esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model of RSST-SR-DCSK
### Signal Slot Structure of RSST-SR-DCSK  
To enhance the transmission rate of the chaotic communication system, RSST-SR-DCSK utilizes a two-dimensional information transmission scheme. In this scheme, one-dimensional information bits consist of \(b_{0}\cdots b_{N-1}\)\(N\) bits of information. The transmitter's reference signal is equipped with \(2^{N}\) scramblers at the front end, and \(N\) information bits are used to select one of these scramblers for scrambling. At the receiver, descrambling is performed, and the corresponding scrambler is determined by detecting the maximum correlation peak through correlation operations between the descrambled reference signal and the information signal. This process allows the retrieval of \(b_{0}\cdots b_{N-1}\)\(N\) bits of information. The other dimension of information bits consists of \(b_{k}\cdots b_{k+M-1}\)\(M\) bits of information transmitted by the information signal. Similar to traditional SR-DCSK, in the subsequent time slot, a bit information sequence of length \(\beta(\beta=P\times R)\), replicated \(P\) times from a reference signal of length \(R\), is transmitted. Figure 2 illustrates the frame structure of the RSST-SR-DCSK system signal, while Figure 3 depicts the frame structure of the DCSK system signal. Through the signal time slot structure, it can be observed that the RSST-SR-DCSK system, by adopting the structure of SR-DCSK, saves time and transmission energy in the reference signal. The information bits utilize a two-dimensional information transmission approach, enhancing the information transmission rate.  
Figure 4 illustrates the time slot structure of RSST-SR-DCSK. In this scheme, the kth frame signal is delayed by \(\beta\), and the reference slot of length \(R\) is replicated \(P\) times from the delayed \(i\)th frame signal. Then, correlation operations are conducted with the signal itself. By making correlation-based decisions, the original two-dimensional information data can be retrieved.  
Figure 3: DCSK frame.  
Figure 2: RSST-SR-DCSK frame.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Determination of Weights  
#### 3.2.1 Determination of Weights for \(R_{\text{lon}}\)  
Figure 2 illustrates how the integrated risk index \(R_{\text{lon}}\) changes with relative speed difference (\(\Delta v\)) and spacing (\(\Delta x\)) under different values of the weighting parameter \(\delta\). The six subplots show the progression from \(\delta\) = 0.0 to \(\delta\) = 1.0. Each subplot represents how risk varies with the two parameters under different weight settings.  
When \(\delta\) = 0.0 (Figure 2a), \(R_{\text{lon}}\) is entirely dependent on the relative speed difference (\(\Delta v\)). The risk value distribution is directly influenced by speed, while changes in spacing (\(\Delta x\)) have little effect. As \(\delta\) increases (Figure 2b-e), the impact of spacing on \(R_{\text{lon}}\) becomes more prominent, while the influence of speed diminishes. The risk contour gradually reflects a balance between the two parameters.  
In Figure 2f, with \(\delta\) = 1.0, \(R_{\text{lon}}\) is determined solely by spacing (\(\Delta x\)). This shows that, under high weighting, spacing becomes the dominant factor, while the effect of speed difference becomes negligible. These changes indicate how relative speed and spacing contribute differently to risk under varying weight configurations.  
Figure 2: Sensitivity Analysis of \(R_{\text{lon}}\).  
The figure also demonstrates that the choice of \(\delta\) has a significant effect on risk assessment. Overemphasizing either factor may lead to neglecting the other, resulting in an incomplete risk evaluation. Therefore, to achieve a more accurate assessment, \(\delta\) needs to balance both influences. In this study, we set \(\delta=0.4\). This value reflects the effect of speed differences while still accounting for spacing risks, facilitating a more comprehensive assessment of traffic safety.
