esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Wireless Energy Transfer
#### 4.1.1 Harvesting Potential  
The input impedance of the harvester must match the transducer's output impedance to maximize extracted power. The PMU can only match the harvester's input resistance through MPPT but can not perform complex impedance matching because dynamically adapting complex impedances is hard to realize. In MPPT, the harvester only extracts energy from the source when its voltage exceeds a predefined fraction of its open-circuit voltage. Typically, piezoelectric sources are modeled as voltage sources with a nonzero internal series impedance. In that case, the load should match the source's internal resistance. We evaluate this assumption by measuring voltages and currents over the piezoelectric source with different load resistances. The results are shown in Figure 9. We see an approximately linear relationship between voltage and current, indicating that maximum power is extracted from the source if the MPPT threshold voltage is half the open-circuit voltage. This result confirms observations from [5; 16]. The same experiment was repeated with different carrier frequencies, clearly showing that maximum extractable power strongly varies with frequency. This shows the requirement for a dynamic MPPT mechanism compared to a fixed threshold voltage.  
Next, we ask how much power AC we can expect to extract at the sensor node at a certain distance. The selected carrier frequency strongly influences this power level. Frequencies that resonate in the structure enable more efficient power transfer. In Figure 9 (bottom), we show the maximum harvestable powers with optimal resistive matching for a band around the transducer's resonance from 200 kHz to 250 kHz. At 3 m, the best frequencies provided an output power of 4 mW. At that frequency, the reader has generated an input power of 141 mW, leading to a transfer efficiency of 2.8%. Repeating the measurement for multiple channels with varying distances shows that no clear trend can be observed. The maximum power over a 1 m channel is about 10 mW with roughly 8.3% transfer efficiency. The 2 m channel shows almost identical power levels to the 3 m channel. However, the frequencies that produced the highest output power varied strongly between channels. The results indicate that distance is not the dominant factor determining transfer efficiency. Strong frequency dependency suggests that positive interference patterns are essential. These are influenced by structure geometry and the exact placement of the transducers, which are generally unknown at the time of the system design.
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 4 Empirical Study  
To evaluate the efficiency of the CDR and the PSO-SVM-based scheduling method, we embedded the CDR into the simulation model, which was built according to an industrial semiconductor manufacturing system (a mixed 5\" and 6\" semiconductor manufacturing system of a semiconductor manufacturer in Shanghai) and conducted a series of simulation experiments. The simulation platform was built in the Tecnomatix Plant Simulation software 2022, which belongs to Siemens. The control layer and the function layer of the simulation platform are shown in Figure 6. In terms of experimental hardware, three Nvidia GeForce RTX 4000 GPUs (Nvidia, Santa Clara, CA, USA) were used to train and validate corresponding models.  
With an average WIP inventory of more than 80,000 pieces, the manufacturing system consists of 11 processing areas, which are oxidation, photolithography, injection, epitaxial growth, dry etching, deposition, sputtering, wet cleaning, and three non-processing areas, namely, the virtual machine, testing, and outsourcing. Among these processing areas, the oxidation area and the photolithography area are bottleneck processing areas. In addition, the production comprises more than 800 machines, belonging to five categories: single-workpiece processing equipment, batch-processing equipment, multi-workpiece processing equipment, cluster tools, and tanks.
## 2 Materials and Methods
### Equation Finding
#### 2.3.2 Derivation of the First-Degree Equation  
Data on ambient temperature, relative humidity, and DPT are gathered into vectors. Subsequently, a matrix A is utilized to represent the variables \(X\) and \(Y\) along with a constant term. The coefficients \(a\), \(b\), and \(c\) of the Equation (1) are then computed through the least squares' method using the matrix inverse.  
