internal classification module capable of detecting jamming attacks.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Ministatellites
#### 3.2.3 SmallSat InSAR (180 kg)  
SmallSat InSAR is a design concept by Jet Propulsion Laboratory (JPL) to demonstrate the technical feasibility and economic viability of a small-satellite SAR constellation [46]. Its design principles were already explained in Section 2, whose volume and mass constraints are derived from the standards of an EELV Secondary Payload Adapter (\"ESPA\" or \"ESPA ring\"), which utilizes the unused payload margins of Evolved Expendable Launch Vehicles (EELVs) of the US Department of Defense [94]. Multiple types of ESPA rings and their combinations will provide small satellites with affordable rideshare (or piggy-back) launch opportunities depicted in Figure 16[95]. Although an ESPA and an ESPA \"grande\" may allow the maximum spacecraft mass of 320 kg and 450 kg in 6-slot and 4-slot configurations, respectively, SmallSat InSAR aimed to drive the spacecraft mass below 200 kg accommodatable by all six slots of an ESPA ring at the same time. By limiting the spacecraft mass and size (1.0 m \(\times\) 0.7 m \(\times\) 0.6 m), shown in Figure 16 (right), multiple SAR satellites can be launched simultaneously for rapid constellation deployment. SmallSat InSAR considered deploying a total of 12 small SAR satellites with two ESPA launches, but its launch schedule is unknown. The peak transmit power of the satellite is designed to be 1 kW.  
Figure 16: Accommodation of secondary payloads in Evolved Expendable Launch Vehicle Secondary Payload Adapter (ESPA) rings: (**left**) Stowage examples for different launch vehicles. (**right**) Volume constraints for similar-sized payloads. Source: NASA/JPL.  
Figure 15: TECSAR and its observation modes: (**left**) Assembly. (**right**) Wide-coverage mode, strip mode, mosaic mode, and spotlight mode. Source: IAI, ELTA Systems Ltd.  
SmallSat InSAR is often compared with the NASA-ISRO Synthetic Aperture Radar (NISAR) Mission in terms of scientific scopes, mass, and cost. The NISAR mission scheduled for launch in 2021 has multiple purposes of measuring land surface motions and ecosystem disturbances. These objectives can be translated into use scenarios specific to India such as monitoring agricultural biomass, Himalayan glaciers, and coastal environments [96]. The main objective of SmallSat InSAR is to map Earth's surface deformation with sub-millimeter-per-year precision via daily visits supported by a twelve-satellite constellation. A rough estimate of the total system cost is 1 to 1.5 times the cost of a single-spacecraft NISAR mission. The JPL team's work dating back to 1999 is based on a systems-engineering philosophy briefly described in Section 2[45]. The idea of systemizing the design process of SAR satellites and relaxing their design goal for miniaturization was referred to by designers of RADARSAT, TSX/TDX, and Micro XSAR to name a few [97; 98; 99; 100].
# Blockchain Applications in Forestry: A Systematic Literature Review
## 3 Research Methodology
### Data Selection  
We retrieved 657 articles from four databases (281 records from Scopus, 139 records from Web of Science, 79 records from IEEE Xplore, and 158 records from ACM digital library). Figure 2 is the flowchart of data identification, based on the PRISMA 2020 guideline [31]. As Figure 2 shows, the search result was processed by identification, screening and inclusion. In total, 87 records were removed from search results, and 570 records were screened. To select the related articles, two screening steps were performed with the inclusion criteria in Table 1. The first step was to screen the titles and abstracts of included articles. A total of 525 articles were excluded after the first screening. These articles were removed because their titles and abstracts: (1) were not related to either topic of blockchain or forestry; or (2) only related to one topic. The second step was to screen the full-text articles. A total of 45 articles were screened at this step, and 24 articles were excluded for specific reasons. The reasons are mainly: (1) only mentioned blockchain in the abstract; or (2) full-text content was not consistent with the abstract and title. 21 academic articles were included as the dataset of this study for further data extraction, analysis and synthesis. Table A1 in Appendix A is the list of 21 included articles. The table includes the author(s), year, title, and publication type of included studies. The quality of included studies was ensured since all retrieved studies are from four high-quality scientific databases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Inclusion Criteria** & **Exclusion Criteria** \\ \hline Language & English & Not English \\ \hline Publication year & Not earlier than 2008 & Before 2008 \\ \hline Availability & Full text available & Full text not available \\ \hline Source type & Original/Research articles & Non research articles \\ \hline \multirow{3}{*}{Subject/Content} & \multirow{3}{*}{Related to the topic of blockchain} & Not related to the topic of blockchain. \\  & & Only mentioned blockchain in abstract. \\ \cline{1-1}  & & The full-text content not consistent with its \\ \cline{1-1}  & & abstract and title. \\ \hline Context & Forestry industry & Not related to the forestry industry \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria to select papers in this review.
