esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 8 Conclusions  
In this paper, we have presented a novel approach to address the challenges associated with Channel Estimation in CF-MMIMO wireless systems. Leveraging the power of DMD, we have introduced a data-driven methodology that captures the temporal dynamics of the wireless channel, offering a promising solution to the complexities posed by rapidly changing environments.  
The integration of DMD/PiDMD/mpeDMD into the CF-MMIMO framework holds significant potential. By extracting dynamic modes from received signal data, we gain insights into the evolving behavior of the channel, enabling more accurate and adaptable CSI estimation. Through extensive simulations, we have demonstrated the effectiveness of the proposed DMD-based approach in enhancing system performance, particularly in scenarios with dynamic and dense wireless environments.  
Our findings underscore the transformative impact of infusing Dynamic Mode Decomposition (DMD) into massive Multiple-Input-Multiple-Output (MIMO) wireless systems. The ability to predict and adapt to changing channel conditions opens up new avenues for optimizing transmission strategies, improving Spectral Efficiency, and ensuring reliable communication. Additionally, the integration of DMD may pave the way for novel applications in wireless systems beyond 5G and 6G, where dynamic and agile communication environments are paramount.  
As the wireless landscape continues to evolve, our research indicates that integrating DMD with CF-MMIMO systems presents a promising avenue for achieving robust and efficient wireless communication. Nonetheless, further investigation is required to delve into the complexities of DMD-based Channel Estimation across a variety of real-world scenarios. Additionally, addressing challenges such as computational complexity and adaptation to different propagation environments remains imperative.  
In closing, the fusion of advanced data-driven techniques such as DMD, PiDMD, and mpeDMD with cutting-edge wireless technologies holds the potential to reshape the future of wireless communications. This integration promises to usher in an era of adaptable, intelligent, and high-performance wireless networks.  
Methodology, software, investigation, Writing original draft and editing, F.P.D.; Validation and suervison, C.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
AP Access Point  
BS Base Station  
BU Beamforming interference  
CDF Cumulative Distribution Function  
DL Down-link  
D Dynamic Mode Decomposition  
DS Desired Signal  
FZF Full Zero Forcing  
LS Least Squares  
MIMO Massive-Multiple Input Multiple-Output  
MMSE Minimum Mean Square Error  
mpeDMD Measasure Preserving Enhanced DMD  
MR Maximum Ratio
## 3 Methodology
### Pixel-Wise Spectral Features  
#### 3.2.1 Time-Series Composition  
Multi-temporal value composition is a common technique for suppressing atmospheric and cloud effects and reconstructing time-series observations when processing time-series optical images [35]. This technology was employed to generate time-series images with lower cloud and shadow contamination. It was also noted that time-series composition could increase observation intervals, resulting in sparser time-series sequences.  
Greater vegetation index values indicated a much more robust vegetation growth. Alternatively, this assumption may not hold true for spectral reflection. For instance,cloudy pixels with higher spectral values and shadowy pixels with lower values are contaminated. Consequently, a mean value composition algorithm was utilized in this study. Following a similar procedure of maximum value composition, mean value composition was applied to multi-temporal Sentinel-2 images to generate a composited image using the following equation.  
\[\text{mvc(i,j)}=\frac{1}{N(i,j)}\sum_{t=1}^{N(i,j)}v(i,j)^{t}\]  
where \(N(i,j)\) is the number of clean observations (not covered by clouds and shadows) at a geographical location (_i,j_), \(v(i,j)^{t}\) is the pixel-wise spectral value of time step \(t\) at location (_i,j_) and \(mvc(i,j)\) is the composited value at location (_i,j_).
