esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 4 Proposed DJSCC Scheme and Performance Limit
### Proposed DJSCC Scheme  
To achieve the corner point (5), we turn this problem into the problem of JSCC with side information at the receiver, and solve it with QSPC and punctured QSPC. The proposed DJSCC scheme consists of \(s\) encoders and one joint decoder, as shown in Figure 2.  
Define  
\[\tilde{R}_{\max}=\max_{i\in\{1,2,...,s\}}\tilde{R}_{i} \tag{15}\]  
and  
\[i_{\max}=\operatorname*{argmax}_{i\in\{1,2,...,s\}}\tilde{R}_{i}. \tag{16}\]  
For source \(V_{i}\), the previous sources \(V_{1},V_{2},...,V_{i-1}\) are completely regarded as side information to construct QSPC and punctured QSPC. For the source \(V_{i_{\max}}\), its message \(\mathbf{v}_{i_{\max}}\) is encoded by a \((MN,MK)\) QSPC. For sources \(V_{i},i\
eq i_{\max}\), the message \(\mathbf{v}_{i}\) is encoded by a \((MN,MK,MP_{i})\) punctured QSPC to adapt a lower rate \(\tilde{R}_{i}\). At the destination, a joint decoder is used to recover the sources, as shown in Figure 5. The joint decoder consists of \(s\) conventional polar decoders working successively. The hard decisions \(\mathbf{\hat{v}}_{1},\mathbf{\hat{v}}_{2},...,\mathbf{\hat{v}}_{i-1}\) from decoder \(1,2,\ldots,i-1\) are fed to \(i\)-th decoder for calculating log likelihood ratios (LLRs) of systematic bits. For parity bits, the LLRs are calculated with channel observations.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 3 Distributed DL Communication: Libraries and Architectures Compared
### Linux Shell Environment
#### 3.1.1 Parameter Server Method  
In the parameter server method, both the broadcast operation, which disseminates parameters to worker nodes, and the gather (allgather) operation, which aggregates updated gradients from workers to the parameter server, occur within a single iteration. Figure 5 provides an overview of the broadcast architecture for each library. In the case of MPI, the parameter server employs the MPI_Bcast function to transmit the parameters created within the parameter server to the CPU memory of each worker. Subsequently, each worker utilizes the cudaMemcpy function to copy data from CPU memory to GPU memory. Due to CUDA-aware MPI's GPU communication capabilities, cudaMemcpy is omitted from the architecture, allowing for direct communication with GPU memory.  
NCCL only supports communication between GPUs, so it cannot communicate with a parameter server that does not have a GPU allocated to it. Allocating GPUs to the parameter server would waste resources. In such cases, the approach involves using MPI_Send to send  
Figure 5: Linux shell broadcast architecture.  
Figure 6: Linux shell gather architecture.  
Figure 7: Linux shell all-reduce architecture.  
data to the CPU memory of a representative worker node (chief worker). Subsequently, the data are copied to the GPU memory of the chief worker using cudaMemcpy, and then NCCL_Bcast is utilized to broadcast these data to the other worker nodes.  
Figure 6 illustrates the gather architecture for the parameter server method in each library. In MPI, the process begins with each worker generating 1 GB of data on the GPU. The cudaMemcpy function is invoked in the reverse order of the broadcast, copying the GPU memory to CPU memory. Subsequently, each worker's data are transmitted to the parameter server. In the case of CUDA-aware MPI, each worker's gradient is configured to be sent directly to the parameter server's CPU memory. NCCL synchronizes the data across all workers, including the chief worker, through the allgather operation. Among these, the chief worker performs a CPU memory copy allocated to that node using cudaMemcpy and then utilizes MPI_Send to transmit the aggregated data to the parameter server.
## 4 The Evolving Value of Module Efficiency  
Module price will drop much faster than other system components in the coming years, due to sustainable technology innovation, continuous oversupply, and the choice of polysilicon makers to move to regions with low electricity rates [14]. Based on the forecast made by BloombergNEF, the best-case integrated module production cost will fall from $0.23/W at the end of 2018 to $0.145/W by 2025 [1], as shown in Figure 9.  
Figure 8: Residential PV system costs in different markets.  
