esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Evaluation
### Data Set Analysis  
We first calculate the information acquisition rate for different probe methods. The probability equals \(n_{i}/N,(ie[1,12])\), where \(n_{i}\) is the number of devices successfully identified by the probe methods, and \(N\) is the total number of devices. Figure 5 shows the probability of obtaining device fingerprints using different probe methods. We found the following patterns in identifying devices:  
1. A single method cannot identify complete fingerprints. For example, probing using the HTTP/HTTPS protocol has a high probability of obtaining brand and model information but a lower probability of obtaining firmware version.
2. Different response data contain different device fingerprint information. If a protocol response does not contain the firmware version, it is more likely that the rest of the protocol responses will contain this information.
3. Different combinations of probe methods have different complementarity in identifying device fingerprints. For example, the probability of identifying firmware version via the SNMP, HTTPS, and SIP protocols is 73.02%, 15.90%, and 50.82%, respectively. Although the probability of identifying the firmware version is only 15.90% using HTTPS, it can reach 80.31% when combined with SNMP, which is higher than the probability of 78.17% achieved by combining the SNMP and SIP protocols.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Brands** & **Number of Models** & **Number of Devices** & **Main Model Series** & **Type** \\ \hline \multirow{3}{*}{Cisco} & \multirow{3}{*}{946} & \multirow{3}{*}{15,186} & CSR, DPQ, ASR, RV & Router, Switch \\  & & & TANDBERG, Codian & Webcam \\  & & & ASA & Firewall \\ \hline \multirow{3}{*}{Huawei (H3C)} & \multirow{3}{*}{776 (114)} & AR, HG, EG, Quidway, WX, CR, SI & Router, Switch \\  & & & Secoway, Eudemon, ASG, SecPath & Firewall \\  & & & IPC, Hisilicon & Webcam \\ \hline \multirow{2}{*}{D\_Link} & \multirow{2}{*}{666} & \multirow{2}{*}{12,270} & DI, DCM, DSL, DIR & Router, Switch \\  & & & DCS, DSH & Webcam \\ \hline \multirow{2}{*}{Juniper} & \multirow{2}{*}{483} & \multirow{2}{*}{1333} & MX, ERS, EX & Router, Switch \\  & & & SRX & Firewall \\ \hline \multirow{2}{*}{HP} & \multirow{2}{*}{370} & \multirow{2}{*}{850} & ProCurve, SuperStack, NR & Router, Switch \\  & & & LaserJet, Officelet & Printer \\ \hline \multirow{2}{*}{Synology} & \multirow{2}{*}{191} & \multirow{2}{*}{718} & DiskStation, CubeStation & NAS \\ \hline \multirow{2}{*}{Dahua} & \multirow{2}{*}{117} & \multirow{2}{*}{1219} & DHI, HCVR, NVR & Webcam \\ \hline \multirow{2}{*}{Fortinet} & \multirow{2}{*}{86} & \multirow{2}{*}{553} & FortiGate, FortiManager & Firewall \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample device models for the real-world test.  
In other words, the complementarity between the SNMP and HTTPS protocols is higher than that between the SNMP and SIP protocols because the HTTPS and SNMP protocols' responses have less duplicate fingerprint information. Therefore, the communication overhead can be significantly reduced by optimizing the probe methods and their order.
## 4 Discussion
### Limitations of This Review  
This review centers on the three topics \"Remote Sensing\", \"West Africa\", and \"Small-Scale Agriculture and Cropping Systems\". A total of 163 selected articles were analyzed. The preselection of those articles was limited by constraints in the search string and in the Web of Science database, which is widely recognized for providing reproducible and transparent search results. However, using a different search system may result in slightly varying number of articles. As databases and search functionalities are regularly updated, the performance results in this study may change over time [207]. The use of Boolean expressions like \"AND\" make the searches highly sensitive, necessitating precise keyword formulations by the authors. For instance, the keyword \"field*\" retrieved numerous for this review irrelevant articles, while the relevant ones were included by adjusting other keywords in the search string. Combining the three topics with Boolean \"AND\" helped to clearly define the scope of this review.  
