esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Asymmetric Partition Support Monitoring System for the Final Mining Withdrawal Space  
### Asymmetric Support Control Scheme of the Final Mining Withdrawal Space Partition  
Due to the influence of many factors such as the geological structure and mining disturbance of the working face, the degree of plastic damage of the surrounding rock in front of and behind the final mining face is obviously different. The distribution of overburden rock and stress shows obvious zoning characteristics, that is, the stopping coal caving area, the support area of the hydraulic support, the withdrawal channel area, and the stopping coal pillar area.  
1. The reasonable space-time layout of the withdrawal space under the scientific stopping coal caving distance and the basic roof fracture position in the stopping coal pillar area ensures that most of the dynamic and static load stresses of the overlying strata act on the caving direct roof and the loose top coal to improve the stress environment of the withdrawal space overburden.
2. The withdrawal space is divided into the support area of the hydraulic support and withdrawal channel area. It can be seen from the stress distribution cloud diagram that the vertical stress depressor area and the maximum shear stress low-value area of the surrounding rock in the support area of the hydraulic support are large but the roof of the area needs to have a certain bearing capacity to ensure that the top coal will not fall close to the support and the roof will not collapse with the withdrawal of the  
Figure 13: Migration characteristics of overburden structures in withdrawal space under typical stopping coal caving distance: (**a**) the whole picture of similar simulation model; (**b**) \(L_{\text{C}}\) = 0 m; (**c**) \(L_{\text{C}}\) = 30 m. The red font is to highlight the message that different pictures want to convey.  
support; therefore, the strength of the roof in the support area should not be too large. The withdrawal channel area is the key point of the whole withdrawal space support. Therefore, in the design scheme, the anchor cable must pass through the low-value area of the maximum shear stress combined with the bolt to tighten the plasticized coal body, and the anchor cable is anchored to the range of the maximum shear stress increase area. The anchor depth at the shoulder socket of the withdrawal channel also needs to reach the range of the maximum shear stress increase area. Therefore, the inclined anchor cable is used at the shoulder socket of the withdrawal channel area and a row of single hydraulic props are set up at 400 mm from the coal wall of the withdrawal channel to prevent the risk of end mining and roof cutting.
3. The vertical stress depressor area and the maximum shear stress low-value zone of the stopping coal pillar area are small and the support requirements are not high as long as the coal wall does not occur in a large area.  
Based on the typical zoning characteristics of the withdrawal space, an asymmetric zoning support control scheme for the final mining withdrawal space is proposed as shown in Figure 14.
## 1 Introduction  
Massive open online courses (MOOCs) have become popular recently. Learners can select MOOCs from different platforms based on their learning interests [1, 2]. According to a report from Class Central, by December 2021, the number of MOOC learners reached 220 million, and the number of courses reached 19,400 [3].  
Since 2011, Udacity, edX, and Coursera have become well-known international MOOC platforms. On the one hand, courses continue to increase, making it difficult for learners to find suitable courses [4, 5]. Some high-quality course resources are also difficult for target learners to find. On the other hand, the massive learning data generated by MOOC platforms contain rich learning information. Therefore, it is necessary to dig deeply into the characteristics of learners and courses so that the learning characteristics and learning experiences of MOOC learners can be incorporated into improved and personalized course recommendations.  
Currently, MOOCs' recommendation algorithms usually focus on data mining learners' course ratings. However, learners' course choices are also based on their personal interests, preferences, knowledge domains, and learning capabilities. For example, knowledge graph enhanced recommendation algorithms were proposed to estimate learners' potential interests automatically and iteratively [6] In fact, their interests, preferences, knowledge domains, and capabilities dynamically change with time [7, 8]. Notably, owing to the large-scale data in the MOOC context, runtime efficiency is low when using a collaborative filtering algorithm to measure learners' similarities. Therefore, based on multidimensional item response theory in psychometrics and the Ebbinghaus forgetting curve, this study proposed a hybrid recommendation algorithm for MOOCs by integrating clustering and collaborative filtering algorithms. These algorithms mine learners' preferences based on course ratings, course attributes, and multidimensional capabilities by integrating memory weights.  
Coursera, an internationally renowned MOOC platform, was selected to verify the effectiveness of the proposed recommendation algorithm. On the theoretical side, this study can enrich and develop personalized recommendations in the research field of MOOCs. In practice, it can promote the effective knowledge dissemination of high-quality course resources to improve learners' learning experiences in the MOOC context.
