esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 3 Method
### Line Buffer Access Pattern and Control Instruction  
#### 3.3.1 Access Pattern of Line Buffer  
Since the image lines are vertically aligned in the LBs, the load addresses are synchronized to the store address of the LB which accepts the input block. We use an example in Figure 7 to demonstrate the pixel access pattern in the underlying BRAMs. The parameters, \(N_{line}\), \(N_{blk}\) and \(H\) are set to \(44,16,and3\), respectively in the example. With the D-SWIM method, we set 3 LBs (\(H=3\)) in the streaming architecture, and each LB is composed of 3 BRAMs (\(N_{bran}=3\)). The store position of input blocks (blk0-blk15) are highlighted in the LBs. In each cycle, one BRAM address is accessed at most to ensure that the previous constraint of BRAM port is not violated. For the blocks that are not aligned to the BRAM boundary, such as blk3-blk5, a byte-wise write enable signal was used to make sure only the positions marked by the enable signal were updated and the other pixels in the same address are not overwritten. Note that the remainder pixels in the last block of each line are duplicated and stored at the beginning of the successive LB. For example, blk2 contains 4 pixels of Line1 (\(N_{remain}=4\)). Thus, these pixels are written to both BRAM2 of LB0 and BRAM0 of LB1, concurrently.  
Note that from the Blk11, the storage pattern in LBs will be the same as that from Blk0. This is because the values of \(N_{remain}\) in continuous lines show a periodic pattern, and the period is determined by \(N_{line}\) and \(N_{blk}\). The period measured in clock cycle (\(P_{clk}\)) or in image line (\(P_{line}\)) is given by Equation (3).  
\[\begin{split}& P_{clk}=\text{LCM}(N_{line},N_{blk})/N_{blk}\\ & P_{line}=\text{LCM}(N_{line},N_{blk})/N_{line}\end{split} \tag{3}\]  
where LCM is the least common multiple. In addition, \(N_{remain}\) of line \(l\) (denoted as \(N_{remain,l}\)) is calculated as Equation (4).  
\[\begin{split} N_{remain,l}=\begin{cases}N_{blk}-N_{line}\%N_{ blk},&l=0\\ N_{blk}-(N_{line}-N_{remain,l-1})\%N_{blk},&l\in[1,P_{line})\end{cases} \end{split} \tag{4}\]  
where \(l\) is the index of the line. With the equations above, the buffer access pattern is deterministic. Thus, in the Figure 7 example, every 4 lines have the same LB storage pattern, and the value of \(N_{remain,l}\) shows a periodic pattern of \(\{4,8,12,0\}\).  
Figure 6: Example of buffer load and store with the line-rolling behavior.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 3 System Model
### Local Computing Model  
For the task, \(R_{i,j}\) and \(R_{i,j}^{off}\) denote the input bit and offload bit of the tasks, respectively. \(j\in\mathbf{M}\), \(i\in\mathbf{N}\). As for the local computing, the size of the local computing data on the \(j\)-th MD after knowing the task size and offloading size is \(R_{i,j}^{loc}=R_{i,j}-R_{i,j}^{off}\). We can get the local computing energy consumption as:  
\[E_{i,j}^{loc}=(R_{i,j}-R_{i,j}^{off})C_{j}P_{i,j}\quad i\in\mathbf{N},j\in\mathbf{M} \tag{1}\]  
where \(P_{i,j}=\kappa_{j}f_{i,j}^{2}\) is the energy consumption per cycle, \(C_{j}\) denotes the cycles required to process one bit input, which depends on the task characteristic, \(\kappa_{j}\) is the effective capacitance coefficient, depending on the MD chip architecture of user \(j\), \(f_{i,j}\) denotes the frequency of the CPU of the \(j\)-th MD in \(i\)-th time slot, and is constrained by \(f_{i,j}\in(0,f_{j}^{max})\).  
According to the CPU frequency, we can get the time consumed by the local computation as:  
\[T_{i,j}^{loc}=\frac{(R_{i,j}-R_{i,j}^{off})C_{j}}{f_{i,j}}\qquad i\in\mathbf{N},j \in\mathbf{M} \tag{2}\]  
After obtaining the time consumed by the local computation, according to the previous assumption, we use (2) as a constraint to perform the computation task on the MD. The user cannot stand for waiting for a long time, so the time limit of the local computation should be within the time block. Thus, we assume that \(T_{i,j}^{loc}\leq T\).
