esa-sceva/satcom-chunk-collection huggingface dataset:
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
###### Abstract  
This paper presents a dedicated digital signal process (DSP) for four pulse amplitude modulation (PAM4) SerDes receivers. It is targeted to implement data recovery and adaptive equalization under ultra-high-speed and large channel attenuation with a small area and high power efficiency. The DSP consists of a clock data recovery (CDR), a 16-tap feed forward equalizer (FFE), a 1-tap decision feedback equalizer (DFE), and an automatic adaptation engine. An adaptive least mean square (LMS) algorithm is utilized to make the system more intelligent in calculating tap coefficients of the FFE and DFE. To address the timing limitation associated with traditional digital DFE that cannot handle large amounts of parallel data at a high speed, speculative techniques and a customized 4-to-1 multiplexer (MUX) unit are employed to remove the summation time and reduce the selection time, respectively. A first-order sigma-delta modulator is used to replace the traditional moving average to calculate average voltages, which could prominently save the hardware resources and power consumption. Additionally, the influence of input quantization resolution on the equalization ability is analyzed. Implemented in a 28-nm CMOS, the DSP could compensate for up to 33-dB loss at 100 Gb/s with a power consumption of 7.22 pJ/bit.  
digital signal process (DSP); wireline transceiver; feed forward equalizer (FFE); decision feedback equalizer (DFE); parallel; multiplexer (MUX); adaptive; least mean square (LMS); sigma-delta
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 6 Experiments and Results
### Evaluation Indicators
#### 6.3.3 Packet Drop Rate  
Figure 6 shows the comparison of packet drop rate for different sending rates.  
As is shown in Figure 6, with the increase of individual data transmission rate, the packet drop rate will increase. Packet drop rate is caused by packet queues overflow. Comparing the three routing algorithms, the packet drop rate of interval type-2 fuzzy set routing algorithm is the lowest, followed by ELB algorithm and the packet drop rate  
Figure 6: The packet drop rate for different sending rates.  
of centralized DSP routing algorithm is the highest. With the increase of the load, the DSP always continues to try the same transmission path to transmit data, resulting in a large packet drop rate. The ELB algorithm adopts the corresponding congestion avoidance strategy according to the congestion state of adjacent satellites, while the interval type-2 fuzzy set routing algorithm can reduce congestion before congestion.  
In conclusion, compared with traditional centralized DSP algorithm and ELB algorithm, the proposed interval type-2 fuzzy set routing algorithm can reduce the average delay, increase the throughput and reduce the packet drop rate.
## 4 Training and Detection Using Deep Learning
### Validation in Training  
In this study, it was determined that the overlap between the detected and actual targets being greater than 0.5 was the correct detection (T). Whereas, overlap less than 0.5 was considered an incorrect detection (F). Figure 9 shows a calculation diagram of TP, FN, FP, and TN. Taking roof outline as an example, TP (true positive) is the situation in which the actual roof was detected as a roof. FN (false negative) is the situation in which the actual roof was not detected as a roof (missed detection). FP (false positive) is the situation where a roof is incorrectly detected in the absence of a roof. TN (true negative) is the situation there is no actual roof and it is not detected as one either. K in Equation (6) is the total number of roof outlines, blue tarps, and roofs completely destroyed. The accuracy of a general deep learning model is not clearly specified and is according to actual needs. The required accuracy of target segmentation is approximately 72% [27]. This study attempted to measure the specific area of roof damage, and at least 90% accuracy was required in the training stage. According to the adjustment of parameters, the learning accuracy was approximately 98%. Further, the _mIoU_ (mean value of _IoU_) is usually used as an evaluation indicator. The calculation formula is shown in Equation (7).  
\[mIoU=\frac{1}{k}\sum\frac{TP}{FP+FN+TP} \tag{7}\]  
Figure 10 is a result of the validation in training. According to the results of the roof outline, certain adjacent houses were mistaken for a whole object. The test result for blue tarps is the best. According to the results of roofs completely damaged, certain ships were mistakenly detected as roofs completely damaged. In the comparison between ground truth and the results of validation, it is found that the target region is accurate and satisfies the accuracy of segmentation. The mIoU of roof outline, blue tarps, and roofs completely destroyed was more than 98%.  
Figure 9: The calculation of TP, TN, FP, FN.
