esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 2 Background and Related Works  
### The MU Transmission in the IEEE802.11 ax WLANs  
The IEEE802.11 ax WLANs separate the channel bandwidth into resource units (RUs) that consist of multiple subcarriers for OFDMA operation. The frame exchange procedure of the downlink (DL) MU transmission in IEEE802.11 ax WLANs is shown in Figure 1. For the DL MU transmission, the AP transmits an MU physical layer protocol data unit (PPDU) after the back-off procedure. The transmission time of different users in the MU PPDU is aligned by appending padding bits to the shorter A-MPDU frames. The RU allocation information of the MU PPDU is indicated in the HE-SIG-B subfield of the preamble. The scheduled stations can receive the A-MPDU frames in respective RUs according to the RU allocation information. After the transmission of the whole MU PPDU is finished, those stations can send the BlockACK/ACK frames to the AP in corresponding RUs.  
The frame exchange procedure of the uplink (UL) MU transmission is shown in Figure 2. For the UL MU transmission, the AP is responsible for the back-off procedure. The AP then transmits the buffer status report poll (BSRP) trigger frame to scheduled stations to collect the buffer status reports (BSR). The scheduled stations respond with the BSRs to indicate the amount of data waiting for transmission and to help the AP estimate the transmission time for them. The AP can allocate the transmission duration and RUs in the basic trigger frame according to the estimations. After receiving the basic trigger frame, each scheduled station can construct its A-MPDU frame and padding bits to stuff the transmission duration in the corresponding RU. After the reception of the trigger-based PPDU, the AP sends the BlockACK/ACK frames to those stations in corresponding RUs.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 6 Applications and Outcomes from Spaceborne LiDAR Data
### Vegetation and Ecosystem Monitoring
It finds that observation periods ranging from 143 to 534 days are required to meet the IPCC accuracy requirement of \(\pm 10\%\) for forest AGB estimates. The findings highlight the importance of sufficient data collection periods for effective REDD+ project monitoring and the variability of required observation periods based on local conditions.
## 5 Experimental Verification
### Binocular Ranging Experiment
#### 5.3.1 Comparison of Parallax Map  
Figure 9a is the disparity map obtained through the SGBM algorithm. The algorithm selects the difference of each pixel to form a difference map, related to which the global energy function is defined and minimized to obtain the optimal difference of each pixel. Figure 9b shows the disparity map calculated by the BM algorithm. The BM algorithm divides the frames of the two cameras into many small squares for model matching. By moving the small squares to match the small squares in the other image, and by finding the pixel positions of different small squares in the other image, combined with the relationship data of the two cameras (rotation matrix and translation matrix in the calibration parameters), the actual depth of the object is calculated to generate the corresponding depth map.  
Figure 8: Feature matching results.  
Figure 7: Feature points extracted by SIFT algorithm.  
Figure 9: Comparison of disparity maps obtained by two algorithms: (**a**) SGBM algorithm; (**b**) BM algorithm.  
Comparing the disparity maps obtained by the two algorithms, it can be seen that the disparity map processed by the SGBM algorithm is more refined than that of the BM algorithm, and with a better effect of the stereo matching. Therefore, the SGBM algorithm is used in this paper for stereo matching and disparity calculation.
## 6 Integration of RFID and WSN  
After reviewing the applications presented in this paper, one main conclusion is that WSN and RFID are interesting and complementary, because they were originally designed with rather different objectives (RFID for identification while WSN for sensing).  
An integration of WSN and RFID allow synergies, WSN uses a variety of sensors like the ones that were mentioned previously, but they cannot identify objects individually while RFID allow the identification of items like container, pallet, boxes or bottles. For this reason, integration of WSN and RFID provides a significant improvement on monitoring and has been faced by recent research.  
A possible solution for the integration of RFID, WSN and software agents, in intermodal containers was proposed [36]. The system is called \"intelligent container\".  
Currently, RFID loggers are roughly 10 times cheaper than wireless sensor nodes (10 \(\upepsilon\) versus 100 \(\upepsilon\)). Both prices will drop; the price of sensor nodes will decrease a bit quicker when their mass production starts, but will always be a multiple of the costs of RFID loggers.  