## 5 Ablation Studies
### Hash Adaptive Matching
#### 5.1.3 Hash Bucket Size  
The size of the hash bucket directly impacts the exploration range of query features in the non-local feature space. As shown in Table 6, to evaluate the effect of hash bucket size on model performance, we conducted ablation experiments, and the results show that the model performs best when the hash bucket size is set to 144. By comparing the results with different hash bucket sizes, we observed a significant decline in image reconstruction quality as the bucket size increased. This is because larger hash buckets weaken the ability to distinguish boundaries, leading to incorrect fusion of features across buckets, which in turn affects the accurate matching of features and reconstruction results. Therefore, properly setting the hash bucket size is crucial for ensuring the model's performance.
## 1 Introduction
The IIR filters have a delay line as the FIR filters, but the taps are connected to the input rather than to the output. They take their name from the fact that the duration of the impulsive response is theoretically infinite, even if after a sufficiently long period of time, the response is negligible. The main advantage of the IIR filters is that the vanishing time of the impulsive response can be set by changing the feedback parameters and by keeping the topology unchanged. Unfortunately, the values of such parameters could make the impulsive response unstable; therefore, some measures are needed to prevent this event. The standard algorithm for the training of the feedback NNs is the Backpropagation Through Time (BPTT [18]), which can be adapted to any feedback topology of NN. This algorithm has been adapted to LRNNs in [17; 18; 19; 20], substantially developing the feedback loop a number of times, sufficient to consider the impulsive response extinct. Such a measure allows one to adapt the structure in spite of the feedback structure being non-casual, and it makes it possible to use the same procedures defined for feedforward structures for training. Nonetheless, some issues remain, as the number of times the loop should be developed is unknown a priori, so the assumed value could be too small, giving rise to interference among different impulsive responses, or too large, in which case it would oversize the computational cost. Furthermore, the stability of the impulsive response remains a main issue to solve [17]. In the present work, a new training algorithm is presented, which at the same time overcomes the problems of training and stability, in this way allowing one to extend the applicability of the LRNNs. The organization of this paper is structured as follows. Section 2 introduces the neural model and provides a detailed description of the newly proposed training algorithm. In Section 3, the method is applied to two different forecasting problems: the prediction of a chaotic time series and the estimation of power demand. Finally, Section 4 presents the conclusions, highlighting the main findings and discussing potential implications.
## 5 Conclusions  
PHM technology has now become an important tool to ensure the operation of modern commercial aircraft, because it can provide a large amount of fault information for maintenance decision making, and as an important part of PHM technology health assessments can accurately assess the operational status of aircraft. Health assessment information obtained from aircraft-condition-monitoring system data can be used to improve aircraft maintenance practices to avoid delays and cancellations. The combination of aircraft system degradation indicators and estimation algorithms can provide maintenance personnel with the ability to arrange disassembly and maintenance plans in advance.  
Based on flight control system health assessment under certain conditions, this paper establishes a health assessment model under uncertain conditions. Firstly, the characteristic parameters that characterize the health status of a flight control system are selected. Secondly, the uncertain factors are included in the flight control system health assessment modeling process, and the uncertainty of the characteristic parameters is quantified and their propagation was simulated. Finally, the actual flight data are taken as an example to verify the effectiveness of the proposed model. The experimental results show that the health assessment under uncertain conditions is more in line with the actual situation of the degradation of an aircraft's system life. More importantly, health assessments conducted under uncertain conditions can reduce the false alarm rate, which can support maintenance decisions.  
In future research, statistical principles can be combined with machine learning and other methods to more accurately quantify the uncertainty of input parameters, and at the same time a failure prediction model can be established based on health indicators.  
Conceptualization, J.C.; methodology, Y.Z.; formal analysis, X.X.; resources, R.C.; writing review and editing, J.C.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61873203), research funds for an interdisciplinary subject, Northwestern Polytechnical University and Key Laboratory Fund (6142806190504), China Academy of Space Technology Innovation Foundation (CAST-2020-02-11), Aeronautical Science Foundation of China (20200033053001) and the project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory (6142806190504).
## 2 Basic Concepts
### The BCC Model  
A significant disadvantage of the CCR model discussed above is the premise of linear homogeneity [56]. [PERSON], [PERSON], and [PERSON] in 1984 [13] developed a model that eliminates this drawback. It was named the BCC model, after the authors' surnames. These models differ from CCR models by adopting variable returns to scale (VRS). Variations in the BCC model make it possible to identify an increasing or decreasing scale effect for each object [60].  
