esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments with Ensemble-Average SNR
Figure 4), Equation (12) indicates that in a given realization, such as \(H^{(i)}[n,k]\), the channel frequency responses of different subcarriers are correlated. Consequently, if \(|\mathbf{H}^{(i)}[n,k_{0}]|^{2}\) is small, it is highly probable that \(|\mathbf{H}^{(i)}[n,k_{1}]|^{2}\) will also be small. As a result, this realization is likely to have a smaller channel gain compared to other realizations. The difference between the pdfs of \(\overline{\left|\mathbf{H}[n,\cdot]\right|^{2}}\) for the three channel models becomes more pronounced when \(K=600\). Realizations with small channel gains exhibit low effective SNRs and, consequently, suffer from high decoded BERs. These realizations, therefore, dominate the overall decoded BER performance in the experiment. This phenomenon is more significant in the EPA model compared to the EVA and ETU models, as the EPA model has a larger variance in \(\overline{\left|\mathbf{H}[n,\cdot]\right|^{2}}\). This larger variance results in more channel realizations with very small channel gains (much less than one), as illustrated in Figure 3:4 This explains the rationale behind the plots in Figure 3:5  
Figure 3:6 Theoretical and empirical pdfs of \(\overline{\left|\mathbf{H}[n,\cdot]\right|^{2}}\). In short, a channel with a larger variance, such as EPA in Figure 7, is more likely to have a realization with a channel gain significantly less than one, or a much smaller \(P_{r}\) in (2). Consequently, this channel realization would result in an extremely high decoded BER, as illustrated in Figure 3:7  
To further illustrate this point, Figure 8 shows examples of \(|H[n,k]|\) against subcarriers for the three channel models. It is evident from the figure that (sub)frame 1 is indeed transmitted at much lower power than the other two in the EPA channel. Moreover, in LTE DL transmission, each codeword must fit into a subframe, which is too short to reflect the channel's ergodic nature (if existing), as evidenced by the nonzero variance of \(\overline{|\mathbf{H}[n,\cdot]|^{2}}\). Therefore, regardless of how large the ensemble-average SNR is, there is always a nonnegligible probability that the system cannot achieve an arbitrarily small decoded BER. Nevertheless, as the ETU channel has a larger fluctuation of \(|\mathbf{H}[n,k]|\) over \(k\) due to long delay spread, its average power has a higher probability of being close to one. Figure 3:8 _Cont._  
This phenomenon has been previously observed from the perspective of capacity in [25; 26; 27]. It was found that to achieve the Shannon (ergodic) capacity, the length of the codebook must be sufficiently long for the fading channel to exhibit its ergodic nature. If a short codebook is used instead, there is a nonnegligible probability that the actual transmission rate, regardless of how small, will exceed the instantaneous mutual information. This nonnegligible error probability corresponds to the event where some codewords are transmitted at an SNR significantly lower than the ensemble-average SNR.
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 3 Results
### Comparison and Analysis
#### 3.2.2 Efficiency Evaluation  
As shown in Table 2, the proposed STDecoder-CD series models have a relatively large number of trainable parameters, but fortunately, their FLOPs (i.e., floating point operations) remain low. That is, STDecoder-CD (especially the Type I model) can maintain a good trade-off between computational complexity/time and accuracy. On the contrary, although some of the compared methods (e.g., SiUNet3+-CD and SNUNet-CD) can provide an acceptable CD performance, the high computational cost limits their real-world applications. Although FC-Siam-Diff and STANet have much lower parameters and FLOPs, their prediction accuracies are worse. ChangeSTAR and ChangeFormer have slightly higher FLOPs, and their CD performance is inferior to STDecoder-CD as well. Here, BIT is an exception: it has smaller trainable parameters and FLOPs with the assurance of accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameters (M)** & **FLOPs (G)** \\ \hline FC-Siam-Diff & 1.35 & 4.71 \\ STANet & 16.93 & 12.98 \\ IFN & 50.71 & 82.26 \\ ChangeSTAR & 52.58 & 19.53 \\ SNUNet-CD & 12.03 & 54.77 \\ SiUNet3+-CD & 27.00 & 216.72 \\ BIT & 12.40 & 10.59 \\ ChangeFormer & 29.75 & 21.19 \\ Type I (ours) & 40.84 & 12.14 \\ Type II (ours) & 52.45 & 18.20 \\ Type III (ours) & 53.89 & 18.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A list of different models’ parameters and FLOPs (when the input image size is 256 \(\times\) 256).  