[PERSON] and [PERSON] describe a deep analysis of RFID and WSN, three forms of new system architecture that combines the two technologies. The first one, mix RFID tags and sensor nodes in the same environment. A station gather information from tags and sensor nodes then transmit it to local host computer or remote server. The second architecture was a new smart node, which makes use of kinds of sensors, to detect interested physical scenario, reading RFID tags, and radio transceiver which transporting sensed data. The last one proposed was to replace the RFID active and semi-active tags by ZigBee motes. The active tag is similar to a mote, but they are not exactly sensor network nodes because they communicate in centralized mode and cannot cooperate with each other through formed ad-hoc networks. Nevertheless, replace all active RFID tags to sensor nodes can be expensive in a large amount of applications [103].  
[PERSON] _et al_. exposed two system architectures to tracking and monitoring animals, integrating WSN and RFID. In both sceneries special wireless sensor nodes that can also read RFID tags are used. This node reads active RFID tags in the long range architecture, while the sorther one passive RFID tag [104].  
[PERSON] and [PERSON] designed an integration scenario where mobile objects and users carrying RFID tags and WSN receive ubiquitous services according to their identity and real-time sensor/actuator information. The main contribution of the proposal was a managed merge of RFID and sensor/actuator information at the context level, as well as a distributed infrastructure that allows the tracking of the context in a real-time manner [105].
## 4 CxQSIoT-Fog-CloudSERP: An Extension of CxQSCloudSERP Ontology Model
### An Overview of CxQSCloudSERP  
CxQSCloudSERP defines Cloud ERP in terms of virtual and concrete services-based business processes [25]. Figure 3 shows the CxQSCloudSERP ontology model of the context-aware services responsible for ERP management. The ontology is designed using the following main classes:  
* High: quality values in \([\text{qmin}_{\text{i}}^{\text{High}},\text{qmax}_{\text{i}}^{\text{High}}]\).
- Medium: quality values in \([\text{qmin}_{\text{i}}^{\text{Medium}},\text{qmax}_{\text{i}}^{\text{Medium}}[\).
- Low: quality values in \([\text{qmin}_{\text{i}}^{\text{Low}},\text{qmax}_{\text{i}}^{\text{Low}}[\). The constraint (\(C_{i}\)) of the quality attribute \(q_{i}\) is defined as follows: \[C_{i}=\text{qmin}_{\text{i}}^{\text{semantic\_value}}\] (1) In CxQSCloudSERP, preferences are defined as an ordered list of QoS attributes that are assigned with semantic values. Based on this semantic value, automatically generate the weight \(w_{i}\) by using Equation (2): \[w_{i}=\frac{e^{P_{i}}}{\sum_{j=1}^{n}e^{P_{j}}}\] (2) The computed priority \(P_{i}\) of the QoS \(q_{i}\) is: \[P_{i}=explicitPriority_{i}+constraintRank_{i}\times n\] (3) where:
- \(n\): Number of QoS attributes.
- \(ExplicitPriority\): A priority level of QoS. It is defined by a value between 1 and \(n\). The \(explicitPriority\) is null when the customer does not specify any preference.
- \(ConstraintRank\): The importance of QoS constraint. There are four priority levels: high, medium, low, and none.
* **Service:** The ontology presents unified business concrete services, such as buy, sale, finance, etc. Each service uses resources and they are linked to other services that provide or require these resources.
* **Business Process (BP):** This BP includes a set of business tasks (_parallel_, _iterative_, _choice_, _repeat_) delivering the same features with varied QoS.
* **Context:** Circumstances are defined as execution contexts. Each execution context responds to a functional need that has a specific language. Execution contexts provide an efficient solution to filter different services of a business process. In our case, we divide the context into three categories: service context, environment context, and customer context.
* **QoS:** A variety of quality attributes are defined in our ontology during ERP specification, design, and execution. QoS attributes are designed and built in such a way that every service is evaluated in perfect conditions and in a real environment.