For the above-described linear production models, this means that if the production object (\(X_{0},Y_{0}\)) is valid under the given economic conditions, then the object (\(tX_{0},tY_{0}\)) where we have the number \(t>0\) will also be valid.  
Since the concept of scale effect is important in mathematical and practical economics, we focus on it in a little more detail. It is believed that for a specific object (a point in the socio-economic space) there are the following [61]:  
* A constant scale effect, if the limiting coefficient of the ratio of the relative increment of the output vector to the relative increment of the cost vector is equal to one;
* An increasing scale effect, if the limiting coefficient of the ratio of the relative increment of the output vector to the relative increment of the cost vector is greater than one;
* A decreasing scale effect, if the limiting coefficient of the ratio of the relative increment of the output vector to the relative increment of the cost vector is less than one.  
A mathematical representation of the variable scale effect can be produced by adding a new variable \(U_{0}\) to the objective function of the original model.  
\[E_{0}=\frac{\sum_{j=1}^{s}U_{j}Y_{j_{0}}+U_{0}}{\sum_{i=1}^{s}V_{i}X_{i_{o}}} \to max! \tag{4}\]  
Subject to the following conditions:  
\(\frac{\sum_{i=1}^{s}U_{j}Y_{i}+U_{0}}{\sum_{i=1}^{s}V_{i}X_{i_{m}}}\leq 1\), \(U_{j}\), \(V_{i}\geq 0\).  
\(U_{0}\) is the scale effect, \(U_{0}>0,U_{0}<0\), \(U_{0}=0\).  
At the same time  
\(U_{0}>0\) is the increasing return to scale;  
\(U_{0}<0\) is the decreasing return to scale;  
\(U_{0}=0\) is the constant return to scale.  
The equation presented (4) is the BCC-Output Model.  
Now, let us imagine a multiplier primal BCC input model with variable slacks [62].  
\[max{\sum_{j=1}^{s}{\mu_{j}y_{j0}}}+U_{0} \tag{5}\]  
Subject to the following conditions:  
\(\sum_{i=1}^{s}{T_{i}X_{i0}}=1\)  
\(\sum_{j=1}^{s}{\mu_{j}y_{j0}}-\sum_{i=1}^{s}{T_{i}X_{i0}}+\sum_{m=1}^{m}{U_{0}}\leq 0\)  
for all studied objects m = 1,..., n; \(\mu_{j}\geq 0\); \(U_{0}\) is free.  
The fractional program is converted to an LP problem using the Charnes and Cooper transformation and the Multiplier BCC Output model is as below [60].  
\[\frac{1}{E_{0}}=min{\sum_{i=1}^{m}{V_{i}X_{i_{0}}}}+{V_{0}} \tag{6}\]  
Subject to the following conditions:  
\(\sum_{r=1}^{s}{U_{r}Y_{r0}}=1\)  
\(\sum_{i=1}^{m}{V_{i}X_{ji}}+{V_{0}}-\sum_{r=1}^{s}{U_{r}Y_{rj}}\geq 0\)  
\(j=1,2...\) \(n\), \(U_{r}V_{i}\geq\epsilon\), \(r=1\), \(2.....\) s, \(i=1,2....\), m, \(V_{0}\) is free (unrestricted in sign).
## 3 Results and Performance Evaluation  
The effectiveness of the proposed method is assessed using AE data obtained from an actual milling machine. Since the primary aim of this method is to detect and diagnose faults in milling machines, this section begins by comparing the fault detection capability of the proposed method with existing time-domain indicators. Subsequently, the method's performance in fault classification is evaluated against other current approaches.
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 3 Materials and Methods
### Topographic Factor (LS)  
Several studies are interested in the effect of topography on erosion. [PERSON] et al. [53] and [PERSON] et al. [22] exposed that topography acts on soil loss by two compounds such as length and degree of the slope. The topography also intervenes by the slope profile shape, depending on whether the slope is concave or convex. On the convex slopes, erosion is very low upstream, but it increases rapidly downstream. On concave slopes, erosion is very high upstream but decrease gradually downstream [54]. The slope can similarly act on erosion through infiltration parameters. With the increasing slope angle, [PERSON] and al. [55] observed a decrease in the runoff rate attributed to the destruction of the crust of the crop under the kinetic energy action of runoff, which increases with the slope.  
