esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### State of the Art  
Channel estimation in OTFS systems has been widely investigated in the literature since the detection performance of the OTFS receiver is based on the estimated channel matrix [12; 13; 14; 15]. In [10], an embedded-pilot-aided method for channel estimation is proposed, achieving good performances in both integer DDs and fractional Doppler scenarios. Channel estimation in massive multiple-input multiple-output scenarios (MIMO) has been investigated in [16], where additional sparsity due to a large number of transmit and receive antennas allows the channel estimation problem to become a simpler sparse recovery problem taking advantage of the angular domain. In [17; 18; 19] a novel tensor-based approach for channel estimation in massive MIMO systems is proposed to further exploit channel sparsity in the delay-Doppler-angle domain and reduce signal processing burden. Low-complexity channel estimation schemes were considered in single-antenna systems in [8], where modified maximum likelihood estimator (M-MLE) and two-step estimator (TSE) algorithms were introduced. The first is based on the asymptotic orthogonality of a delay-Doppler parameter matrix for large OTFS frames, which allows for the simplification of the cost function. On the other hand, the second one has lower complexity and relies on disjoint delay and Doppler estimation. Under the assumption of having good separability of the received pilot replica due to fine delay and Doppler resolutions, in both algorithms, the IPI is cancelled, and at each iteration, a residual received vector is used for estimating channel parameters of the next path. Similarly, IPI cancellation is performed in the low-complexity channel estimation scheme for discrete Zak transform-based OTFS (DZT-OTFS) proposed in [20]. Sparse Bayesian learning (SBL) algorithms for channel estimation are also considered in [21; 22], but M-MLE and TSE algorithms are shown to perform better then SBL in [8] when delay and Doppler resolutions are fine enough. In [23], different channel estimation schemes for the fractional DD case are proposed with the scope of improving the spectrum efficiency and reduce the peak-to-average-power-ratio (PAPR) by sending pilots in the time-frequency (TF) domain. The problem of high-precision channel estimation in IPI scenarios has been addressed in [11], where an iterative delay-Doppler IPI cancellation (DDIPIC) algorithm was proposed. In this case, the IPI cancellation is performed indirectly through refinement procedures carried out at each iteration. These refinements of the channel parameters take time, increasing the computational burdening of the channel estimation algorithm. In more detail, the computational complexity of the cost function, which is evaluated multiple times during each iteration, increases with the cube of the iteration index due to matrix inversion. However, in [11], the DDIPIC algorithm is shown to perform better than the M-MLE algorithm. Hence, in this work, we considered only the comparison between the proposed approach and the state-of-the-art DDIPIC algorithm.
## 1 Introduction
In recent years, the acceleration feedback enhanced controller (AFE controller) has drawn great attention due to its simple structure and low requirements for operating conditions. [PERSON] et al. [17] proposed an acceleration-limited proportional derivative controller to enhance the stability of admittance control. [PERSON] et al. [18; 19] showed that the joint acceleration feedback with an accelerometer damps out the oscillations substantially in explicit force control. In addition, an AFE controller can effectively suppress the system disturbance, and have good compatibility with the traditional industrial controller [20]. [PERSON] [21] proposed a new position controller that is suitable for use as the internal position servo of an admittance controller with bounded actuator forces. The novel position controller approximately behaves as a proportional-integral-derivative (PID) controller with an acceleration feedforward in normal situations and as a sliding mode controller when the actuator force is saturated [22; 23]. The admittance controller employing the new position controller realizes smooth transitions between saturated periods and unsaturated periods. Moreover, it quickly responds to changes in the applied force even when the actuator force is saturated, leading to better stability and smoothness. The classic AFE controller can suppress the disturbance by setting the gain K, but high-gain is difficult to obtain in most cases due to the constraints of the control structure in the actual system. In this work, we proposed a reconfigurable SCARA robot with extensible physical space by adjusting the number of modular arms. First of all, the upper arm of the SCARA robot was designed in three different modules, including the forepart module, the middle part module and the rear-end module, which are connected through bolt connectors. Secondly, the multi-objective optimization method based on finite element method was employed to optimize the bolt connectors between the modules. Then, an improved pre-filter AFE PID controller was devised to suppress the disturbance inside the robot. Finally, the experimental setup of the reconfigurable SCARA robot was established to evaluate the structural design and pre-filter AFE PID controller.
# CSEM Optimization Using the Correspondence Principle
## 2 Methodology
### Gradient Preconditioning  
The gradient calculated according to Equation (27) is subjected to a preconditioning process [26], used to compensate for its attenuation with depth. We implement two different approaches. The first approach, preconditioning 1, involves calculating the ratio of the gradient to the squared modulus of the electric field in each model sample, requiring calibration to avoid amplifying the gradient at the edges. In practice, at each frequency, the division is performed by the sum of the normalized field with a constant:  
\[g_{precond}=\frac{g}{\Big{(}\frac{E}{\max(E)}+\delta\Big{)}}\,, \tag{47}\]  
where \(E=E_{x}^{2}+E_{y}^{2}+E_{z}^{2}\), and \(\delta\) can be chosen by evaluating the square modulus of the field in the region of interest.  