\[Z=aX+bY+c \tag{5}\]
## 3 Results and Discussion
### TGA Analysis of the Biopellets before and after Torrefaction  
Figure 12 compares the thermal decomposition and the corresponding weight loss of the prepared biopellets as a function of temperature before and after torrefaction. Regardless of the types of lignocellulosic raw materials used, the rate of weight loss of the torrefied biopellets was slower than that of the non-torrefied ones. The lower mass loss rate in TGA analysis of biopellets after torrefaction results from torrefaction's removal of moisture and volatile matter from the biopellets. This explains why the non-torrefied biopellets begin to lose weight before reaching 300 \({}^{\circ}\)C, whereas the torrefied biopellets do not commence weight loss until after surpassing 300 \({}^{\circ}\)C. Moisture and volatile matter are easily vaporized at relatively low temperatures, while fixed carbon is resistant to decomposition at high temperatures. It is widely recognized that moisture and volatile matter are the two primary components that contribute to the mass loss of biopellets during thermal decomposition. In addition, torrefaction increases the fixed carbon content of biopellets. Fixed carbon is a non-volatile component that does not decompose during thermal decomposition. Therefore, the increase in the fixed carbon content also contributes to the lower mass loss rate of the torrefied biopellets. Notably, MT, with the highest fixed carbon content of approximately 35% after torrefaction, displayed the slowest thermal decomposition characteristics, retaining 60% of its weight even at temperatures as high as 800 \({}^{\circ}\)C. This can be attributed to MT's elevated fixed carbon content, rendering it more resistant to decomposition at elevated temperatures.  
Figure 11: Ignition and combustion time of the biopellets before and after torrefaction: (**a**) ignition time; (**b**) combustion duration.  
Our TGA results are in agreement with those of [PERSON] et al. (2012), who found that torrefied biomass exhibited slower thermal decomposition rates and higher residual mass compared to non-torrefied biomass [54].
# Dynamic SDN Controller Load Balancing
## 5 DCC Two Phase Algorithm
### Phase 2: Decreasing Load Differences Using a Replacement Rule
: _Replacement Rule. In a partition \(\Theta\), a controller \(c_{i}\) has incentive to replace its coalition a with controller \(c_{j}\) from coalition \(b\) (forming the new coalitions \(a^{new}=\left(a^{old}\backslash c_{i}\right)\cup c_{j}\) and \(b^{new}=\left(b^{old}\backslash c_{j}\right)\cup c_{i}\) if it satisfies both of the following conditions: (1) The two clusters \(a^{new}\) and \(b^{new}\) that participate in the replacement do not exceed their capacity \(K\cdot P\). (2) The RV satisfies: \(RV\left(C_{i},C_{j},a,b,Cnt\right)<0\) RV defined in Equation (14))._  
In order to minimize the load difference between the clusters, we find iteratively a pair of controllers with minimum \(RV\), which then implement the corresponding replacement. This is repeated until all \(RV\)'s are larger than or equal to zero: \(\mathrm{RV}\left(C_{i},C_{j},a,b,Cnt\right)>0\). Procedure 1 describes the replacement procedure. Regarding the time complexity of Line 1 in Procedure 1, i.e., find the best replacement, it takes: \(\frac{M}{k}\left(k-1\right)+\frac{M}{k}\left(k-2\right)+...+\frac{M}{k}\left( k-\left(k-1\right)\right)=\frac{M^{2}}{k^{2}}\cdot\frac{k\left(k-1\right)}{2}= \frac{M^{2}\left(k-1\right)}{2k}=o\left(M^{2}\right)\) time. Line 3 invokes the replacement within \(O(1)\) time. Since in each iteration the algorithm chooses the best solution, there will be a maximum of \(M/2\) iterations in the loop of Lines 2-5. Thus, in the worst case Procedure 1 takes an \(O(M^{3})\) time complexity. In practice, the number of iterations is much smaller, as can be seen in the simulation section. Figure 4: Clusters’ loads after replacement on different sides with reference to the average. Figure 3: Clusters’ loads after replacement on the same side with reference to the average. ```
1:\(CL=\{cl_{1},cl_{2},...,cl_{k}\}\) Clusters list, where \(CL_{i}=\{cl_{i1},cl_{i2},...,cl_{i(M/K)}\}\) distance constraint \(Cnt\)
2:\(CL=\{cl_{1},cl_{2},...,cl_{k}\}\) Clusters list after replacements procedure:
3:\(bestVal\leftarrow\min_{c_{i}\in CL_{x},c_{j}\in CL_{y},x\
eq y,1\leq x,y,z\leq k} RV(c_{i},c_{j},CL_{x},CL_{y},Cnt)\)
4:while\(bestVal<0\)do
5: invoke replacement \(RV\)