While the module price fall will continue following a learning rate of 28.5%, we expect the industry average module efficiency to rise by 2.5% relatively, on an annual average basis. For utility PV projects with fixed tilt, BloombergNEF expects that the per DC watt system capex will drop by 33% by 2025, compared with 2018 [7]. The rate is 32% for commercial PV. We estimated the correlation between different cost components and area or power, and regrouped the spending, based on which the percentages of area-proportional costs and fixed costs are derived, as shown in Figure 10.  
Module price reduction occurs faster than all other components in a PV system, resulting in a rising share of area-proportional and fixed non-module costs in the total system spending. As tracking is becoming a standard option for utility PV in sunny places [15], and the percentage of area-proportional and fixed non-module costs is similar for tracking utility and commercial PV, here we use the tracking utility to illustrate the evolving value of module efficiency, as shown in Figure 11. As time goes on, the value of module efficiency becomes greater, increasing the incentive to adopt high-efficiency technologies.  
Figure 10: Estimated percentage of area-proportional and fixed non-module costs in PV capex.  
Figure 9: Forecast of best-case integrated production cost for c-Si modules. Source: BloombergNEF Note: Integrated production refers to the operation model of sourcing polysilicon from the spot market and implementing the rest of the process using an in-house facility.
# Mechanism and Characterization of
## 2 Results  
### Mechanical Properties  
Good mechanical properties are crucial for practical applications. Figure 1a shows the stress-strain curves of the four composites, all of which exhibit a linear growth trend. Among them, MSNR shows the most pronounced growth curve, indicating increased stiffness when dual-component fillers are added. Figure 1b presents the tensile strength, elongation at break, and Young's modulus of the four composites. It is evident that MSNR has the highest tensile strength, which is attributed to the synergistic effect between the MWCNTs and SiO\({}_{2}\) that reduces filler agglomeration in the NR matrix, allowing for the fillers to be better dispersed. Upon stretching, dispersing stress is generated, resulting in MSNR having an excellent tensile strength. In addition, MSNR exhibits the highest elongation at break, indicating that this material can withstand higher stress concentrations without failure. Furthermore, the Young's modulus of MSNR was increased by 28.74%, 3.35%, and 3.54% compared to NNR, SNR, and MNR, respectively, demonstrating that MSNR has a good resistance to deformation. Specific mechanical properties are shown in Table S2, and compared with other studies on the mechanical properties of natural rubber latex, the reinforcement effect of the two-component filler is better [2, 5, 7, 16, 17, 18].  
Figure 1c-f show the cyclic stress-strain curves of NNR, SNR, MNR, and MSNR. It can be observed that for all four composites, the area enclosed by the curves decreases with an increasing number of cycles, eventually converging. This is due to the viscoelastic nature of rubber materials, where deformation increases with strain, and upon unloading, the rubber cannot fully return to its original state, resulting in residual deformation [28].  
Stress-strain cycling curves were used to evaluate energy loss in composites. A larger hysteresis area indicates a higher energy loss in the material, which is due to the friction generated between the filler and the NR molecular chains when strain occurs [29]. The initial stress-strain cycling curves of the extracted NNR, SNR, MNR, and MSNR are shown in Figure S1a-d. It can be seen that the hysteresis area of MSNR is the largest, and the energy loss within MSNR is large enough to effectively absorb and consume the externally applied energy, thus effectively reducing the propagation of vibrations and impact energy.
## 2 Methods  
We included peer-reviewed papers published from January 1991 to February 2021 and excluded conference proceedings and documents written in languages other than English. We first searched the Scopus database interrogated with the term \"remote sensing,\" which returned 254,392 documents (see Appendix A for the flowchart). We then searched terminology used to describe vegetation under management, i.e., \"(pasture* OR grassland* OR rangeland*) management\", which returned 31,938 documents. We then combined queries (#1 AND #2), resulting in 1582 documents. We introduced grazing (i.e., graz*) to streamline this third list to select only articles that describe vegetation under a grazing regime, obtaining 633 papers. After eliminating conference papers and proceedings, we obtained 262 articles. We previewed the articles using the search strings described in Table 1,and by reading the abstracts to eliminate unrelated papers, reduced the number of papers to 214. Articles that made the final round (i.e., 214) were grouped into original research and review papers. From these documents, the following information was extracted:  
* The geographical location/site of a study.
