esa-sceva/satcom-chunk-collection huggingface dataset:
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 3 Results
### Large Ideal Reflector Array
#### 3.1.3 Spectral Distribution of Retroreflected Energy  
The octave band transfer function spectra at collocated source-receivers are influenced by multiple factors, and Figure 9 shows the results for space-reversal and focusing space-reversal patches (2 m surface distance). Mostly the lower limit of the transfer function, seen at low frequencies, was \(-\)12 dB which is the same value as the expectation for a simple reflection. Small space-reversal patches presented a 6 dB/octave spectral slope, consistent with expectation from diffraction loss. For 1.2 m patches the diffraction loss slope was only seen in the lower part of the spectrum, and it was not present at all for the 2.4 m patches. Focusing space-reversal patches introduced another frequency-dependent phenomenon, and consequently positive spectral slopes can be seen for all patch sizes in Figure 9. Focusing made a difference in the 2 kHz and 4 kHz octave bands for the 0.6 m patches; for the bands 500 Hz and above for the 1.2 m patches; and over the entire spectrum for the 2.4 m patches.  
Figure 8: Spatial distribution of reflected energy level over a 2.4 \(\times\) 2.4 m square for 25 source positions 2 m from the 7.2 \(\times\) 7.2 m reflector, with space-reversal patches. Small (0.6 m) and large (2.4 m) patch sizes are shown, for non-focusing and focusing space-reversal patches. Source locations are indicated by the green circles (the source for the bottom left subplot of each quadrant is outside of the receiver square). Values are speech-A-weighted.  
Since the 500 Hz and 1 kHz bands have the greatest weight in speech-A-weighting, high transfer function values for those bands are most associated with the high weighted values reported throughout this paper.
###### Abstract  
This paper describes the design, development, and testing of both hardware and software for a visual servoing-based system that enables agents within a heterogeneous marine robotic swarm to share energy. The goal of this system is prolonging the active operational time of the swarm as a whole, allowing it to perform long-term environmental monitoring and data collection missions. The implementation presented in the paper features an over-actuated autonomous surface platform docking up to four floating sensor nodes at a time and replenishing their batteries using wireless inductive charging. Mechanical solutions for each robot segment related to the docking procedure are presented, along with pertinent high-level and low-level control structures. A node featuring an extended Kalman filter and additional heuristics is used to fuse measurements from a neural network trained for object detection and a hue thresholding image processing algorithm, in order to track the docking target and achieve visual servoing. Finally, experimental results in both a controlled environment and challenging conditions on-site are presented. Once deployed, the developed system successfully enables the approach and docking of the designated target, showing its feasibility in different real-life conditions.  
marine robotics; multi-vehicle system; visual servoing; energy sharing +
Footnote †: journal: Applied Sciences
## 3 Optimization Problem Solving  
Due to the fractional structure of the objective function in problem P1 and the strong coupling between the optimization variables, directly obtaining a solution is challenging. To address this issue, we adopt an alternating optimization strategy to decouple P1 into two sub-problems: (1) fixing the IRS phase-shift matrix and optimizing the BS transmit beamforming, and (2) fixing the BS transmit beamforming and optimizing the IRS phase-shift matrix. For sub-problem (1), we utilize the Dinkelbach-SCA algorithm. Specifically, since the objective function is in a fractional form, the Dinkelbach algorithm is employed to transform it into a parameter subtraction form. Considering the presence of non-convex constraints, the SCA algorithm is used to convert the non-convex problem into a solvable convex optimization problem. The CVX toolbox [34] is then utilized to obtain the optimal BS transmit beamforming. For sub-problem (2), the Riemannian Conjugate Gradient (RCG) algorithm is employed to optimize the IRS phase-shift matrix. The optimization process is then iteratively alternated between BS transmit beamforming and the IRS phase shift until convergence.