## 5 IETF ROLL Working Group
### Leveraging Upon CoAP to Realize the IoT
#### 6.3.3 Advanced Interaction Patterns  
The base CoAP provides good support for the simple interaction patterns between clients and servers. However more advanced interaction patterns such as the communication between a group of devices requires extensions to the base protocol. In fact, it is anticipated that constrained devices will often naturally operate in groups (e.g., all window shutters on a given side of building may need to be lowered or raised as a group). Draft-ietf-core-groupcomm [113] discusses fundamentals and use cases for group communication patterns with CoAP. Building upon IPv6 multicast capabilities, the draft describes how CoAP should be used in both constrained and unconstrained networks and provides guidance for deployment in various network topologies. Although this draft has been adapted as a working group draft, it is still (at least in certain parts) in an explorative mode and will require additional investigation before conclusive results become available.  
The CoAP observe option allows clients to register with servers to be notified whenever the state of a resource changes [89], much like the publish/subscribe paradigm in conventional web services. In [117] a new CoAP option \"Condition\" was proposed to extend the observe option. This option can be used by a CoAP client to specify the conditions the client is interested in. Several condition types, _i.e.,_ filtering options, have been identified based on realistic use cases. Using conditional observations, the CoAP server will send a notification response with the latest state change only when the criterion is met. Using this mechanism a client can for example indicate that it is only interested in temperature values above 25 \({}^{\circ}\)C and not in all state changes. The feasibility of implementing this conditional observe on a constrained device is evaluated and proven in [118]. The correct operation for a simple scenario showed that the use of conditional observations can result in a reduced number of packets and power consumption compared to that which is normally observed in combination with client-side filtering.
## 4 Data Preparation and Machine Learning Models
The TF-IDF (TF--term frequency, IDF--inverse document frequency) text model was used to obtain the \"features-objects\" matrix. The ensemble methods based on decision trees used for the classification include extra-trees classifier (ET), random forest classifier (RF), AdaBoost classifier (AB), gradient boosting for classification (GB), CatBoost classifier (CB), multi-layer perceptron classifier (MLP), the support vector classification (SVC), and logistic regression classifier (RF). The result tables show only the best results. The list of methods and classes implementing them are presented in Table 3. At the first stage, the classification by groups of features was performed separately for each group, then a conclusion was given about the effectiveness of the group. At the second stage, the most effective groups of features were combined, and the classification was carried out according to several groups of features. The task of the second stage was to build the most accurate classification, as well as to identify specific features that most determine the author's style. All the given estimates are calculated on the basis of cross-validation for five blocks with the stratification by the answers. The part of the test sample is 25%. The tables show the maximum numbers of features, as well as the significances of the features which are obtained for the entire data set as a whole. It should be noted that at the stages of cross-validation, the smaller quantity of features is possible when classifying by words, service words, and also to a lesser extent by letters and symbols. Figure 2: The syntax tree for a sentence from [PERSON]’s Sonnet 98.
[6] treated a projector and a camera as a module, and two modules were set in opposite directions. As a result, patterns projected by the opposite projector could blind the other camera, avoiding pattern superposition. [PERSON] et al. [7] utilized the fact that the fixed unit of a projector and a camera will see a clear version of its own pattern and blurred ones that come from other units. The overlapping patterns can be distinguished by the blurring patterns. [PERSON] et al. [8] recovered the depth information of overlapped and non-overlapped regions by considering the correlation between multiple projectors and the infrared images, as well as that between the infrared images. [PERSON] et al. [9] mentioned intensity-blending algorithms for correcting the overlap area. [PERSON] et al. [10] proposed hierarchical patterns that can be separated from each other. [PERSON] et al. [11] separated the superposition grating by acquiring partial derivation of color fringes in different directions from different projections. [PERSON] et al. [12] categorized the interfered regions into flat regions and boundary regions under the guidance of texture segments, then applied different Markov random field (MRF) models to calculate the final depth results. [PERSON] et al. [13] uploaded their own specifically selected group of temporal phase shifts for each projector, resulting in simple and efficient separation between projected patterns. All of the aforementioned studies either kept the special position relationship between projectors, or projected specific patterns and dealt with the superimposed images using complicated algorithms. In our previous work, we designed a particular projection order to project the phase-shifting gratings. But six images are needed to be captured if the four-step phase-shifted method is used to acquire a wrapped phase from two projectors [14]. In this study, we utilized a color camera for fringe projection profilometry. Two projectors projected red and blue stripes, respectively. The color camera captured them at the same time and used its optical characteristics to separate the superposition grating. The proposed method did not require complex projection coding and separation algorithms, and there was no special requirement for device placement. Four images are sufficient for the wrapped phase using the four-step phase-shifted method from two projectors. The rest of this paper is organized as follows. Section 2 analyzes optical characteristics of the color camera and presents the proposed method. Sections 3 and 4 respectively describe the simulation and experiments. Section 5 discusses the advantages of the proposed method and Section 6 summarizes our chief conclusions.