## 4 Discussion
### Comparison with Carbonaceous Chondrite Samples  
In carbonaceous chondrites, insoluble organic matter (IOM) accounts for 70% of all organic matter [39]. Its origin, besides interstellar radical synthesis, might be related to impact-induced reduction reactions, promoted by metal droplets or minerals [40; 41]. These metals could act as reductive agents, originating reduced forms of organic matter (e.g., aromatic rings, extensive conjugation, and CO\({}_{2}\) reduction) [42; 43]. Isotope measurements concluded that aqueous alteration in carbonaceous chondrites would have happened in the early solar system (around one million years after its formation), at a distance of 2.5 astronomical units from the proto-sun, and at that time, carbonaceous chondrite's parent bodies (i.e., planetesimals) could be from partially to fully oxidized [44; 45; 46]. Modeling asteroid Ryugu (i.e., Cl-type sample) for its parent planetesimal suggests that an aqueous alteration would have occurred at T < 100 \({}^{\circ}\)C in an alkaline environment (pH-8-9) [47]. Looking into the Pourbaix diagram for Ni(s), Fe(s), and Al(s), these metals act as reducing agents, oxidizing into Ni\({}^{2+}\), Fe\({}^{2+}\)/Fe\({}^{3+}\), and Al\({}^{3+}\) (same species used in this work); hence, the availability of these ions is expected to be very significant compared to specific soluble organic molecules detected in extraterrestrial rocky bodies. Rubin and Ma [17] comprise most minerals detected in meteorites, which also contain metal ions used in this work (e.g., olivine, (Mg,Fe)\({}_{2}\)SiO\({}_{4}\), hematite, \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\), corundum, Al\({}_{2}\)O\({}_{3}\), pentlandite, (Fe,Ni)\({}_{9}\)S\({}_{8}\), calcite, CaCO\({}_{3}\), and dolomite, CaMg(CO\({}_{3}\))\({}_{2}\), among many others). Hence, the amount of active metal content is in a bigger proportion relative to the 1 to 4 ribonucleoside-metal ratio used in this study (i.e., the amount of ribonucleoside could be at least more than 1:100 lower than oxidized metal content) [17; 39].
# Automatic Simplification of Lithuanian Administrative Texts
## 4 Methods
### Evaluation  
#### 4.4.1 Metrics  
We employed a set of evaluation metrics to assess text simplifications performed by our text simplification models. These metrics provide a quantitative evaluation of the results:  
1. **BLEU (Bilingual Evaluation Understudy) Score** measures the n-gram overlap between the simplified output and reference sentences. BLEU scores range from 0 to 1, where higher scores indicate greater proximity to the reference [78].
2. **ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score** assesses n-gram overlap between simplified and reference texts, with scores between 0 (no overlap) and 1 (perfect overlap) [79]. We employed three ROUGE variants: (a) ROUGE-1: Unigram overlap; (b) ROUGE-2: Bigram overlap; (c) ROUGE-L: Longest Common Subsequence overlap. We used ROUGE to assess model performance during fine-tuning and to evaluate the automatically simplified sentences.
3. **SARI (System Aggregated Referenceless Evaluation Metric)** was developed specifically for text simplification tasks and evaluates simplified sentences by considering words kept, added, and deleted during simplification [80]. SARI scores range between 0 and 100, where higher values indicate better performance. Studies have shown that SARI correlates well with human judgments [81; 82].
4. **BERscore** leverages pre-trained contextual embeddings from BERT to identify similarities between candidate and reference phrases. BERscore correlates well with human evaluation [83].
5. **LIX** is a readability metric that combines word and sentence factors in its formula, i.e., it considers the percentage of longer words (>6 characters) and average words per sentence. LIX scores are interpreted as follows [84; 85]: 60--very difficult text, \(\leq\)40--universal readability, 20--very easy text.
6. **RIX** is a simplified version of LIX. For universal readability, a RIX score of 8 or below is recommended [85].
7. **SacreBLEU** is a variant of BLEU. It offers a standardized way to calculate scores, which facilitates more consistent model comparisons [86]. We utilized this metric to monitor model performance during the fine-tuning process.
## 2 Materials and Methods
### Construction of the B-Camera
#### 2.2.3 Communication Unit  
Either the USB or Bluetooth port can be used for communication between the sensor array and the processing unit. When mobility is prioritized, we can use a Bluetooth breakout such as the HC-06. The array data include the ID of each sensor, the three components of measured magnetic fields \(\{B_{x},B_{y},B_{z}\}\) and a flag to indicate the end of a scan, so that the PDU can immediately process the data and display the image when all of the data in the array have been received.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 2 QKD Networks with QKP
### Trusted Repeaters for Distance Extension  
In 2002, [PERSON] et al. proposed the idea of using trusted nodes for quantum-key-distribution networking [20]. On the basis of the point-to-point QKD system, the \"one-time-pad\" cryptographic algorithm is used to realize the key distribution of both parties in the long-distance communication through the \"hop-by-hop\" encryption method. At present, many quantum-key networks based on key relay have been built in countries around the world. In 2008, the European countries established the famous SECOQC (secure communication based on quantum cryptography) network. In 2010, Japan established a six-node QKD network in Tokyo. China has also established several quantum-key networks, and the world's first wide-area quantum key network - \"Beijing-Shanghai trunk line\" also uses key-trusted repeaters to generate end-to-end keys.  
