esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement and Results
### Discretization Influence  
Considering that using _Gradient Projection_[11] to solve the optimization problems at hand implies the discretization of the trajectory in \(N\) steps (of constant acceleration), here we focus on the influence of the discretization factor \(N\). We will compare here the trajectories traced by the four proposed functionals for a discretization factor \(N\in\{1,...,20\}\) and evaluate the error (as the sum of the square of the difference between one curve and the reference one, set to _N_=100, see Equation 8). We consider the curve _N_=100 as accurate enough for the calculation of the error evolution. The parameter configuration for this case is the same as for previous subsections (Table 1).  
\[\varepsilon\left(N\right)=\sum_{i=0}^{N_{ref}}\left(x_{N}\left(i\frac{t_{f}}{ N_{ref}}\right)-x_{N_{REF}}\left(i\frac{t_{f}}{N_{ref}}\right)\right)^{2} \tag{8}\]  
where \(\varepsilon\left(N\right)\) refers to the square error for an _N_-discretized curve \(x_{N}(t)\), in comparison with the _Nref_ = 100 reference trajectory \(x_{N_{ref}}(t)\).  
Having a look at the Figure 10, it is easy to realize the correctness of the trajectories calculated by the optimization algorithm (_Gradient Projection_), _i.e._, the highest errors are located for low values of the discretization factor, whereas the error value decreases as \(N\) gets higher.  
Observing this graph, it is also straightforward to infer that setting a value of \(N\) = 20 should be enough to achieve a proper resolution in the curve for the four functionals. We can see that the functional \(J_{D1}\) needs a higher value for the discretization factor than that of \(J_{D2}\) to achieve the same level of accuracy. Due to the need of higher accelerations for the trajectories obtained by \(J_{D1}\) in comparison with those calculated by \(J_{D2}\), a higher value for the discretization factor \(N\) is thus  
accuracy. This is explained by the same reasons as regards the comparison between \(J_{D1}\) and \(J_{D2}\), that is, the greater acceleration values of the trajectories, the higher \(N\) must be to maintain accuracy.
## 2 Materials and Methods
### Data Fitting to Models
#### 2.4.2 Modified Logistic (LOG) Model  
The LOG model (Equation (2)) also assumes that the rate of gas production is proportional to the amount of gas already produced, the maximum production rate and the ultimate gas yield [22; 38]. The model is characterized by an exponential rise to the maximum and a final stabilization phase. The model comprises the same parameters as the GOM model.  
\[M_{t}=\frac{S}{\left\{1+\exp\Bigl{(}4R_{m}\frac{(\lambda-t)}{S}+2\Bigr{)} \right\}} \tag{2}\]  
The two models (LOG and GOM) were fitted to the data (cumulative methane yield and residence time) obtained from the BMP assay. The code was written in MATLAB(r)R2016b software in order to execute data fitting of models using the Levenberg-Marquardt algorithm [39] with the optimization function [Isqcurvefit]. The iteration process was initiated with arbitrary values of 1, 0 and 0 for S, R\({}_{m}\) and \(\lambda\) respectively. Afterwards the kinetic constants were plotted against the temperature for each substrate. Curve fitting was executed on the kinetic constants to establish a mathematical relationship to the temperature. Consequently, the kinetic parameters were inserted to the LOG and GOM models to estimate the methane yield of husks, pulp and mucilage at any temperature ranging from 21 \({}^{\circ}\)C through 37 \({}^{\circ}\)C.
## 3 Results and Discussion
The best approximation of true measurements by activity estimates obtained by the algorithm is achieved at the minimum value of the approximation error, which corresponds to 2000 iterations. It is noted that the algorithm's execution time has no importance and could be optimized by parallelization of the main loop, with the number of iterations in this proof-of-concept study limited for practicality reasons. Without the optimization of the mutation function, the ability to obtain a \"better\" population decreases with the growth of the number of iterations. Such a behavior could also be used as a termination condition of the algorithm. The dependence of the MAPE percentage error on the number of iterations of the algorithm is shown in Figure 6. Supporting other assessment metrics, this MAPE error is easy to interpret and has a scale for assessing the quality of modeling results, allowing an assessment of the quality of the model. The mean absolute percentage error of MAPE (Figure 6) indicates the quality of the approximation and if MARE \(\leq\) 10%, the approximation accuracy is excellent; 10% < MARE \(\leq\) 20%, the approximation accuracy is good; 20% < MARE \(\leq\) 50%, then this accuracy is satisfactory; and if MARE > 50%, then the solution is deemed unsatisfactory. Such dependence of the approximation accuracy (percentage error) on the number of iterations of the algorithm is subsequently shown in Figure 7.  