6:\(bestVal\leftarrow\min_{c_{i}\in CL_{x},c_{j}\in CL_{y},x\
eq y,1\leq x,y,z\leq k} RV(c_{i},c_{j},CL_{x},CL_{y},Cnt)\)
7:endwhile
8:return\(CL\)
```  
**Procedure 1** Replacements  
**input:**  
\(\quad\
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s PDE-Based Proposal  
[PERSON] et al. [65] proposed a design model-based inspection method to address IR problems of 3D range images. Their method combined the DE algorithm and an EFT-based 3D point descriptor to improve the IR results. This point property-based DE algorithm was named PDE, in which the control parameters \(F_{i}\) and \(CR_{i}\) were set adaptively as proposed in [66]. Initially, the contributed algorithm randomly selects three point pairs \(n\) (\(n<n_{p}\)) times to form the candidate solution pool (CSP). The solutions in CSP are approximate and provide pre-registration for the two tested image models. In practice, with the increasing complexity of the geometry of the image the rate of false solutions becomes higher. Thus, PDE generates the population from two different sets. One set, \(X_{1}\), is randomly generated, and the other one, \(X_{2}\), is generated based on the CSP. Additionally, their method made use of the \(k\) best individuals of \(X_{2}\) to replace the \(k\) worst individuals of \(X_{1}\) as the current population. This strategy was aimed at speeding-up the convergence of the algorithm. Moreover, the mutation strategy proposed in [66] was also included in PDE. In the experimental section, the authors conducted several IR tests and it was considered both synthetic and range image datasets. PDE was compared against three deterministic (non-coarse) algorithms and other five heuristic algorithms. They validated the robustness of the EFT-based point descriptor, and the resultant outcomes demonstrated it suitability to achieve reliable IR results. Moreover, according to the analysis of the statistical results, in most of the cases, PDE showed significant better results compared to the other five heuristic-based IR algorithms. Finally, the authors concluded that PDE was insensitive (robust) to outliers, and PDE can obtain the global optima solutions successfully for all tested situations. However, the performance of PDE is dependent on the image content because the lower the similarity between the images, the more incorrect the correspondence of the image points.
## 3 Intermittent Receiver-Driven Data Transmission  
### MAC Protocol  
In IRDT, each receiver sends its own ID to inform other nodes that they are ready to receive a data packet. A sender node waits for a receiver ID, and when it acquires an ID from an appropriate receiver, it establishes a link with it by returning an SREQ packet. After obtaining an acknowledge packet for SREQ (RACK), the sender transmits a data packet and terminates the communication upon receipt of an acknowledge packet for the data (DACK). Carrier sense multiple access with collision avoidance (CSMA/CA) is used for sending packets. However, especially when a node transmits an ID packet or an SREQ packet, it terminates the transmission of those packets if the channel condition is busy. If the channel is idle, it transmits an ID packet or an SREQ packet after a random backoff period. Otherwise, when it transmits a data packet, a RACK packet, or a DACK packet, a binary exponential backoff mechanism is utilized.  
Here, all nodes contain two timers, which are set immediately before starting to wait for an SREQ packet, a RACK packet, a data packet, or a DACK packet. \(T_{ws}\) is the time allocated for waiting for an SREQ packet following the transmission of an ID packet. Furthermore, \(T_{wd}\) is the time allocated for waiting for a data packet, a RACK packet, and a DACK packet. After the transmission of a RACK packet, an SREQ packet, or a data packet, respectively, as shown in Figure 3. If a time \(T_{ws}\) passes before receiving an SREQ packet after the transmission of an ID packet, the receiver node enters sleep mode, as shown in the figure. The receiver node also enters sleep mode if the period \(T_{wd}\) before receiving a data packet after transmitting a RACK packet extends beyond a certain limit. On the side of sender nodes, if  
Figure 3: \(T_{ws}\) and \(T_{wd}\) timers in IRDT.  
a RACK packet and a DACK packet are not received from the receiver after a lapse of \(T_{wd}\), they begin to wait for reception of another appropriate ID packet. Note that, for the CSMA/CA backoff algorithm, \(T_{ws}\) is shorter than \(T_{wd}\).  