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Physician/Emergency Room Scheduling  
The next kind of schedule that is common in Canadian papers is physician scheduling in emergency rooms. This type of rostering falls under type one and is very similar to nurse scheduling. The two types of scheduling typically contain the same constraints and considerations with a few important distinctions. For example, physician shifts usually need to be filled by only one physician, but nursing shifts need to be filled by several nurses [17]. Another notable difference is the amount of work and stress that they undertake. This specific distinction in scheduling is important, as almost a quarter of ER's in Canada are unsatisfied with their job, and a notable portion of this is directly related to scheduling woes. This affects patients as well, as over 44% of patients waited over five hours on their last emergency visit in Quebec, 2016 [31]. The other main difference is that arrival rates in the ER are random, so being able to predict them would change how the roster is made. Many schedules are still done by hand and can take a day or more to complete, so any improvements done will have a direct and palpable impact [16]. All of this culminates in a vibrant field with a large degree of innovation. A summary of each papers contributions is seen in Table 3. Some considerations that fall in this category are as follows.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Penalty Min** & **Penalty Mean** & **Penalty Max** & **Range** \\ \hline Tabu Search (fixed) & 1270 (5) & 1425 & 1520 & 250 \\ Tabu Search (random) & 1235 & 1342 & 1495 & 260 \\ Great Deluge & 1135 & 1720 & 3620 & 2485 \\ ID Walk & 1160 & 1391 & 1810 & 650 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative Results.
## 3 Existing Cobot Programming and Control Methodologies
### Programming of Industrial and Collaborative Robots  
Traditional industrial robots have fulfilled manufacturers' automation needs since their introduction in the 1950s [5]. Relieving human workers from repetitious and laborious tasks, these robots have been segregated from humans for safety reasons. Resolutely devoted to its assigned task, the industrial robot blindly follows assigned orders, unable to accommodate sudden changes in circumstances. Requiring relatively infrequent reprogramming, tasks assigned to industrial robots are typically simple, repetitive operations designed to be performed from a fixed location [29]. While some industrial robots are mounted on sliding rails or mobile platforms to increase their mobility, most are stationary because of their bulk and requirement to be housed within protective cages [30]. Their ability to operate at high speed allows them to excel at high-volume manufacturing in the mass production era [31].  
A cobot is designed to perform precision tasks at relatively slower speeds while demonstrating high flexibility and interactiveness and allowing a much more diverse range of operational tasks than industrial robots [7]. In summary, both industrial and collaborative robots are practical but have different roles to play in the industry. Directly comparing attributes between the two, such as speed, without considering these different roles [32] can potentially result in misguided conclusions. If applied to cobots, similar programming techniques for industrial robots may not make sufficient allowance for the differences between the two, which are comparably outlined in Table 2.  
As modern technology progresses, many manufactured products become more complex, as do their manufacturing processes [33]. Today's intricate manufacturing assemblies and processes are generally beyond the scope of industrial robots, especially where part of the workload must be shared with human workers. With a more compact and streamlined form and the addition of sensors and other safety features, cobots can operate outside protective cages and work safely and collaboratively with humans. This allows a level of automated task flexibility and functional expansion, opening up new opportunities for product diversity in manufacturing [34]. As a result, a much wider variety of low-volume, on-demand product manufacturing is now possible, which is the embodiment of agile manufacturing [7; 35]. Accompanying this more flexible and adaptive capability, however, is the need for more frequent reprogramming of cobots [7].
