esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Materials Characterization
The Mn 2p core-level spectra for all compositions are shown in Figure 3 and the corresponding BE values are given in Table 2. The BEs of the Mn 2\(p_{1/2}\) and Mn 2\(p_{3/2}\) signals are given by the positions of the maxima of the main peaks. The asymmetric Mn 2\(p_{3/2}\) main peak is located at 642.3-642.6 eV with a (2\(p_{3/2}\)-  
Analysis of the 3\(s\) region is more useful to assess the oxidation state of manganese, due to mainly the exchange interaction between the 3\(s\) and 3\(d\) electrons [14,15]. The 3\(s\) splitting (Table 2) suggests that the samples have a Mn\({}^{3+}\) and Mn\({}^{4+}\) mixture. The values of Mn average oxidation state in KMn4, KMn8 and HKMn are gathered in Table 2 and were obtained as suggested by [PERSON] at al. [16] taking into account the doublet separation from Equation (1). \[v_{\text{Mn}}=9.67-1.27\Delta\text{E}_{3s}/\text{eV} \tag{1}\]  
where \(v_{\text{Mn}}\) is the average oxidation state and \(\Delta\text{E}_{3s}\) splitting magnitude of 3\(s\). Therefore, taking into account the above results, the sample obtained at high temperature presents the highest amount of Mn(III). On the other hand, the sample prepared by hydrothermal method probably contains oxygen vacancies. As previously stated, the K amount in the formula of HKMn is considerably less than in the other samples [17]. The material morphology has a great influence on the electrochemical behaviour and therefore was analyzed by SEM and HRTEM. Some representative images are depicted in Figure 4. KMn8 (Figure 4c,d) consists predominantly of large micrometer-sized crystals exhibiting well-defined crystallographic faces whereas KMn4 (Figure 4a,b) exhibits cloud-like particles with diameters ranging between 175 and 260 nm. These large particles appear as aggregates of smaller individual particles with diameter close to 20 nm. On the other hand, HKMn (Figure 4e,f) present flower-like nanostructures, the individual nanoflowers are micro-sized and comprise a large number of thin nanosheets with a thickness of 15 nm. Moreover, striations along [1] orientation are clearly seen with spacings of ~7 A in all samples. Figure 4: SEM (**left**) and HRTEM (**right**) representative images of (**a,b**) KMn4, (**c,d**) KMn8, and (**e,f**) HKMn.
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 3 Control Strategies of the DBI
### Selected Control Strategies  
This work presents a comparison among the possible control strategies of the DBI. In particular, four control strategies were selected, including a classical linear control scheme and three non-linear control strategies, namely FCS-MPC, flatness-based control, and sliding mode control. Linear control is a widely used strategy in the industry since the implementation and design are well known and simple. For these reasons, linear control was selected as one of the strategies to be analyzed. The reminder of the control strategies to be analyzed in this document were selected based on their qualities in terms of (control) robustness, fast dynamic performance, and flexibility to incorporate restrictions and constraints, among others. Furthermore, the development of microprocessor technology has enabled the use of advanced control strategies [24]; therefore, FCS-MPC, flatness-based control, and sliding mode control were selected as additional control strategies to be analyzed.  
The general control diagram of the four strategies for the DBI is presented in Figure 5. Since a PV module is used as the input source, the four control schemes are composed by a common part, which is the maximum power point tracking (MPPT) of the PV module and the PV voltage control. In order to obtain the maximum power output of the PV panel, the conventional perturb and observe (P&O) algorithm was implemented in the four control schemes due to its simplicity and effective tracking [25].  
Figure 5: Global control strategies for DBI.  
In order to regulate the voltage of the input capacitor, the PI controller was used. The reference of the PV voltage \(v_{\mathrm{pv}}^{*}\) is obtained from the MPPT algorithm. Then, the second-order harmonic in the PV voltage measurement \(v_{\mathrm{pv}}\), inherent to single-phase inverters, is attenuated by the notch filter.
## 4 Discussion
### Strength and Limitations  
To our knowledge, this is the first systematic review and meta-analysis focusing on RCT studies in which the efficacy of LLBERT therapy programs in musculoskeletal patient care has been analyzed. Moreover, we selected studies that were exclusively performed on patients in post-surgery recovery, analyzing the effects of LLBERT on strength, muscle size, and pain perception in the operated lower or upper limbs.  
