esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hierarchical Optimization Algorithm
### Optimization Model  
According to the analysis in Section 2, the frequency of the mission operation involves four terms: the beat-note \(f_{\text{het}}\), the USO \(f_{\text{USO}}\), the pilot-tone signal \(f_{\text{PT}}\), and the ADC sampling\(f_{\text{ADC}}\) frequencies. The variable \(f_{\text{het}}\) is affected by the Doppler shift and changes dynamically over time, while the remaining three terms remain unchanged. The variation range of the inter-satellite beat-note frequency\(f_{\text{het}}\) is determined by the lower and upper bounds of the beat-note frequency, namely \(f_{\text{lower}}\), and \(f_{\text{upper}}\). Therefore, the optimization goal is to make the sideband readout displacement noise meet the mission requirement, minimize the total readout displacement noise, and maximize the feasible range of beat note frequency by reasonably allocating the values of each frequency or frequency band, and modulation depth \(m\), respectively. The optimization model is as follows:  
\[\begin{split}\Gamma_{1}(f_{\text{upper}},f_{\text{USO}},f_{\text{PT }},f_{\text{ADC}},m,P_{\text{local}})&=\min\bigl{(}\delta x^{\text{ total}}\bigr{)}\\ \Gamma_{2}(f_{\text{upper}},f_{\text{lower}})&=\max \bigl{(}f_{\text{upper}}-f_{\text{lower}}\bigr{)}\\ s.t.\\ \left\{\begin{array}{l}\delta x^{\text{sideband}}<\frac{1}{10} \delta x^{\text{total}}\\ n_{1}*f_{\text{ADC}}=f_{\text{USO}}\\ n_{2}*f_{\text{PT}}=f_{\text{USO}}\\ f_{\text{ADC}}>2f_{\text{upper}}\\ |f_{\text{ADC}}-f_{\text{PT}}|<f_{\text{lower}}\\ f_{\text{lower}}>=2\;\text{MHz}\\ f_{\text{upper}}<=25\;\text{MHz}\\ f_{\text{USO}}<5\;\text{GHz}\\ f_{\text{PT}}<98\;\text{MHz}\\ 0.44<m<0.61\end{array}\right.\end{split} \tag{9}\]  
where \(\Gamma_{1}\) and \(\Gamma_{2}\) represent the two objective functions.
# Effects of Corner Set--Backs on Wind Loads and Wind Induced Responses of Rectangular Tall Buildings
## 3 Analysis of Test Results
### Local Wind Force
#### 3.2.2 Power Spectral Densities of Local Wind Force Coefficients  
Figure 10 presents the power spectra densities of local wind force coefficients at 2/3 H of the five models. As shown in Figure 10a, the power spectra densities of drag force coefficients vary gently with the reduced frequency. Corner set-backs have little effects on the power spectra densities of drag force. From Figure 10b, it can be seen that as the reduced frequency is approaching the Strouhal number of 0.10, the spectrum of the benchmark model has a spike. Although narrow-band characteristics are still presented in the power spectra of lift forces of 5%, 10%, 15% and 20% corner set-back models, their peak values are  
Figure 9: The local wind force coefficients. (a) Mean drag force coefficients along height. (b) RMS drag force coefficients along height. (c) RMS lift force coefficients along height.  
lower than that of the benchmark model at different extent, which indicates that the corner set-backs can weaken the energy of the vortex shedding. The power spectra of 20% recessed model have the lowest peak and the widest frequency band. It is also found that the peak values for the power spectra of 10% and 15% corner set-back models are similar to that of the benchmark value, even larger than that of the 5% and 20% corner set-back models.  
Figure 10: The power spectra of local wind force coefficients at 2/3 H of the five models. (**a**) The power spectra of drag force coefficients. (**b**) The power spectra of lift force coefficients.