Figure 5: Dependence of the approximation accuracy on the number of iterations of the proposed evolutional method. Together, the analysis results of the algorithm show that, as the number of iterations increases, the reliability of the approximation also increases. This correlates with the values of the MAPE error, whereby the value decreases with increasing the number of iterations of the algorithm. The utilization of these three aforementioned metrics together provides a means to evaluate the modeling results obtained from the perspective of algorithm implementation, alongside the quantitative and qualitative aspects of the results obtained.
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 2 Modeling and Motivation
### Motivation  
#### 2.2.1 Challenges in SOC Estimation for Reconfigurable Circuits  
Accurately estimating the State-of-Charge (SOC) of supercapacitors in reconfigurable circuits presents unique challenges. Let us consider a scenario where an external charging current, denoted as \(i_{c}\), supplies the circuit. This current can be constant or time-varying, and for a more general analysis, we assume it changes over time. The actual current flowing through the supercapacitor, representing the system input, is denoted by \(u\).  
Figure 3 illustrates the impact of reconfigurable circuitry on SOC estimation. The figure depicts a 20-second charging sequence with a time-varying charging current. To highlight the effects of circuit switching, we intentionally open switch \(s\) (turning it OFF) during two intervals: from 0 to 5 s and from 10 to 15 s.  
Note that when the switch is turned off, meaning \(s=0\), the charging current through the supercapacitor drops to zero, as shown in the figure. This abrupt change in current poses a significant problem for conventional SOC estimation techniques that rely solely on the external charging current (\(i_{c}\)) and a single supercapacitor model. In such cases, the input current error approaches 100%, rendering the SOC estimation inaccurate and unreliable.  
Furthermore, Figure 3 demonstrates that the supercapacitor voltage can be maintained at a constant level when the switch is OFF. This ability to isolate and hold the supercapacitor's voltage highlights the potential benefits of reconfigurable circuits in energy management and power control applications.
# From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
## 4 Neural Network Models for Thresholding Prediction Scores  
### From Classic Strategies to Neural Networks  
S-cut and CS-cut strategies (as well as their scaled counterparts) can be easily implemented as very simple, one-layered neural networks (see Figure 1a).  
Figure 1: Basic neural network thresholding models. (**a**) Neural network realizing the CS-cut thresholding: CS-Net; (**b**) naive generalization of the CS-cut thresholding.  
Typically, a _dense_ neural layer performs the following calculation:  
\[\mathbf{y}=\text{dense}_{act}(\mathbf{x})=f_{act}(\mathbf{W}\mathbf{x}+\mathbf{b}), \tag{11}\]  
where \(\mathbf{W}\) is a weight matrix, \(\mathbf{b}\) is a _bias_ vector (both are trainable parameters of the layer), and \(f_{act}\) is a nonlinear activation function. Setting the _Heaviside step_ function \(H(\cdot)\) as \(f_{act}\), forcing \(\mathbf{W}\) to be the identity matrix, and \(\mathbf{b}=-\mathbf{t}\), i.e., setting the bias vector to the negative threshold vector of the CS-cut strategy, and feeding the net with score vectors \(\mathbf{s}\) (or \(\mathbf{ss}\)), we have the following:  
\[\mathbf{\hat{y}}=H(\mathbf{s}-\mathbf{t}), \tag{12}\]  
where  
\[H(\mathbf{x})=\begin{cases}0,&\text{if}\quad x<0,\\ 1,&\text{if}\quad x\geq 0,\end{cases} \tag{13}\]  
which is exactly equivalent to the CS-cut strategy, from Formula (7). We call this architecture _CS-Net_. If \(\mathbf{t}\) is further forced to share one (and the same) value for all \(L\) positions, then such a restricted neural network realizes the S-cut strategy. With sorted scores at the input, one can even turn this model into R-cut or DS-cut thresholding equivalents.  
The benefit of formulating thresholding strategies in neural network terms comes when the normal neural connections of a dense layer are retained. Figure 1b shows a natural generalization of the previous model, i.e., a fully connected neural network layer that maps score vectors to prediction vectors. This model conditions its decision about the \(i\)-th label prediction, not only on the \(i\)-th score value but on _all_ values in the score vector, via a dense weight matrix \(\mathbf{W}\).  