# A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps
## 4 Hardware Implementation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Video Standard** & **Image Size** & **Required Clock Cycles (\#)** & **Processing Speed (fps)** \\ \hline Full HD (FHD) & \(1920\times 1080\) & 2,076,601 & 113.8 \\ \hline Quad HD & \(2560\times 1440\) & 3,690,401 & 64.0 \\ \hline \multirow{3}{*}{4K} & UW4K & \(3840\times 1600\) & 6,149,441 & 38.4 \\  & UHD TV & \(3840\times 2160\) & 8,300,401 & 28.5 \\ \cline{1-1}  & DCI 4K & \(4096\times 2160\) & 8,853,617 & 26.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Processing speeds for various video standards. As mentioned in Section 2, the computation error of the proposed hardware architecture is within \(\pm\)0.5 LSB compared with the floating-point algorithm. This may cause a possible issue of slight color shift, as illustrated in Figure 8. The color around the train headlights is slightly stronger due to the limited bit width of every signal in the hardware implementation. Except for this kind of minor shortcoming, the results of two methods shown in Figure 8 are virtually identical. The results of the implemented hardware in Table 7 are very similar to those of [PERSON] et al. 's algorithm. Small differences in the results were caused by the limited bit widths to achieve compact and fast hardware designs. In the floating-point algorithm, \(k_{h}\) in Equation (5) could be fine-tuned to get the best results, as shown in Table 2. However, when converting to the fixed-point representation for hardware implementation, we allocated four bits to the fraction of \(k_{h}\). As a result, \(k_{h}\) could only be coarse-tuned in the hardware design at intervals of 0.0625 (=1/2\({}^{4}\)). Accordingly, even when \(k_{h}=0.9\)
## 2 Materials and Methods  
### Study Design  
This study is an interventional clinical trial with factorial assignment (registered within ClinicalTrials.gov, identifier: NCT04624438). For the sake of simplicity, a pre-post design with two random parallel groups (both experimental) was used. Quadriceps femoris (QF) function was assessed by MVC and RTD-SF on three occasions: prior to the intervention (pre-test), after 3 weeks of training (which served as the adjustment of exercise threshold; mid-test), and after 6 weeks of training (post-test; Figure 1). Isometric training involved either electromyostimulation (EMS) or the voluntary (VOL) exercises of QF. One week prior to intervention, all subjects participated in one practice session to familiarize themselves with stimulation parameters and training protocols and to determine the intensity of EMS needed to achieve 60% of MVC. Prior to the pre-test, body mass and percent of body fat were assessed using a bioelectric impedance method (In Body 720; InBody Co., Ltd. InBody Bldg, 625, Eonju-ro, Gangnam-gu, Seoul, 06106 Korea) and body height with a standard stadiometer. After the pre-test, the participants were randomly assigned into either an EMS or VOL group. Neither participants nor evaluators were blinded to the intervention or the assessment.  
Both groups of participants performed unilateral isometric strength training three times per week for six weeks. Training density and intensity were selected in line with previous recommendations and knowing the limitations of EMS [24; 25; 36]. Participants were advised to refrain from any type of resistance training targeting the leg muscles for the duration of the study. All sessions were performed at the same time of the day for each subject (always between 8 a.m. and 2 p.m.) and under similar environmental conditions (-22 \({}^{\circ}\)C and \(\sim\)60% humidity).  
Figure 1: An overview of the study.
## 2 Materials and Methods
### Materials Characterization  
The crystalline structure of the as-synthesized samples was characterized by X-ray diffraction (XRD, PANalytical X'Pert PRO MPD Diffractometer, Almelo, The Netherlands) with Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5418A) in the range 20 = 10-80\({}^{\circ}\). The morphology of the layers and heterostructures was examined using scanning electron microscopy (SEM, FEI Inspect S model, Eindhoven, The Netherlands). The morphologies of the as-synthesized sensors were investigated by atomic force microscopy (AFM, Model Nanosurf\({}^{\text{EasyScan}}\) 2 Advanced Research, Liestal, Switzerland), using the non-contact mode (scan size of 1 \(\upmu\)m \(\times\) 1 \(\upmu\)m).  
