esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### System Specification
Figure 5: Visualization of the data path with the transformation functions and the data quality measurement points. CONSTANT MAX_NUM_WIN, WINDOW_SIZE, TIMEOUT UP VARTABLES numWin, nowl, now2, now3, now4, dropList, ui, pi1, pi2 Step(t) ==/ \ IP numWin[t] < MAX_NUM_WIN THEN/ \ numWin' = [numWin EXCEPT! [t] = numWin[t] + 1] / \ nowl' = [nbox2 EXCEPT! [t] = nowl[t] + WINDOW_SIZE] / \ now2' = [nbox2 EXCEPT! [t] = nowl[t] + ui[t] / \ IF now3[t] < now2'[t] + TIMEOUT_UP THEN/ \ dropList' = [dropList EXCEPT! [t] = <<FALSE>> \ o dropList[t]] / \ now3' = [nbox3 EXCEPT! [t] = now2'[t] + pi1[t]] / \ IF now3'[t] > now4' = [nbox4 EXCEPT! [t] = now3'[t] + pi2[t]]  ELSE/ \ UNCHANGED <=now4>> ELSE/ \ dropList' = [dropList EXCEPT! [t] = <<TRUE>> \ o dropList[t]] / \ UNCHANGED <=now3, now4>> / \ ui' = [ui EXCEPT! [t] = (UPLOAD LATENCY[numOp[t] + 1])] / \ pi1' = [pi1 EXCEPT! [t] = (PROC_TIME_ALG_1[numOp[t] + 1])] / \ pi2' = [pi2 EXCEPT! [t] = (PROC_TIME_ALG_2[numOp[t] + 1])]  ELSE/ \ UNCHANGED vars  
In this formal specification, we defined 3 constant values. _MAX_NUM_WIN_ acts as a termination condition that describes the length of the measurement process, which tells us how many data windows we can have. If we did not set this up, the model checker would never stop. _WINDOW_SIZE_ stands for the length of the window in time. TIMEOUT_UP is the configured timeout value of the upload process. _numWin_, _now\({}_{1}\)_, _now\({}_{2}\)_, _now\({}_{3}\)_, _now\({}_{4}\)_, _dropList_, \({u}_{i}\), \({p}_{i1}\) and \({p}_{i2}\) are the variables that we check after every step. _numWin_ is the main counter of the system and it is always compared to _MAX_NUM_WIN_. _now\({}_{i}\)_ contains the current time value in the \(i\)th process. _dropList_ is the tuple of the dropped and undropped windows. It contains only Boolean values, _TRUE_ if the window has to be dropped, and _FALSE_ if it can be processed. The latencies of the upload process and the algorithms are assigned to the \({u}_{i}\), \({p}_{i1}\) and \({p}_{i2}\) variables, respectively. _UPLOAD_LATENCY_, _PROC_TIME_ALG_1 and _PROC_TIME_ALG_2 are input variables that take values from predefined intervals.
## 3 SDMob Architecture
### Enhanced SDMob
#### 3.2.1 Handling Anchor Density  
The RSSI measurements that provide the vital information for localization generate bursty traffic since all the anchors try to forward the control beacon as soon as they receive it. The constrained network cannot always deliver this bursty traffic to the controller especially if MN resides in a dense area. Although the CSMA-based MAC layer handles the shared spectrum but with bursty traffic in a dense network the delivery ratio drops significantly. With more burstiness in the traffic, CSMA is forced to retransmit more packets due to congestion in the network. This bursty traffic can push the CSMA-based MAC layer to its limits. If the number of retransmissions for a certain packet crosses a certain threshold, the packet will be dropped. Some mobility patterns tend to keep the mobile node in areas with more density. The situation gets worse with multiple mobile nodes moving in the same area.  
The accuracy of the filter substantially depends on the RSSI reports arriving on time. However, this bursty traffic can be lost in the anchors or be delayed. By enabling the re-sampling procedure, the particle filter can converge again after a period of not receiving any/enough measurements. But most importantly, we introduce a timer called the congestion timer. The congestion timer randomly delays the transmission of the RSSI report to the controller in order to decrease the burstiness. This timer can be tuned based on the density of the neighbors but excessively increasing this timer may increase the handover delay. By setting the congestion timer to a reasonable value, the delivery ratio at the control plane gets boosted but on the other hand setting it too high may lead to not receiving the packets in time for the filter.