## 2 Background  
In the DAA research, the ability to test small UAS DAA systems in BVLOS flights for normal air traffic scenarios is critical to the success of system development, performance testing, and system validation. As an intermediate solution to mitigate the operational safety risks for small UAS DAA system tests, the RAVEN project applied for a new SFOC for BVLOS flight operations in a restricted airspace at the RAVEN test site in Argentia, NL, Canada [10]. During a BVLOS flight, UA often fly out of the visual range of the Pilot-in-Command (PIC); thus, extra safety features have to be installed on the UA to prevent the aircraft from flying beyond the restricted area assigned by the SFOC. Depending on the safety features installed onboard the UA and at the associated GCS, Transport Canada evaluates whether the applicant's proposed operations are safe enough to warrant issuing a BVLOS SFOC. In the RAVEN BVLOS SFOC application, with the previous operating experience learned from Aerosonde UAS (the RAVEN project had carried out a number of environment monitoring BVLOS missions with Aerosonde UAS at the Clarenville--Random Island area, in Clarenville, NL, Canada back to 2007 as described in Appendix A), extended communication links, a First Person View (FPV) video system, a flight termination system (FTS), and Geo-fence and Return-To-Launch (RTL) functions enabled in the autopilot are added to prove to Transport Canada that the RAVEN project has the capability to prevent RAVEN UA from flying away during a BVLOS flight. After these safety measures were designed, implemented, and demonstrated in flight tests, the RAVEN project was granted an SFOC for BVLOS flight operations at the RAVEN Argentia test site from Transport Canada, represented by the red box (6 km by 8 km) on the map in Figure 1, and has carried out a series of testing missions for the development of the RAVEN-proposed small UAS DAA system.  
Figure 1: Map for the Argentia BVLOS operating area, Newfoundland, Canada.
## 4 Experimental Results
### Dataset and Exploratory Analysis
#### 4.1.3 N-Gram Analysis  
In our analysis results, as depicted in Figure 6, we observed the prevalence of certain bi-grams in the dataset, offering a deeper insight into the collective mindset of the authors of these posts. The bi-gram \"don't know\" emerged as the most frequent, appearing 47,675 times. This was closely followed by \"feel like\" and \"don't want\", with occurrences of 44,661 and 38,755 times, respectively. Additionally, the analysis highlighted the recurrence of expressions like \"I'm going\", \"fuck fuck\", and \"like I'm\", each appearing over 12,000 times. Furthermore, expressions such as \"want die\", \"don't think\", and \"I'm tired\"  
Figure 5: Word cloud analysis.  
were notably recurrent. The phrase \"want die\" is particularly alarming, as it directly conveys a disturbing inclination toward ending one's life. The frequent appearance of \"suicidal thoughts\", which was mentioned 5910 times, further accentuates the serious nature of the communications within this dataset.  
The aforementioned phrases illustrate that individuals experiencing suicidal ideation often communicate their melancholy and negative emotions and sentiments in their online posts, both implicitly and explicitly. Our analysis showed that these expressions often involve negations of verbs like \"want\" or \"feel\". This linguistic trend suggests a prevailing reluctance among these individuals to continue enduring their suffering. Recognizing these expressions in social media posts can be pivotal in the early detection of suicidal tendencies, potentially averting the exacerbation of their mental health conditions.  
Figure 6: Bi-grams analysis.  
Intriguingly, the analysis also unearthed the presence of seemingly positive terms in these posts, such as \"get better\", \"high school\", and \"best friends\". At first glance, these terms may appear contradictory to the overarching theme of negativity and despair. However, it is crucial to understand that the mere frequency of such ostensibly positive terms does not negate the underlying distress. Their inclusion in discussions about suicidal ideation often highlights the contrast between what is desired or remembered fondly and the current state of suffering, thereby emphasizing the depth of the individual's struggle.  
Therefore, it is imperative to approach the analysis of such posts with a nuanced perspective. Focusing solely on specific terms or phrases may not provide an accurate representation of the individual's mental state. It is more beneficial to monitor the changes in mood over time and consider the full context of their communication. The identification of n-grams commonly utilized by individuals experiencing suicidal ideation can contribute to the timely detection of this complex mental health concern.
## 3 Implemented Results of Multi-Cloud Services
### Sequential Workflow of Multi-Cloud Services  
Figure 3 depicts a sequence diagram of data uploading and management in the private cloud, with the data manager as the actor. The data manager first uploads KOMPSAT-3/3A Level-1G data through the SaaS system, which stores the original data in the Manila service storage. Then, the Tiling process is performed for web mapping visualization, with processing results sent to the system as an alert when completed. Simultaneously, the ODC indexing process registers the data in the ODC Core system, which already has a module for indexing through the auxiliary files of the KOMPSAT series. The SaaS system includes a calibration function that processes TOAR/TOCR and stores them in Manila. Finally, the data manager can select data for public access through the SaaS management function. The synchronization module will synchronize data to AWS S3 once the public setting is changed to 'On'.  
