esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
On the other hand, this also means that sensor network protocols and algorithms must possess self-organizing capabilities. The management and topology control mechanism must be flexible and robust. Considering these unique characteristics, Designing suitable routing protocols in WSN is very challenging due to the inherent characteristics [4]. Though WSN share some common features with MANET (Mobile Ad-hoc NETwork), those protocols proposed for MANET are not well suited for WSN for some reasons. For example, keeping the routing state is not necessary; the collected data has much redundancy; getting the data is more important than knowing which nodes sent the data. Some special routing tactics (such as data aggregation, clustering and data-centric methods) is proposed for routing algorithms in WSN to minimize energy consumption. All routing protocols can be classified based on different standards. According to the network structure, there are flit-based, hierarchical-based and position-based. Considering protocol operation, these routing algorithms can be classified into query-based, negotiation-based, quality of service (QoS)-based. Among these routing protocols, position-based protocols utilize position information to relay the data to the desired regions and show the advantage of energy-awareness. Since, there is no addressing scheme like IP-addresses in position-based routing and they are spatially deployed on a region, position information can be utilized in routing data in an energy efficient way. For instance, if the sensed region is known, the query can be limited only in some particular region to save energy significantly according to the position information of sensor nodes. Although there are some previous efforts on surveying WSN [1, 3, 4, 5, 10, 11], the scope of the survey presented in this article is distinguished from these surveys. The surveys in [1, 5, 10] addressed the general issues and techniques, such as the physical constraints on sensor nodes, applications, and architectural attributes. The survey in [3, 4, 11] emphasized on the general routing algorithms. Due to the importance of position information in routing and the availability of many literatures on this topic, a detailed survey becomes necessary and useful. Our work concentrates upon position-based routing algorithms in WSN and the different approaches are described and categorized. The rest of this article is organized as follows. Firstly, we discuss some related works, such as localizing algorithms, coverage and connectivity. Then, a classification and comprehensive survey of position-based routing techniques in WSN is presented. The different routing algorithms of the same category and the different category of position-based routing are compared separately. Finally, some future research directions and open issues related to position information in WSN are also discussed.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
###### Abstract  
The orthogonal time frequency space (OTFS) is a modulation designed to transmit data in high Doppler channels where the usage of the orthogonal frequency division multiplexing (OFDM) is challenging. The random padded OTFS (RP-OTFS) modulation, introduced recently, is an OTFS-like waveform optimized for more precise estimation of channel state information (CSI) and, in the case of integrated sensing and communication (ISAC), for radar detection as well. One of the main drawbacks of the RP-OTFS is the high level of interference between carriers (the inter-carrier interference--ICI) of Doppler-delay (DD) grid. In the article, we optimize the RP-OTFS waveform in terms of reducing the level of pilot-to-data interference and also offer a way to reduce the data carrier interference. The reduction in the pilot-to-data interference is achieved due to the introduction of the following: (1) redistributing interferences along the DD grid, and (2) special DD grid configuration. In turn, the reduction in data carrier interference is achieved by extrapolating the estimate of channel state information. The proposed approach allows us to reduce the influence of the interference component and, as a result, to improve the probability of correct demodulation in the ISAC RP-OTFS system. Various DD grid configurations for different use cases from a radar point of view are considered in the article. The questions of choosing appropriate values of the DD grid parameters depending on the operating environment are also discussed here. In simulations, the ICI-reduced RP-OTFS is compared with its predecessor, the regular RP-OTFS, and classical modulations: OFDM and zero-padded OTFS, and benefits of its usage are shown: lower bit error rate (BER) of the transmission and higher detection probability of the radar detection.  