However, this study had some potential limitations. Regardless of the risk of bias already mentioned, and although all selected studies were RCT (only one of them was an RCPT), we found high levels of heterogeneity in the assessment methods used to evaluate the effects of LLBERT on strength and muscle size. Additionally, several studies could not be included in our meta-analysis because they did not adequately express strengthened outcomes [33; 39; 47]. A lack of homogeneity was also observed in the intervention protocols (especially regarding the number of sessions, program duration -from four to 24 weeks, and type of exercises performed) and controls (HRT, LLRT, and specific physical therapy protocols). However, curiously, the training workloads used (sets, repetitions, and rest intervals) were very similar between studies, with sets of 30-15-15-15 repetitions and 30 s of rest between sets being predominant. Additionally, nine of the selected studies used an arterial occlusion pressure of 80%, and three used pressures between 60 and 40%. This imbalance was even more pronounced when data were grouped by outcomes (strength: nine articles, seven used 80% of AOP, one used 40%, and one used incremental pressures--between 40 and 80%; muscle size: ten articles, eight used 80% of AOP and two used incremental pressures; pain perception: seven articles, six used 80% of AOP, and one used incremental pressures). Therefore, despite being a pooled-type analysis, moderation tests were not performed.
## 2 Results
### Analysis of Histone-Related Genes  
The analysis revealed significant differential expressions of the genes encoding histone modification enzymes and histone proteins in the _I114_ disruption lines compared to the WT (Figure 5A, Table S4). The expression of several histone protein genes (_H2A_, _H2B_, _H3_, and _H4_) was altered in the mutant lines (Figure 5B). As can be seen in the heatmap, _Solyc06g075930.1_, _Solyc06g075800.1_, and Solyc11g066160.1 (encoding H2B and H4) were downregulated in both M7_5 and M29_2. However, Solyc11g010240.2_, _Solyc03g071620.1_, _Solyc05g051515.1_, _Solyc06g074080.3_, _Solyc06g005430.1_, and _Solyc09g066100.3_ (encoding histone H2A, H2B, H3, and H4) were upregulated specifically in the M7_5 line, while _Solyc07g052940.3_ (Histone _H3_) was upregulated only in the M29_2 line. These changes in histone protein expression may influence the nucleosome assembly and chromatin structure. Both the M7_5 and M29_2 lines exhibited decreased expression of _Solyc03g044380.3_ (_Histone-lysine N-methyltransferase_) (\(-\)2.45- and \(-\)1.45-fold change, respectively). The M7_5 line also showed a significant downregulation of _Solyc03g112690.1_ (\(-\)5.00-fold change).  
Conversely, _Solyc11g066840.2_ (_Histone-lysine N-methyltransferase_) was upregulated only in the M29_2 line (1.11-fold change). Downregulation of the _listone methyltransferase_ (_HKMTs_) suggests a potential reduction in histone methylation, potentially affecting gene expression. Both the M7_5 and M29_2 lines exhibited decreased expression levels of _Solyc03g044380.3_ (_Histone-lysine N-methyltransferase_) (\(-\)2.45- and \(-\)1.45-fold change, respectively). The M7_5 line also showed a significant downregulation of _Solyc03g112690.1_ (\(-\)5.00-fold change). Overall, the two _I114_ disruption lines displayed distinct expression profiles for histone-related genes. The unique expression patterns observed in each line likely reflect the specific impact of each L1L4 mutation on gene regulation. The differentially expressed histone-related genes encode core histones (H2A, H2B, H3, H4), linker histone H1, and histone modifiers such as histone-lysine N-methyltransferase and histone deacetylase (HDAC) (Figure 5C).
# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 3 Experimental Results
### Testing of Different Multilayer CNN Models and Determination of the Most Suitable Architecture  
As shown in Table 3, this study designs five models comprising 1-5 convolutional layers. The first layer is a fractional-order convolutional layer with two 3 \(\times\) 3 convolutional windows; it is used to perform the 2D spatial convolution operations. The second to fifth convolutional layers use 16 kernel convolutional windows with different sizes of convolutional windows (3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, 9 \(\times\) 9, 11 \(\times\) 11) [51] and 16 MP windows for feature enhancement and extraction. Finally, a fully connected network with two hidden layers using the adaptive moment estimation optimization method [46; 48] adjusts the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **1** & **2** & **3** & **4** & **5** \\ \hline Training CPU Time (min) & \textless{}30 & \textless{}240 & \textless{}7 & \textless{}10 & \textless{}180 \\ Average Accuracy (\%) & 90.99\% & 90.34\% & 95.92\% & 95.28\% & 95.71\% \\ \hline \end{tabular}
\end{table}
Table 4: Comparisons of average training CPU time and average accuracy (%) for five different CNN models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & & \\  & **Convolution** & **Convolution** & **Convolution** & **Convolution** & **Convolution** & **Stride** & **Padding** \\  & **Window** & **Window** & **Window** & **Window** & **Window** & & \\ \hline
3 & 3 \(\times\) 3, 2 & 5 \(\times\) 5, 16 & 7 \(\times\) 7, 16 & - & - & 1 & 1 \\
4 & 3 \(\times\) 3  
connecting weighted parameters with Equations (11)-(13) such that the predetermined classification is obtained. This study uses the same training dataset, specifically, 466 feature patterns (282 tumor and 184 tumor-free screenshots), to train and test the five different CNN models. We randomly generate initial parameters and train each model at least five times, thus recording the required training time and classification accuracy rate to compare the average training CPU time (min) and average accuracy (%) of models, as seen in Table 4. The testing results indicate that the three-layer convolutional layer model (Model #3 shown in Table 3) has an average training CPU time of lower than 7 min and an average accuracy (%) of larger than 95% with 466 untrained feature patterns. While the average accuracy (%) of the four- and five-layer models can reach larger than 95%, these two models require more training CPU time. Therefore, Model #3 is the most suitable model for developing an automatic breast tumor screening classifier in clinical applications.
## 2 Materials and Methods
### Methods of Extracting Muscle Synergies  
NMF analysis was performed to extract muscle synergies from the preprocessed surface EMG data [15]. Muscle synergies are expressed by Equation (1):  
\[X=W^{*}H+e \tag{1}\]  
\(X\) refers to the matrix of the surface EMG data, represented by an \(m\times n\) matrix (\(m\), number of muscles measured; \(n\), number of data points). The NMF analysis factorizes \(X\) into muscle weighting (\(W\)) and activation coefficient (\(H\)) components and solves the calculation such that \(e\) (the error between \(X\) and \(W^{*}H\)) is minimized. \(W\) consists of an \(m\times k\) matrix (\(m\), number of measured muscles; \(k\), number of muscle synergies), and \(H\) consists of a \(k\times n\) matrix (\(k\), number of muscle synergies; \(n\), number of time data points). In this study, surface EMG data were measured from 10 muscles, and a propulsion cycle was interpolated to 100 points, which means \(m=10\) and \(n=100\).  
\(k\) was determined based on the variance accounted for (_VAF_). The _VAF_ is a measure of the reproducibility of the surface EMG data matrix reconstructed from the factorized \(W\) and \(H\) relative to the original matrix \(X\)[26]. It is defined as the coefficient of determination obtained from the uncentered Pearson's correlation coefficient multiplied by 100, which is calculated using the following equation (2):  
\[\text{VAF }=\left(1-\frac{\sum_{i=1}^{m}\sum_{j=1}^{n}\left(e_{i,j}\right)^{2}}{ \sum_{i=1}^{m}\sum_{j=1}^{n}\left(E_{i,j}\right)^{2}}\right)\times 100 \tag{2}\]  
where \(i\) indicates the number of muscles (\(1<i<m=10\)) and \(j\) indicates the number of time data points (\(1<j<n=100\)). In this study, two criteria were set: (1) the number of muscle synergies with a _VAF_ \(>90\%\) and (2) increasing the number of muscle synergies by one, and not increasing the _VAF_ by \(>3\%\). The smallest \(k\) value that satisfied both criteria was adopted [27].  
The K-means method, a nonhierarchical clustering analysis, was used to identify muscle synergies common to all participants for muscle weighting (\(W\)) components extracted by the NMF analysis. The corresponding \(H\) component was extracted for each \(W\) component of the identified muscle synergy, and the mean value was calculated.