The second strategy (referred to as preconditioning 2) involves essentially applying a depth gain factor by multiplying the gradient by an exponential [27]:  
\[g_{precond}=g\times e^{kz/\delta}, \tag{48}\]  
where \(k\) is a dimensionless parameter (close to 1.0), \(z\) is the depth related to the seabed, and \(\delta\) is the skin-depth [28] at the frequency of the gradient calculation.
## 4 Prospects for Research on the Optimization of MOT Parameters
### Some Suggestions for Parameter Optimization Research
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Years**} & **Research Team** & \multirow{2}{*}{**Working Model**} & \multirow{2}{*}{**Optimization Method**} & \multirow{2}{*}{**Performance Index**} \\  & **Member** & & & \\ \hline
2021 & [PERSON] & Rate equation model & Bayesian optimization & Trapping rate \\
2023 & [PERSON] & \begin{tabular}{c} Monte Carlo simulation based on \\ motion equation \\ Actual system \\ \end{tabular} & \
\begin{tabular}{c} Control variable method \\ Bayesian optimization \\ \end{tabular} &
\begin{tabular}{c} Atomic beam flux \\ Atomic group fluorescence \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._to guarantee. Although the MOT principle has been proposed for more than 30 years, owing to the complexity of cold atom theory and the optical magnetic principle, it is still difficult to reproduce the atomic motion process that can guide practical applications through numerical simulation [89; 90; 91]. In the study of atomic MOT parameter optimization, although the team establishing the numerical model has achieved the optimization of various performance indicators, comparative analysis with the actual system is lacking, and the accuracy of the numerical model cannot be guaranteed. To fully exploit the advantages of numerical models in parameter optimization, we should continue to promote the comparison and verification of numerical models with actual systems and gradually build more accurate numerical models on this basis.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 4 Simulation Results
### Comparison with the State-of-the-Art Methods
#### 4.2.5 Scenario 5  
In this scenario, we keep the number of anchor nodes fixed and vary the time steps of the measurements. Specifically, we fix the number of anchor nodes to \(N=10\), while varying the number of time steps, \(T\) from 3 to 10, with a fixed parameters of the measurement error distributions are \(\mu_{m}=6\,\)deg, \(\mu_{p}=6\,\)deg, and \(\mu_{n}=4\,\)dB. The results in Figure 8 show that the performance of the considered method improves as the time steps increase. We can see from the figure that the proposed method has low localization accuracy when \(T=3\) and \(T=4\), because there are not enough data points to estimate the standard deviation of measurement errors at each anchor node. This leads to significant errors in the weight calculations and consequently, large positioning errors. However, when  
Figure 6: Comparison of RMSE as mean standard deviation, \(\mu_{n}\), of RSS measurement error distribution.  
\(T\geq 5\), the proposed method achieves high localization accuracy, with RMSE values close to the CRLB.
# Development of Lift Control System Algorithm and P-M-E Analysis in the Workplace
## 2 Control Network Development
### Ladder Logic  
The ladder logic network is established based on the pre-selected PLC prerequisites, such as the input signal from the hall-call. Some of the ladders that are responsible for tracking the status of different pushes are described in Figures 2 and 3.  
Ladder diagrams are interpreted from left to right and from top to bottom. The rungs, which are also referred to as networks, have several control elements with a single output coil. Table 2 describes the logic input information from the ground floor as illustrated in network 1 of Figure 2a.  
Figure 1: Flowchart representing the algorithm execution in different phases.  
It is important to take a look at the ladders tracking the touch sensors. We can see that from Figure 2, the normal open contact here is responsible for receiving and storing the information received from the touch sensor. After this, the second symbol stores the position of the lift. The information is sent to the counter for processing and the position of the lift is stored in the counter ready for execution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Address** & **Comment** \\ \hline indcart\_Gnd & Q0.0 & indicator of ground floor request \\ Max Entries\_Qu & VWO & maximum no. of entries in the queue/starting address of table \\ Req\_Gnd\_Floor & 10.0 & request coming from ground floor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Logic input information from the ground floor as seen in Figure 2.  
Figure 3: Ladder diagrams for: (**a**) Combining, storing and checking all required conditions; (**b**) Driving the conditions in the output; and (**c**) Automated movement of the doors of the lift.  