_LS_ factor means the role of the slope length (_L_) and its steepness (_S_) in soil erosion. To obtain the _LS_ value map, we chose to use the Unit Stream Power Erosion and Deposition (USPED) method for calculating the _LS_ factor because it was obvious that it could be completed with the spatial analysis tools included in a standard ArcMap installation [56]. The USPED is a physically established model that integrates a spatial module. In RUSLE, \(L\) is dependent on linear distance \(\lambda_{i}\), which is the horizontal length from the start of sediment transport to point \(i\) on the slope. Thus, they are inherently a single-dimensional function. The USPED uses the area of upland as an alternative, contributing flow at distance \(i\). The area is switched in place of the former slope length. The \(L\) calculation for point \(i\) on a slope is shown in Equation (7).  
\[L=(m+1)\left(\frac{\lambda_{A}}{22.1}\right)^{m} \tag{7}\]  
where \(L\) is the slope length factor at some points on the study site landscape, \(\lambda_{A}\) is the region of upland flow, \(m\) is an adaptable value depending on the soil's vulnerability to erosion and 22.1 is the unit plot length.  
The \(S\) calculation is shown in Equation (8).  
\[S=\left(\frac{\sin\left(0.01745\times\theta_{deg}\right)}{0.09}\right)^{n} \tag{8}\]where \(\theta\) is the slope value in degrees, 0.09 is the value of the slope gradient constant and \(n\) is an adaptable value reliant on the soil's vulnerability to erosion.  
In our case study, we used 12 m digital elevation model, which is optimal for this kind of work [52].
## 4 Experiments and Analysis
### Baselines  
To verify the effectiveness of the proposed BiLSTM-BGAT-GCN model, we conducted comparisons with several leading baseline models, with the results presented as follows:  
Peng-two-stage [7] and its related variants are a series of models in the field of sentiment analysis, which extract emotional pairs, opinion terms, and construct triplets through a two-stage process. Among them, Peng-two-stage+IOG [17] combines IOG technology to enhance performance; GTS-CNN [17] and GTS-BiLSTM [17] incorporate grid tagging schemes with CNN and BiLSTM techniques for feature extraction; Dual-MRC [8] constructs two machine reading comprehension problems to extract relevant information; IMN + IOG [17] combines the advantages of interactive multi-task learning networks and IOG; CMLA [7] uses the attention mechanism for collaborative extraction. RINAATE [7] is based on word dependency in sentences and extraction rules; Li-unified-R [7] is an improved version of its original OE component; OTE-MTL [15] achieved triplet extraction through multi-task learning; JET-BERT [16] simultaneously extracts triplets using a position-aware tagging approach; BMRC [8] converts the task into a machine reading comprehension task; and EMC-GCN [24] processes words and edges in sentences through multichannel graphs. These models possess unique characteristics and advantages, providing sophisticated and efficient solutions for complex text sentiment analysis.
## 4 Analysis of Results
It is evident that the RBFNN achieves significantly better equalization performance. Figure 7: Simulation results of different probabilistic shaping schemes for 400 GHz photonic terahertz OFDM signals in the back-to-back (BTB) configuration. According to Figure 8b, both schemes lead to an improvement in the Q-factor. For the woRBFNN system, the Q-factor increases by 1.21 dB, indicating that LUT-PS provides a degree of performance improvement. For the LUT-PS-DFE system, the Q-factor gain remains relatively constant at approximately 2.07 dB. In contrast, the LUT-PS-RBFNN system exhibits an increasing Q-factor gain with increasing ROP, reaching a maximum of 23 dB, which is 21.79 dB and 20.93 dB higher than that of the woRBFNN system and that of the LUT-PS-DFE system, respectively.
## 3 Results and Discussion
### Fiber and DSP Setup  
We consider a standard single-mode fiber with a span of \(L\) = 120 km, transmitting a dual-polarized 50-GBaud signal. The wavelength of lasers is 1552.6 nm. Table 1 shows the fiber's parameters, which will be applied to all models mentioned in this chapter, including SSFM, RP and MRP models. We intentionally chose a larger group velocity coefficient, \(\beta_{2}\), to demonstrate that even when the symbol length affected by nonlinear inter-symbol interference is longer, the model's performance remains strong. We also note that using other reasonable dispersion values does not significantly impact the improvement in model performance. For the signal output by the model, we process the waveform-wise signal through a standard DSP process to remove linear distortion in the fiber optic, its specific structure is shown in Figure 4.  