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 4 Discussion
### Internal Heating Source  
The significance of effective insulation in bee hives for both safeguarding bees during summer and maximising honey production aligns with the necessity to preserve internal warmth throughout winter. The hive must maintain a temperature inside the bee clusterat a level of 24-32 \({}^{\circ}\)C despite the considerable variation in the outside temperature [49]. Initial findings indicate that thermal insulation's physical properties could be instrumental in maintaining this warmth and supplementary experiments with internal heat sources confirm it.  
These experiments, comparing hives insulated with black agglomerated cork to control hives, demonstrated the ability of black agglomerated cork not only to retain heat but also to maintain lower humidity levels. Such humidity control is critical for preventing the spread of pathogenic fungi, as emphasised by [50], and can be attributed to the inverse relationship between temperature and humidity: in an environment with a constant amount of water vapour, an increase in temperature is associated with a decrease in humidity. These findings emphasise the superior thermal insulation properties of black agglomerated cork, showcasing its ability to enhance hive thermal regulation. This advantage becomes particularly crucial during the colder months, transitioning into spring--a challenging period for bee colonies as identified by [51].  
While enhancing hive insulation is primarily beneficial for bee survival during winter, increased indoor temperatures can have other repercussions. For instance, elevated temperatures during overwintering might adversely affect bee microbiota [52], accelerate the growth of the Varroa mite population [53], or prompt too-early brood restarting [54]. All these facets necessitate in-depth exploration through comprehensive field studies.
## 1 Introduction
Thirdly, the equivalent-time sampling method employs the high-speed ADC with wideband input and programmable time delay line to repeatedly sample the wideband echo, obtaining one or more sampling points of the wideband signal in each pulse repeated period (PRP), and finally performs timing recovery in the time domain [17; 18], which is able to receive wideband echo at the cost of sampling time. Although the equivalent-time sampling method is simple, the positions of the radar and target are almost stationary or quasi-static in the equivalent-time sampling period, which is difficult to meet the requirement of high-speed flight for space targets imaging. In order to reduce the complexity of the system and achieve the real-time imaging of space targets at any distance, this paper presents a novel space-borne high-resolution SAR system with non-uniform hybrid sampling technology. The non-uniform hybrid sampling technology is firstly applied in SAR imaging system for the detection of space targets. The non-uniform hybrid sampling technology optimizes the transmitted and receiving timing of SAR signals, reducing the requirement of the sampling rate of ADC for the reception of the wideband echoes from space targets. Meanwhile, according to the oversampling requirement of SAR imaging in the azimuth direction, we establish a theoretical model of non-uniform hybrid sampling parameters and relative velocity between the SAR system and the space targets. Finally, guided by the simulated results of SAR imaging with different parameters, an X-band SAR experimental system is constructed to verify the effectiveness of the proposed non-uniform hybrid sampling technology. The experiment results show that the imaging resolution with non-uniform hybrid sampling technology is better than 8 cm, achieving high-resolution imaging for space targets at any distance. This paper is organized as follows: Section 2 introduces the design of the space-borne X-band SAR system. Section 3 presents the proposed non-uniform hybrid sampling technology in detail. Section 4 shows the imaging results of simulation and indoor experiment. Section 5 finally gives the conclusion.
## 3 Control Method
### Concept of ESN-MPC  
In real-life circumstances of the control target, the system state changes dynamically, and unexpected events may occur. MPC, as a control method, can help predict future events and optimize control signals at each control period. The objective of the MPC is to control and optimize system performance for a predicted finite-time future. However, to realize real-time optimization, the MPC generally requires full calculation results within the control period. Therefore, this framework cannot be easily applied in a system where the control period is too short to complete the calculation. Instead of performing real-time optimization within the subsequent control period, the forecasted future disturbance is used to establish the future optimal control. The ESN predicts the future disturbance, which is multiple steps ahead of the period, to calculate the optimal control signals required by the MPC in the future. Figure 2 shows the controller structure in the proposed method. The upper left part illustrates the prediction sequence of a future disturbance in the system. The difference in time between the current calculation and optimization calculation is termed the time margin. The result predicted using the ESN is applied to the MPC in the future to achieve optimization at the time when the control signals are required. In other words, predicting the future disturbance provides a time margin to achieve the optimization calculation.  