## 5 Discussion
### KPIs on the User Updates to the Risk Analysis  
A different outcome was recorded in the area concerning the accuracy of the information. In contrast to our hypothesis, usage of the RA tool led to a higher number of false  
Figure 13: Outcome of the KPIs calculated on the final risk evaluations performed by the users. The RA tool had, on average, a larger percentage of false positive entries (**b**) and a lower percentage of true positive ones (**a**) when compared with the legacy tool. However, the percentage of false negative entries (**c**) was lower in the RA compared to the legacy tool.  
positive entries. However, by looking at the RA risk evaluations in the user tests (File S4), it is possible to see that the users erred by not updating the information regarding the smallest contact area in the event of an impact. In the RA, this information had to be inserted in a long string. However, in the legacy tool, this information was organized in a specially designed entry field. Given that the current layout requires a user to manually scroll sideways in the field (and the entire text is not shown in multiple lines), it is possible that users did not see this or realize that they needed to look more carefully within a long string to make the correct update. We believe that this design error led to an increased percentage of false positive entries. Despite this, the results showed that the RA tool was less prone to false negative entries compared to the legacy tool. We believe that this lower rate of false negative entries was possibly due to the highlighting feature provided. Considering this information, we believe that the designed RA tool can better support engineers when frequent changes to risk evaluations are necessary and some fine tunings are necessary to reduce the amount of false positive entries. Therefore, the future version of RA will integrate special entry fields for HRC-related information such as the smallest contact area, similar to the legacy tool, to further support the user and reduce the rate of false positive entries. Moreover, we believe that the RA tool could improve the overall compliance to machinery directive requirements by reminding safety experts about a needed review. However, further experiments are needed to prove this last hypothesis.
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 2 Register Consistency in Non-Atomic Memory  
Shared communication variables (registers) used by a mutual exclusion protocol can (hopefully) be only _output variables_[29] (also called _exterior variables_ in [26]). Each output variable is owned by a distinct process, which is the only one that can write a value on it. Output variables, though, can be consulted by all the other processes. Instead, a not-output variable can be read/written by any process. It can be anticipated that this is the most complex case that can occur.  
Register consistency conditions arise when multiple operations can be executed simultaneously on the same register. The so-called Single Writer Multiple Readers (SWMR) case is the first scenario to be considered [22; 30]. A register operated under SWMR is _safe_ if a read that occurs when there is no overlapping write returns the most recently written value, whereas a read that happens concurrently to a write operation may return (_flickering_) a non-deterministic value belonging to the domain (type) of the register. The register is _regular_, under SWMR, if it is safe, but, in addition, a read that occurs simultaneously to a write operation returns either the value of the overlapping write or the most recently written value. Finally, a register is _atomic_ under SWMR when the read and write operations never occur simultaneously but are executed according to a definite order.  
The most complex scenario is the Multiple Writers Multiple Readers (MWMR) one. As in [22; 23], the Raynal condition [3] is assumed. Under MWMR, a register is _safe_ if multiple concurrent writes on the register cause _scrambling_ of its value, that is, a non-deterministic value belonging to the register type is actually written. Any read operation executed in overlapping with one or multiple writes returns a non-deterministic value belonging to the type of the register. The register is _regular_; if a read occurring in overlapping with write operations, either returns the last value written to the register or the value of any overlapping write. Finally, an MWMR register is _atomic_ if read/write operations never overlap and a read operation always returns the value of the last executed write with respect to the moment the read was issued.  
Of course, the use of only output variables requires a mutual exclusion algorithm to be correct under atomic registers and SWMR-safe or SWMR-regular non-atomic registers. When non-output variables are admitted, the atomic algorithm's correctness has to be checked under the more challenging MWMR-safe or MWMR-regular non-atomic registers.  
A specific contribution of this paper is a rendering of the non-atomic SWMR/MWMR registers in an Uppaal model. In particular, the approach starts by transforming a mutual exclusion algorithm into an Uppaal model tailored to only use atomic registers. Model correctness is then thoroughly assessed. After that, the model is modified according to the requirements of the intervening SWMR/MWMR scenarios, and the correctness is re-checked under safe or regular non-atomic registers.
## 4 Discussion
### Limitations and Future Directions
#### 4.4.4 Future Directions  
Future research should focus on expanding the sample size and extending the data collection period to enhance robustness of the findings. Additionally, exploring advanced signal processing techniques and machine learning algorithms could improve the differentiation of respiratory signals in multi-occupant environments. This technology holds promise for future real-world applications, providing a non-intrusive and reliable method for RR monitoring in various settings.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 3 Methods
### Infrared Difference Dust Index (IDDI)  
As Figure 2c shows, the frequency distribution of FY-4A IDDI had two peaks for the dusty pixels. In order to not miss the dusty pixels or misclassification, thresholds of 2-40 were set for dust, where the dust pixels began to rise and fall obviously, as the threshold values of IDDI.