# OISE: Optimized Input Sampling Explanation with a Saliency Map Based on the Black-Box Model
## 4 Experimental Results and Discussion
### Experimental Results

Table 1: ILSVRC2012 Val classification and positioning error (%) of VGG-16, GoogleNet, and Resnet50 (the lower the better). OISE achieves excellent positioning error without compromising classification performance. Except for OISE, all of them are white-box models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Model** & **Accuracy** \\ \hline ILSVRC2012 & VGG-16 & 75.26 \\  & Resnet50 & 84.43 \\ \hline \multirow{3}{*}{PASCAL VOC 2007} & VGG-16 & 78.64 \\  & Resnet50 & 86.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean accuracy (%) of ILSVRC2012 and PASCAL VOC 2007 on different models. Except for OISE, all of them require white-box models. Figure 3: Average decrease and increase. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **CAM** & **Grad-CAM** & **OISE** \\ \hline Average Decrease (\%) & 49.6 & 47.8 & 47.4 \\ Average Increase (\%) & 18.9 & 19.6 & 19.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results on recognition (lower is better in Average Decrease, higher is better in Average Increase). The method can explain the reason to people in a clearer way, and is more convenient compared to the white-box method, for arbitrary networks, without internal access, without reimplementing each network architecture, and for existing image networks, and is considered to be completely black-box. This method provides a more precise positioning capability than traditional CAM series methods. Grad-CAM is unable to perform multi-target detection, and Score-CAM improves on Grad-CAM's shortcomings with more accurate localization and a poor classification task. OISE has excellent performance in both localization and classification tasks. However, the method also has some limitations, such as that it requires a lot of computation when generating and updating the mask, and the optimal parameters for optimizing the mask may be different for each update due to the use of gradient descent, which results in a different final recognition saliency range each time. More recognition results are shown in Figure 5.  
Figure 4: Identification results. (**a**) The original image. (**b**) The result of using the OISE algorithm. (**c**) The result of using CAM. (**d**) The result of using Grad-CAM. The visual description of using OISE gives a good description in terms of importance. The reason why the image was identified as a bulldog was pointed out.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 1 Introduction
Though [23] considered resource allocation for blocklength and power, this did not include a latency constraint because it was based on infinite-blocklength coding and a Poisson field of interferers. Thus, achieving low-latency short-packet communications over a large spatial scale is still an open problem. In this paper, we focus on long-distance and short-packet communication. We aim to minimize the overall error probability while limiting the power cost under a time constraint. We prove the convexity of the overall error probability with respect to the blocklength and power under a particular condition when the relays are placed equidistantly. We also find a blocklength power allocating scheme to minimize the error probability, where more power should be allocated to the relays that have fewer encoded symbols. By scaling the problem conditions, we simplify the problem and obtain an analytical expression of the optimal error probability. Thereby, we find that the optimal relaying strategy is to allocate the blocklength and power equally to each relay. Then, we analyze the theoretical limits of the optimal solution. The discussion includes two cases, where the number of relays is fixed and the number of relays is variable. When there is a fixed number of relays in the communication system, we find that the error probability decreases superexponentially with the blocklength and the logarithm of the signal-to-noise ratio. When the number of relays is not fixed, we find there is an optimal number of relays in the relaying communication system. Therefore, the performance of relay communication has a theoretical upper limit. Under certain circumstances, no matter how many relays are added, the performance of the communication system cannot be improved. Based on a numerical simulation, we solve the optimization problem and compare it to our theoretical result. The rest of this paper is organized as follows. Section 2 presents the system model. In Section 3, we form an optimization problem for the overall error probability in terms of the distance, blocklength, and signal-to-noise ratio. Then, we give a theoretical solution for the optimal bit-error rate. In Section 4, we analyze the theoretical asymptotic limit and approximate results of the optimal solution. In Section 5, we verify the correctness of our conclusions by a numerical simulation. Finally, conclusions and future suggestions are provided in Section 6.
# UAV-Assisted Hybrid Scheme for Urban Road Safety Based on VANETs
## 4 Experiments
### Packet Delivery Ratio (PDR):  
Figure 7 shows the variation of PDR for the UAVa and AODV protocols. Here, we can consider according to the increasing number of nodes that packet delivery ratio is increased for both AODV and UAVa protocols, but for UAVa protocols packet delivery ratio is better than AODV. The highest PDR is achieved for UAVa, again proving its effectiveness. It depends on both vehicles and RSUs or UAVs (both RSUs and UAVs can carry packets), which can be checked at all times in the data transmission.