Another limitation lies in the potential bias, as the second filter and all classifications were performed manually, introducing some subjectivity. The boundaries between certain categories were challenging to establish, leading to a degree of uncertainty. However, we have defined these categories based on our best knowledge and judgment, consistently aiming to base the processes on well-established and pre-defined criteria. Table S1 is important for providing a comprehensive overview and for ensuring transparency.  
For instance, the first author affiliation is considered, and although some studies are authored or co-authored by (West) African researchers, they may be linked to non-African countries or funded projects through collaboration. A specific first author example is [PERSON], who has two publications from 2014 and 2015 affiliated with Germany [63; 64] followed by three publications affiliated with WASCAL in Burkina Faso [34; 65; 66]. Studies with affiliations from West Africa account for less than 21%, a relatively low percentage. Additional factors contributing to this underrepresentation could include high publication fees and limited access to research funding in West Africa.  
A debatable aspect of this review is the inclusion of low-resolution RS studies, such as those utilizing MODIS or AVHRR data. While these studies are limited in their ability to directly analyze small-scale agriculture, their inclusion was justified as they serve as an additional data source in RS-data-scarce regions. Despite their low spatial resolution, their high temporal resolution is particularly valuable in seasonally cloud-prone areas. This approach aims to provide a comprehensive overview of the potential and progression of Earth Observation in predominantly small-scale agricultural regions.  
Additionally, some relevant publications may not have been included in the search results, despite efforts to minimize this risk. While limitations in this review exist, it still provides a thorough overview of RS on small-scale agriculture and cropping systems in West Africa.
## 3 CGST Codec Simulation for Processing Images
### CGST-Compressed Image Restoration Using the Frequency Method  
The problem of image blurring from the output of an extrapolation block can be leveled by applying image restoration methods after the codec operation. However, the increased spatial frequency of processed images complicates analyzing the distorting function. It makes the use of classical approaches challenging due to the issues in determining object boundaries and the simultaneous blurring of close-in-tone areas [50].  
We utilized a frequency image restoration technique called a Gaussian low-pass filter to minimize edge effects. In our simulation, we employed the standard deviation of the Gaussian distribution, which ensured that the spatial frequency graphs of the original and processed images from the codec output were almost identical, as shown in Figure 4. As a result, we obtained the following processed images that are provided in Figure 5.  
Figure 4: Spatial frequencies of the original (red), compressed by modified CGST (blue), and restored images with a Gaussian low-pass filter technique (green).  
The quality of the recovered images was evaluated based on the following criteria: the mean square error (MSE) and the Minkowski norm (MN) to check the proximity of the RGB channels' brightness levels and structural similarity (SSIM) [51] to assess the structural distortions' impact. However, the SSIM algorithm in the spatial domain is highly sensitive to scaling and image shifting when processing RGB channels with an extrapolation block. To overcome this limitation, a new criterion called the complex wavelet SSIM (CW-SSIM) was introduced, which extends the SSIM algorithm to the region of the complex wavelet transform and is insensitive to non-structural distortions [52]. The averaged results obtained by applying these criteria to the entire dataset of restored images after the Gaussian filter are presented in Table 1.  
The results indicate that there is only a slight improvement in image quality after filtering. While the effect of increased spatial frequency was reduced, the decrease in the CW-SSIM indicates that a processed image's blurring worsened.  
Thus, using the CGST codec for image processing led to several distortions. Applying conventional methods to eliminate these distortions turns into neutralizing each other's effects. For instance, applying a Wiener filter to a restored image after Gaussian low-pass filtering will reduce the blurring impact but increase the spatial frequency. As part of our study, we aimed to evaluate whether restoring images compressed by a CGST codec using neural network (NN) methods is possible. These methods can theoretically adapt the mapping of input data to a given output space for any distortion model.