The specific implementation process is shown in Figure 2. [PERSON] and [PERSON] need to share the key \(\mathrm{K_{A}}\) on the premise that the point-to-point key distribution is completed. First, key sequences \(\mathrm{K_{A}}\) and \(\mathrm{K_{B}}\) are shared by Alice-trusted relay pair and Bob-trusted relay pair, respectively (step 1). Then a bit-by-bit exclusive-or (XOR) with \(\mathrm{K_{A}}\) and \(\mathrm{K_{B}}\) is executed at the trusted relay node. The encrypted bit sequence is sent to [PERSON] (step 2). Finally, [PERSON] receives the bit sequence and decrypts it using \(\mathrm{K_{B}}\). \(\mathrm{K_{A}}\) was shared by [PERSON] and [PERSON] successfully (step 3). Since the relay node also knows the \(\mathrm{K_{A}}\) when relaying, the relay node is required to be trusted. Subsequently, schemes such as bit transmission, random routing, multipath transmission, and XOR storage are proposed to reduce or even eliminate the requirement for node credibility.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
###### Abstract  
Long Term Evolution-Metro (LTE-M), as a special communication system for train control, has strict requirements on adjacent channel interference (ACI). According to the 3 rd Generation Partnership Project (3 GPP) protocol of the European Telecommunications Standards Institute (ETSI) standards, this paper presents the required isolation degree for LTE-M systems to resist ACI. Aiming at the scenario of leaky cable transmission and antenna transmission adopted by the underground LTE-M system of the subway, the isolation degree required for LTE-M system deployment is deduced by combining the channel description with the principle of ACI. For the coexistence of a LTE-M system and an adjacent cellular system in a subway ground scenario, the Monte-Carlo (MC) method is used to simulate several conceivable scenarios of the LTE-M system and the adjacent frequency cellular system. In addition, the throughput loss of the LTE-M system is estimated by considering signal to interference plus noise ratio (SINR). Simulation results demonstrate that adjacent frequency user equipment (UE) has negligible small interference with the LTE-M underground system when using the leaky cable radiation pattern, whereas for the LTE-M ground system, the main interference comes from the adjacent frequency UE to the LTE-M base station (BS). Finally, interference avoidance solutions are presented, which can be utilized as a reference in the design and deployment of LTE-M systems in the rail transit environment.  
rail transit; LTE-M; adjacent channel interference; isolation degree 2022222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222218 May 202022222222222222222222222222222222222222222  
coupling loss (MCL), is one method for estimating the need for isolation for worst-case interference. The other one is based on the Monte-Carlo statistical method, which is used to model random processes and obtain reliable data through a sufficient number of simulations. This method is appropriate for a more precise assessment of interference coexistence between systems in the case of multiple interferences [8].
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 4 Results and Discussion
### THz Raman Spectra of Aqueous Solutions  
The THz Raman spectrometer system was used to measure signals of aqueous solutions. Figure 9 shows the THz Raman spectra of different concentrations of uracil aqueous solution (a) and salicylic acid aqueous solution (b) at a laser power of 50 mW and integration time of 50 s. The Raman peaks in the figure are identical to those for the solid state, and the Raman signal intensity increases with increasing concentration. The lowest measurable concentration of 2-hydroxy BA solution is 0.015 mol/L. This experiment breaks through the difficulties of THz measurement of aqueous solutions and makes it possible to detect low-concentration liquids.  
Figure 7: Spectra of gallic acid in different containers with a laser power of 50 mW and integration time of 100 s.  
Figure 8: THz Raman spectra of four nucleobases: (**a**) cytosine, (**b**) uracil, (**c**) guanine, (**d**) and adenine.  
Figure 9: (**a**) THz Raman spectra of uracil aqueous solution at different concentrations. (**b**) THz Raman spectra of 2-hydroxy BA aqueous solution at different concentrations.