* The type of sensor used (i.e., optical, multispectral, hyperspectral, SAR).
* Whether a single sensor was used or a combination of sensors together (i.e., fusion).
* The scale with which pasture was monitored (i.e., \(\leq\)5 ha, \(\geq\)10 ha, \(\leq\)50 ha and \(\geq\)100 ha);
* The approach for retrieving vegetation parameters for estimating pasture indicators and how this was validated. Information on the adoption of remote sensing approaches by end users.
* Whether environmental (climate and anthropogenic) variables and machine learning were considered.  
We exported results using the Research Information System (RIS) tag format by creating a custom CSV file to format and analyse data. We defined \"UAS\" as unmanned aerial systems remotely controlled or programmed to fly autonomously with onboard high-resolution sensor(s). In contrast, satellites orbit the earth with onboard sensor(s) often lower than UAS in spatial resolution.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 4 Performance Evaluation
### Computation Efficiency  
The computational and message complexity of the proposed ODCR protocol is O(1) per node. Initially, each node broadcasts one message of its velocity and its position to neighbors. Then, once CHs are selected, each CH broadcasts one declaration message (such as the \"hello message\" presented above). When the \(k\)-hop nodes (\(k\geq 1\)) hear the declaration message they broadcast an acknowledgement message, join to the cluster and rebroadcast the declaration message to the \(k\)+1-hop nodes to find the shorted path to the destination. Hence, it has been observed that each node sends, at most, three messages to perform the operation.  
Figure 7: Scenario 2, where \(x\) is not a member of C.
## 2 Materials and Methods
### Distribution of Training Sessions and Matches  
The training and competition volume, internal load, external load, recovery, and perceived well-being of the players were monitored daily over seven consecutive days during a UEFA U16 Development Tournament in Eerikkila, Finland. Throughout the observation period, three training sessions (Sessions 1, 3, and 6), three matches (Sessions 2, 4, and 7), and one gym session (Session 5) were conducted. All sessions took place at the Eerikkila Sport and Outdoor Resort, and data were collected by members of the national team's technical staff, who are also the authors of this research. All training and match sessions were conducted on the same natural grass playing surface. The team consistently used a 1-4-3-3 formation, comprising two center-backs, two full-backs, one central midfielder, two central midfielders, two wingers, and one striker. The research did not alter the training session designs planned by the national team coach. Our analysis focused exclusively on the team's primary on-field training sessions. These sessions included warm-up activities, the main phase of training, and subsequent cool-down activities. The warm-ups consisted of dynamic mobility work, core activation, and low-intensity passing drills. For cooling down, short-duration (<10 s) lower limb and lower back static stretches and 2 min of walking were performed. The training sessions encompassed a wide range of activities, including physical aspects (such as warm-up routines and circuit-based exercises), technical components (including rondos, ball control, passing drills, and finishing exercises), and tactical elements (including positional games, pressure tasks, specific movement patterns, and set pieces). All activities were supervised by the national coach and the rest of the coaching staff. The established periodization by the technical staff during the tournament was structured into three microcyles, each consisting of one training session and one match. In response to the organizational constraints that this structure generates, the head coach adopted a mixed approach [24]. This approach involved dedicating part of each session to recovery strategies or load reduction to enhance the players' readiness for the next match. In the remaining portion of the session, the coaches increased the intensity by incorporating the typical MD\(-\)1 training structure to emphasize team movements and tactical preparation. Accordingly, recovery strategies, passing drills, rondos, and collective positioning were included as elements typical of an MD\(+\)1 session. Conversely, MD\(-\)1 contents encompassed reaction speed tasks, small-sided games, offensive situations, competitive finishing games, and set pieces.
## 3 Results and Discussion
### Adsorption and Catalytic Oxidation Performance of CoPc@s-BC
When both CoPc@BC and H\({}_{2}\)O\({}_{2}\) were added, a strong, four-line spectrum with a peak intensity of 1:2:2:1 was easily detected, which is the typical characteristic spectrum of the DMPO-OH adducts [47]. This result indicates that \(\cdot\)OH was generated during the reaction and was responsible for the catalytic oxidation of reactive red X-3B. Similar results were found for the CoPc@s-BC/H\({}_{2}\)O\({}_{2}\) reaction system; the only differences were the peak intensities, which were proportional to the amount of the formed \(\cdot\)OH radical during the reaction. An obvious higher amount of \(\cdot\)OH was observed for the CoPc@TP-BC/H\({}_{2}\)O\({}_{2}\) reaction system. Therefore, the CoPc@TP-BC had the highest catalytic activity for the decoloration of reactive red X-3B dye wastewater, which is in good accordance with the results illustrated in Figure 7.  