# Assisted Migration Field Tests in Canada and Mexico: Lessons, Limitations, and Challenges
## 6 Challenges  
### Making Large Multisite Provenance Field Trials More Affordable  
Provenance tests were first used to identify superior seed sources for reforestation, and subsequently, to quantify safe seed transfer distance [72]. New analytical approaches have extended their application by providing estimates of impacts of seed transfer [73] and climate change [34; 46; 74; 75; 76; 77] on the growth and survival of tree populations. However, few older multisite provenance trials contain a sufficiently wide climate range among populations or sites to allow accurate estimation of seed transfer or climate change impacts [3]. Examples of trials containing wide population or site sampling include _Quercus petraen_ in the European Union [78]; _Pinus sylvestris_ in Scandinavia [79]; the Assisted Migration Adaptation Trial in Canada and the USA [80]; and trials of _Pinus sylvestris_ in the former Soviet Union [81; 82; 83].  
Remarkably few older provenance trials contain test sites located outside the contemporary species distribution [78; 84], limiting the ability to assess assisted migration outside of range. Further, costs of provenance trials can be prohibitive where numerous tree species are planted, or unjustifiable where few seedlings are planted. Nonetheless, collaboration among jurisdictions (provinces, states, and countries) can reduce costs to individual jurisdictions, while increasing the climate range of test populations and sites [84], arguably the most significant limitation of early provenance trials. Careful selection of populations (i.e., sampling widely and uniformly across climates) could reduce numbers of populations needed in future trials. Additionally, careful blocking, incomplete block alpha-design [85], and use of single-tree plots can reduce the number of seedlings needed for each population to have adequate statistical power to characterize patterns of adaptive variation. More strategic selection of populations and test sites using geographic information systems and climate models, sampling a wider climate range, and careful prioritization of species to be tested could reduce costs while increasing effectiveness [34]. Demonstration of similar climate clines for adaptation traits among a wide range of tree species mitigates the need for provenance trials for all species [6; 86].
## 4 Case Study
### Results and Discussion
#### 4.5.2 Prediction Results Considering the ER  
Although the VMD-SE-PACF-ELM model greatly improves the prediction performance, its prediction results fail to show the fluctuation characteristics of the DD well. Considering that the ER obtained after VMD decomposition contains some of the fluctuation characteristics of the original sequence, it is necessary to extract the sequence and to explore and analyze the deformation features embedded in it. Due to the strong nonlinearity and nonstationarity of the ER, direct modeling and prediction of it with the ELM model will lead to the prediction results being messy and impractical. Therefore, we propose to perform a secondary decomposition of the ER by the VMD algorithm to obtain subsequences with relatively stable deformation characteristics, which are recorded as ER-VMFs. Just like the original sequence, the decomposition modulus of the ER is first determined by the SE method. By analysis, the optimal decomposition modulus of ER is 2, and the obtained subsequences are denoted as ER-VMF\({}_{1}\) and ER-VMF\({}_{2}\). PACF analysis is then performed for each ER-VMF to determine the input variables for the prediction model. The specific analysis process of the ER is shown in Figure 15.  
As can be seen in Figure 15, the VMD-decomposed ER is able to achieve better prediction results. The prediction results of the proposed model can be obtained by summing it up with the prediction results of the VMD-SE-PACF-ELM model. The prediction results and evaluation indicators of the models before and after considering ER are shown in Figure 16. The left side of the figure is a bar graph of the performance evaluation indicators (RMSE, MAE and R\({}^{2}\)), and the right side is a graph of the predicted results.  
From Figure 16, the following conclusion can be drawn: the prediction results of the model without considering ER can reflect the overall trend of the time series, but cannot well capture the fluctuation characteristics of the original data. Through the analysis of ER, the prediction results of the hybrid model not only become more accurate, but also better reflect the fluctuation characteristics of the DD. Therefore, the analysis of ER has practical engineering significance.  
Figure 14: Comparison of prediction results before and after VMD optimization.