Figure 7: Visualized results of STDecoder-CD series models and the compared methods on the CD_Data_GZ dataset. Note: due to limited space, we could not show the prediction results of all of the algorithms listed in Table 1.  
However, we must admit that training STDecoder-CD with the SAM optimizer needs a second round of forward/backward propagations to update \(\varepsilon\), and this will lead to around 2\(\times\) computational cost per update. This may be the main disadvantage of the proposed models, and the best way to address this problem is to apply the transfer learning strategy like BIT does, and it may be better to pretrain the singular TransCNN module on large CV (i.e., computer vision) datasets such as ImageNet and then freeze these pretrained weights within TransCNN. This will be carried out in our future work.
# Projections of Climate Change Impact on Stream Temperature: A National-Scale Assessment for Poland
## 5 Conclusions  
In conclusion, this national-scale assessment provides preliminary but valuable insights into the anticipated impact of climate change on stream temperature in Poland. Leveraging an ensemble of EURO-CORDEX projections and the SWAT model, the study offers a preliminary understanding of the dynamics shaping freshwater ecosystems in the face of environmental change. The validated stream temperature model demonstrates its reliability across diverse geographical locations, instilling confidence in the projected outcomes. The results reveal a uniform warming tendency across the country, with discernible increases in stream temperatures during both summer and winter seasons. The choice of emission scenarios (RCP4.5 and RCP8.5) introduces variations in the magnitude of temperature changes, emphasizing the critical role of mitigation strategies in influencing future climate outcomes. As global temperatures continue to rise, the findings of this study underscore the urgency of implementing adaptive strategies to safeguard the ecological integrity of Poland's freshwater systems. The integrated approach, combining advanced climate models and a hydrological model, positions this study as a valuable resource for policymakers, researchers, and environmental managers striving to develop targeted conservation strategies. In summary, this research contributes to the growing body of knowledge on climate change impacts, emphasizing the consequences on stream temperature and, by extension, freshwater ecosystems. While this study provides essential insights, future work could focus on refining the model to capture more complex hydrological and seasonal processes, particularly those relevant to cold-region dynamics. Incorporating elements such as freeze-thaw cycles, groundwater influences, and riparian shading could increase model accuracy.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflict of interest.
## 3 tRNA Core Modifications and Their Impact on Translation and Stress Adaptation
### tRNA Core Modifications and Their Relation with Stress Adaptation Mechanisms  
#### 3.3.1 s\({}^{4}\)U8 Is Implicated in the Response to UV Stress  
The s\({}^{4}\)U8 modification is omnipresent in prokaryotic tRNAs [14] and has been reported early on to carry a protective role against UV radiation (Table 2) (reviewed in [141]). This modification is sensitive to UVs [142], and when cytosine is present at position 13, an intramolecular s\({}^{4}\)U8:C13 cyclobutane pyrimidine dimer is formed between the aromatic bases that are stacked on each other in tRNA 3D structures [142]. This UV-triggered cross-linking is a widespread phenomenon, since in _E. coli_ for instance, about two-thirds of the tRNAs harbor the s\({}^{4}\)U8 modification, with half of them also carrying cytosine at position 13. Thus, UV radiation likely targets about one-third of the _E. coli_ tRNAs, including the entire pool of several isoacceptor tRNAs. Some of these photo-crosslinked tRNAs exhibit a reduced ability to be charged by their aminoacyl-tRNA synthetase [143; 144]. The accumulation of uncharged tRNAs--which may be directed to the A-site of the ribosome--causes protein synthesis to stall and leads to growth delay in _E. coli_ and _Salmonella typhimurium_[145; 146; 147]. In addition, ribosome stalling gives rise to the production of ppGpp, which triggers the stringent response and a massive decrease in stable RNA production. This seemingly allows for a faster recovery of the UV-exposed cells upon antibiotic treatment [148]. Interestingly, _E. coli_ strains selected for their abnormal sensitivity to UVs, were found to carry mutations in the gene responsible for s\({}^{4}\)U8 formation [149; 150] and were unable to produce ppGpp after irradiation [147]. These mechanisms--initiated by a photo-cross-linking reaction at the s\({}^{4}\)U8 modification site--eventually ensure better survival after UV stress [147; 150].