## 5 Discussion
Compared to their default values, e.g., the carboxylation efficiency (0.07 umol m\({}^{-2}\) s\({}^{-1}\) (umol m\({}^{-2}\) s\({}^{-1}\))\({}^{-1}\)), our study obtained similar values on average but with obvious seasonal variation. Other parameters,  
Figure 11: T/ET (**a**) and WUE (**b**) in different stages of rice growing. In (**a**), red circles and blue squares represent T/ET for the early rice and late rice, respectively. In (**b**), red circles and blue squares represent the canopy WUE (WUE\({}_{\rm e}\) = GPP/T) for the early rice and late rice, respectively; black circles and green squares represent the ecosystem WUE (WUE\({}_{\rm e}\) = GPP/ET) for the early rice and late rice, respectively. e.g., the stomatal conductance coefficient, m, varies around 20 in our study site, which is similar to that of the wetland (25.8) other than that of the crop (8.4) in [PERSON] et al. (2019). We want to note that the PML-V2 model was evaluated using only one set of EC equipment at the Xiangtang site in our study area. However, because the study area is quite small, we assume that the parameters that were retrieved using the EC measurements at the Xiangtang site are representative of the characteristics of rice ecosystems in this area. Therefore, parameters of the PML-V2 model were assigned the same values for each rice grid in the study area. However, our results showed that GPP and ET of rice paddy fields exhibited substantial spatial variations (Figures 10 and 11) because meteorological and land surface conditions were different for different grids. Moreover, simple relations are expected to exist between the multi-year daily values of GPP, ET, and the meteorological and land surface variables. Compared to the meteorological variables which were at 0.01\({}^{\circ}\) resolution, the NDVI data can be acquired at a much finer resolution (0.0025\({}^{\circ}\)), which was approximately equal to 250 m. Therefore, it is expected that the spatial distribution of estimated GPP was mainly correlated with the NDVI. The stepwise regression analysis showed that the NDVI can explain over 90% of the spatial variations in the multi-year daily values of GPP of the early rice. In addition, the slopes of NDVI in the regression equations for both the early rice and late rice were close to each other (14.14 v.s. 14.11). Compared to that of GPP, ET exhibited much smaller spatial variation due to the high evaporation rate of the saturated soil surface of paddy fields, which is comparable to the transpiration rate of the canopy; therefore, meteorological variables such as downward shortwave radiation, air temperature, and wind speed entered the regression equation. Besides GPP and ET, the stepwise regression also showed that, together with meteorological variables, the NDVI can explain over 90% of the spatial variations in the multi-year daily values of T/ET, and over 80% for those of WUE (Table 5).
## 4 Discussion
### Couple or Uncoupled ACWR  
Another parameter is whether a coupled or uncoupled ACWR was used. The coupled ACWR includes the acute load within the chronic load, while the uncoupled ACWR does not include the acute load in the chronic load. Among the studies in this review, eight studies [12; 19; 21; 25; 26; 27; 28; 29] used the coupled ACWR, and three studies [31; 32; 33] used the uncoupled ACWR. In half of the studies using the coupled ACWR, a relationship between the ratio and the likelihood of injury was observed [12; 19; 25; 28]. In studies with the uncoupled ACWR, only one study [33] observed a relationship, reporting that an increase in the ratio by 0.1 raises the probability of non-contact injury five days later, with 62% of injuries occurring when the ratio was at 1.2.  
In the same study, another form of the ratio was investigated, the exponentially weighted moving averages (EWMA) ACWR. According to this, more emphasis is given to the recent training load compared to the older load [22]. Researchers observed that an increase of 0.1 above the value of 0.97 leads to a 30% increase in the probability of non-contact injuries.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis
### IEEE 69-Node Distribution Network
#### 5.2.1 Accuracy  
As the size of the network increases, the solution space of the fault location problem increases, which causes an intelligent algorithm with randomized search operations to be more prone to local convergence. As can be seen in Figure 16a, the accuracy of Algorithm 2 dropped below 50% for some of the test cases. The accuracy of Algorithm 3 was higher than that of Algorithm 2, but it produced misjudgment for fault cases 3, 7, and 9, where the branch nodes were distorted. As mentioned previously, the algorithm in this paper does not contain random operation and makes use of the redundancy of fault information in the whole network to tolerate the distorted information; thus, its accuracy was almost not affected by the network size. The test results confirm that Algorithm 1 maintained an accuracy of 100% for the various fault cases, as seen in Table 9.  