## 5 Conclusions  
The experimental research validated a rapid technique, based on non-destructive techniques, to estimate the compression strength and the elastic modulus of the stones that characterize the wall textures of the buildings damaged in the area of Central Italy during the 2016 earthquake. From the St. Agostino church in Amatrice, a well-known building that symbolizes the damage caused by the 2016 Central Italy earthquake, some sandstones were taken and tested in the laboratory under compression test and by ultrasonic and impact tests. The average compression strength of Amatrice sandstone was found of about 125.2 MPa. Moreover, the experimental methodology made it possible to compare the average value of the static modulus obtained from the compression tests (34894 MPa) with the average one estimated from the impact tests (32121 MPa). Very similar values were obtained from the experimental results.  
Subsequently, from these tests, it was possible to elaborate a series of correlations between the compression strengths, obtained from the laboratory test, and the values offered by the non-invasive tests. In particular, the applied Sonreb methodology replaced the sclerometric test obtained with the impact hammer, which accurately estimated the value of the elastic modulus in relation to what is already possible with the ultrasonic test. In fact, this experimental research validated the possibility of replacing the Schmidt hammer with the impact test, which offers more information related to the dissipated energy.  
The results of this research tend to prove that the obtained experimental results can be applied in situ for a quick and non-destructive estimate of the residual strengths and mechanical characteristics of stones, or other damaged masonry structures with similar texture. For different textures, this estimate can be achieved after specific calibration with limited sampling of cores and laboratory compression tests. The described methodology may have useful applications in the evaluation of the mechanical characteristics of masonry damaged by earthquakes and in the design of safety and strengthening works.  
Conceptualization, A.G. and G.L.; Tests, A.G.; Writing, A.G.; Supervision, G.L.; Project administration, A.G. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The author greatly thanks the Superintendence Archeology and Fine Arts Landscape of Rieti, the Special Superintendent Office for the areas affected by the earthquake of 24 August 2016, and the Curia of Rieti for providing the opportunity to take the stones and carry out the scientific research. The authors would like to thank [PERSON], [PERSON] and [PERSON] for their valuable technical support during the execution of the laboratory tests.  
The authors declare no conflict of interest.
# Quantum Game Application to Recovery Problem in Mobile Database
## 3 Quantum Game-Based Recovery Model
### Recovery Modeling Using Quantum Game
#### 3.2.1 Recovery Algorithms and Its Parameters
In general, game theory may be summarized as follows: (1) a set of players (the negotiation algorithms chosen); (2) a pool of strategies for each player (the strategies take into account the assumed values of significant coefficients in each protocol, as well as possible environmental changes); and (3) the benefits or payoffs (utility) to any player for any possible list of the players' chosen strategies. It should be noted here that any number of strategies can be generated for each player (protocol) by running any number of experiments using different parameter values, but this of course increases the model's complexity. Therefore, we consider it best to choose a number of strategies to express different degrees of performance levels that can be relied upon in decision-making. In the suggested game-based recovery model, the assumption is that each player utilizes pure strategy and not a mixed one, as each strategy handles specific protocol parameters, and these parameters differ from one protocol to another. There are no general parameters used for all protocols. For pure strategies, it is far easier to obtain multiple solutions (of course if they exist) for the NE, and then select the best fitting one.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 2 Related Work
For path planning, we use a fully distributed version of the priority-based planning paradigm [12] as described in Section 3.2.3. Priorities are assigned implicitly by the order in which data is received from nearby robots. No token passing procedure or specific schedule is in place ensuring robust execution also with limited probabilistic communication and robot failures. Other decentralized path planning methods--not considered here--include push-rotate-update [34] and a method where the path is planned by a sensor network [35].