## 2 Material and Methods
### Tree-Ring Data  
Spruce and Chinese pine forests with no significant human influence were selected (located on N105.901\({}^{\circ}\) E38.775\({}^{\circ}\) and N105.911\({}^{\circ}\) E38.745\({}^{\circ}\), respectively) as target tree species. An increment borer with an inner diameter of 5.15 mm was used to drill the tree-ring cores from the vertical slope direction, and two cores were drilled from each tree. After the cores were naturally dried, they were fixed in the sample wooden slot with manual white glue. After the latex dried, the sample was ground step by step with sandpaper of different mesh sizes (from 180 mesh to 1000 mesh) until the xylem cells were clearly visible under the microscope. Visual cross dating was carried out under the microscope. Then LINTAB 6 with TSAP software was used to measure tree-ring width with an accuracy of 0.01 mm. The initial dating results were cross-checked using COFECHA software [21], and any errors in dating were corrected. Samples with severe fractures that made it difficult to cross-date were removed, and, finally, 55 cores from 31 spruce trees and 31 cores from 19 Chinese pine trees were used for subsequent analysis.  
After obtaining accurate tree-ring data, ARSTAN software was employed to build species-specific chronologies [22]. Smooth spline with a 50% frequency cutoff equal to 32 years was employed to detrend the non-climatic signals. Then, the detrended series was synthesized using the Robust Bi-weight averaging method to develop three types of chronologies: standard chronologies (STD), residual chronologies (RES), and ARSTAN chronologies (ARS). Here we used the residual chronologies for subsequent analyses. A common period analysis was conducted from 1982-2018, and the signal-to-noise ratio (SNR), expressed population signal (EPS), and variance in the first eigenvector (PC1) were calculated to evaluate the quality of the chronologies. Finally, the chronologies were truncated using the 0.85 threshold of subsample signal strength (SSS) [23].
## 2 Materials and Methods
### Methodologies
#### 2.3.1 Geo-Informatic Tupu  
Geo-informatic Tupu records the spatial and temporal composite information of LUTs with Tupu units, which have the composite characteristics of the quantitative representation of the \"spatial pattern\" and \"temporal sequence feature\" under the condition of multiple spatio-temporal conditions, making LUTs analysis more intuitive and accurate [56]. The Tupu unit is formed by the combination of spatial units and time-series units, and its selection and collection are two key steps in creating the Tupu. The main steps are as follows:  
1. Composite Tupu unit. The spatial resolution of 30 m \(\times\) 30 m was selected as the basic spatial geographical unit for Tupu analysis. Remote-sensing image data of three stages in the study area were selected to determine the time-series units of Tupu from 2000 to 2010 and from 2010 to 2020.  
2. Tupu construction. The map algebra method is used to carry out the map algebra superposition operation of the two stages of land-use Tupu units, and to realize the Tupu fusion. The specific equation is as follows: \[C=10A+B\] (1) where \(C\) is the Tupu code for land-use, that is, the land-use type of the Tupu unit. \(A\) and \(B\) are the code value of the land-use unit at the beginning of the research phase and at the end of the research phase, respectively. The basic principle of geo-informatic Tupu is to overlay two periods of land-use data, with land types coded at the beginning of the study as the tens-digit and those at the end set as the units-digit, and synthesize the two into a land-use change map with a 2-digit code. For instance, Code 12 means the transition of cultivated land to forest land. Code 26 signifies the transition of forest land to built-up land. The rest of the code follows the same rules. Moreover, the ratio of LUCs represents the percentage of the conversion area of land-use types to the total conversion area of study area, which is used to further reflect the quantitative change characteristics of Tupu units for LUTs. The calculation formula is: \[P=\frac{S_{ij}}{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}S_{ij}(i\
eq j)} \times 100\%\] (2) where \(P\) represents the ratio of land-use change; \(S_{ij}\) denotes the area of the Tupu unit where the initial \(i\)-th land-use type is transformed into the final \(j\)-th land-use type; \(n\) is the number of land-use types.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 5  
QoS optimization in smart healthcare applications can be challenging due to several factors [72]. Major obstacles include network congestion, interoperability issues, real-time demand, and financial constraints. When too many users or devices attempt to access the same network at once, network congestion can occur, resulting in delayed or unstable connectivity. In healthcare, where many systems and devices may need to exchange information, interoperability refers to the capacity of various systems and equipment to operate together seamlessly. Real-time demands refer to the demand for very low latency in specific healthcare applications, such as remote surgery or monitoring critical patients. The cost of adopting and sustaining QoS might be a significant obstacle.