## 3 Preliminaries
### Shapelet and Shapelet Transformation  
As defined in [33], shapelets are subsequences of sequence datasets that can maximally distinguish between sequence classes, assuming \(M\) is the length of the shapelet and \(N\) is the length of the sequence in the sequence dataset. Broadly speaking, shapelets are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Terminal Type** & **Communication Service** & **QCI Value** \\ \hline Terminal of train operation control & Train control data transmission service & 1 \\  & Emergency call & 1 \\ \hline The cab-integrated radio & Transmission dispatching order & 2 \\ communication equipment & Wireless train number calibration & 2 \\  & In-vehicle speech & 2 \\ \hline Handheld mobile station & Emergency call & 1 \\  & Speech call & 3 \\ \hline Train video surveillance system & Video Surveillance & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: QCI value and its corresponding services.  
Figure 2: The construction process of the dataset for the detection of LTE-R cells with degraded communication performance.  
not necessarily subsequences of sequence data in the strictest sense. Any sequence that can distinguish between sequence classes in terms of shape and has a length that satisfies \(M\leq N\) can be considered as a shapelet of a sequence dataset.  
For the convenience of these calculations, the distance between the \(i\)-th sequence data in a one-dimensional sequence dataset, \(X_{i}\), and the \(k\)-th shapelet, \(S_{k}\), is defined by Formula (1).  
\[d_{i,k}=\min_{j=1,\ldots,J}\frac{1}{M}\sum_{m=1}^{M}\big{(}X_{i,j+m-1}-S_{k,m} \big{)}^{2},\ J=N-M+1 \tag{1}\]  
In (1), \(d_{i,k}\) represents the distance between \(S_{k}\) and \(X_{i}\). Accordingly, the smallest distance between all \(J\) subsequences of length \(M\) in \(X_{i}\) and \(S_{k}\) is \(d_{i,k}\).  
After finding the \(K\) optimal shapelets in the one-dimensional sequence dataset, the distance between the \(K\) optimal shapelets and certain sequence data can be obtained using (1). These distances are used as new features of the sequence data, which map the sequence data to a new feature space. The above process is also called shapelet transformation. Through shapelet transformation, we can transform the one-dimensional sequence dataset \(X\) into \(D\), where \(D\) is \(\{(D_{1},y_{1}),(D_{2},y_{2}),\ldots,(D_{N},y_{N})\}\), \(D_{i}\) is \(\{d_{i,1},d_{i,2},\ldots,d_{i,K}\}\) and the value of \(y\) is 0 or 1. In general, the value of \(K\) is much smaller than \(N\), so shapelet transformation can effectively reduce the dimensions of sequence data.  
The approach proposed in this paper extracts corresponding shapelets from the drive test data of each LTE-R cell in a supervised manner by modifying the CNN network and performing shapelet transformation to realize feature extraction.
## 3 Results and Discussion
### Physicochemical Analysis
commercial juice due to that natural apple juice has no added additional components (preservatives, stabilizers, and dyes) and/or any previous treatment carried out, such as in the commercial juice produced. An important point is that the ice fraction can be frozen again, and then centrifugation or other external force applied to separate the C\({}_{\text{s}}\) from the C\({}_{\text{i}}\), as recently reported by [PERSON] et al. [19] to recover as much cryoconcentrate as possible. The pH and TTA were changed significantly (\(p\leq 0.05\)) along CBCC cycles compared to the fresh sample. Specifically, an opposite effect was observed for pH and TTA. This inverse behavior could be attributed to the high content of organic acids in the samples, mainly malic acid. A comparable phenomenon was described by [PERSON] et al. [33] in pomergnate juice. The density (\(\rho\)) increased as the solutes increased at each cycle. Thus, an increment close to 36.4% in relation to fresh apple juice was reached in the last cycle. These experimental behaviors were in concordance with previous data for orange juice [19] and pineapple juice [20] under cryoconcentration. The CIELab colorimetric space was used to characterize the change in color during the CBCC cycles between the fresh juice and cryoconcentrates. The samples showed significant changes in the L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\) values after the applied CBCC technique. In particular, the initial L\({}^{\star}\) value decreased at each cycle, which indicated that concentrate samples were darker than the initial juice. The darkening is due to the increase in TSSC cycle by cycle, which also generates the increase of bioactive compounds [15]. Furthermore, these results are in agreement with different cryoconcentrated liquid samples [19; 28; 34]. For a\({}^{\star}\) and b\({}^{\star}\), the values increased as the number of cycles increased. This trend indicates a slight deviation towards the red color (a\({}^{\star}\)) and a large increase in the yellow color (b\({}^{\star}\)), which allow for a clear difference with the natural color of the fresh juice (Figure 3). The total color difference (\(\Delta\)E\({}^{\star}\)) between fresh juice with each cryoconcentrated samples can be estimated as not noticeable (\(\Delta\)E\({}^{\star}\leq 3\)) and well visible (\(\Delta\)E\({}^{\star}\geq 3\)) according to the human visual discrimination threshold [35]. For the first cycle, the \(\Delta\)E\({}^{\star}\) values were over eleven CIELab units, and in the final cycle, \(\Delta\)E\({}^{\star}\) reached a value of twenty-five CIELab units. Therefore, all cryoconcentrate samples showed visual differences with fresh juice. These values confirm that CBCC process intensifies the natural color of fresh juice, in CIELab values terms.