We quantify the accuracy of the model by evaluating and comparing the symbol-wise data solved by the MRP model and the SSFM. Normalized root mean square error  
\begin{table}
\begin{tabular}{c c} \hline \hline Nonlinear Parameter \(\gamma\) & 1.2 W\({}^{-1}\).km\({}^{-1}\) \\ Fiber attenuation \(\alpha\) & 0.2 dB-km\({}^{-1}\) \\ Second-order dispersion \(\beta_{2}\) & \(-27.77\times 10^{-27}\) s\({}^{2}\).m\({}^{-1}\) \\ Third-order dispersion \(\beta_{3}\) & \(17.68\times 10^{-41}\) s\({}^{3}\).m\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fiber parameters.  
Figure 4: The setup of the system simulated by the MRP model.  
(RMSE) and error vector magnitude (EVM) are used here to evaluate the model, and their definitions are as follows:  
\[RMSE=\sqrt{\frac{1}{2}\left(\frac{\mathbb{E}\left\{\left|R_{x}-\hat{R}_{x}\right|^ {2}\right\}}{\mathbb{E}\left\{\left|R_{x}\right|^{2}\right\}}+\frac{\mathbb{E} \left\{\left|R_{y}-\hat{R}_{y}\right|^{2}\right\}}{\mathbb{E}\left\{\left|R_{y }\right|^{2}\right\}}\right)} \tag{16}\]  
\[EVM=\frac{1}{2}\left(\sqrt{\frac{\mathbb{E}\left\{\left|\hat{R}_{x}-T_{x} \right|^{2}\right\}}{\mathbb{E}\left\{\left|T_{x}\right|^{2}\right\}}}+\sqrt{ \frac{\mathbb{E}\left\{\left|\hat{R}_{y}-T_{y}\right|^{2}\right\}}{\mathbb{E} \left\{\left|T_{y}\right|^{2}\right\}}}\right) \tag{17}\]  
where \(R\) are the symbol-wise data passed through DSP at the receiving end, and \(T\) are the initial data sent by the transmitting end. Due to the model being a stationary process, the expectation, \(\mathbb{E}\), are transformed into a sum of the points in the sequence.
## 4 Simulation Results and Discussion  
In this section, we first perform simulation experiments in six different environments using Matlab to verify the effectiveness of the APF-IRRT* algorithm. Then, a comparison of the proposed algorithm against RRT*, P-RRT* and IRRT* algorithms on a variety of planning problems is presented. Lastly, we perform the proposed APF-IRRT* algorithm in a real laboratory environment to test the path planning on a real mobile robot.
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 1 Introduction
An IoT communication network comprises different layers such as application, security, transport, network, and link layer. The communication protocols at the link layer can be classified into three categories: (1) short-range protocols include Bluetooth and ZigBee, (2) medium-range protocols include Wi-Fi, 4G/LTE, and 5G, and (3) Long-range protocols include Narrowband-IoT (NB-IoT), and LoRa (Long Range). The LoRa network is a low-power wide-area network that uses the spread-spectrum modulation technique. In this work, the LoRa network is proposed to implement a microgrid architecture based on IoT connectivity. A voltage-based droop control method for the transition from grid-connected mode to the standalone mode was developed in Refs. [33; 34]. The authors of [35] designed (active-reactive power) PQ and (voltage-frequency) VF control algorithms to minimize voltage and current spikes, there predominantly appear when the microgrid is switched between grid-connected and islanded operating modes. This manuscript is an extension of the work published in [36], by means of the following contributions:  
1. A proposed design of a partially grid-connected microgrid based on the IoT communication network. 2. A comprehensive simulation model using Matlab/Simulink was designed to evaluate the performance of the load-shedding algorithm and the associated communication protocol. 3. A prototype design of IoT-based smart meter using ZigBee communication protocol. 4. A LoRa based communication network is proposed to link the smart meters with the microgrid controller. The rest of the paper is organized as follows. Section 2 describes the operation, control, and modeling of modern microgrids. Section 3 demonstrates the simulation models and designs used to describe the control of each component in the PV microgrid. Section 4 elaborates the architecture and design of the IoT communication network in addition to the communication protocol requirements. Finally, Section 5 displays the results and discussion followed by the conclusions.