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies
#### 3.4.3 Optical Wireless Communication Technology (OWC)  
The OWC has surfaced since the 4G era, improvements are now expected to meet the requirements of the future 6G communication [63]. OWC was born out of the need for high-speed and large-capacity data transmission coupled with advances in semiconductor illumination [93]. The OWC comprises technologies such as light fidelity, visible light communication (VLC), optical camera communication, and FSO communication [63; 94]. The VLC is a more efficient wireless communication alternative to the radio frequency approach, which promises faster data rate, larger capacity, and higher reliability, and is ideal for short-distance high-speed application scenarios [93]. Like IRSs, VLCs are unable to be propagated through walls [95; 96; 97]. In 6G application scenarios, a more adaptable approach for effective VLC implementation can require collaboration with ML technology [19; 93].
## 3 A flexible Approach to a Reachable Network Topology with a Minimum Connection Degree
### Scenario Description  
To ensure the data transmission from the sensor nodes to the data sink, full reachability is required for each cluster in the third and second layers. For the sensor node SN_A3, the distance is farther between SN_E3 and the data sink, and high transmission power is required if data are directly delivered from SN_E3 to the data sink. Certain sensors become unable to deliver data to a data sink because of limited transmission power. In another scenario, SN_A3 transmits data to SN_A4, and SN_A4 forwards data to cluster head CH_A. CH_F acts as a relay node between CH_A and the data sink. Therefore, the power consumption from SN_A3 to the data sink is divided between SN_A4, CH_A and CH_F.  
In a hierarchical network, multi-hop delivery is an effective approach to equalize the power consumption of nodes in a long transmission. A sufficient condition of fully reachable networks is to ensure that the degree of each node is greater than one. However, the impact of transmission power depends on node degree. Therefore, to save node energy for a node, a proper node degree is required, and the transmission range must be adjusted by node density. According to the above description, a transmission power optimization with a minimum node degree is proposed to construct a fully reachable, energy-efficient network. The proposed approach will be illustrated in the following subsection.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Dataset  
We used 6300 utterances of clean speech data from the TIMIT database [43] to train the neural networks. TIMIT database consists of 10 sentences, each spoken by 630 English speakers. In order to make sure that various noisy utterances are considered during simulations, we selected 10 different noise types including: car, construction, office, railway, cafeteria, street, incar, train, bus from ITU-T recommendation P.501 database [44] and _white_ noise from NOISEX-92 database [45]. In the case of matched noise conditions, two-thirds of each noise clip was used for training and the rest for testing. For each pair of the clean speech utterance and the noise waveform, a noisy speech utterance was artificially generated with an SNR value randomly chosen from \(-3\) to 6 dB in 1 dB scale. As a result, a total of 63,000 utterances (about 54 h) were used so that the entire database was mixed with each noise type.  
The test set consisted of 1400 utterances of clean speech data from TSspeech [46], spoken by 12 male and 12 female English speakers. For the experiments in the matched noise conditions, we used the same noise types as used for training. For the experiments in the mismatched noise conditions, noises including kids, traffic, metro, and restaurant from ITU-T recommendation P.501 database were applied. Noisy speech utterances were generated with the SNR value ranging from \(-6\) to 9 dB with 3 dB step in which \(-6\) and 9 dB cases represented the unseen SNR conditions.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.16 Se-Leach  
According to the authors in [91], a protocol named stability enhancement for LEACH (SE-LEACH) is proposed to expand the stable zone of WSN by balancing the load and ensuring that all nodes dissipate power in the same way. Because the most critical requirement for the job of cluster head is the best candidate selection, the selection criteria consider node density, residual energy, distance from the base station, and the candidate's power dissipation.