The problem with such a natural generalization is the O(\(L^{2}\)) space complexity of this model. For very large number of labels, such a naive model is infeasible. One possible solution to this problem is to add a hidden layer of \(h\) neurons, where \(h<<L\), reducing the space complexity to O(\(Lh\)). Another possibility would be to apply sparse weight matrices, such as in [42], but this approach would limit the generality of the model.
# Impact of Audio Delay and Quality in Network Music Performance +
###### Abstract  
Network Music Performance (NMP) refers to network-based remote collaboration when applied to music performances, such as musical education, music production and live music concerts. In NMP, the most important parameter for the Quality of Experience (QoE) of the participants is low end-to-end audio delay. Increasing delays prevent musicians' synchronization and lead to a suboptimal musical experience. Visual contact between the participants is also crucial for their experience but highly demanding in terms of bandwidth. Since audio compression induces additional coding and decoding delays on the signal path, most NMP systems rely on audio quality reduction when bandwidth is limited to avoid violating the stringent delay limitations of NMP. To assess the delay and quality tolerance limits for NMP and see if they can be satisfied by emerging 5G networks, we asked eleven pairs of musicians to perform musical pieces of their choice in a carefully controlled laboratory environment, which allowed us to set different end-to-end delays or audio sampling rates. To assess the QoE of these NMP sessions, each musician responded to a set of questions after each performance. The analysis of the musicians' responses revealed that actual musicians in delay-controlled NMP scenarios can synchronize at delays of up to 40 ms, compared to the 25-30 ms reported in rhythmic hand-clapping experiments. Our analysis also shows that audio quality can be considerably reduced by sub-sampling, so as to save bandwidth without significant QoE loss. Finally, we find that musicians rely more on audio and less on video to synchronize during an NMP session. These results indicate that NMP can become feasible in advanced 5G networks.  
QoE; NMP; audio delay; audio quality Article
# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 3 Algorithm Description
### Operation of the Proposed Algorithm  
The proposed algorithm was designed having in mind the mobility of the end nodes and their direct consequences in the operation of the network. As each ED has a generic moving pattern, controlled from specific parameters as shown in Tables 1 and 2, it does not remain within the boundaries of a single SF. Due to their mobility, nodes may not be able to retain the same SF parameter during their operation as their distance from the SN may vary. Therefore, EDs should amend their transmission parameters in order to ensure the successful reception of their message. The proposed approach MOTILO (moving-based time-division multiple access for LORA) is described concisely below, while the corresponding pseudocode is depicted in Algorithm 1. \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(N\) & Total number of nodes in the cluster \\ \(ED\) & End Device/Node \\ \(p\) & Indicates the network load, i.e., 10\%, 20\%,..., 100\% \\ \(Load\) & Creates network load according to variable \(p\) \\ \(EOS\) & Defines the end of simulation in ms \\ \(ToA(1,2,3,4,5,6)\) & Defines time on air according to SETS 1, 2, 3, 4, 5 and 6 \\ \(WuB_{CH->ED}\) & WuB duration between the CH and the ed \\ \(WuB_{ED->CH}\) & WuB duration between the ed and the CH \\ \(GT\) & The duration of guard time \\ \(j\) & Defines the current cycle \\ \(gtss\) & Defines the lists of global transmission slot schedule \\ \(list\) & Defines the list of each ED’s slot schedule \\ \(S_{initial}\) & Defines the estimated SF \\ \(slot_{SF}\) & Defines the time slot of the initial estimated SF \\ \(SF_{new}\) & Defines the higher SF (\(SF_{initial}+1\)) \\ \(slot_{SF+1}\) & Defines the time slot of the higher SF reserved (\(SF_{initial}+1\)) \\ \(newslot\) & Defines the last slot for the schedule of one ED’s higher SF option \\ \(Latency_{ED->SN}\) & Data latency from ED to SN (LoRa message) \\ \(Latency_{ED->CH}\) & Data latency from ED to CH (WuB message) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**LoRa Radio Settings** & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** & **\#6** \\ \hline Spreading Factor & 12 & 11 & 10 & 9 & 8 & 7 \\ Coding Rate & 4/6 & 4/6 & 4/6 & 4/5 & 4/5 & 4/5 \\ Bandwidth (kHz) & 500 & 500 & 500 & 500 & 500 \\ Transmission Power (dBm) & 10 & 10 & 10 & 10 & 10 & 10 \\ Carrier Frequency (MHz) & 868 & 868 & 868 & 868 & 868 & 868 \\ Data Rate (kb/s) & 0.976 & 1.790 & 3.255 & 7.03 & 12.5 & 21.87 \\ Payload (B) & 8 & 8 & 8 & 8 & 8 & 8 \\ Preamble length (symbols) & 8 & 8 & 8 & 8 & 8 & 8 \\ Time on Air (ms) & 264 & 132 & 66 & 31 & 18 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LoRa radio settings.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 2 Background
### RDMA in DCN  
As the DCN bandwidth continues to increase, from 10 Gbps to 100 Gbps or even higher, network transmission becomes the bottleneck for DC applications. Specifically, traditional kernel TCP cannot meet the demand of latency-sensitive or bandwidth-intensive applications. Given the advantages mentioned in the previous section, RoCEv2 is introduced to deploy in production DCs.  