## 5 Experiment and Analysis  
### Simulation Experiments  
#### 5.1.1 Pusher Voltage-Motor Speed Coupling Model  
Electric actuators are a class of devices that convert the rotational motion of a motor into linear motion and comprise a motor, gear structure, screw, and other components. [PERSON] et al. studied electric actuators' synchronous control systems and derived the electric actuator simulation structure that forms the basis for the coupled actuator voltage-motor speed model in this paper (Figure 11) [30].  
After the system outputs the motor speed, it converts it into the push rod retraction, joystick tilt angle and, finally, the harvester traveling speed. A second-order system to describe the controlled object can be approximated by neglecting secondary factors under certain accuracy conditions [31]. After data processing, the system transfer function is expressed as follows:  
\[G(s)=\frac{4531}{(s^{2}+0.335s+0.0108)} \tag{18}\]  
The Bode plot in Figure 12 was plotted using MATLAB according to the transfer function. The ideal suppression of harmonics was in the high-frequency region, and resonant spikes were successfully suppressed at resonant frequencies. The gain margin of the system was approximately infinite and the phase margin was close to satisfying \(\varnothing m\approx\pi-\beta\pi/2\), \(\beta\) was the system damping ratio, and the above transfer function corresponds to a damping ratio of 1.675. Therefore, we could conclude that this closed-loop system's accuracy and stability are high.  
Figure 11: Electric actuator simulation model.  
Figure 12: Bode Plot.
# Soil Hg Contamination Impact on Earthworms'
## 4 Discussion  
### Hg Contamination in Soils and Its Effect on Earthworms  
Mercury is a highly toxic heavy metal occurring in soils worldwide due to industrial activity. However, few studies have investigated the impact of Hg on soil fauna and their microbiome, and artificially Hg-spiked soils have typically been used instead of soils from Hg-contaminated sites [46]. In this study, we demonstrated the impact of Hg contamination and accumulation in earthworms using long-term Hg-contaminated soils [24]. Whereas laboratory experiments using Hg-spiked soil often aim for concentrations of up to 500 mg kg\({}^{-1}\)[47; 4], the total Hg concentrations in our incubated soils (36 mg kg\({}^{-1}\)) were in the range of total Hg concentrations found in other Hg-polluted sites, such as in a leachate-contaminated soil (THg 1.3 \(\mu\)g g\({}^{-1}\) dw, [48]) or a landfill (THg 0.9-97.5 \(\mu\)g g\({}^{-1}\), [23]).  
Interestingly, the impact of Hg contamination on the earthworms differed between our two experimental setups. In experiment 1, the earthworms gained biomass during the incubation, even when incubated with a 5-cm layer of Hg-contaminated soils in addition to the mixture of garden soil and quartz sand, which is considered highly suitable for earthworms [13]. Contrastingly, in experiment 2, the natural soil collected in the vicinity of a long-term Hg-polluted site did not meet the physiological needs of the incubated earthworms, as their biomass decreased during the incubation in both the Hg-contaminated and uncontaminated treatments. The direct impact of Hg on the earthworms' biomass might have been outweighed by the rich soil quality in experiment 1 and the  
Figure 8: Heatmap of abundant and responsive bacterial and fungal OTUs of each soil and earthworm gut part that was significantly increased or depleted in the Hg-contaminated treatment in experiment 2. The heatmap was computed using the responsive index of each OTU abundance \(\times\) log\({}_{2}\)-fold change increase or decrease in the Hg-contaminated treatment in comparison to the uncontaminated one. Sto = stomach, UgT = upper gut tissue, UgS = upper gut soil, LgT = lower gut tissue, LgS = lower gut soil.  
poor quality in experiment 2. However, the accumulation of both THg and MeHg in the earthworms compared with in the soil matrix was considerable in both experiments, in line with observations from previous field studies of a correlation between Hg bioaccumulation and soil total Hg, organic matter and clay contents [23; 27; 48]. Furthermore, the Hg and MeHg bioaccumulation might have affected the earthworms over a longer period than the incubation duration, for example by impacting their burrowing behavior or respiration [49].