Figure 1: A schematic of the HEV powertrain.
## 3 Satellite Characteristic Fundamentals  
Satellite telecommunications are a form of radio frequency telecommunications through satellite radio links between ground-based transceiver stations and artificial satellites in orbit around the Earth. These systems, made possible by the birth and development of launch technologies (rockets) starting in the second half of the twentieth century, often represent the only solution that is applicable in the absence of terrestrial infrastructures, or when such infrastructures are difficult to locate, and with an overall cost that is lower than the realization of equivalent terrestrial communication systems. In the following, a brief description of the main satellite characteristics are provided.
## 3 Problem Formulation
### Application Model  
In this paper, DAG is used to model the application, which can be expressed as \(\textbf{G}(\textbf{V},\textbf{E})\)[24]. \(\textbf{V}=\{n_{0},n_{1},\)..., \(n_{|V|-1}\}\), representing the set of tasks of the DAG. Each task \(n_{k}\) is related with two attributes, i.e., the resource consumption \(h_{i}^{n_{k}}\) and the execution time \(ET_{k}\) on the FPGA. \(\textbf{E}=\{e_{0},e_{1},\)..., \(e_{|E|-1}\}\) is the set of directed edges. The directed edge \(e=(n_{i},n_{j})\) is used to express the data dependency between tasks \(n_{i}\) and \(n_{j}\). \(n_{i}\) is called the parent task of \(n_{j}\), and \(n_{j}\) is called the child task of \(n_{i}\). Noting that \(n_{j}\) cannot be executed until \(n_{i}\) is finished. For convenience, we use \(pn\) and \(cn\) to denote parent task and child task of a task respectively. All the parent tasks of \(n_{i}\) form the parent task set \(\textbf{PN_{i}}=\{pn_{0},pn_{1},\)..., \(pn_{|PN_{i}|-1}\}\). All the child tasks of \(n_{i}\) form the child task set \(\textbf{CN_{i}}=\{cn_{0},cn_{1},\)..., \(cn_{|CN_{i}|-1}\}\).  
If there is a path from \(n_{i}\) to \(n_{j}\) in **G**, \(n_{i}\) is the ancestor task of \(n_{j}\), and \(n_{j}\) is the descendant task of \(n_{i}\). We use \(\textbf{AN_{i}}\) and \(\textbf{DE_{i}}\) to represent the set of ancestor tasks and descendant tasks \(n_{i}\) respectively [25]. Noting that \(\textbf{PN_{i}}\) is a subset of \(\textbf{AN_{i}}\), and \(\textbf{CN_{i}}\) is a subset of \(\textbf{De_{i}}\). The task without descendants is called the sink task. For the sake of generality, we assume that there is only one sink task in **G**. When there are multiple sink tasks in **G**, a virtual sink task \(n_{s}\) can be added to **G**. Noting that the added virtual sink task is connected with all sink tasks and its execution time is set as 0.  
Figure 2 shows a DAG model of an example application. The DAG contains eight tasks and nine directed edges. Table 1 shows the parameters of tasks of the application, including the execution time \(ET\) of the task in the FPGA, the number of CLB resources required by each task, and the parent task set **PN**, child task set **CN**.  
Figure 1: Dynamically partially reconfigurable (DPR) system.
# An Improved Lightweight Dense Pedestrian Detection Algorithm
## Proposed Algorithm
### GSConv  
GhostNet makes use of the \"halved\" SC operation to retain channel interaction information. Nevertheless, the 1 \(\times\) 1 dense convolution consumes more processing resources, and the results of utilizing \"channel shuffle\" has no effect on the SC results, and GhostNet returns to SC, which may affect a variety of factors. Many lightweight models use similar thinking to design the basic architecture, using only DSC from the beginning to the end of the deep neural network; however, the shortcomings of DSC are immediately magnified in the backbone, both for image categorization and detection. To ensure the DSC output is as near to SC as feasible, GSConv [29], a hybrid convolution of SC, DSC, and shuffle, is utilized. As shown in Figure 6, the information generated by SC (channel-dense convolution operation) is permeated to each part of the information generated by DSC using shuffle, which is a uniform mixing strategy. This method allows the information from the SC to be completely mixed into the output of the DSC, exchanging local feature information uniformly over different channels.  