However, several core challenges arise when RDMA is deployed in modern data center networks (DCNs). Specifically, implementing RDMA directly over Ethernet-based DCNs leads to a significant decline in RDMA throughput. This is attributed to the fact that the RDMA NICs (RNICs) only support the basic Go-Back-N (GBN) retransmission mechanism, which is a result of the extreme reduction in bandwidth utilization when packet loss occurs. As a result, Priority Flow Control (PFC) is employed to facilitate lossless RDMA networks [25]. However, PFC introduces its own set of problems, such as head-of-line (HoL) blocking, deadlock, and unfairness issues [25; 26; 27; 28], which negatively impact RDMA's performance and scalability.  
In response to the aforementioned challenges, many studies are proposed to improve intra-DC RDMA's performance. These solutions encompass approaches targeting lossless and lossy networks, and many of them specifically focus on congestion control, retransmission mechanisms, or multipath transmission. Protocols such as DCQCN [25], TIMELY [29], HPCC [30], Swift [31], PCN [32], and PACC [33] are designed for lossless RDMA networks. They have been developed to enhance application performance through the design of a single-path congestion control mechanism. These end-to-end transport protocols identify congestion at switches and subsequently inform end hosts to adjust their sending rates accordingly. Among these approaches, certain studies (e.g., HPCC) have employed newlydesigned INT-capable switches to achieve more precise congestion control. Tagger [34], GFC [35], and TCD [36] contribute to resolving specific PFC issues such as HoL blocking, PFC storms, and deadlock, as identified in previous studies. IRN [28] and ConWeave [37] operates in lossy RDMA networks with an enhanced packet retransmission and reorder mechanism. In IRN design, the sender emits all packets within a sliding window (i.e., a bitmap), and the switches drop packets when encountering buffer overflow. IRN relies on redesigned NICs to support its proposed enhanced packet loss recovery mechanism and end-to-end flow control capabilities. ConWeave aims to prevent out-of-order packets within existing load-balancing frameworks, which necessitates redesigning switches to incorporate both load-balancing and out-of-order packet reordering functionalities. In terms of load balancing, MP-RDMA [38], PLB [39], and Proteus [40] aim to leverage multiple equal-cost paths and reduce congestion through fine-grained load balancing. With regard to RDMA scalability, SRNIC [41] represents a significant stride in enhancing RDMA scalability by developing a newly scalable RNIC.