## 3 Data and Methods
### Restoration of Denudation Thickness  
In this study, the Late Cretaceous denudation thickness calculation was performed for 20 vertical wells in the northeastern area of the Sulige gas field (Table 1). The principles of sonic transit time data acquisition are as follows: (1) pure mudstone with a thickness of more than 1.5 m is selected, and the position at 1/2 of the corresponding curve is recorded; (2) the sonic transit time at the point where the borehole diameter suddenly increases or decreases is discarded; and (3) the values of the abnormal jump segments of the sonic transit time are discarded. On the basis of the sedimentary characteristics of the Ordos Basin and referring to the sonic transit time reported by previous researchers [28], in this study, we took \(\Delta t_{0}=600\)\(\mu\)s/m. By fitting calculations, we obtained the denuded thicknesses of the 20 wells in the study area. As shown in Table 1, the Late Cretaceous denudation thickness in the northeastern part of the Sulige gas field was between 820 m and 1200 m, which is consistent with the results of denudation thicknesses in this region by [PERSON] et al. [29].  
On the basis of the denudation thickness data of the above 20 well locations, we constructed a planar distribution map of the Late Cretaceous denudation thickness in the northeastern part of the Sulige gas field (Figure 4). As shown in the figure, the amount of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Well Name** & **Regression Equation** & **R\({}^{2}\)** & **Denudation** \\  & & & **Thickness/m** \\ \hline H2 & y = \(-5.3096\)x + 2106.6 & 0.7236 & 1094.16 \\ H3 & y = \(-5.97\)x + 2379.5 & 0.8406 & 1217.50 \\ S153 & y = \(-5.7101\)x + 2428 & 0.8186 & 1013.06 \\ T33 & y = \(-5.722\)x + 2489.9 & 0.8089 & 958.30 \\ T34 & y = \(-5.9268\)x + 2423.7 & 0.8058 & 1147.38 \\ T35 & y = \(-4.7313\)x + 1909 & 0.7325 & 944.78 \\ T37 & y = \(-5.3937\)x + 2347.5 & 0.8532 & 903.72 \\ T39 & y = \(-5.6174\)x + 2247.6 & 0.7121 & 1137.84 \\ T41 & y = \(-5.2292\)x + 2187.8 & 0.7334 & 964.72 \\ T42 & y = \(-5.4778\)x + 2220.2 & 0.7497 & 1081.48 \\ T96 & y = \(-5.5588\)x + 2224.2 & 0.8084 & 1126.08 \\ Z114 & y = \(-4.5804\)x + 1928.7 & 0.7017 & 834.94 \\ Z53 & y = \(-6.0197\)x + 2560.1 & 0.6148 & 1066.72 \\ Z71 & y = \(-5.6598\)x + 2428.4 & 0.8614 & 982.48 \\ Z72 & y = \(-5.3412\)x + 2333 & 0.7280 & 886.72 \\ Z79 & y = \(-5.6666\)x + 2322.8 & 0.6782 & 1092.16 \\ Z30 & y = \(-5.7186\)x + 2265.7 & 0.7416 & 1180.46 \\ T32 & y = \(-4.8294\)x + 1890.5 & 0.7053 & 1022.14 \\ T43 & y = \(-5.5877\)x + 2265.8 & 0.7076 & 1101.82 \\ SD011-104 & y = \(-5.8218\)x + 2481.4 & 0.8459 & 1026.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Restoration statistics of the denudation thickness of 20 wells in the northeastern part of the Sulige gas field in the Ordos Basin.  
denudation in the northeastern and southwestern parts of the study area was relatively large, forming a strong denudation zone in the northeast-southwest direction, with a denudation thickness between 1070 and 1190 m; in the northwestern part, the denudation was relatively small, with a denudation thickness between 860 and 950 m.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Evaluation Criteria  
Following the convention of MS COCO [91], we adopt the following six metrics for evaluation: _AP_ (Average Precision), _AP\({}_{50}\)_, _AP\({}_{75}\)_, _AP\({}_{S}\)_, _AP\({}_{M}\)_, and _AP\({}_{L}\)_. _AP\({}_{50}\)_, _AP\({}_{75}\)_ are the AP values obtained with IoU fixed at 0.5 and 0.75. _AP\({}_{S}\)_, _AP\({}_{M}\)_, and _AP\({}_{L}\)_ calculate the average AP values for all small, medium, and large targets, respectively.