To speed up the prediction calculation, the feed image in the CNN almost has to go through a similar conversion procedure in the Backbone: spatial information is gradually delivered to the channel. And each space (width and height) compression and channel extension of the feature map will result in some semantic information being lost. Dense convolution amplifies the hidden connections between each channel, whereas sparse convolution eliminates these connections. GSConv protects these relationships as much as feasible while being time-efficient. The advantage of GSConv is particularly noticeable for lightweight detectors, owing to the improved nonlinear expression capabilities provided by the addition of the DSC layer and shuffle. However, if it is employed at all phases of the model, the network layer becomes deeper and the inference time increases dramatically. As a result, GSConv is only used in the Neck section.
## 3 Multiagent DRL Design for Proposed Method
### PPO-Based Multiagent DRL Framework for Autonomous Control
#### 3.3.1 State  
The appropriate choice of the state is important because each agent determines the action using the state. In this study, the PV and load have a large influence when determining the volt-var curve of each inverter, so the amount of PV generation and load is set as the state. In the case of day-ahead training for real-time operation, learning is carried out by putting the \(i\)-th PV generation and load as the state predicted on the previous day. The set of states for the \(i\)-th inverter at time step \(t\) is denoted as \(s_{t}^{i}=\left\{P_{t}^{pv,i},S_{t}^{load}\right\}\), where \(P_{t}^{pv,i}\) represents the active power of the \(i\)-th PV generation, and \(S_{t}^{load}\) is the normalized apparent power of the load consumption. In addition, various pieces of system information can be helpful when determining the volt-var curve for the inverter; however, additional information is not used because the communication burden must be reduced for real-time control.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Vertex Deletion and Vertex Addition  
#### 3.1.1 Vertex Deletion  
Let \(G\) be a graph and \(v\in V(G)\) a vertex of \(G\). By \(G-v\), we denote the graph we obtain from \(G\) when removing \(v\) and all its incident edges, i.e.,  
\[G-v=(V(G)\setminus\{v\},E(G)\setminus\{\{v,u\}\mid u\in N_{G}(v)\}).\]  
With the graph operation of _vertex deletion_ defined, we now consider the tree-width and path-width of \(G-v\).  
Theorem 3.1: _For a graph \(G\) and vertex \(v\in V(G)\), it holds that_  
\[\begin{split} tw(G)-1&\leq tw(G-v)\leq tw(G)\quad \text{and}\\ pw(G)-1&\leq pw(G-v)\leq pw(G).\end{split}\]  
Proof: By removing \(v\) from every bag of a tree-decomposition (path-decomposition) for \(G\) and deleting all resulting empty bags, we obtain a tree-decomposition (path-decomposition) for \(G-v\). Consequently, we obtain \(\operatorname{tw}(G-v)\leq\operatorname{tw}(G)\) and \(\operatorname{pw}(G-v)\leq\operatorname{pw}(G)\). Adding \(v\) and its incident edges to \(G-v\) results in \(G\), such that the lower bounds follow from the upper bounds of Theorem 3.1.
###### Abstract  
This study aims to develop a dynamic pad monitoring system (DPMS) for measuring the surface topography of polishing pad. Chemical mechanical planarization/polishing (CMP) is a vital process in semiconductor manufacturing. The process is applied to assure the substrate wafer or thin film on wafer that has reached the required planarization after deposition for lithographic processing of the desired structures of devices. Surface properties of polishing pad have a huge influence on the material removal rate (MRR) and quality of wafer surface by CMP process. A DPMS has been developed to analyze the performance level of polishing pad for CMP. A chromatic confocal sensor is attached on a designed fixture arm to acquire pad topography data. By swing-arm motion with continuous data acquisition, the surface topography information of pad can be gathered dynamically. Measuring data are analyzed with a designed FFT filter to remove mechanical vibration and disturbance. Then the pad surface profile and groove depth can be calculated, which the pad's index PU (pad uniformity) and PELI (pad effective lifetime index) are developed to evaluate the pad's performance level. Finally, 50 rounds of CMP experiments have been executed to investigate the correlations of MRR and surface roughness of as-CMP wafer with pad performance. Results of this study can be used to monitor the pad dressing process and CMP parameter evaluation for production of IC devices.  