Keywords:integrated sensing and communication (ISAC); OTFS modulation; random-padded OTFS; pilot-to-data interference; vehicle detection +
Footnote †: journal: Sensors
# A Fair Cooperative MAC Protocol in IEEE 802.11 WLAN
## 4 Performance Analysis
### Throughput  
According to the analysis in [26], A Markov model can describe the behaviour of a single node in the IEEE 802.11 standard. In the exponential back-off process, each node selects a random value in the contention window of [0, _CWmin_] at the first transmission. If the packet transmission fails due to errors or a collision occurs, the length of the contention window is doubled, i.e., \(CW=2^{i}\)\(CW_{min}\) and \(i=0,1,2\),..., \(m\). After retransmission \(m\) times, the packet will be dropped. When a network has \(n\) nodes, the probability that there is at least one node transmitting a packet in a given time slot can be expressed as:  
\[P_{tr}=1-(1-\tau)^{n} \tag{3}\]  
where, \(\tau\) denotes the transmission probability of a user in a particular time slot. For a no-collision scenario, the value of \(\tau\) is obtained as [27]:  
\[\tau=\frac{2}{CW+1}. \tag{4}\]  
With the assumption of at least one node transmitting, the probability of a successful transmission can be calculated as follows:  
\[P_{s}=\frac{n\tau\ (1-\tau)^{n-1}}{P_{tr}}. \tag{5}\]  
The saturated throughput can be expressed by dividing the successfully transmitted payload information in a time slot, with the average length of a time slot, and we have  
\[S=\frac{P_{s}P_{tr}E[P]}{(1-P_{tr})T_{i}+P_{tr}P_{s}T_{s}+P_{tr}(1-P_{s})T_{C}} \tag{6}\]  
where \(E[P]\), \(T_{i}\), \(T_{s}\), and \(T_{c}\) denote the average packet length, the duration of an empty time slot, the successful transmission time, and the transmission time with a collision, respectively. In the basic access mode and RTS-CTS mode, we have  
\[\begin{split} T_{S}^{bas}=H+E[T_{D}]+T_{SIFS}+T_{ACK}+T_{DIFS}\\ T_{S}^{\prime ts}=H+E[T_{D}]+T_{RTS}+T_{CTS}+\ 3T_{SIFS}+T_{ACK}+T_{DIFS} \end{split} \tag{7}\]  
and  
\[\begin{split} T_{C}^{\text{bas}}=H+E[T_{D}^{*}]+T_{DIFS}\\ T_{C}^{\prime ts}=T_{RTS}+T_{DIFS}\end{split} \tag{8}\]  
where \(H\), \(E[T_{D}]\), and \(E[T_{D}{}^{*}]\) denote the header of the physical layer and MAC layer, the average time to transmit a MAC frame (without MAC header), and the average time to transmit the longest packet payload involved in a collision, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Relay ID** & \(R_{SD}\) & \(R_{Sri}\) & \(R_{iD}\) & \(CG_{i}\) & \(CL_{i}\) & \# ACK \\ \hline \(R_{1}\) & & & & & \\ \(R_{2}\) & & & & & \\ \(\cdots\) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: CoopTable of the FC-MAC protocol.  
In the cooperation mode, we have  
\[T_{S}=P_{Coop.}T_{S}^{Coop}+\big{(}1-P_{Coop}\big{)}.T_{S}^{Dir} \tag{9}\]  
where \(P_{Coop}\), \(T_{S}^{Coop}\), and \(T_{S}^{Dir}\) denote the cooperation probability in the network, the successful transmission time in cooperation mode, and the successful transmission time with no cooperation, respectively. The expression of \(T_{S}^{Dir}\) is the same as (7) and  
\[\begin{split} T_{S}^{Coop-\text{{b}ss}}=2H+2E[T_{D}]+2T_{SIFS}+T_{ ACK}+T_{DIFS}\\ T_{S}^{Coop-\text{{r}ts}}=2H+2E[T_{D}]+T_{RTS}+T_{CTS}+T_{HTS}+ \ 5T_{SIFS}+T_{ACK}+T_{DIFS}.\end{split} \tag{10}\]
###### Abstract  
Cu-CHA zeolites have proven to be effective for NO\({}_{\rm x}\) reduction, but a drawback in using CHA zeolites is the cost associated with using expensive organic structure-directing agents. To overcome this drawback, we are reporting here the synthesis of Cu-CHA zeolite catalysts in both their NH\({}_{4}\)-form as well as K-form that do not require the use of organic structure-directing agents. After comprehensive characterization by XRF, XRD, [27] Al NMR spectroscopy, FE-SEM, SEM/EDS, N\({}_{2}\)-adsorption/desorption, NH\({}_{3}\)-TPD, H\({}_{2}\)-TPR, and XPS, the zeolite catalysts were tested for NO\({}_{\rm x}\) conversion by NH\({}_{3}\)-selective catalytic reduction (NH\({}_{3}\)-SCR). Cu-NH\({}_{4}\)-CHA zeolite catalysts exhibited remarkable activity and thermal stability over a wide temperature window, outperforming their counterpart K-forms. Among the NH\({}_{4}\)-forms of CHA zeolite catalysts, the 0.1 M Cu-NH\({}_{4}\)-CHA showed the best catalytic performance, achieving 50% NO\({}_{\rm x}\) conversion at a temperature as low as 192 \({}^{\circ}\)C, and reaching full conversion of NO\({}_{\rm x}\) at 261 \({}^{\circ}\)C. These Cu-based CHA zeolite catalysts are promising thanks to their environmentally friendly synthesis and offer the opportunity of maximizing DeNO\({}_{\rm x}\) strategies in applications for NO\({}_{\rm x}\) pollution abatement.  