## 3 Results and Discussion  
### Direct Roxarsone Reduction Experiment  
In the direct roxarsone reduction experiment, minor growth of the strain MR-1 was observed with lactate as a carbon source (Figure 1A). [PERSON] et al. [15] suggested that _S. oneidensis_ MR-1 was able to use roxarsone as a terminal electron acceptor. Therefore, it was speculated that the minimal cell growth might be due to the anaerobic respiration using roxarsone as the e-acceptor, even though experiments were not performed to confirm the usage of roxarsone as the e-acceptor by MR-1 in this study. The concentration of roxarsone was found to decrease gradually from the initial 97.5 um to 2.6 \(\pm\) 1.2 um at 30 days in the incubations with MR-1, while in the uninoculated control the concentrations remained at similar level from 2 days to 30 days at 63.8 um (Figure 1A). For comparison, in the previous work by [PERSON] et al. [15], roxarsone concentrations decreased much faster to undetectable levels within 5 days from the initial concentrations of 0.1 to 2.5 mM. It seems that incubation conditions might affect metabolic activities of the microbes, e.g., concentration of electron donor, which was 50 mM in the previous work and 10 mM in this study. During the incubations, the pH of the media was rather stable, ranging from 6.8 to 7.2.  
In the case of the strain Cellu-2a, the maximum growth was observed up to OD\({}_{600}\) of 0.090 \(\pm\) 0.005 at 2 days, using sucrose as a carbon source (Figure 1B). Since the strain Cellu-2a has been reported to grow fermentatively with sucrose in the previous study [21], the rapid growth was assumed to be due to fermentation. The roxarsone concentration decreased rapidly to 5 days and reached to 16.8 \(\pm\) 1.4 um at 10 days from the initial 97.6 \(\pm\) 0.1 um (Figure 1B). In the abiotic control, no obvious reduction of roxarsone was observed throughout the incubation (Figure 1B). For comparison of both strains, it seemedthat Cellu-2a was better for roxarsone reduction than MR-1, possibly by forming larger cell mass through fermentative growth.
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations on the Effect of Coolant Boiling Caused by Accumulated Debris
#### 3.4.2 Experimental Studies Using the Bottom-Heated Method
Based on the temperature variations measured, owing to the thermocouples (see Figure 16b), and under the assumption that heat loss rate did not vary significantly within a fairly limited water temperature, the effective rate for bubbling, \(Q_{\text{g,eff}}\), was determined at nearly 18.49 L/min [71], which is within the range expected (i.e., 10-20 L/min) for \(P=2580\) W with \(H_{\text{w}}=60\) cm. Therefore, though further investigations with more elaborate considerations may still be required, to a certain degree, the reliability of the gas-injection approach for simulating coolant boiling was validated by the bottom-heated experiments, and additional gas-injection experiments with a wider range of \(Q_{\text{g}}\) are expectable to elucidate the impact of boiling intensity on flow regime transition. In a word, the bubbles generated from sodium boiling are confirmed to have a promoting impact on pool convection and a dampening effect on particle inertia. Therefore, the flow regime characteristics may generally behave as lower-numbered ones in this case, as a result forming a more flattened bed.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 2 Waveform Models for SC-FDMA, FBMC, and LP-FBMC  
### Sc-FDMA  
Figure 1 shows the structure of an SC-FDMA transmitter for the \(k\)th uplink user. The number of users is denoted by \(K\), the number of carriers allocated to each user is denoted by \(N\), and the number of symbols per data frame is denoted by \(M\). The \(m\)-th complex data symbol transmitted to the \(n\)-th subcarrier under SC-FDMA is expressed as \(x_{n,m}^{(k)}\), and the \(m\)-th complex data symbol vector is expressed as \(\mathbf{x_{m}^{(k)}}=[x_{0,m}^{k},x_{1,m}^{k},...,x_{N-1,m}^{k}]^{T}\), where \([\cdot]^{T}\) denotes the transposed matrix. In Figure 1, \(\mathbf{X_{m}^{(k)}}\) is the \(N\)-point discrete Fourier transform (DFT) output for input \(\mathbf{x_{m}^{(k)}}\). If the \(n\)-th component of \(\mathbf{X_{m}^{(k)}}\) is expressed as \(X_{n,m}^{(k)}\), then the \(k\)-th user's transmit signal \(s^{(k)}(t)\) can be expressed, as follows [6]:  