pad dressing; dynamic measurement; CMP; pad uniformity; pad lifetime Article
## 3 Results
### The Synergistic Effect between P and T for the NDVI  
The synergistic effect between P and Tmax had the strongest explanatory power for the NDVI, with Qnpmax varying from 0.41 to 0.81 (the mean value of Qnpmax was 0.68), followed by Qnpave and Qnpmin (varying from 0.38 to 0.77; the mean value of Qnpmin was 0.64). Spatially, Figure 5 shows that the explanatory power of the synergistic effect between P and T for the NDVI was enhanced from northwest to southeast as a whole. Among them, the synergistic effect between P and Tmin had the strongest explanatory power for the growth and distribution of the NDVI in Gansu, Heilongjiang, and Ningxia Huizu Zizhiqu (a). The NDVI in Shaanxi and Shandong provinces was mainly affected by the synergistic effect between P and Tave (b), while that in other areas, like in Liaoning, Jilin, Nei Mongol Zizhiqu, and other provinces was mainly influenced by the joint effect of P and Tmax (c). In addition, the explanatory power of P and T for the NDVI in Xinjiang Uygul Zizhiqu was generally weak, and the synergistic effect between P and Tmax had a greater explanatory power for the NDVI in this area than Tmin and Tave. The explanatory power of P and T for the NDVI in Shanxi province descended, with Qnpmax ranking at the top, followed by Qnpave and Qnpmin in sequence. However, the overall Q value was significantly lower than that of surrounding areas, which may be attributed to coal mining and other factors.  
The annual change rate of the explanatory power for the synergistic effect between P and T for the NDVI (d-f) shows that the overall explanatory power between P and T for the NDVI in the study area was weakened in the central area and strengthened in the east and the west. Specifically, the synergistic effect between P and T on the NDVI was weakened in both Shaanxi and Ningxia Huizu Zizhiqu, while the opposite occurred in Xinjiang Uygul Zizhiqu, Qinghai, and another five provinces in the eastern part of the study area (Heilongjiang, Jilin, Liaoning, Shandong, and Hebei). In addition, there were differences in the annual change rate of the synergistic effect between P and T. For example, Qnpmin declined in Gansu and Henan provinces, while Qnpave and Qnpmax rose in these areas; Qnpave and Qnpmin dropped in Shanxi, while Qnpmax rose.  
Figure 4: Spatial distribution of multiple correlation coefficients among NDVI, P, and T.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 4 Intra-Cavity PTS Gas Sensors  
Several dissimilar configurations for registering the PT-induced RI modulation have been developed over the years. One of the approaches involves inserting the gas sample into the resonator of a _probe_ laser. By exciting the gas sample via an auxiliary _pump_ source, the resulting RI change would directly influence the parameters of the _probe_ laser e.g., its output beam intensity or frequency.
## 6 Discussion
### Challenges and Future Directions
#### 6.3.3 Lack of a Comprehensive Multi-Scenario Baseline Dataset  
In the field of traffic prediction, despite the existence of high-quality datasets, such as the PeMS dataset, current data resources often focus on specific regions or types of roads (such as highways) and are not sufficient to comprehensively cover diverse traffic scenarios, such as urban streets, rural roads, or those in different countries and regions. Additionally, existing datasets often lack traffic flow data during changing weather conditions, holidays, or large events, as well as data from daily peak and off-peak periods, all of which are important factors affecting traffic flow. Effective traffic prediction models need to process multiple types of data, such as real-time location and speed information from floating car data, road network data, and exogenous data, like weather and population density. However, the lack of a unified and widely accepted baseline dataset makes it difficult to compare model performance between different studies, affecting the evaluation and validation of models.  
In future research, creating a comprehensive, diverse, and widely applicable standard dataset is crucial. It can improve models' accuracy and generalization ability, promote the reproducibility of research results, and further technological innovation.