The decision of the sender regarding whether to send an SREQ packet is taken on the basis of its routing protocol. In this way, a sender node can select a receiver node flexibly, which can enhance the communication reliability and save considerable amounts of energy. Therefore, in the network layer, the routing protocol should be designed to use multiple receiver nodes in a flexible and efficient manner. A specific example is shown in Figure 1(b), where receiver nodes 1 and 2 are in intermittent operation mode. Sender node 3 checks the ID received from node 2 and accepts node 2 as an appropriate receiver.
## 2 Materials and Methods  
### Materials  
A sample of slag generated in the smelting process of copper (S1) and one generated from the smelting process of primary/secondary lead concentrates (S2) along with a synthetic ferro-silicate slag (S3) are studied in this research. S3 is included in this research to account for slags with extreme chemical compositions (low silica and alumina and high iron oxide contents) in order to investigate how such compositions will perform in cementitious mixtures. The alkali content in this slag is also considerably lower than in S1 and S2. Due to the high heavy metal and sulphates content of the slags, they are first pyrometallurgically treated, as described here, in order to recover valuable metals and remove toxic heavy metals and sulphates. S1 was treated in a top-submerged lance furnace by combustion at 1200-1300 \({}^{\circ}\)C (20% coke addition) for 4 h, followed by rapid cooling in water. The rationale behind rapid cooling, which is applied to all three slag sources, is to avoid crystallisation and promote chemical reactivity [14; 15; 22; 23]. The treated slag is referred to as TS1 (short for treated S1). S2 was treated in an electric-car furnace at a similar ultimate temperature (with 5% coke added) for 2 h and then rapidly cooled in water (treated slag labelled TS2). The synthetic slag (S3) was first produced by mixing metallurgical materials such as Waelz kiln slag, Waelz kiln oxide, PrSnSb oxide drops, PbAs converter dust and silica sand. It was then combusted in a top-submerged lance furnace (15% coke addition) at a similar temperature for 3.5 h, followed by rapid cooling (TS3). Table 1 shows the oxide composition of the slags, along with the metal contents before and after treatment. It is observed that all slags originally contained considerable amounts of zinc and lead, which are successfully reduced and recovered by the applied treatments (compare the zinc oxide and lead contents of each slag in the raw and treated states). Heavy metals are also reduced as a result of treatment. However, the high pH of the cement pore solution can promote the leaching of elements such as lead [24; 25; 26], which needs to be measured and evaluated via batch-leaching experiments, as laid out later in Section 2.2. From the chemistry standpoint, the slags are primarily composed of ferro-silicates, with the lowest iron oxide content and the highest silica content belonging to TS1 among the three slags (16.9% and 50.6%, respectively). TS3, in contrast, contains the highest iron oxide and the lowest silica content (31.0% and 21.3%, respectively), and TS2 contains intermediate amounts of each oxide (21.6% and 39.0%, respectively) in comparison with TS1 and TS3. TS2 also contains the highest calcium oxide and alumina contents in comparison
## 3 Model Design
### Non-Working Time
#### 3.2.1 Sensitivity of Interest  
In non-working hours, people's travel is more casual and unconstrained, so one of the reasons for people to travel may be interests. Therefore, the two nodes with the same interest are more likely to meet the two nodes with different interests. As we can see, interests lead to the motion of nodes. However, it is impossible that the movement of a node depends entirely on interests, so the probability that a node moves due to interests is called the sensitivity of interest in this paper. When people use mobile devices, our interests can be easily obtained and analyzed from our related operations. The node's sensitivity of interest is an M*1 probability vector \(P_{i}=\left[p_{i1},\ldots,p_{iM}\right]^{T}\), where \(A^{T}\) represents the transpose of a matrix. \(p_{ij}\) indicates the probability that node \(i\) will move because of the \(j^{\text{th}}\) interest. Indeed, \(p_{ij}\) is used to compare the sensitivity of nodes to different interests. Hence, without loss of generality, we technically define \(\sum\limits_{j=1}^{M}p_{ij}=1\) for \(\forall i\), and \(P_{i}\) can be considered as a discrete probabilistic distribution. The weight of interest \(j\) occupying node \(i\) is defined as:  
\[W_{ij}=\frac{p_{ij}}{\sum\limits_{i=1}^{m}p_{ij}} \tag{3}\]  
Node \(i\)'s sensitivity to all interests is defined as:  
\[p_{i}=\sum\limits_{j=1}^{M}W_{ij}\cdot P_{i} \tag{4}\]  