###### Abstract
Previous research has also shown a correlation with physical disability, as measured by the Expanded Disability Status Scale (EDSS), which evaluates neurological impairments and walking ability, even in individuals who are not severely disabled [12]. Preserving physical functions such as walking and activities of daily living (ADLs) is essential for the overall health and quality of life (QoL) of people with MS. Notably, postural instability during gait significantly impacts QoL [8]. Most individuals with MS exhibit postural control and gait abnormalities, even in the early stages of the disease [2; 9]. It has been previously shown that people with MS demonstrate reduced trunk stability during seated arm movements when compared to healthy individuals, reinforcing the clinical observation that core stability is often impaired in this population [13]. Specifically, this population performs worse on tests assessing core muscle strength and endurance [14]. These deficits negatively impact responses to both self-initiated and unexpected disturbances, as well as muscle activation patterns, ultimately affecting the performance of daily activities. During reaching, stepping, or unexpected perturbations, the transverse abdominis muscle is activated before other trunk or limb muscles, highlighting its key role in core stabilization [15]. [PERSON] et al. [16] found that people with MS exhibit altered core muscle activation during walking, which may contribute to increased fatigability. A range of rehabilitative interventions has been proposed for core muscle training in individuals with neurological disorders, including dynamic neuromuscular stabilization, respiratory training, Pilates, Ai Chi, adapted sports, training on unstable surfaces, electrical stimulation, feedback-based training, and robotic therapy. Although core stability training programs have been suggested as an effective method to improve postural balance in individuals with neurological conditions [15], no previous review has specifically examined the effects of core exercise on gait and exercise capacity in people with MS. Therefore, the objective of this study was to systematically review the impact of core training on gait and exercise capacity in people with MS.
## 4 Comparison and Discussion
### Comparison of Interference Mitigation Approaches  
As shown in Figure 2 earlier, the interference mitigation schemes have been classified into five categories which are power control approach, MAC approach, cognitive radio approach, UWB approach, and signal processing approach. In this subsection, the five approaches are compared with each other in terms of robustness to mobility, lossy channel support, self-learning ability, effectiveness, and cost as shown in Table 4. The robustness to mobility is considered as the ability of a mitigation scheme in mobile environments and densely populated WBASNs. The lossy channel support is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interference Mitigation Scheme** & **Throughput** & **QoS Guarantee** & **Collaboration** & **Channel Parameter** & **Collision Rate** \\ \hline FDCR [38] & High & No & Yes & RSSI5  & Lower \\ Adaptive CR-MAC [2] & Medium & Yes & Yes & RSSI & Low \\ C-RICER [3] & N/A4 & No & Yes & RSSI & N/A \\ \hline \hline \end{tabular}  
* Fast Dynamic Cognitive Radio; 2 Cognitive Radio-MAC; 3 Cognitive-Receiver Initiated CyclEd Receiver;
* Not Applicable; 5 Received Signal Strength Indicator.  
\end{table}
Table 3: Comparison of the interference mitigation schemes in the cognitive radio approach.  
examined with respect to high fading channels. Effectiveness can be evaluated whether the technique is robust to the interference. Cost is evaluated on the basis of channel parameters, convergence time, and acceptable performance.
## 5 Conclusions  
This paper proposes a compressive sensing-based speech enhancement technique that uses the dominant columns group orthogonalization of the sensing matrix (DCGOSM). The proposed method attempts to find the sensing matrix in such a way that each column can either contain the noise component or the clean speech component, which is obtained by calculating the dot product of column contributions for the noise and speech samples. The proposed technique greatly reduces the speech recovery time by iterating only through the speech-dominating columns of the sensing matrix, as opposed to other OMP-based techniques, which require iterations through all columns of the sensing matrix. Another advantage of the proposed technique is that it provides uniform improvement for different quality measures, unlike the other sensing matrix optimization techniques that only improve the measures defined in the objective function. In this work, the PSO is adopted for the optimization of the sensing matrix. However, in the future, other optimization algorithms can also be tested. Furthermore, the OMP can be replaced with multiple column selection approaches such as CoSaMP and STOMP.  
Methodology, V.S.; Software, V.S.; Validation, V.S.; Formal analysis, P.D.S.; Supervision, P.D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used are publicly available.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Techniques**} \\ \hline
**Configuration (\(k\),\(m\),\(n\))** & **OMP** & **CoSaMP** & **StOMP** & **Proposed** \\ \hline (8,32,64) & 0.7956 & 0.8071 & 0.3439 & **0.3261 \({}^{*}\)** \\ (16,64,128) & 0.9434 & 0.7652 & 0.3694 & **0.3527** \\ (32,128,256) & 1.3928 & 1.2097 & 0.4637 & **0.4165** \\ (64,256,512) & 2.8048 & 2.0487 & 0.9845 & **0.8542** \\ \hline \hline \end{tabular}  
* The bold font indicates the best obtained result for the selected measure in that row. \({}^{*}\)\(k\) = sparsity, \(m\) = rows in sensing matrix, \(n\) = columns in sensing matrix.  