Figure 2: Ladder diagrams for: (**a**) Reception and storage of information from touch sensor on the ground and first floor represented as networks 1 and 2; and (**b**) Storage of information related to the current position of the lift.  
The ladder diagram in Figure 3a is responsible for combining and storing all the conditions received (i.e., ladders for checking all required conditions). Based on the results of this condition, the output (i.e., motor drive) is driven by the ladder seen in Figure 3b. The motor executes its action from the output received from the other ladders. The examples of the conditions include: next floor waiting for service, the current lift position and next destination, etc.  
The door of the lift is also automated based on the movement of the lift, which means that when the lift is in motion, the door of the lift will be closed and will remain open for the rest of the time. The ladder to achieve this goal is shown in Figure 3c.
## 1 Introduction
### Transportation Sector  
Digital firms are seriously challenging the existing practices in the Nigerian transportation sector. For example, E-transport companies, such as Nairaxi, Uber, OPay, LaraTransport, and Kobo360, rely on the power of technology to digitalize transport services in Nigeria, and hence build resilience against shocks, improve inclusion, and put Nigeria on the path of sustainable development. For instance, Nairaxi utilizes disruptive technologies, such as blockchain, AI, and IoT, to build a multi-service platform and digital payment technology that make it possible for people to order a ride service at an affordable rate. The digital platform also has a feature that allows people to shop, track and have groceries and other items delivered to them at the comfort of their homes. The firms that render similar services in the transportation sector include Uber, Taxify, and OPay.  
On the other hand, Lara Transport is an AI chatbot that has helped ease people's movement in Lagos State, Nigeria. This chatbot provides critical information, such as step-by-step directions, fare estimations, and alternative means of transportation from one place to another. Furthermore, Kobo360 is another digital transportation firm that has transformed the transportation services in Nigeria. Kobo360 leverages big data and other technologies to build a formidable logistic platform that deals with logistic frictions, thereby reducing transportation costs in the supply chain. The logistic platform helps achieve an inclusive, sustainable, and efficient supply chain by connecting cargo owners, truck owners, drivers, and cargo recipients. Specifically, the firm enables farmers to increase their earnings by reducing agricultural wastages and helping manufacturers to expand their market share [27]. During the COVID-19 pandemic, when the Nigerian government put several measures in place to control the spread of the coronavirus, Kobo360 became a household name, as it helped to build resilience against the pandemic.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Controller Functions
#### 5.4.6 Service Chaining  
Service chaining involves directing network traffic through a sequence of network services such as intrusion detection systems, load balancing, firewalls, etc. [397]. In policy-based service chaining in conventional SDN, the sequence of network services is predefined based on network policies, such as those enforced by the network administrators. In dynamic service chaining, the network functions in the service chain can be updated based on varying network conditions, such as real-time network traffic, which can be used in KDN architecture. For example, in a KDN, by anomaly detection using the knowledge plane, it can be detected whether there is a threat to the network or not. If a threat is detected, the service chain can be updated by adding a firewall [398].
# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 4 Experiments
### Experimental Settings
#### 4.1.3 Baselines  
In this section, we briefly summarize the ten baseline methods and compare them to the NRCLS.  
1. LibFM [36]. As a generalized recommendation method based on matrix discretization, it is widely used in news recommendation for its excellent performance.
2. Wide Deep [37]. The method combines both DNNs and simple linear transformations to improve the model performance.
3. NPA [3]. This method learns news representation through CNN and Attention Network and learns user representation by focusing on how important it is for the user to view the news through the Attention Network.
4. NAML [15]. This learns a unified news representation through multi-news perspectives, focuses on the importance of the user's news browsing, and learns the user's representation through an attention network.
5. LSTUR [38]. The method learns a user's short-term preferences based on the user's browsing history and uses user ID embedding as the user's long-term preferences.
6. NRMS [39]. This focuses on the correlation between news words and the importance of news, so as to learn user representation by using Multi-head Attention Networks.
7. FIM [40]. FIM uses hierarchical null convolution to extract different levels of user and news representation and then predicts the probability of how users will click on the news via 3D-CNN.
8. GNewsRec [41]. This models the long-term interests of the user by using GCN, and bidirectional LSTM is used to model short-term user interests. In this method, the effects of candidate news on users' short-term interests are also considered.
9. UaG [27]. Each user is represented as a single graph, and uses a novel heterogeneous graph pooling technique to obtain high-quality user representation.
10. AMM [42]. A news recommendation method employing BERT to learn matching representation of each browsed-candidate news pair as user-news matching signal.  
Our method adds the consideration of the potential relationship between long- and short-term user preferences and candidate news to the baseline method, which enables our model to achieve superior recommendation performance than the baseline method.