## 2 Materials and Methods
### Key Component Parameter Design and Analysis  
#### 2.3.1 The Effect of the Rotation Speed on the Material Sieving  
The BSF sand mixture particles rise to a certain height on the trammel screen surface under the combined actions of the friction force of the screen surface and its own centrifugal force, and the particles finally roll down to the bottom of the screen surface under the action of gravity in a cyclic, periodic motion. Under the determined diameter, length, screen size and form, material, and other parameters of the tremmel screener, the penetration rate of the mixed material in the tremmel screener is correlated with the rotation speed of the tremmel screener. The movement force in the trammel screen of the BSF sand particles was analyzed in this study (Figure 7) to ensure that the mixed material particles could pass through the effective screening area in time and reduce the screening loss. At the highest point, the force of the BSF sand particles on the tremmel screener surface is illustrated in Figure 7(I):  
\[\left\{ \begin{array}{c}mg\cos\beta+N_{2}-\frac{mw^{2}D}{2}=0\\ f_{2}-mg\sin\beta=0\\ f_{2}=\mu N_{2}\end{array} \right. \tag{7}\]  
Through calculation, this yields:  
\[w=\sqrt{\frac{2g(\mu\cos\beta+\sin\beta)}{\mu D}} \tag{8}\]  
To prevent the BSF sand particles from being discharged from the top of the tremmel screener, \(\beta\) should be greater than 0, i.e., when \(\beta\) = 0, the BSF sand particles will be discharged from the top in the vertical direction. Accordingly, when the tremmel screener rotates, it is necessary to ensure that the gravity of the BSF sand particles is higher than the centrifugal force, such that the BSF sand particles can fall back to the bottom, which is expressed as follows:  
\[mg-\frac{mw^{2}D}{2}>0 \tag{9}\]  
Through calculation, this yields:  
\[w<\sqrt{\frac{2g}{D}} \tag{10}\]  
Moreover, to ensure that the BSF sand particles can pass through the sieve holes of the trammel screen, the force of the particles in the sieve holes should meet the requirements (Figure 7(II)):  
\[\left\{ \begin{array}{c}\frac{mw^{2}D}{2}+mg\cos\theta-f_{1}>0\\ N_{1}-mg\sin\theta=0\\ f_{1}=\mu N_{3}\end{array} \right. \tag{11}\]  
Through calculation, we obtain:  
\[w>\sqrt{\frac{2g(\mu\sin\theta-\cos\theta)}{D}} \tag{12}\]  
Equations (9)-(12) indicate that the highest point of the trammel screener is affected by the rolling friction coefficient \(\mu\), inner diameter of the trammel screener \(D\), the angle \(\beta\) between the line connecting the BSF sand particles and the center of the circle and the vertical center line, etc. The angle \(\beta\) between the line connecting the BSF sand particles and the center of the circle and the vertical center line are correlated with the rotational angular velocity \(w\) of the screener under the determined friction coefficient \(\mu\) between the sand material and the screener surface, as well as the obtained inner diameter of the roller screen \(D\). Thus, the  
Figure 7: Stress analysis of BSF sand at the critical location.
## 4 Trends and Future Work
### Real-World Usage Scenario  
A real-world usage scenario for the real-time estimation of earthquake magnitude using machine learning models is presented below. The process follows multiple stages, from data acquisition to result visualization. This scenario describes the workflow of an artificial intelligence-based system for early magnitude estimation.  
Figure 8: Evolution of ML and DL approaches for earthquake magnitude estimation, highlighting major advancements and future research directions.  
The process begins with seismic stations continuously monitoring ground motion using broadband seismometers and accelerometers, along with digital seismic digitizers. These devices record waveforms in three components (Z, N, E) and transmit the data in real-time to a centralized processing system. There, the first 2 to 3 s of the P-wave are automatically extracted for analysis. To enhance signal quality before processing, filtering, and noise removal techniques are applied.  
Once the preprocessed waveform data reaches the processing system, an AI-based model is applied for magnitude estimation. Deep learning architectures such as CNNs, Bi-LSTMs, and Transformers analyze waveform features to predict earthquake magnitude. The system is optimized for low-latency predictions, ensuring rapid magnitude estimation within milliseconds. Additionally, models are trained on regional seismic datasets to improve accuracy and adaptability to different tectonic settings. After magnitude estimation, the results are displayed and processed for further analysis. A real-time monitoring dashboard provides a software interface where raw and processed waveforms are visualized alongside estimated magnitude values and confidence levels. This scheme is graphically represented in Figure 9, illustrating the key stages from seismic data acquisition to magnitude estimation and visualization.
## 5 Analysis of Displacement Effect
### Pressure Distribution in Grout Diffusion Zone  
The penetration and diffusion model of grout considering the displacement effect is developed based on the continuity conditions of the pressure and flow rate pressure at the interface between grout and groundwater. The distribution of fluid pressure in the penetration and diffusion zone of grout is analyzed. The relationship curve between the grout pressure and diffusion distance is shown in Figure 4.  
Figure 4: Pressure distribution in grout diffusion zone.  
Figure 4 shows that the grout pressure rapidly decreases with the increase in the diffusion distance of grout. The pressure attenuation mode considering the displacement effect is concave. Under the influence of groundwater, the pressure balance between grout and groundwater rapidly balances. That is, the pressure and flow rate at the interface between grout and groundwater are continuous. The resistance in the diffusion of grout increases, and therefore, the grout pressure rapidly decreases. The pore water in the stratum is driven away after the grout injection. Its penetration movement is actually a deceleration movement, which is consistent with the results obtained by [PERSON] [34]. The results show that the column penetration and diffusion mechanism of Bingham fluid considering the displacement effect are consistent with the practice.