\end{table}
Table 6: Reconstruction time comparison (in seconds).
## 3 Results
### RQ1: What Are the Existing Recognition-Based Methods?
#### 3.2.1 Publications Years  
Figure 3 illustrates the distribution of chosen research papers according to their respective years of publication. According to the data presented in the figure, it is evident that the year 2021 had the highest volume of published studies, with a total of 10 publications. Subsequently, the papers published in the year 2022 totaled eight, while the years 2018 and 2019 each yielded three publications. In the years 2016, 2017, 2020, and 2023, there was a single article published in each respective year.
## 2 Materials and Methods
### Methods
#### 2.2.2 Post-Processing Methods  
To remove the optimal support structure designs in realizing the final shape of specimen geometry, three alternative post-processing procedures were explored: two mechanical routes, SB and GB, as well as an electrochemical route, ECP. Supports were eliminated using the high kinetic energy of speeding particles in blasting processes. To avoid damaging the specimen geometry, a protective shield was used. The region of the specimen geometry not protected by the shield was exposed to the blasting as a result. The specimen geometry served as the anode in the ECP process, which followed the anodic oxidation concept. The thinnest structures in the specimen geometry are the support structures, which were eliminated first and fully.  
Sand Blasting (SB)  
SB employed a normal corundum with a particle size of 500-700 (KALO GmbH Sandstrahltechnik, Eching, Germany) to remove the support structures. The support structures were irradiated for 90 s at a pressure of P = 7.3 \(\times\) 10\({}^{5}\) Pa. The chemical composition of the standard corundum provided by the supplier is given in Table 3. The general blasting process is depicted schematically in Figure 1a.  
Glass Bead Blasting (GB)  
Glass beads (KALO GmbH Sandstrahltechnik, Eching, Germany) with a particle size of 70-110 (m) were utilized to remove the support structures using GB. The support structures were irradiated for 90 s at a pressure of P = 2.4 \(\times\) 10\({}^{5}\) Pa. Table 4 shows the chemical composition of the glass beads provided by the supplier. Figure 1a also shows a schematic illustration of the typical blasting process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Filling** & **Down Skin** & **Up Skin** & **Support** \\ \hline Laser power (W) & 350 & 100 & 300 & 150 \\ \hline Scan speed (mm/s) & 700 & 1000 & 400 & 650 \\ \hline Layer thickness (\(\upmu\)m) & 50 & 50 & 50 & 50 \\ \hline Hatch distance (mm) & 0.12 & 0.08 & 0.08 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Laser parameters used for the fabrication of different areas within the specimen.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Al2O3** & **TiO2** & **Fe2O3** & **SiO2** & **CaO** & **MgO** \\ \hline Normal corundum & 95.65 & 2.42 & 0.12 & 0.92 & 0.35 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition of the normal corundum (sand) (wt. %).  
Electrochemical Polishing (ECP)  
The electrolyte used to remove the optimal support structure designs by ECP was a mixture of phosphoric acid and sulfuric acid (E268 A, Poligrat GmbH, Munich, Germany). Figure 1b depicts the experimental setup. The electrolyte was heated to roughly 65 \({}^{\circ}\)C, and the temperature was monitored throughout the experiment with a thermometer. A titanium spiral was utilized as the counter electrode (cathode), and a magnetic stirrer was used to generate a consistent current in the electrolyte. A voltage of 6 V within the trans-passive potential region was applied and a recorded current drop was attributed as the removal of support structures, and the experiment was terminated.