Based on the above experimental results, a possible catalytic reaction mechanism is proposed in Figure 10. Firstly, the reactive red X-3B dye molecules were adsorbed onto the nanocomposites from solution. Thanks to the incorporation of flexible spacers, the contact between dye molecules and the CoPc pendants became easier; therefore, both the adsorption capacity and the adsorption rate were enhanced by the spacers. Secondly, the adsorbed dyes were catalytically decolorized in-situ by the \(\cdot\)OH radical. Due to the presence of spacers, the free movement and the reaction homogeneity of CoPc on CoPc@s-BC were improved; thus, more \(\cdot\)OH is produced during the oxidation of dye molecules. Figure 9: 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) spin-trapping electron paramagnetic resonance (EPR) spectra of reactive red X-3B dye wastewater (initial concentration 100 uM, pH = 2, T = 50 \({}^{\circ}\)C). The inset shows the magnification of the marked region. These two processes cooperate in a synergistic manner and effective enhancement of the catalytic efficiency of CoPc can be reasonably expected. The decoloration was significantly associated with the spacer length of CoPc@s-BC. The most preferable spacer length arose from TP; thus, CoPc@TP-BC has the highest catalytic efficiency.
## 4 Discussion
### Residual Drift  
Residual drift is largely affected by the Maxwell stiffness adopted. On average, the ratio to model type HB is about 1.17 (type MB) to 2.01 (type LB). Focusing on type LB, the variation in residual drift is:  
* Uninfluenced by the damper type; the ratio is very similar for linear dampers (2.07) and non-linear dampers (1.94).
* Larger for increasing damping (1.38 for low damping to 2.50 for high damping).
* Moderately influenced by the type of damper distribution (1.74; 1.88; 2.40 for distributions Q, R, S).
* Much larger for the top half of the building (1.56 for first storey, 2.79 for roof).  
The influence is much larger for moderate intensity (2.35 for intensity level D vs. 1.66 for intensity level C).  
Result dispersion follows similar trends to the median: dispersion is larger for LB than for MB, increases with increasing damping, is moderately affected by the distribution type, and is much larger at the roof than in the first storey.
## 3 Results and Discussion
### Nutrient Concentrations from Preserved Samples
#### 3.2.1 Nitrate-N Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{7}{c}{**Tile Water**} \\ \cline{3-8} \multicolumn{1}{c}{**Treatment 1**} & \multicolumn{1}{c}{**Treatment 2**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{3-8} \multicolumn{1}{c}{} & **0** & **0.5** & **1** & **2** & **5** & **7** & **14** \\ \hline Baseline & Control & 0.900 & 0.900 & 0.900 & 0.875 & 0.428 & 0.132 & 0.900 \\ Baseline & Filtered & 0.900 & 0.563 & 0.900 & 0.900 & 0.430 & 0.043 & 0.667 \\ Baseline & sulfuric Acid & 0.900 & 0.900 & 0.055 & 0.604 & 0.900 & 0.754 & 0.900 \\ Baseline & Sodium Omadine & 0.795 & 0.060 & 0.900 & 0.205 & 0.140 & 0.019 & 0.849 \\ \hline \multicolumn{8}{c}{**Pond Water**} \\ \hline \multicolumn{8}{c}{**Treatment 1**} & \multicolumn{1}{c}{**Treatment 2**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{3-8} \multicolumn{1}{c}{} & **0** & **0.5** & **1** & **2** & **5** & **7** & **14** \\ \hline Baseline & Control & 0.900 & 0.900 & 0.900 & 0.900 & 0.900 & 0.736 & 0.900 \\ Baseline & Filtered & 0.900 & 0.900 & 0.627 & 0.900 & 0.115 & 0.046 & 0.518 \\ Baseline & sulfuric Acid & 0.448 & 0.857 & 0.900 & 0.728 & 0.692 & 0.003 & 0.770 \\ Baseline & Sodium Omadine & 0.422 & 0.003 & 0.526 & 0.826 & 0.161 & 0.002 & 0.770 \\ \hline \multicolumn{8}{c}{**Stream Water**} \\ \hline \multicolumn{8}{c}{**Treatment 1**} & \multicolumn{1}{c}{**Treatment 2**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{3-8} \multicolumn{1}{c}{} & **0** & **0.5** & **1** & **2** & **5** & **7** & **14** \\ \hline Baseline & Control & 0.900 & 0.900 & 0.900 & 0.152 & 0.499 & 0.900 & 0.345 \\ Baseline & Filtered & 0.775 & 0.900 & 0.273 & 0.900 & 0.055 & 0.900 & 0.900 \\ Baseline & sulfuric Acid & 0.169 & 0.501 & 0.014 & 0.770 & 0.900 & 0.690 & 0.900 \\ Baseline & Sodium Omadine & 0.291 & 0.200 & 0.231 & 0.358 & 0.032 & 0.708 & 0.900 \\ \hline \end{tabular}
\end{table} 