## 2 The Constrained Multi-Objective Component Deployment Problem
### Reliability Objective
To calculate this value, each probabilistic transition \(c_{i}\xrightarrow{p_{ij}}c_{j}\) in the model is expressed as a tuple of transitions \(c_{i}\xrightarrow{p_{ij}}l_{ij}\xrightarrow{1}c_{j}\), the first following the original probability and then terminating in one of the states with probability \(1\). Considering that the execution of a software system is never initiated in a network link \(l_{ij}\) and the only predecessor of link \(l_{ij}\) is component \(c_{i}\), thus the expected number of visits of a network bus is equivalent to  
\[v_{ij}=v_{i}\cdot p_{ij}. \tag{6}\]  
The reliability of a deployment architecture \(\mathbf{d}\in\mathcal{D}\) can be computed by taking into account the expected number of visits and reliabilities of execution and communication elements:  
\[R\ \approx\ \prod_{i=1}^{n}R_{i}^{v_{i}}\cdot\prod_{(i,j)(\text{if used})}R_{ ij}^{v_{ij}}. \tag{7}\]
## 2 Materials and Methods
### Automatic Deviation Correction Control Strategy
#### 2.2.1 Prediction Method of the Driving Cycle EndPoint
The predicted deviation is greater than the initial deviation, and the initial deviation is greater than the maximum correction amount (\(\mid e_{1}\mid\;>\;\mid e_{0}\mid\) and \(\mid e_{0}\mid\;>\;\mid\)[\(e\)]). While \(e_{1}\;>\;e_{0}\) indicates that the position deviation of the roadheader continues to increase, \(\mid e_{0}\mid\;>\;\mid\)[\(e\)] indicates that the deviation of the anchor point of the windlass at the anchor point is large, which is not suitable for eliminating the lateral deviation of the roadheader in a driving cycle. If the maximum allowable adjustment amount in a single cycle is [\(e\)], then the coordinate of the trajectory endpoint \(P_{1}\) is (\(e_{1}\;\pm\;\mid\)[\(e\)], \(L_{\rm c}\)). When \(e_{1}\) is greater than zero, the negative sign is taken and when \(e_{1}\) is less than zero, the positive sign is taken, as shown in Figure 5.
## 4 Conclusions  
Results for 'health effect #1' show that even a decrease in MRL for 30% (0.7 MRL) increases the MOET for 40% to 50% depending on the recall method while MOET values are doubled or almost tripled when the \"zero\" scenario is employed for the two recall methods. This confirms the working hypothesis that the \"zero residue\" concept has a significant potential in decreasing cumulative exposure effects and has a great sustainability potential. A certain limitation of the study is that organic food products were not sampled. As for 'health effect #2', the level of MOET for all four scenarios are considered as very low risk.  
Future research should target other pesticides with chronic effects on other human organs and include other age groups. Other dimension of research could correlate overall sustainability and health effects of different agricultural practices.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14020880/s1](https://www.mdpi.com/article/10.3390/app14020880/s1)]([https://www.mdpi.com/article/10.3390/app14020880/s1](https://www.mdpi.com/article/10.3390/app14020880/s1)), Table S1: Detailed description on occurrence of specific active substances in different types of fruits and vegetables; Table S2a: Estimated daily intake of active substances for two recall periods for scenario \"1.0 MRL\", and 'health effect #1'; Table S2b: Estimated daily intake of active substances for two recall periods for scenario \"0.7 MRL\", and 'health effect #1'; Table S2c: Estimated daily intake of active substances for two recall periods for scenario \"zero\", and 'health effect #1'; Table S3: acceptable daily intake values and list of references.  
Conceptualization, I.D. and B.U.; methodology, N.S., N.T. and B.U.; validation, I.D. and B.U.; formal analysis, N.T., A.S., M.S., S.V. and K.R.; investigation, A.S., M.S., S.V. and K.R.; data curation, N.S. and N.T.; writing--original draft preparation, I.D. and B.U.; writing--review and editing, all co-authors; visualization, I.D. and B.U.; supervision, B.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical Review--This study was performed according to the Codex of professional ethics of the University of Belgrade (Kodeks professionalne etike Univerzitateu a Beogradu 193/2016).  
All participants involved in the field survey gave verbal consent.  
Data Availability Statement: Data are available upon request.  
The authors declare no conflicts of interest.