The optical properties of all of the samples were recorded using the UV-VIS analysis (PerkinElmer Lambda 950 UV/Vis spectrophotometer, Connecticut, USA) with an integrating sphere, in the range of 300-800 nm. The band gap \(E_{g}\) of the semiconductor materials was determined by plotting the Kubelka-Munk function against energy (eV). For electrochemical measurements, a Voltalab Potentiostat model PGZ 402 (Wuhan Cortrest Instruments Corp., Wuhan, China) was used with a three-electrode cell system comprised of an Ag/AgCl/KCl saturate electrode (reference electrode), a platinum wire (counter electrode) and Ti-TiO\({}_{2}\) layers, and CuMnO\({}_{2}\) film (working electrodes) in 10\({}^{-1}\) mol/L Na\({}_{2}\)SO\({}_{4}\) aqueous solutions. The potential range was established at \(-\)0.8-0.6 V for the Ti-TiO\({}_{2}\) layers, and 0.2-1.1 V for the CuMnO\({}_{2}\) film, respectively, with a 25 mVpotential step.  
The electric properties of the obtained UV photodetectors were measured using the Keithley 2450 SourceMeter SMU Instrument (Keithley Company, Cleveland, OH, USA).The parameter of current-voltage (_I-V_) demonstrates the _n_-TiO\({}_{2}\)/p-CuMnO\({}_{2}\) heterojunction behavior in dark state, UV and Vis illuminated conditions. The measurements were recorded in forward bias with \"-\" at the \"n\" area (99.99% Ti electrode) and \"+\" on the gold wire placed on the p-type CuMnO\({}_{2}\) semiconductor film. The applied voltage varied over ranges between \(-\)2 V and 2 V with a step rate of 10 mV/s. The UV detection characteristics were evaluated by a current-time measurement at a bias of 2 V under 20 s of lamp illumination at \(\lambda\) = 365 nm with a light power of 0.1 mW/cm\({}^{2}\). For visible irradiation, a Vis lamp at a wavelength range of 450-700 nm with a light power of 0.2 mW/cm\({}^{2}\) was used. A Solar 4000 Sensor (AMPROBE) (Octopart Inc., New York, NY, USA) device was used to calibrate the light power of the UV and Vis lamps that were used for the opto-electrical measurements.
## 3 Results and Discussion
### Limit Test for Heavy Metals and other Physicochemical Parameters  
Metal contamination may be introduced in many ways from raw materials since CMSC is synthesized from plant biomass. Therefore, ICP-MS analysis is prescribed by United States Pharmacopoeia (USP), European Pharmacopoeia (EP), and British Pharmacopoeia (BP) to evaluate heavy metal content (limit test). According to Table 5, only elements such as Na, Mg, Al, K and Ca were found in the sample. However, other toxic heavy metal contaminants, such as lead, mercury, arsenic and cadmium (not shown in data), were not found in the sample, indicating that CMSC can be used as a pharmaceutical excipient. The density of CMSC was found to be 11.55 g/mL (Table 4). Color measurement was carried out to determine the color formation that is known to be caused by a reaction in carboxymethylation. The color of CMSC is white as compared to commercial CMC, which was light brown and the total color difference was 0.1053 abs (Table 6). The CMSC color appearance could be due to reprecipitation with acetone.
## 4 Results and Analysis
### Comparative Studies
#### 4.4.4 Performance on the Established Dataset (English)  
To test the performance and to prove the language independence of the proposed author identification system, experiments were carried out with one of the most popular and widely used datasets, the C50 dataset, which is a subset of RCV1 [43]. The training and test sets included 2500 documents each (50 per author), nonoverlapping with one another. Here also, the dataset was partitioned into an 80%-20% train-test split for the experiment. The performance of our proposed system was compared with the works proposed by [PERSON] et al. [4] where the authors used article-level GRU and obtained an accuracy of 69.10% on the C50 dataset, [PERSON] et al. [12] worked with word and character unigrams and an SVM classifier on the mentioned dataset and achieved 88% accuracy, and [PERSON] et al. [13] used bag-of-terms model and an SVM and obtained 80.80% accuracy. The accuracy obtained on the said dataset is provided in Table 13 where it can be observed that our system outperformed the existing systems.