Table 4: Significant differences between methods for nitrate-N measurement were calculated using Tukey honestly significant difference (HSD) method and the \(p\)-values are presented for each treatment comparison and day. Significant differences (\(p<0.05\)) are highlighted in yellow for convenience. the addition of sodium omadine showed some ability to maintain nitrate-N concentrations, it did not perform as well as the addition of sulfuric acid or the control.
# Nanomaterials for Healthcare Biosensing Applications
## 3 Applications of Inorganic Nanomaterials in Healthcare Biosensing
### Quantum Dots
coli_ antibodies, were employed as capture probes for _E. coli_ and chit-coated CdTeQds acted as reporter probes in a sandwich immunoassay. The bacteria were extracted from the sensing solution using IMS prior to fluorescence analysis. The sensor showed a low cross-reactivity against non-specific bacteria and achieved a detection limit of 30 cfu mL\({}^{-1}\) with a wide linear concentration range of 10\({}^{2}\)-10\({}^{8}\) cfu mL\({}^{-1}\). Wu et al. employed this strategy by using multicoloured QDs for the detection of the lung cancer biomarkers with carboxyl functionalised micro-magnetic beads (CMMB) acting as immune carriers [160]. Fluorescence measurements revealed a successful detection of CEA, cytokeratin-19 fragments (CYRFA-21), and neuron-specific enolase (NSE), with LODs of 38 pg mL\({}^{-1}\), 364 pg mL\({}^{-1}\) and 370 pg mL\({}^{-1}\), respectively. MicroRNA or miRNA is a vital biomarker for early stage diagnosis of cancer. Therefore, sensitive and rapid determination of miRNA is important for POC testing. Deng and co-workers fabricated a strip biosensor labelled with QDs that quantified miRNA-21 [161]. The photostable QDs improved the detection efficacy of the biosensor. The sensitivity of the system was further enhanced using a target-recycled amplification strategy based on sequence-specific and enzyme-free hairpin strand displacement mechanism. The sensing platform was functional in a range of 2-200 fmol, with a limit of 200 amol. The analysis was comparable with results obtained from real-time polymerase chain reaction (PCR). In addition, 16 out of 20 clinical tumour samples provided positive signals. Another similar study enhanced the resonance light-scattering (RLS) intensity of cadmium tellurium quantum dots (CdTeQDs) with the help of a hybrid mixture of cDNA probes (CDTEQD-P) [162]. The CdTeQD-Ps exhibited low intensity in the absence of miRNA-122 and coexisted stably in the solution. However, they formed proportionate aggregates by complexation with miRNA-122 with increased intensity. The enhancement in the RLS intensity could be observed for concentrations between 0.16 and 4.80 nM, with a low limit of 9.4 pM. Such fluorescence properties of metallic nanoparticles have been extensively harnessed for the construction of optical biosensors (Table 3). Figure 8: **(a)** Experimental steps for developing quantum dot-based microfluidic chips for virus detection. **(b)** Schematic representation of the biosensor and sample loading process. **(c)** Cross-section of the microfluidic chip. **(d)** Principle of multiple viruses recognition and simultaneous subtyping on the microfluidic device [164]. \begin{table}