organic structure-directing agent; selective catalytic reduction; Chabazite; zeolite
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 6 Conclusions  
By considering the M-distributed fading channels, this paper has investigated the PLS problem. The theoretical expressions of the SOP, the probability of SPSC, and the ESC are derived, respectively. In this paper, a limitation of the theoretical analysis is that both the exact SOP and ESC are with a complex integral, and closed-form expressions for them are not derived. Alternatively, tight lower bounds for SOP and ESC are derived, which are in closed-forms. Numerical results show that the SOP and its lower bound are monotonously increasing functions with respect to \(\gamma_{\rm th}\). Moreover, when \(E(\gamma_{\rm B})\) and \(E(\gamma_{\rm E})\) are large, the gap between the SOP and its lower bound is very small. The exact expression for probability of SPSC has the same form as that for the lower bound of SOP when \(\gamma_{\rm th}=0\). With the increase of \((\alpha_{\rm E},\beta_{\rm E})\), the probability of SPSC increases for small \(E(\gamma_{\rm B})\) and decreases for large \(E(\gamma_{\rm B})\). Moreover, for large \(E(\gamma_{\rm B})\) and \(E(\gamma_{\rm E})\), the difference between the exact ESC and its lower bound is small enough. Therefore, the derived expressions are accurate and can be used to evaluate the system performance without time-intensive simulations. Moreover, the derived results are extended to many well-known channels, which can provide some insights for practical system design.  
After deriving the expressions of the secure performance indicators over the M-distributed fading channel, the natural next step is to consider the secure transmission schemes to further improve the PLS.  
Conceptualization, S.-H.L., J.-Y.W.; methodology, S.-H.L., R.-R.L., X.-T.F.; software, S.-H.L.; validation, R.-R.L. and X.-T.F.; investigation, S.-H.L.; resources, S.-H.L., R.-R.L. and X.-T.F.; writing-original draft preparation, S.-H.L.; writing-review and editing, R.-R.L., X.-T.F., A.-L.T. and J.-Y.W.  
This work is supported by National Natural Science Foundation of China (61701254), Natural Science Foundation of Jiangsu Province (BK20170901), the open research fund of Key Laboratory of Intelligent Computing & Signal Processing, Anhui University, and the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security (AGK2019002).  
The authors thank the anonymous reviewers and the Editor for their helpful comments and review of this article.  
The authors declare no conflict of interest.  
Figure 11: ESC versus \(E(\gamma_{\rm B})\) for different fading channels.
## 3 Results
### Hazard Assessment
#### 3.2.1 Qualitative Evaluation
The municipality of Montalbano Jonico is represented by 19 census tracts in LH2, LH5, LH7, LH8, and LH12 scenarios, including the town center, and 8 census tracts in the MH4, MH5, MH8, MH11,  
Figure 5: Histogram showing the number of census tracts intersected by flood hazard (FH), landslide hazard (LH), and multi-hazard (MH) for each municipality under investigation. and MH14 hazard combinations. Moving towards the south, Scanzano Jonico shows only three census tracts located in MH5 scenario, bordering Montalbano Jonico. Lastly, within the southern sector (Figure 6d), the municipality of Tursi is represented by 33 census tracts in the LH2, LH4, LH5, LH6, LH7, LH8, LH9, LH10, LH12, and LH13 scenarios (including the city town area) and 13 census tracts belonging to MH1, MH5, MH8, MH9, MH12, and MH15; Colobraro shows 12 census tracts related to LH2, LH3, LH4, LH5, LH6, LH7, LH8, and LH12 and 9 census tracts related to MH3, MH6, MH7, MH9, MH10, and MH13; Valsinni is characterized by 11 census tracts falling into LH2, LH7, LH12, and LH13 and 9 census tracts corresponding to MH7, MH10, MH13, and MH14; Rotondella is represented by one census tract associated with LH1 and two census tracts associated with MH11 and MH14 scenarios; Nova Siri has three census tracts intersected by the LH4, LH7, and LH12 landslide hazard combinations and six census tracts characterized by the MH5, MH9, and MH13 scenarios. As shown in the qualitative investigation and looking at the hazard spatial distribution, the central and southern sector of the study area is the most exposed to multi-hazard scenarios.