###### Abstract  
Today's wireless sensor networks expect to receive increasingly more data from different sources. The Time Slotted Channel Hopping (TSCH) protocol defined in the IEEE 802.15.4-2015 version of the IEEE 802.15.4 standard plays a crucial role in reducing latency and minimizing energy consumption. In the case of convergecast traffic, nodes close to the root have consistently heavy traffic and suffer from severe network congestion problems. In this paper, we propose OSCAR, an novel autonomous scheduling TSCH cell allocation algorithm based on Orchestra. This new design differs from Orchestra by allocating slots according to the location of the node relative to the root. The goal of this algorithm is to allocate slots to nodes according to their needs. This algorithm manages the number of timeslots allocated to each node using the value of the rank described by the RPL routing protocol. The goal is that the closer the node is to the root, the more slots it gets in order to maximize the transmission opportunities. To avoid overconsumption, OSCAR sets up a mechanism to adjust the radio duty cycle of each node by reducing the slots allocated to inactive nodes regardless of their position in the network. We implement OSCAR on Contiki-ng and evaluate its performance by both simulations and experimentation. The performance assessment of OSCAR shows that it outperforms Orchestra on the average latency and reliability, without significantly increasing the average duty cycle, especially when the traffic load is high.  
wireless sensor network; TSCH; convergecast scheduling; RPL; MAC; low latency; low power +
Footnote †: journal: [PERSON]; [PERSON] OSCAR: An Optimized Scheduling Cell Allocation Algorithm for Convergecast in IEEE 802.15.4e TSCH Networks. Sensors **2021**, 21, 2493. [[https://doi.org/10.3390/s21072493](https://doi.org/10.3390/s21072493)]([https://doi.org/10.3390/s21072493](https://doi.org/10.3390/s21072493))
## 5 Results and Discussion
The results are shown in Figure 6. Simulation was performed with the help of Equation (13) using a common value of the electron effective mass \(m_{\text{e}}=0.22\)\(m_{0}\)[26] and corresponding 2 DEG parameters measured for our HEMT structures in the Hall experiment (Table 1). At the temperature of 77 K, the theoretical simulations are in a good agreement with experimental results (Figure 6a). The main feature in the transmittance spectrum is the monotonic  
Figure 4: Relative spectrum of the stack of UID GaN, GaN:C buffer and AlN nucleation layers at room temperature. Thick and narrow solid lines correspond to FTIR and TDS experiments, respectively. Dashed line represents the results simulated at the best-fit parameters for the reflectance spectra. amplitude increase with the increase of incident photon energy visible up to 15 meV. This steep shoulder is a fingerprint of free carrier absorption due to high-frequency Drude conductivity of 2 DEG in AlGaN/AlN/GaN HEMT structures. Figure 5: Transmittance of the whole heterostructure (WS) at temperatures of 77 K (**a**) and 300 K (**b**). Thick and thin solid lines correspond to FTIR and TDS experiments, respectively. Black dashed lines show modeling for single-pass THz TDS measurements, while blue and red dashed lines represent results of calculation for multi-pass FTIR data at the best-fit parameters. We noticed that at room temperature, the numerical modelling suggested a significantly flat Drude part of the transmittance spectrum in comparison to that measured in the experiment (see dashed and solid lines in Figure 5b). However, the numerical modelling fitted much better to the experiment if the effective electron mass \(m_{\rm e}=0.34\ m_{0}\)[15] was used (compare dash-dot and solid lines in Figure 6b). The renormalization of the electron effective mass starting from the temperature of 134 K was also found in AlGaN/GaN heterostructures but without AlN spacer conducting the optical Hall effect experiments with \(m_{\rm e}=0.34\ m_{0}\)[15]. The numerical modelling was performed using the \(m_{\rm e}=0.2\ m_{0}\) and \(m_{\rm e}=0.34\ m_{0}\)[15]. The numerical modelling was performed using the \(m_{\rm e}=0.2\ m_{0}  
free 2D electrons [16] and the THz TDS of 2D plasmons [15]. These two independent works revealed that the electron effective mass changes from 0.22 \(m_{0}\) at 80 K to 0.36 \(\pm\) 0.03 \(m_{0}\) at 300 K and to 0.34\(m_{0}\) at 294 K, respectively. It is worth noting that the use of calibration samples would allow for further development of the IR-THz spectroscopy method for use as a non-destructive characterization tool of the UID GaN, GaN:C buffer, AlN nucleation and 2 DEG layers of different commercial AlGaN/GaN HEMT structures.