Figure 15: IEEE 69-node distribution system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Equipment Number** & \(\left[K_{1},K_{2},K_{3},K_{4}\right]\) & **Preset Fault Section** & **Preset Information Distortion** \\ \hline
1 & \(\left[1,1,1,1\right]\) & \(x_{52}\) & \(I_{8}\): \(-1\to 0\) \\
2 & \(\left[0,1,1,1\right]\) & \(x_{15}\) & \(I_{49}\): \(0\to 1\) \\
3 & \(\left[1,1,1,1\right]\) & \(x_{57}\) & \(I_{20}\): \(0\to-1,I_{51}\): \(1\to 0\) \\
4 & \(\left[1,0,1,1\right]\) & \(x_{16}\), \(x_{32}\) & \(I_{8}\): \(1\to 0\), \(I_{25}\): \(0\to 1\), \(I_{39}\): \(0\to-1\) \\
5 & \(\left[0,1,1,1\right]\) & \(x_{64}\) & \\
6 & \(\left[1,1,0,1\right]\) & \(x_{10}\), \(x_{36}\) & \(I_{15}\): \(0\to-1\) \\
7 & \(\left[1,1,1,0\right]\) & \(x_{12}\), \(x_{35}\) & \(I_{7}\): \(1\to 0\), \(I_{32}\): \(1\to 0\), \(I_{41}\): \(0\to-1\) \\
8 & \(\left[1,1,1,1\right]\) & \(x_{56}\), \(x_{67}\) & \(I_{35}\): \(-1\to 0\), \(I_{63}\): \(1\to-1\) \\
9 & \(\left[1,1,1,1\right]\) & \(x_{14}\), \(x_{23}\) & \(I_{17}\): \(1\to 0\), \(I_{34}\): \(-1\to 1\), \(I_{43}\): \(0\to 1\) \\
10 & \(\left[1,1,1,1\right]\) & \(x_{39}\), \(x_{59}\) & \(I_{24}\): \(0\to 1\), \(I_{63}\): \(-1\to 0\) \\
11 & \(\left[1,0,0,1\right]\) & \(x_{27}\), \(x_{34}\) & \\
12 & \(\left[1,0,1,1\right]\) & \(x_{7}\) & \(I_{34}\): \(-1\to 1\), \(I_{62}\): \(-1\to 0\) \\ \hline \end{tabular}
\end{table}
Table 9: Test cases for IEEE 69-node distribution network.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter
### Modulation Waveforms
#### 2.1.4 Ufmc  
UFMC modulation technique was proposed by [PERSON] et al. to solve the ICI problem in OFDM systems [42]. UFMC modulation enjoys the following advantages of relaxing the requirement of CP, having high spectrum efficiency, relaxing carrier synchronization, and being suitable for fragmented debris spectrum utilization. In addition, UFMC supports short burst asynchronous communication because the filter lengths depend on the sub-band widths. Nevertheless, similar to OFDM, the performance of UFMC is also inevitably affected by the carrier frequency offset (CFO), which has attracted a lot of research interest in order to mitigate the significant impact of CFO on UFMC based system performance [114; 115; 116].
## 3 Results
Table 2 shows a performance comparison of several ADCs proposed in recent years [6; 24; 25; 26; 27]. The FoM was used to compare the power \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Switching Scheme** & **Average Switching** & **Energy** & **Area** & **Control Logic** & **Dependency on** & **Common-Mode Shift** \\
**S** & **Energy (CV\({}^{2}\)_net_) & **Saving** & **Reduction** & **Complexity** & **the Accuracy of V\({}_{\text{cm}}\)** & **Mode Shift** \\ \hline Conventional & 1363.3 & Reference & Reference & Low & No & 0 \\ SC [7] & 852.3 & 37.48\% & 0\% & Low & No & 0 \\ MCS [8] & 255.5 & 81.26\% & 50\% & Low & No & V\({}_{\text{ref}}\)/2 \\ RFM [9] & 128 & 90.61\% & 74.7\% & Low & No & V\({}_{\text{ref}}\)/2 \\ VPSS [10] & 69.08 & 94.93\% & 75\% & Low & Very high & V\({}_{\text{ref}}\)/4 \\ SMS [11] & 63.75 & 95.32\% & 75\% & Low & Very high (all bits & V\({}_{\text{ref}}\)/4 \\ Tri-level [12] & 42.41 & 96.89\% & 75\% & High & \
\begin{tabular}{c} very high (all bits \\ except MSB) \\ \end{tabular} & \({}_{\text{ref}}\)/2 \\ VMS [13] & 31.88 & 97.66\% & 75\% & High &
\begin{tabular}{c} Very high (all bits \\ except MSB) \\ \end{tabular} & \({}_{\text{ref}}\)/4 \\ Proposed & 47.5 & 96.52\% & 75\% & Low & Very low (only LSB) & V\({}_{\text{ref}}\)/4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of switching schemes for 10-bit SAR ADCs.