## 2 Results and Discussion
### Determination of MTBE in Contaminated Soil Samples  
For further elucidation of applicability of the PPy@MIL-101(Cr) coated fiber, it was evaluated for HS-SPME sampling of MTBE in contaminated soil samples, followed by GC-FID quantification. For this purpose, six soil samples were collected from the area of an oil-refining company and some gas stations in different locations of Kermanshah City (Kermanshah, Iran) and subjected to the developed method. When volatile compounds are to be determined, sampling is critical, and precautions should be taken to prevent loss of analytes. Therefore, sample is needed to be analyzed as soon as possible after sampling. Otherwise the sample should be stored in a clean and tightly closed container and transported to the laboratory under cool conditions. In this research, the soil samples were collected according to international standard method of ISO 10381-1 [33], and then they were immediately transferred to the laboratory, in a closed polyethylene container, and stored in a refrigerator, at -18 \({}^{\circ}\)C. On the test time, the sample containers were removed from the refrigerator and allowed to reach to ambient temperature, and then they were analyzed by using the developed method, without any pretreatment step. For more assurance of the validity of the obtained data, each sample was also fortified with a known amount of MTBE and analyzed three times. The results are shown in Table 2.
# Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity
## 6 Conclusions and Future Work  
The RIS-assisted DC system is proposed by utilizing the RIS resource and radio resource from two different RIS and BS, respectively. The joint resource allocation for RIS-assisted DC system is formulated as an optimization problem and the user scheduling time fraction is derived for an \(\alpha\)-fair scheduler. Further, a heuristic for solving given RIS-assisted DC problem with the derived optimal user scheduling time fractions is presented. The exhaustive simulation results of throughput are presented for different user, BS, and blocker densities. The proposed system provides significant throughput gain of 50% and 48% in certain scenarios when compared to the existing benchmark and DC systems. It is also shown that the coverage with increased number of BSs in the system decreases and saturates beyond certain RIS density. For RIS to be advantageous as a standalone and RIS-assisted DC system, the RIS deployment should be carefully designed for any BS. The proposed heuristic categorizes the users based on the available channel parameters. In the future, we consider evaluating the performance of various RIS-assisted wireless systems during uplink and with imperfect phase estimation.  
Conceptualization, Y.R.; methodology, Y.R. and M.I; validation, Y.R., T.M. and M.I.; formal analysis, Y.R.; investigation, Y.R.; writing--original draft preparation, Y.R.; writing--review and editing, Y.R., T.M., M.L., T.O. and Y.T.; visualization, Y.R., T.M. and M.I.; supervision, M.I.; project administration, T.O. and Y.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 2 Related Work
In fact, a recent study introduced the IoT-UAV architecture [36], which entails the synergistic collaboration between IoT and Unmanned Aerial Vehicles (UAVs). In this system, the IoT network detects the fire and transmits an approximate location to a UAV, enabling it to locate the precise fire location and perform fire suppression. Utilizing UAVs in forest fire fighting offers several advantages. First, UAVs have the ability to access difficult-to-reach areas, facilitating intervention in challenging terrain. Second, they exhibit a faster response time for deploying fire extinguishing measures. Third, UAVs provide real-time aerial reconnaissance, enabling effective planning of fire suppression strategies. Moreover, their overhead view allows for precise identification of the fire's point of ignition. UAVs can also utilize remote extinguishing systems, reducing risks to human operators. They are capable of dropping water or fire retardant to delay the fire's spread. However, UAVs have certain limitations when it comes to fire suppression. These limitations include limited payload capacity for carrying water or fire retardant, short flight duration, restricted battery life, and difficulties in navigating smoke-filled environments. To overcome these limitations, the collaboration between UAVs and Unmanned Ground Vehicles (UGVs) has gained attention in the field of firefighting forest management [14]. UGVs offer unique advantages that can be leveraged in fire suppression efforts. They excel in navigating rough terrain and reaching remote areas that may be inaccessible for human-operated vehicles. UGVs demonstrate robustness in collision scenarios and can operate effectively in adverse weather conditions and low visibility. Equipped with powerful pumps and larger water tanks, UGVs can hold more water and fire retardant than UAVs, increasing the likelihood of successful fire suppression. Furthermore, UGVs can remain on-site for longer durations, monitoring the fire and preventing re-ignition. They can be outfitted with cameras and sensors to collect valuable data on the fire and the surrounding environment, contributing to future fire management planning. Additionally, UGVs can be remotely operated, minimizing risks to human operators. When combined with UAVs, which provide aerial surveillance and real-time data, coordinated operations between UAVs and UGVs enhance the overall efficiency of the firefighting system.