## 3 Theoretical Analysis
### Range Characteristic between MBS and EGT
Along with an increase in synthetic aperture time and look angle, the MESR will also increase slightly. The reason might be related to MBS's motion status, which can also be shown by the speed components of MBS's sub-point on Earth's East-North-Up (ENU) orientation. The ENU changes continuously when Moon is at four different orbit points shown in Table 6; notably, the North-Up speeds at the ascending node and descending node are faster than the speeds at the apogee and perigee, the MESRs in ascending node and descending node are also larger than those in apogee and perigee. In addition, the limit wavelengths in the ascending node and descending node are longer than those in the apogee and perigee, according to Equation (8). When the Moon is in apogee and perigee, the speed components are more closely related to the orientation of EGT's speeds, i.e., the east orientation, thus decreasing the MESRs under the same parameters. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Apogee** & **Perigee** & **Ascending Node** & **Descending Node** \\ \hline Fitting speed & 2.4093 \(\times\) 10\({}^{3}\) m/s & 2.1404 \(\times\) 10\({}^{3}\) m/s & 2.4133 \(\times\) 10\({}^{3}\) m/s & 2.4680 \(\times\) 10\({}^{3}\) m/s \\  & (2.4093 \(\times\) 10\({}^{3}\) m/s) & (2.1404 \(\times\) 10\({}^{3}\) m/s) & (2.4133 \(\times\) 10\({}^{3}\) m/s) & (2.4680 \(\times\) 10\({}^{3}\) m/s) \\ Fitting smallest & 3.9849 \(\times\) 10\({}^{8}\) m & 3.4905 \(\times\) 10\({}^{8}\) m & 3.6872 \(\times\) 10\({}^{8}\) m & 3.8429 \(\times\) 10\({}^{8}\) m \\  & (3.9849 \(\times\) 10\({}^{8}\) m) & (3.4905 \(\times\) 10\({}^{8}\) m) & (3.6872 \(\times\) 10\({}^{8}\) m) & (3.8429 \(\times\) 10\({}^{8}\) m) \\ MESR & 3.8981 \(\times\) 10\({}^{-5}\) m & 4.1425 \(\times\) 10\({}^{-5}\) m & 0.0015 m & 0.0013 m \\ (second order) & (2.2900 \(\times\) 10\({}^{-4}\) m) & (2.2197 \(\times\) 10\({}^{-4}\) m) & (0.0057 m) & (0.0052 m) \\ MESR & 1.6570 \(\times\) 10\({}^{-5}\) m & 1.5199 \(\times\) 10\({}^{-5}\) m & 7.2718 \(\times\) 10\({}^{-4}\) m & 6.5845 \(\times\) 10\({}^{-4}\) m \\ (third order) & (4.7207 \(\times\) 10\({}^{-5}\) m) & (4.3035 \(\times\) 10\({}^{-5}\) m) & (7.6067 \(\times\) 10\({}^{-4}\) m) & (6.8873 \(\times\) 10\({}^{-4}\) m) \\ MESR & 1.4007 \(\times\) 10\({}^{-5}\) m & 1.4067 \(\times\) 10\({}^{-5}\) m & 7.2616 \(\times\) 10\({}^{-4}\) m & 6.5655 \(\times\) 10\({}^{-4}\) m \\ (fourth order) & (2.5570 \(\times\) 10\({}^{-5}\) m) & (2.3305 \(\times\) 10\({}^{-5}\) m) & (7.3516 \(\times\) 10\({}^{-4}\) m) & (6.6817 \(\times\) 10\({}^{-4}\) m) \\ \hline \end{tabular}
\end{table} 
Table 4: The fitting parameters for MBS (0\({}^{\circ}\), 0\({}^{\circ}\)) with a look angle (left looking) about 0.1\({}^{\circ}\), with the synthetic aperture time being about 80 s (contents outside brackets) and 150 s (contents inside brackets). The time of apogee, perigee, ascending, and descending nodes are consistent with the time in Tables 2 and 3.