## 3 Methodology  
### Search Strategy  
A systematic literature review (SLR) is a tool used to identify research articles related to a predetermined topic [26]. In this study, we carried out an SLR to identify the key factors that could affect farmers' adoption of DATs. The well-defined review protocol guarantees a robust systematic review process [27]. The protocol contains three components: the formulation of research questions, the specification of the literature search strategy, and the establishment of inclusion and exclusion criteria. In this study, the ISI Web of Science database (accessed on 12 July 2023) was used as the primary tool for sample collection. The Web of Science database is an informative retrieval platform, which contains more than 9000 world-authoritative academic journals [28]. A web-based academic search engine, Google Scholar, was further applied since it was identified as a useful supplement to traditional academic citation databases [29]. The keywords used for this search are indicated in Table 2. Finally, to refine the search results, inclusion and exclusion criteria were applied for further validation of related publications. For instance, we limited the studies to those available in full text and published in English, excluding a portion of the gray literature, such as seminars summaries, books, reviews, and editorials. We did not apply any filters for the publication year to prevent missing any relevant literature. Note that in this article we mainly focused on on-farm technologies adopted by farmers. Off-farm technologies (in the agrifood value chains or more broadly food systems) adopted by agribusiness firms or supply chain management entities were not part of our review focus.
## 3 Results and Discussions
### Optimizing Printing Parameters with MOPSO
#### 3.4.2 Optimized Printing Parameters for an Angled Strut
As in the previous section, we compared the optimized examples selected from the Pareto optimal set with the non-optimized examples obtained from the validation set up, as shown in Table 10. The clear difference from the optimization is displayed in the bold in Table 10. The printing parameters for the non-optimized examples are (PI = 54.18 mol/m\({}^{3}\), PA = 4.62 mol/m\({}^{3}\), LT = 20.00 \(\mu\)m, CT = 1.00 s), (PI = 50.12 mol/m\({}^{3}\), PA = 6.08 mol/m\({}^{3}\), LT = 28.00 \(\mu\)m, CT = 2.00 s), and (PI = 56.77 mol/m\({}^{3}\), PA = 3.13 mol/m\({}^{3}\), LT = 120.00 \(\mu\)m, CT = 1.00 s) for example 1, 2, and 3, respectively. The comparison between Point C and the non-optimized point 1 shows the 16% surface quality increase by the proposed optimization framework. In addition, Point D shows 18% surface quality increase even in the fastest printing speed. Therefore, like the previous vertical strut, the comparison between the optimized points and the non-optimized cases, it is clearly seen that the proposed optimization framework significantly increases the efficiency of the printing process. The clear difference between the Pareto sets of the upper and lower side profiles, shown in Figure 10, is expected for other angled struts. The degree of effect from the light penetration from the upper layer on the surface roughness of the lower layer will be different because the area affected by the light penetration is different, depending on the printing angle. The optimization method presented here can be easily applied to other 3D printed geometries that may have different side slopes. It is very important to know the configuration of the Pareto optimal set between these two extremes. Different points on this set allow us to have different printing set-up and corresponding printing time and RMSE, but its printing time and roughness are limited by these two extremes. Therefore, this Pareto optimal set can be a guideline to make better printing conditions depending on the need. The usability of MOPSO is even more obvious when the result from MOPSO is compared with the non-optimized printing case. The approach described in this section can be extended to other printing angles or complex surface profile. It can also be possible to use different objective functions, such as the mechanical strength, total mass, or other properties of AM parts. Based on these results, we conclude that the MOPSO for PuSL helps to optimize the cost functions of interest. In addition, it offers a design guideline for the performance measure for AM parts.