## 4 Conclusions  
Wireless sensor networks, based on the Internet of Things, for monitoring agro-climatic parameters in real-time, have been developed to benefit farmers, agromomists, and meteorologists. Different blocks were combined together to perform efficient roles of interconnectivity, interoperability and remote monitoring on a real-time basis. AgriLogger is designed and implemented to realize modern precision agriculture, it was tested and deployed into the field for sensing climatic changes throughout different seasons of the year, which therefore will allow farmers to reliably collect data from locations previously inaccessible due to lacking communication networks. Such a system can be easily installed and maintained. This paper successfully applies the wireless sensor networks on agro-ecology fields by investigating environmental situations. However, the big challenge facing AgriLogger functioning relies on the fact that, although the battery consumption could keep the sensor working for more than 16 years, the collecting phase lasts only 2 years due to the limit imposed by the size of the internal memory of the device. Moreover, some ecological effect can cause data transmission failure in WSN since it covers only short ranges. Therefore, more investigations and adjustments are needed in order to finalize the industrial use of AgriLogger.  
Conceptualization, M.I. ([PERSON]) and G.B.; Methodology, M.I. ([PERSON]) and G.B.; Software, M.I. ([PERSON]); Validation, M.I. ([PERSON]), G.B. and S.M.; Formal Analysis, G.G.; Investigation, M.I. ([PERSON]) and G.B.; Resources, A.M. and M.I. ([PERSON]); Data Curation, M.I. ([PERSON]); Writing--Original Draft Preparation, M.I. ([PERSON]); Writing--Review & Editing, G.B.; Visualization, G.B.; Supervision, G.B. and S.M.; Project Administration, S.M.; Funding Acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 4 Performance Evaluation  
This section conducts a simulation on the method proposed in Section 3. First, we experiment with the overlay node placement algorithm for hop-by-hop multipath overlay routing. Then, the performance of next-hop selection algorithms on the generated overlay network is evaluated.
## 3 Result and Discussion
### Energy Dissipation Analysis of the Premium Connection Under Modal Shapes  
Through the mutual verification of the modal test and finite element analysis, it can be concluded that the first five modal shapes of the tubing mainly appear in the x-axis direction, while the vibration amplitude in the y-axis direction is small. Based on the results of the finite element modal analysis, the amplitude of each vibration mode is extracted and used as the displacement amplitude applied to the finite element model to analyze the energy dissipation at the sealing surface of the premium connection. The constraint condition of the premium connection is shown in Figure 15. In the post-processing results of the finite element modal analysis of Section 3.1, the displacement amplitude of the first modal frequency along the y-axis of the tubing is 1.376 mm, the internal pressure is set to 80 MPa, and the friction coefficient of the sealing surface is 0.02.  
Figure 16 shows the hysteresis curve of the first modal shape boundary condition. It can be seen from the diagram that the applied displacement amplitude is attenuated from 1.376 mm to 0.09 mm at the sealing surface. It can be seen from the analysis in Figure 16 that due to the large axial displacement load on the tubing, the contact length and equivalent stress of the sealing surface increase, resulting in a large gross slip between the sealing surfaces. The proportion of gross slip accounts for 93.9% of the displacement amplitude, and the micro slip only accounts for 6.1% of the displacement amplitude, resulting in the shape of the hysteresis curve between the sealing surfaces being a parallelogram. At this time, the energy dissipation value generated by the connection sealing surface interface is large.  
Figure 16: First-order vibration mode hysteresis curve.  
Figure 15: Boundary conditions set up for the premium connection.  
The second mode shape of the tubing also mainly occurs in its x-axis direction, and the amplitude of the vibration mode in its y-axis direction is small. The displacement amplitude along the y-axis direction of the tubing under the second-order natural frequency is 2.258 mm, and an internal pressure of 80 MPa is added. The friction coefficient of the sealing surface is 0.02. The hysteresis curve obtained by applying the displacement amplitude to the finite element model of the premium connection is shown in Figure 17. In Figure 17, the proportion of gross slip accounts for 94.1% of the displacement amplitude, which is higher than the 93.9% of gross slip under the first modal shape. It is concluded that under the same boundary conditions, with the increase in the applied displacement amplitude (modal shape), the proportion of the gross slip region in the hysteresis curve increases, and the energy dissipation value also increases.