\[s^{(k)}(t)=\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}p(t-mT_{s})X_{n,m}^{(k)}e^{j\frac{2 n\pi(n)}{T}(t-mT_{s})}, \tag{1}\]  
where \(T\) is the data symbol duration, \(T_{s}\) is the symbol duration, including the cyclic prefix (CP), and \(p(t)=\left\{\begin{array}{ll}1&\text{for }0\leq t<T_{s}\\ 0&\text{elsewhere}\end{array}\right.\). The variable \(\alpha(n)\) is the subcarrier index that is allocated to the \(n\)th element of \(N\)-point DFT output of the \(k\)-th user. We consider the block sub-band (SB) allocation, and one guard subcarrier between adjacent subcarrier blocks, which are common to FBMC and LP-FBMC for fair comparison [19]. Therefore, \(\alpha(n)\) is expressed as \(\alpha(n)=k(N+1)+n\).
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.4 Weightless  
Weightless [26] is a set of standards developed by the Weightless Special Interest Group (Weightless-SIG). Three different standards exist: W, P, and N. Weightless-W uses TV white space frequencies, and offers a data rate between 1 kbps and 10 Mbps using a narrow-band signal, based on QAM and several other modulations. This is interesting because it takes advantage of a part of the unused ultra-high frequency spectrum. However, access to TV white space is not permitted worldwide as the regulation differs for different countries, which makes it impossible to build a small antenna that could access all the usable frequencies [27]. Weightless-N is based on UNB for upward only communications, using the ISM sub-GHz bands, and a DBPSK modulation. It is similar to Sigfox technology-wise, which means that it shares the same advantages and limitations, but has a higher data rate (30 to 100 kbps). Nwave [28] is the main company supporting Weightless-N technology. Weightless-P [29] is the latest Weightless standard. It offers bidirectional connectivity based on the ISM sub-GHz bands as well, using channels 12.5 kHz wide which results in a data rate between 0.2 kbps and 100 kbps. GMSK and QPSK modulations are used. It also officially supports acknowledgments and FotA updates [30]. Ubiik [31] is the main provider of Weightless-P technology today.  
Figure 5: Countries with a LoRaWAN deployment [20].
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 3 Materials Fabrication
### In-Situ Nanoparticle Reinforced Cu Alloys
#### 3.2.3 Fabrication Technique  
In-situ nanoparticle reinforced Cu alloys can be fabricated by simple casting process. In our research, all as-cast ingot (15 Kg) with NPFG structure was fabricated by centrifugal casting in the vacuum chamber with a medium frequency electrical furnace. Fe, Co were added to the melt at 1573 K and held for 30 min before casting. The fabrication technique is readily applicable to mass production and industrial applications.  
Schematic diagram of the preparation technology for tin bronze alloy with NPFG structure is displayed in Figure 7[64]. Actually, the whole process is very simple, including solid (Fe, Co)-liquid doping, uniform distribution of all atoms, in-situ generation of nanoparticles, formation of Cu adsorption layer, nucleation catalysis by encapsulated nanoparticles. It can be seen that in-situ nanoparticles can act as both grain refiners and dispersion strengthening phase to optimize the microstructure. Apart from ensuring that iron-rich nanoparticles could meet thermodynamic conditions so as to in-situ precipitate as precursor in the melt, appropriate casting technology, such as induction heating and centrifugal casting, should be carried out in order to assure intensive shearing or convection in the liquid metal. Proper size distribution range and spatial distribution of nanoparticles in molten solution can be promoted, resulting in high nucleation efficiency of nanoparticles and intensive nanoparticle-matrix interaction in Brownian motion.  