## 6 Experimental Results
### Experiment 3: Feature Importance Using Pearson Correlation  
As presented in Figure 6, the experimental results are executed again using the Pearson coefficient for measuring the correlation between the input features and the target features. As explained, all conducted ML algorithms achieved low results, and both LR and SVM algorithms achieved an accuracy of 58.73% on the accumulative feature \(k=700\). Starting from \(k=700\) to the end of the accumulative features where \(k=4900\), the accuracy remains the same score, forming a straight line. The LDA algorithm achieved the second-best results of 58.51% with the accumulative feature \(k=700\), and the stated results continued in a straight line to the end of the accumulative features where \(k=4900\). As noticed from the figure, most ML algorithms start increasing with \(k=\)100 until the accumulative feature \(k=700\), where the results remain without change to the end of the features.  
Figure 5: Accuracy of Chi\({}^{2}\) feature selection method with ML models.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 5 Mathematical Analysis
### Comparison between LLL and KZ Lattice Reduction  
Lattice reduction based decoding is a reliable technique and utmost necessary for a large scale MIMO system. It utilizes a lattice structure (signal constellation) for reducing the receiver complexity. The main idea of this decoding is to solve the closest vector problem (CVP) [30]. The standard approach to solve this CVP is to have first lattice reduction and then local search. In addition, for large-scale MIMO systems, it is advantageous to have the basis vectors as short as possible, which is known as the shortest vector problem (SVP) [30].  
As in [33; 48], a lattice can be defined as  
\[L(B)=\left\{\sum_{i=1}^{n}x_{i}b_{i}\right\};x\in\mathbb{Z}^{n} \tag{26}\]  
where \(B=[b_{1}........b_{n}]\) is called the basis of lattice L. Both KZ and LLL algorithms utilize the Gram-Schmidt orthogonalization and one can compute the same as follows  
\[\hat{b}_{i}=b_{i}-\sum_{j=1}^{i=1}\mu_{i,j}\hat{b}_{j},fori=1,...,n \tag{27}\]  
where \(\mu_{i,j}=\left\langle b_{i},\hat{b_{j}}\right\rangle/\left\|\hat{b}_{j} \right\|^{2}\). Using matrix notation, Gram-Schmidt orthogonalization can be written as \(B=\hat{B}\mu^{T}\), where \(\hat{B}=\left[\hat{b}_{1}........b_{n}\right]\) and \(\mu\) is a lower-triangular matrix with unit diagonal elements.  
Now, in the case of KZ reduction, a basis B is considered to be reduced if it maintain the following conditions:  
1. \(b_{1}\) represents the shortest nonzero vector of lattice L.
2. \(|\mu_{i,j}|\leq 1/2\), for\(1\leq j<i\leq n\).
3. If \(L^{n-1}\) represents the orthogonal projection of L on the orthogonal complement spanned by \(b_{1}\), then the projections \(b_{i}=\mu_{i,1}b_{1}\) of \(b_{2},...,b_{n}\) yield a KZ basis of \(L^{n-1}\).  
Similarly, a basis is LLL reduced if it satisfies the following conditions;  
1. \(|\mu_{i,j}|\leq 1/2\), for\(1\leq j<i\leq n\).
2. \(\left\|\hat{b_{i}}\right\|^{2}+\mu_{i,i-1}^{2}\left\|\hat{b_{i-1}}\right\|^{2 }\geq\delta\left\|\hat{b_{i-1}}\right\|^{2}\), for \(1<i\leq n\).  
One can decompose the LLL algorithm as mentioned above; Clause (a) is the size reduction and Clause (b) is the Lovasz condition. The value parameter \(\delta\) is chosen to have a tradeoff between quality and complexity. Its value can be anything between \(1/4\) and \(1\), but the optimal is \(3/4\).  
Particularly, in this section, we compare the most commonly used lattice reduction techniques, namely Lenstra-Lenstra-Lovasz (LLL) and Korkine-Zolotarev (KZ) algorithms. From the computational complexity point of view, KZ is much more expensive compared to the LLL algorithm but is much more efficient.