## 3 Results and Discussion
### Analysis of the Effects of YLDI on the Volatile Compounds of Green Tea Using GC-MS/MS  
#### 3.3.1 Comparative Analysis of the Volatile Compounds in Tea Samples  
In this study, a total of 70 volatile compounds were targeted and analyzed by GC-MS/MS, with detailed quantitative information depicted in Figure 2A and Table S4. No  
Figure 1: PLS-DA results of tea samples exposed to yellow light with different intensities based on GC-E-Nose. (**A**) Score plots (R\({}^{2}\)Y = 0.987, Q\({}^{2}\) = 0.882); (**B**) Permutation tests with 200 iterations (R\({}^{2}\) = 0.717, Q\({}^{2}\) = \(-\)0.465).  
tably, 61 volatile compounds were consistently identified across five different light intensity conditions. In addition, certain volatile substances were detected solely under specific light intensity treatments. For instance, 3-octanone was observed solely in the samples subjected to 4000 Lux and 6000 Lux treatments; (_E_)-2-hexenal was found solely in the samples exposed to 500 Lux and 1000 Lux; _cis_-3-hexenyl salicate and (_3Z_)-3-hexen-1-yl benzoate were detected solely in samples treated with 1000 Lux.  
Figure 2: Information of volatile compounds in tea samples exposed to yellow light with different intensities based on GC-MS/MS. (**A**) Volatile compounds in tea samples under different treatments; (**B**) Content comparison of different volatile categories. Different letters indicated significant differences (_p_ < 0.05).  
The effects of YLDI on different classes of volatile compounds also displayed substantial variations (Figure 2B). Alcohols reached the highest in the samples processed under 1000 Lux, significantly exceeding those under 500 Lux, 2000 Lux, and 4000 Lux (\(p<0.05\)). Aldehydes attained the maximum in the tea samples processed under 4000 Lux (152.32 \(\mathrm{\SIUnitSymbolMicro g}\)/L), followed by the samples processed under 1000 Lux and 6000 Lux, with significantly lower concentrations in the samples processed under 500 Lux and 2000 Lux (\(p<0.05\)). Ketones achieved the maximum in the samples subjected to 1000 Lux (99.32 \(\mathrm{\SIUnitSymbolMicro g}\)/L), which was significantly greater than the levels observed under other light intensities (\(p<0.05\)). Heterocyclic compounds were ordered as 4000 Lux > 6000 Lux > 2000 Lux > 500 Lux > 1000 Lux, with the tea samples treated under 4000 Lux significantly higher than other treatments (\(p<0.05\)). Esters and Alkenes were the most abundant in tea samples exposed to 6000 Lux. Aromatic hydrocarbons and phenolic compounds were more abundant in the 4000 Lux and 6000 Lux samples, significantly higher than other treatments (\(p<0.05\)). Geranic acid exhibited the highest concentration in the 6000 Lux treated sample, significantly higher than other samples (\(p<0.05\)). In summary, these results indicate that YLDI significantly influences both the numbers and concentrations of aroma components in green tea, thereby affecting the overall manifestation of the fragrance profile.
# MIDOM--A DICOM-Based Medical Image Communication System
## 2 Background
### Cbpc2  
CBPC2 is an upgrade of the CBPC lossless image compression method. It uses the same procedure as the original version, but implements Run-Length Encoding (RLE) method in addition to Golomb-Rice coding to provide additional compression. Although the procedure is the same, the encoder is more complex and therefore it consumes more computing power.  
RLE is a simple lossless data compression method. The core idea is to count the successively repeating elements of the original series and write them as a pair (element, consecutive repetition number). An example is shown in Figure 2 where the first four 0s are encoded as a pair (0, 4), then a single 1 as (1, 1) and, finally, three 4 s as (4, 3).  
This method is suitable only for series with a high number of successively repeating values since, on the contrary, it can lead to the expansion of the original series. Considering that medical images have a lot of large areas of uniform colour, with a fairly good prediction most of the values converge towards zero which makes RLE a suitable extension for CBPC.  
Because of the aforementioned properties of the RLE, it is usually used as an auxiliary rather than a primary encoding method. It is activated when a region with a high number of successively repeating elements is found. The minimum number of element repetitions for activating the RLE method should be high enough to avoid expansion of the series and make the cost of switching the encoding methods acceptable. In this context, switching to RLE is called run-mode and is a part of the LOCO-I algorithm, which is accepted as a fundamental part of the JPEG-LS standard.  
Figure 2: An example of run-length encoding.