# The BER Performance of the LDPC-Coded MPPM over Turbulence UWOC Channels
## 1 Introduction
In [31; 34], curve-fitting methods were used to obtain high accuracy conditional SER and BER formulas in the additive white Gaussian noise (AWGN) channel, and the corresponding closed-form SER and BER expressions in the presence of turbulence were derived, based on Gauss-Laguerre integration and the cumulative distribution function (CDF) of the transmitted optical irradiance. This paper analyses the BER performance of low-density parity-check (LDPC)-coded MPPM UWOC systems with aperture averaging over turbulence channels. To the best of our knowledge, there is no detailed BER performance evaluation of LDPC-coded MPPM UWOC systems over turbulence channels with different fading models. LDPC is a powerful coding scheme with a sparse parity check matrix [35], which can approach the Shannon capacity in the AWGN channel. The LDPC code outperforms Turbo and Reed-Solomon (RS) codes in bursty-error channels and can be very effective for high-speed optical systems because of its low hardware complexity and low latency [36; 37]. In this work, Quasi-Cyclic (QC) LDPC codes described in IEEE.802.16 standard [38] are investigated due to their pros, such as simple construction, easy hardware implementation, lower computational complexity of the encoding and decoding, and flexible adjustment of the code length and the code rate [39]. GG and EW distributions are adopted in the study as aperture averaging alleviates intensity scintillation and makes GG and EW distribution suitable for modelling fading in all the considered turbulent scenarios, as outlined in [7]. The main contributions of this paper are as follows: (i) the initial log-likelihood ratio (LLR) for LDPC decoding is derived, based on Gaussian distribution and Jacobian logarithm, and then simplified (ii) based on Gauss-Laguerre integration and the CDF of GG distribution, the closed-form expression of the BER without LDPC, but experiencing GG fading, is presented, and (iii) Monte-Carlo (MC) simulation is used to verify the derived BER expressions and the efficiency and applicability of the LDPC scheme to mitigate turbulence-induced fading. The remainder of this paper is organized as follows: Section 2 describes the LDPC-coded MPPM UWOC system, channel model and general assumptions. The soft demapping and decoding are detailed in Section 3. For comparison, the BER performance without LDPC code over the GG fading channel is analyzed in Section 4. Numerical and simulation results are presented, compared and discussed in Section 5. Finally, Section 6 concludes the paper.
# Design and Testing of a New Bionic Corn-Ear-Picking Test Device
## 2 Materials and Methods
### Test Results and Analysis  
The results of the test data are shown in Figure 4. Due to the fibers connecting the ear and the corn stalk, when the peduncle was subjected to both the bending and twisting forces, the force value would continue to grow. When the force reached the breaking point, the value decreased rapidly to 0, at which point the corn ears dropped. Stretching fracture and bending fracture tests were performed on the corn peduncle. The tests results showed that when the fresh corn was at late milk ripe period, the fracture force of the corn peduncle in forward stretching had a range of 301.36 N-701.53 N, with an average value of 456.43 N. The fracture force, when the corn was subjected to bending, was 31.17 N-114.5 N, with an average value of 51.86 N. When the corn was in the mature period, the fracture force of the corn peduncle in forward stretching had a range of 166.23 N-384.95 N, with an average value of 278.80 N. The fracture force when the corn was subjected to bending was 19.28 N-65.43 N, with an average value of 38.50 N (Figure 5). It can be seen that greater forces were required for harvesting fresh corn with a high moisture content than those for mature corn with low moisture content. This is because straw with a high moisture content can be regarded as a plastic material, whereas straw with a low moisture content can be regarded as a brittle material. Based on this, the flexibility of the straw can be weakened, and its brittleness can be enhanced, so the breaking force will be significantly reduced. Moreover, whether the fresh corn or the mature corn, the force required to break corn peduncle by reverse bending was much lower than that for forward stretching, and the force was reduced by about 90%.  