## 4 Application and Results
### CSE Corpus Annotation
#### 4.2.3 Specifications Definition  
The produced specification file holds the annotation schema and uses XML DTD [34] representation. It describes the model and the CSE ontology by turning the abstract ideas and entities/relations into tags and attributes. Following a clear and simple approach for the specifications file helps to build better and more accurate prediction models at a later stage. An excerpt of the produced specification file is depicted in Figure 10:  
Figure 8: Core concepts.  
Figure 10: Excerpt of the specifications file.  
Figure 9: Excerpt of Object properties.
## 3 Results  
### Qualitative Observation of the Threshold Conditions  
First, we use another slightly degraded laser cavity with larger instability, then single shot randomly without warm-up. Figure 2 shows the output pulses with various temporal profiles in the test range from 2.9 to 3.4 m) while the attenuation ratio of the attenuator was fixed. In most pulses, the modulations originated from the mode-locking mechanism can be clearly seen since the unwanted etalon effect was prevented [40]. It can also be found that, with deeper modulations, the pulses under nearly the same pulse energy will possess higher peak power, such as the comparisons between Figure 2b,c as well as Figure 2d,e. After firing hundreds of pulses, we found that all the pulses like Figure 2b,d  
Figure 1: Schematic diagram of experimental setup and the photo of air breakdown. BS: beam splitter; PD: photodetector.  
cannot induce air breakdown. On the contrary, all the pulses like Figure 2c,e can induce air breakdown. By using mode-locked Q-switched pulses, the qualitative observation implied that the peak power could dominate the threshold condition of air breakdown. In the forthcoming section, the threshold conditions were investigated through a quantitative method to support this observation.
# Using Fast Frequency Hopping Technique to Improve Reliability of Underwater Communication System
## 4 Conclusions  
This article is devoted to an underwater acoustic communication system, which has been set to have specific requirements resulting from working in the difficult propagation conditions that occur in shallow coastal waters. The specificity of the work environment of the underwater communication system results in numerous limitations of digital data transmission. The transmitted signal undergoes strong multipath propagation and the influence of the Doppler effect. These phenomena and the narrow frequency band of the system, resulting from the low operating frequency (from several to several dozen kHz) and design restrictions of ultrasonic transducers, result in relatively low data transmission rates being achieved. In turn, transmission losses and noise are the main factors that limit the range of the system and determine the choice of operating frequency. An additional significant limitation is the low speed of acoustic wave propagation, which results in a strong delay in the reception of transmitted signals, which in two-way communication limits the effective transmission rate.  
An underwater communication system using the fast frequency hopping technique has been described in detail. The influence of acoustic wave propagation conditions in shallow coastal waters on the quality of data transmission in the considered communication system was examined. The results of experimental studies confirmed the thesis that increasing the order of diversity \(L\) allowed an improvement of detection conditions to be achieved and thus a reduction in the transmission bit error rate. It can also be stated that the FFH technique is effective in a channel with fading despite the fact that the available operating bandwidth is relatively narrow for a communication system. Unfortunately, an increase in the order of diversity \(L\) reduces the system's transmission rate.  
This research received no external funding.  
Special thanks to [PERSON] and [PERSON] for technical support in experimental tests.  
The author declares no conflict of interest in this work.
## 4 Discussion
### Limitations of the Study  
This study has several limitations that should be acknowledged. First, the tinnitus group was significantly older than the control group, which may have influenced vascular findings such as vertebral artery stiffness and pulsatility index. Arterial stiffness is known to increase with age due to reduced vascular compliance and endothelial dysfunction. Although our results remained statistically significant, we recognize that age could have contributed to the observed differences. Future studies should consider age-matched cohorts or include age-adjusted analyses to validate these findings more robustly.  
Second, we did not use a tinnitus handicap inventory, which could have provided additional insights into the severity and impact of tinnitus on patients' quality of life. However, such inventories are primarily used for assessing tinnitus severity rather than diagnostic purposes.  
Third, the relatively small sample size may limit the generalizability of the findings and highlights the need for further studies with larger cohorts.  
Finally, we were unable to employ advanced imaging modalities, such as magnetic resonance imaging (MRI), to evaluate cranial vascular structures due to financial constraints, which limited our ability to explore certain vascular parameters in more detail.  
Despite these limitations, our study has notable strengths. It is one of the few clinical investigations exploring the relationship between vertebral artery stiffness and idiopathic subjective tinnitus. The use of Doppler ultrasonography, a non-invasive, accessible, and cost-effective imaging modality, allowed for a detailed evaluation of vertebral artery parameters, including stiffness and pulsatility index, providing valuable insights into the vascular contributions to tinnitus. Furthermore, the inclusion of a healthy control group ensured robust comparisons between case and control groups. Our findings open new avenues for understanding tinnitus as a condition potentially influenced by vascular health and highlight the clinical utility of non-invasive vascular assessments.