## 1 Introduction
Moreover, it is crucial to recognize that the output of optimization routines alone does not guarantee the attainment of the absolute optimal solution; rather, it necessitates the judicious selection of parameter sets to reduce problem complexity and achieve a feasible solution. Consequently, particular attention was devoted to problem simplification during the search for the optimal material parameters by combining the finite element method and genetic algorithm optimization. As a result, the developed F.E. model employed a uniformly adopted lamination scheme in its sub-parts to approximate manufacturing specifications and was subjected to load conditions that simulate the race car's operation. Finally, it should be noted that the optimal stacking sequence is dictated by the monocque's most heavily loaded region, potentially leading to over-engineering. This issue can be mitigated by subdividing the laminate geometry into smaller local sections to enhance accuracy. However, due to the increased computational demands, which were beyond available resources, this approach was not pursued in the scope of this study. The first step towards the analysis of the monocque's response under loading is the specification of its geometry. During the design phase, considerations of chassis layout emerged, with different designs presenting varying advantages and disadvantages. Consequently, the selection and design of the new chassis type were executed through a meticulous examination of cost, performance, space, and safety-related constraints. The fabrication of a \"hybrid\" composite chassis was selected as the optimal choice, as it balances performance, cost, and manufacturing complexity (Figure 1). The primary criteria were achieving higher stiffness and lower mass compared to a spaceframe design and re ducing manufacturing costs compared to a full monocoque chassis (Figure 2). Additionally, the autoclave oven's space limitations for where the structure would be manufactured influenced the decision. Figure 1 summarizes this information, abstractly illustrating the advantages of each criterion, namely rigidity, mass, and manufacturing complexity, to clarify the ranking of alternative chassis designs. The designs are categorized by the extent to which they possess each property; for example, the green bar for the hybrid chassis indicates medium levels of weight, thus its medium length, rigidity, and manufacturing cost, positioning it on the x and z axes for length and height, accordingly.
## 4 Collaborative Distributed Scheduling Approach
### One-Step Collaborative Distributed Scheduling Approach
#### 4.2.1 Scheduling Process  
For O-CDSA, we design that a node does not go to sleep until it has sent out all its data packets in its FIFO queue. We divide the Idle state into I\({}_{0}\) and Ix states. I\({}_{0}\) refers to the Idle state with an empty FIFO and Ix refers to the Idle state with its FIFO queue not empty. At the DD stage, the working state model of a single node can be described as a DTMC model, which is illustrated in Figure 3. After waking up, a node stays in an I\({}_{0}\) state. After it receives a data packet, it may change to Ix state or begin to transmit a data packet from its FIFO queue according to its scheduling probability. If it fails to transmit the data packet, it will re-transmit the data packet again or shift to Ix state. It either changes to I\({}_{0}\)/Ix state or re-transmits another data packet from FIFO queue again after it has successfully transmitted a packet. After having transmitted all data packets in its FIFO queue, it may either shift to its Sleep state, or stay in I\({}_{0}\) state and then begin scheduling state again for a while. If a node goes to Sleep, it will remain this state until the next wake-up time.  
The transmissions of child nodes influence their parent node. Any transmitting of the child nodes will conflict with the transmitting process of their parent node. Thus, in order to schedule a node's state effectively, the node should correctly estimate the states of its child nodes. We introduce a behavior model of child nodes with two states: Mute (M) and Viral (V), indicating the effects of the child nodes on their parent node. The state M corresponds to all its child nodes not in Transmit state, while the state V corresponds to at least one of its child node in Transmit state. Therefore, a node can send out its data packets only when the behavior model of its child nodes is in M state. Transitions between M and V are illustrated in Figure 4.  
In order to transmit packets successfully, a node must compete with other nodes close to it and data packets always conflict with each other. Thus, in order to schedule a node's state effectively, the node should also learn how busy the communication channel is. We introduce a parameter \(\beta\), which indicates the ratio of a data package that has been successfully transmitted to its next-hop. Generally the \(\beta\) of a node reflects how busy the communication channel around the node is.