## 2 Materials and Methods
### Micro-CT  
The whole left hind limb was dissected with the knee straight after cardiac perfusion with 4% PFA and stored in the same PFA solution at 4 \({}^{\circ}\)C until image acquisition. Micro-CT was performed on the knee joint using a Quantum FX scanner (Perkin-Elmer, USA) at an isotropic voxel size of 20 \(\upmu\)m (X-ray source voltage, 90 kVp; current, 160 \(\upmu\)A; 10 \(\times\) 10 mm field of view; 3 min acquisition time). Morphometric analysis was performed using CTAn software v.1.13 (Bruker, USA) for the cancellous bone in the proximal tibial epiphysis (stack of 40 axial-sections; 0.8 mm height) to evaluate the following bone structural parameters: bone volume (BV), bone volume fraction (bone volume/total volume, BV/TV), trabecular number (Tb.N), trabecular separation (Tb.Sp), and bone mineral density (BMD).
# Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
## 2 System Overview of Proposed Solution
### Proposed Elimination of Temporal Domain  
It is worth noting that the proposed feature extraction solutions in Sections 2.1 and 2.2 generate a sequence of feature vectors per video sequence. A classification model for such a sequence can be generated using deep learning or, more specifically, long short-term memory (LSTM) [18] (or even 1D-CNNs). However, in this work, to reduce the computational complexity, we propose to collapse all feature vectors of a video sequence into one feature vector. For the HEVC feature variables, the temporal domain can be eliminated by computing the averages and the standard deviations of each feature variable. This results in a feature vector with a length of 67x2 variables for each video sequence.  
Figure 3: Appending HEVC features with motion-vector-based features.  
As for the phases of the motion vectors of a given video frame, the temporal domain is eliminated by projecting them using PCA, as illustrated in Figure 3. Consequently, all phases are represented using a single 1D list with a length equal to the number of frames in a video sequence. The length of the final feature vector per video sequence is equal to the number of video frames in the sequence minus one, as motion estimation starts from the second video frame.  
Once the temporal dependencies of the feature vector sequence are eliminated using this approach, classical machine learning techniques can be used for model generation. In this work, we used random forests.
# Metal-Organic Frameworks as Promising Textile Flame Retardants: Importance and Application Methods
## 2 Textile Finishes
### Environmental Impact of Flame-Retardant Compounds  
Of the two groups, halogenated organic flame retardants (organochlorines and organobromines) are the most effective in inhibiting flame spread because they are able to neutralize free radicals produced during the combustion process [48]. For phosphorus-based flame retardants, it is impossible to describe a single mechanism [49]. However, one of the main mechanisms has been described by [PERSON], [PERSON] [4], who stated that the flame retardancy of phosphorus compounds occurs through the formation of a carbon layer generated by phosphoric acid, which protects the material from oxygen, thus preventing the formation of flammable gases.  
Halogenated FRs have long been used because they are effective primarily in the gas phase and have been widely developed and marketed. However, they have been characterized as toxic and bioaccumulative [50]. They are therefore classified as persistent organic pollutants, as is the case with hexabromocyclododecane [51].  
The substances released during the incineration of halogenated compounds are generally organohalogenates, especially organochlorines and organobromines, which are known to be highly toxic and carcinogenic [33].  
When hazardous wastes are released into the air, water, or land, they can quickly disperse throughout the environment, contaminating more areas and causing greater health risks [52]. Thus, there is growing concern about the environmental impact of chemical agents released into the biosphere and the proper disposal of potentially toxic materials.  
With increasing environmental responsibility, halogen-based flame retardants are gradually decreasing in the market, and halogen-free additives have become a popular research topic due to their advantages such as less smoke and safety due to non-toxicity. In view of this, conventional flame retardants have become the target of close attention due to their harmful aspects to health, opening space for the study and development of new solutions that offer efficiency and the desired performance.