Due to a large force being exerted on corn by stretching, it is not suitable for fresh corn. A method of picking corn that imitates the manual breaking of corn is proposed, and a fresh corn picking test stand was designed based on this principle. In addition to it being applicable regardless of the physical characteristics of the fresh corn and reducing the amount of picking damage, this method can significantly reduce the energy consumption during the picking process, and it can preserve the intact corn stalks for a full-scale corn harvesting.  
Figure 4: The results of mechanical testing. (**a**) Tensile breaking test of corn ear. (**b**) Bending fracture test of corn ear.
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 4 Comparison of Traditional Sliding Mode Control and Improved Sliding Mode Control
### Improved Sliding Mode Control
\[s_{k+1}=s_{k}-\varepsilon T\text{sgn}(s_{k})-\lambda TS_{k} \tag{26}\]  
According to the above approach rate, an improved sliding mode control law is designed as  
\[{u_{k}}^{\prime}=\frac{r_{2,k+1}-M(x_{k})}{N(x_{k})}+\frac{1}{hN(x_{k})}( \alpha g(e_{1,k})+\beta(e_{1,k})^{\frac{q}{p}})-\frac{1}{mN(x_{k})}(s_{k}- \varepsilon T\text{sgn}(s_{k})-\lambda TS) \tag{27}\]  
After the above analysis and calculation, when \(0<U_{K}<1\),  
\[u_{k}={u_{k}}^{\prime} \tag{28}\]  
When \(U_{K}>1\) or \(U_{K}<0\),  
\[u_{k}=0.5+\frac{0.5{u_{k}}^{\prime}}{|{u_{k}}^{\prime}|} \tag{29}\]  
The following proves the stability of the system, using Lyapunov stability theory to analyze. The chosen Lyapunov function is  
\[V_{k}=\frac{1}{2}{s_{k}}^{2} \tag{30}\]  
The system can be stable if and only if the Lyapunov function satisfies the following conditions:  
\[\Delta V_{k}={s_{k+1}}^{2}-{s_{k}}^{2}<0,\;s_{k}\
eq 0 \tag{31}\]  
According to the Lyapunov theorem, the states in any initial position will approach the sliding surface \(S_{k}\). When \(T\) is sufficiently small, the existing and reaching conditions of the discrete-time sliding mode can be expressed as follows:  
\[\left\{ \begin{array}{c}(s_{k+1}-s_{k})\text{sgn}s_{k}<0\\ (s_{k+1}+s_{k})\text{sgn}s_{k}>0\end{array} \right. \tag{32}\]\[s_{k+1}-s_{k}=h(r_{2,k+1}-(M(x_{k})+N(x_{k})u_{k}))+\alpha g(e_{1,k})+\beta e_{1,k} \prescript{\frac{g}{2}}{r}-(he_{2,k}+\alpha g(e_{1,k-1})+\beta e_{1,k-1}\prescript{ \frac{g}{2}}{r}) \tag{33}\]  
Substituting the control law (27) for PFDTSMC into Equation (33), we obtain  
\[(s_{k+1}-s_{k})\text{sgns}_{k}=-\lambda T|s_{k}|-\epsilon T|s_{k}|<0 \tag{34}\]  
When the sampling period T is sufficiently small, we obtain the conclusion of \(2-\lambda\text{T}>>0\). Therefore,  
\[(s_{k+1}+s_{k})\text{sgns}_{k}=(2-\lambda T)|s_{k}|-\epsilon T|s_{k}|>0 \tag{35}\]  
As can be seen from Equations (34) and (35) above, the improved control law satisfies the stability condition of Equation (32). Therefore, the stability of the system is proved by derivation.