Figure 6: DSC curve of melted Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy at cooling rate of 20 K/min, with the upper-left enlarged image of area 1.[64]. The exothermic phenomenon during the cooling process was recorded and details can be seen clearly in the upper-left inset.
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
## 2 Traditional Management Techniques and Methods
### Agricultural Restoration Method  
Agricultural restoration in saline-alkaline soils involves the use of agronomic practices to rebuild soil health, structure. Physical improvement methods, such as the use of gravel barriers, deep plowing, and land exchange, have been shown to effectively mitigate soil salinity. A study by [PERSON] et al. [66] revealed that gravel barriers can significantly reduce soil salinity levels. Additionally, soil subsoiling technology, when combined with optimized fertilization strategies, offers a moderate yet effective approach to enhancing soil quality. This method involves increasing the application of organic fertilizers while reducing the overuse of chemical fertilizers. Over time, this not only improves soil fertility, but also enhances the physical structure of the soil by increasing porosity and promoting the formation of soil aggregates. These improvements create a healthier environment for plant root development [67].  
Another effective strategy is the implementation of crop rotation systems. By systematically planting different types of crops, the rate of soil salinity accumulation can be slowed. Various plants exhibit distinct nutritional needs and differing degrees of salt tolerance, which helps balance soil nutrient consumption and replenishment. This approach reduces the risk of salt accumulation that often results from monocropping practices [68].
## 4 Implications, Limitations, and Future Directions  
The findings of this review highlight the significant role of badminton footwear modifications in optimizing movement efficiency, stability, and injury prevention. The biomechanical implications of forefoot bending stiffness, torsional stiffness, lateral-wedge hardness, insole hardness, shoe sole design, midsole hardness, and shoe heel design demonstrate that moderate stiffness and cushioning provide an optimal balance between energy return and joint protection [11, 12, 19]. Footwear modifications that enhance force redistribution, proprioceptive feedback, and lower-limb coordination can contribute to both improved performance and injury mitigation [16, 18].  
Despite these insights, several limitations must be acknowledged. First, publication bias may exist, as studies with significant results are more likely to be published, potentially skewing the synthesis of findings. The review was limited to English-language publications, which could introduce language bias by excluding non-English studies. Most studies were conducted on competitive male athletes, limiting generalizability to female players and recreational athletes [15]. Laboratory-based studies used controlled movement tasks, which may not fully represent in-game biomechanics, where fatigue, reactive footwork, and external perturbations affect performance [33]. The variability in study protocols regarding midsole hardness testing (62C vs. 68C), lateral wedge inclines (5\({}^{\circ}\) vs. 10\({}^{\circ}\)), and shoe stiffness classifications introduces methodological inconsistencies [39]. Additionally, longitudinal effects of footwear adaptation remain unclear, as most studies examined immediate biomechanical responses rather than long-term neuromuscular adaptations [37]. The absence of electromyographic (EMG) analysis in these studies also limits our understanding of how different shoe conditions influence muscle activation and coordination strategies [17].  
Future research should expand gender-based analyses to explore sex-specific biomechanical responses to footwear modifications [15]. Real-world testing using wearable motion capture and force-sensitive insoles could improve ecological validity, enabling researchers to examine how footwear modifications interact with fatigue and reactive footwork [42]. Investigating the longitudinal effects of specific shoe features on neuromuscular adaptation will provide deeper insights into injury prevention and performance enhancement [44]. Additionally, incorporating electromyographic (EMG) analysis will allow for a more comprehensive evaluation of muscle activation patterns in response to footwear modifications [20] Advances in 3D printing, AI-driven gait analysis, and adaptive cushioning technologies may contribute to personalized footwear solutions that optimize both athletic performance and injury prevention [28]. Another limitation is the exclusion of non-English studies, which may lead to language bias, although this was based on scientific rather than resource constraints.