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
## 1 Introduction
### Smart Homes
#### 1.1.4 IoT-Based Supply Chain Management  
IoT seeks to simplify the actual processes of business and information systems [11]. One can easily trace items in a supply chain from the point of manufacturing to the point of final distribution by using sensor technologies, such as RFID and NFC. Real-time data is recorded and processed for future reference. RFID tags connected to cargo can also record information regarding product quality and ease of use.  
The proposed mechanism (SDS) achieves the required goals in five steps:  
* Step 1: The service is separated into patterns and the attributes of each pattern are examined before using the utility function to determine the utility value for each network feature.
* Step 2: Network attribute weights are calculated using the link selection attribute (LSA). Based on this, signal inference is completed.
* Step 3: The network attribute score is calculated using the network attribute utility and weights.
* Step 4: Network settings for different scenarios are calculated using the LSA.
* Step 5: Based on the evaluation of network attributes, unpleasant networks are prioritized. This allows the user to select the network with the highest score.  
The key contributions of this work are as follows:  
* The link selection attribute (LSA) specifies the network selection criteria that match the predefined values from the data corpus. This selection strategy ensures that only the best network is selected. This mechanism has been explained in Section 3.2 with the help of Figure 3.
* The performance of IoT devices in a heterogeneous network was analyzed by calculating results in terms of round trip time, network throughput, and energy consumption.  
* The results were obtained by simulation with the NS2 simulator.
* The proposed strategy allows users to choose the most appropriate network, improve interoperability between devices, and reduce unnecessary handovers between different networks.
* Finally, the results are compared to state of the art protocols, such as high-speed packet access (HSPA), content-centric networking (CCN), and dynamic source routing (DSR).  
The rest of this paper is divided into further sections. The current literature on heterogeneous networks is placed in Section 2. Section 3 contains a description of the proposed model, which accomplishes the target output in five steps. Performance analysis is described in Section 4. The conclusions and future research directions are given in Section 5.
# Widely-Tunable Ring-Resonator Semiconductor Lasers
## 1 Introduction
The advantage of this approach is that the alignment tolerances are significantly reduced, as devices are defined using lithography alignment marks after bonding of larger III/V pieces. Such an approach is currently widely used by both academia (e.g., University of California Santa Barbara (UCSB) [5; 8], Ghent University [9; 10]) and industry (Intel [11], Juniper Networks (formerly Aurrion) [12], Hewlett Packard Enterprise (HPE) [13]). This approach still has a theoretical disadvantage, as it uses expensive III/V wafers, and there is a size mismatch between typical Si wafers (12\({}^{\prime\prime}\)), and typical III/V wafers (3\({}^{\prime\prime}\)). Due to this reason, direct growth of III/V on silicon is currently being actively researched, with some very impressive demonstrations [14; 15]. Direct growth has a potential to further reduce cost, but currently still has problems with lifetimes of the devices, despite using quantum dots, that are significantly more resilient to defects and dislocation impairments, than quantum wells. Direct growth, although a topic of great current interest, lies outside the scope of this manuscript. All the devices demonstrated in this manuscript use III/V quantum wells bonded to patterned SOI wafers. This approach gives the best of both worlds: high-gain from optimized III/V materials, and superior passive and waveguide technology of the SOI platform. The paper is organized as follows. In Section 2, we introduce basic concepts of laser tuning utilizing Vernier effect and ring resonators, and provide theoretical considerations on the influence of ring resonators to laser linewidth and other key parameters. In Section 3, we compare tuning and linewidth performance of a number of laser structures that utilize two different ring lengthsfor achieving wide tunability. In Section 4, we introduce methods to further improve the linewidth: use of an on-chip external cavity, and use of an on-chip high-\(Q\) resonator. We study their placement, and performance influence, and select a high-\(Q\) ring internal to the laser cavity as the optimal design. We then demonstrate that tuning of lasers with three rings of different circumference is straightforward, provided the PIC has on-chip monitor photodetectors. Lastly, we give a brief overview of widely-tunable lasers in literature in Section 5, and give conclusions in Section 6.