# Quadrotor Formation Strategies Based on Distributed Consensus and Model Predictive Controls
## 6 Simulation Preparation
### Transformation of the Variable of the Discrete State-Space Model  
The state-space model coded in the formation strategies is the discretized state-space model with a sampling time of 0.1 s. That is, the controls for the leader and the followers are expected to be updated every 0.1 s. For the convenience of coding the control algorithm in the horizontal motion, a transformation of variables is applied to the horizontal state-space model (13). The states and the controls in the horizontal state-space model (13) are arranged as \(\mathbf{x}_{h}\) and \(\mathbf{U}_{h}\) in Equation (43), respectively. The transformation of states \(T_{s}\) and the transformation of controls \(T_{u}\) are considered and shown as follows:\[\mathbf{x}_{h}=\left[\begin{array}{c}\Delta x\\ \Delta y\\ \Delta u\\ \Delta v\\ \Delta\theta\\ \Delta\phi\\ \Delta q\\ \Delta p\end{array}\right]=\left[\begin{array}{cccccccc}1&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\\ 0&0&1&0&0&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&0&\frac{-1}{g}&0&0&0\\ 0&0&0&0&0&\frac{1}{g}&0&0\\ 0&0&0&0&0&\frac{-1}{g}&0\\ 0&0&0&0&0&0&\frac{-1}{g}&0\\ 0&0&0&0&0&0&\frac{1}{g}\end{array}\right]\left[\begin{array}{c}\Delta x\\ \Delta y\\ \Delta u\\ \Delta v\\ \Delta\tilde{\theta}\\ \Delta\tilde{q}\\ \Delta\tilde{p}\end{array}\right]=T_{\text{s}}\widetilde{\mathbf{x}}_{h}, \tag{43}\] \[\mathbf{U}_{h}=\left[\begin{array}{c}\Delta M_{\theta}\\ \Delta M_{\phi}\end{array}\right]=\left[\begin{array}{cc}\frac{-I_{yy}}{g} &0\\ 0&\frac{I_{xx}}{g}\end{array}\right]\left[\begin{array}{c}\Delta\widetilde{M }_{\theta}\\ \Delta\widetilde{M}_{\phi}\end{array}\right]=T_{\text{u}}\widetilde{\mathbf{U}}_{h}.\]  
Applying the above transformation to the states and the controls in Equation (13), the normalized horizontal state-space model considering the modified state \(\widetilde{\mathbf{x}}_{h}\) and the modified control \(\widetilde{\mathbf{U}}_{h}\) can be obtained. The normalized system matrix \(\widetilde{A}_{h}\) and the control matrix \(\widetilde{B}_{h}\) are shown as follows:  
\[\widetilde{A}_{h}=\left[\begin{array}{cccccccc}0&0&1&0&0&0&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&0&1&0&0&0\\ 0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&1\\ 0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0\end{array}\right],\quad\widetilde{B}_{h}=\left[\begin{array}[] {c}0&0\\ 0&0\\ 0&0\\ 0&0\\ 0&0\\ 1&0\\ 0&1\end{array}\right]. \tag{44}\]  
The discrete state-space model that is adopted by the formation strategies in the horizontal motion is obtained by applying the Matlab function _c2d_ to the normalized continuous state-space model (44).
## 1 Introduction
In contrast, DBT methods automatically detect drill cuttings, exhibit superior robustness and computational efficiency, adapt more effectively to target appearance changes, and can leverage deep learning detection advances to enhance tracking performance and stability. ByteTrack [15], a representative DBT method, improves target trajectory consistency and reduces ID switching by optimizing data association and refining the matching of low-confidence detections, thus achieving high accuracy and robustness in real-world scenarios. Consequently, this study adopts ByteTrack as the tracking framework and introduces a velocity estimation module tailored to the motion characteristics of drill cuttings, achieving high-precision velocity measurement by mapping pixel displacement to physical displacement. The primary contributions of this study are as follows:1. Enhancement of the YOLOv11 detection head. We optimize the multi-scale feature fusion capability of the YOLOv11 detection head by integrating the lightweight feature extraction module Ghost Conv and the feature-aligned fusion module FA-Concat, resulting in an improved model named YOLOv11-Dd (drilling debris). 2. Development of a speed detection module. Leveraging the robust tracking capabilities of ByteTrack, we design a high-precision speed detection module that calculates the real-time movement speed of drilling debris by mapping the pixel displacement of detection box centers between consecutive frames to physical space. 3. Construction of a custom dataset and comprehensive experimental validation. We build a drilling debris splashing simulation dataset comprising 3787 images, covering various ejection angles, speeds, and material conditions. The experimental results demonstrate that our YOLOv11-Dd detection head achieves a 4.65% improvement in detection performance compared to YOLOv11, with an mAP@80 of 76.04%. The proposed drilling debris speed measurement method achieves an accuracy of 92.12% in the speed detection task. The remainder of this paper is organized as follows: Section 2 reviews the related work; Section 3 discusses the optimization of the detection head and the design of the speed detection module; Section 4 presents the experimental validation and analysis based on the custom drilling debris splashing simulation dataset; and Section 5 concludes the paper.