When selecting the next hop node, the interest sensitivity of all neighbor nodes around the source node \(i\) is calculated in turn, and the difference of interest sensitivity between the node \(i\) and the neighbor node \(j\) is compared. Select one or more nodes with the smallest difference as the next hop node to improve the message forwarding success rate. The difference is defined as: \(\bar{P}=\left|p_{i}-p_{d}\right|\)
# Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation
###### Abstract
The reference model is also proposed to resolve the performance accuracy of the FPGA system for peripheral designs. (3) Several interesting results are found through case studies: firstly, using reduced input data set could produce inaccurate performance evaluation results, compared with the real input data set; secondly, FPGA-aware design space exploration could be very useful, i.e., small configuration change results in noticeable performance variation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stage** & **Method** & **Speed** & **Accuracy** & **Configurability** & **Design Efforts** & **Cost** \\ \hline \multirow{2}{*}{Early-stage} & Software simulator & Low (100 KHz-1 MHz) & Low & High & Very low & Low \\  & Accelerated simulation & High (N/A) & Very low & High & Very low & Very low \\ \hline \multirow{3}{*}{Late-stage} & RTL software simulator & Very Low (100 Hz-10 KHz) & High & Medium & Medium & Low \\  & RTL emulator & Media (500 KHz-2 MHz) & High & Medium & Medium & High \\  & FPGA simulator & High (4 MHz-80 MHz) & High & Medium & Medium & Medium \\  & Test chip (real chip) & Very High (500 MHz-2 GHz) & Very high & Low & Very high & Very high \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Typical full system performance simulation methods for modern out-of-order cores.
## 1 Introduction
### Effects on the Immune System  
During spaceflight, exposure to microgravity could potentially induce modifications in the cellular function of the human immune system. For example, it has been hypothesized that microgravity exposure could lead to an increase in the production of inflammatory cytokines [148] and stress hormones [149; 150], alterations in the function of certain cell lines (NK cells [151; 152], B cells [153], monocytes [154], neutrophils [154], T cells [5; 155]), and impairments of leukocyte distribution [156] and proliferation [155; 157; 158]. The resultant immune system dysfunction could lead to the reactivation of latent viruses such as Epstein-Bar Virus (EBV), Varicella-Zoster Virus (VZV), and Cytomegalovirusovirus (CMV) [31; 32]. Persistent low-grade pro-inflammatory responses microgravity could lead to space fever. [159] Studies are currently underway to evaluate countermeasures to improve immune function and reduce reactivation of latent herpesvirususes [33; 160; 161; 162]. Microgravity exposure could also lead to the development of autoantibodies, predisposing astronauts to various autoimmune conditions [163; 163]. (Table S2) Most importantly, studies have shown that bacteria encountered within the space environment appear to be more resistant to antibiotics and more harmful in general compared to bacteria encountered on Earth [164; 165]. This is in addition to the threat of novel bacteria species (e.g., _Methylobacterium animali_ sp. Nov. [76]) that we have not yet discovered.  
Upon returning from the space environment astronauts remain in an immunocompromised state, which has been particularly problematic in the era of the COVID-19 pandemic. Recently, NASA has recommended postflight quarantine and immune status monitoring (i.e., immune-boosting protocol) to mitigate the risk of infection [77]. This is similar to the Apollo and NASA Health Stabilization Programs that helped establish the preflight protocol (pre-mission quarantine) currently used for this purpose.  
Further research is required to understand the mechanisms of antibiotic resistance and the modifications in inflammatory cytokine dynamics, in order to develop immune boosters and surrogate immune biomarkers.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 2 Materials and Methods
### Equipment  
The Catapult S5 OptimEye GNSS system was used in the present study. Accessing GPS and Global Navigation Satellite System (GLONASS) satellite constellations, this GNSS system ensures high-quality data even in challenging performance environments ([[https://www.catapultsports.com](https://www.catapultsports.com)]([https://www.catapultsports.com](https://www.catapultsports.com)) (accessed on 1 October 2020)). Eight 10-Hz GNSS units were worn at once on each participant during the test (Figure 1). The eight GNSS units were placed near the mid-back area using a custom-made strap, with slightly different positions. While the tightness of strap was adjusted to fit individual body sizes, the relative positions of the GNSS units on the strap remained consistent across all participants.