It describes the process after sharing settings are made in the private cloud SaaS, as shown in the sequence diagram in Figure 4. AWS ODC Core has a separate system for external users, managed through the private cloud Synchronous module. The synchronization process is initiated once the sharing settings are made on the private cloud SaaS management page. When synchronization is performed through the sharing settings, the synchronous system can communicate with AWS S3 because it is connected to an external network, and accepted data is synchronized. After synchronization, the KOMPSAT data processed by the SaaS is stored in the API S3. At the same time, the synchronous system requests indexing from the AWS ODC core service. Access to the data stored in S3 is required to perform indexing, which differs from the block storage method used for storing KOMPSAT data in the private cloud. However, since the ODC project is software that already takes the cloud into account, it provides a Python API for indexing through S3. In this study, we installed this API in the AWS ODC Core service to process it. The processed data can be checked through Explorer, and additional ODC extension projects can be added to provide them to external users.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 3 Proposed Algorithms  
For the \(q\)th-matched filter output, the posterior probability-based symbol detection is combined with FDE, resulting in the proposed PS-FDE algorithm. Figure 3 shows that PS-FDE can be divided into three parts: FDE, information merging, and symbol decision. The output of FDE, after appropriate mapping and information separation, is decomposed into information sequences at different times. Subsequently, based on the correlation function of the CPM signal, the extracted information is delayed and merged with the current symbol. The likelihood value of the merged information is calculated and used to adjust the weights during the merging process. Finally, the received symbols are obtained through hard decisions and de-mapping of the merged information.  
For convenience, \(c(p,q;l)\) is defined as a cyclic sequence, \(c(p,q;-l)=c(p,q;2L-l)\). The following pseudo-symbol is defined, \(v_{n,p,q}^{(l)}=\exp(j\pi h\alpha_{n-l,p})c(p,q;l)\), and \(u_{n,q}\) is expressed as follows:  
\[u_{n,q}=\sum_{l=1-1}^{L-1}\sum_{p=0}^{K-1}v_{n,p,q}^{(l)}, \tag{14}\]  
where \(l=\{1-L,\ldots,0,\ldots,L-1\}\).  
Figure 3: CPM receiver of PS-FDE.
# Feedback Devices for Cardiopulmonary Resuscitation: A Narrative Review
## 4 Equipment Support of CPR Feedback Physiological Parameter Monitoring  
In a comprehensive questionnaire survey encompassing 1652 emergency physicians in China, a variety of methodologies for quality monitoring of CPR were examined. Among the participants, 93.1% (1514) stated that they assessed the quality of CPR by directly observing the actual procedures being performed. A limited proportion of respondents--16.0% (256) and 13.8% (228)--reported the routine utilization of audio-visual feedback devices during chest compression and end-expiratory carbon dioxide monitoring during CPR, respectively. The predominant impediment to the usage of these tools was cited as a lack of necessary equipment. Additionally, a mere 1.3% (22) of respondents indicated the routine use of invasive arterial pressure monitoring during CPR. The primary deterrent to the implementation of this method was the challenge associated with establishing arterial access [44].  
In the context of clinical practices in China, the predominant approach for assessing the quality of CPR performance remains reliant on emergency physicians. The lack of availability of advanced equipment and technological constraints has impeded widespread adoption of audio-visual feedback devices and monitoring mechanisms which can evaluate blood flow perfusion efficacy directly during emergency CPR. Consequently, clinical cardiopulmonary resuscitation quality feedback still needs physiological parameter monitoring equipment, particularly non-invasive physiological parameter monitoring equipment. End-tidal carbon dioxide is the most ideal physiological parameter in cardiopulmonary resuscitation feedback monitoring because of its non-invasive nature, lower acquisition cost compared to multi-parameter monitors, and recommendation by the AHA to use the waveform of carbon dioxide in CPR guidelines.
## 3 Establishment and Simulation of the Error Model
### Ray Tracing and Error Representation
#### 3.2.4 The Radial Motion Error along Y Axis  
Similar to the derivation process of radial runout error along the X axis, the radial motion error along the Y axis is this:  
\[\begin{array}{c}\delta_{y}=\mathrm{LY}_{lab}-\mathrm{LY}_{lab(t=0)}+\delta_ {abc}(1-cos\theta)+\delta_{abc}sin\theta-\left(U_{x}+U_{y}^{7}\right)\left( \varepsilon_{z}-\varepsilon_{pz}+\varepsilon_{pz(t=0)}\right)\\ \hskip 14.226378 pt+\left(U_{z}+U_{z}^{7}\right)\left(\varepsilon_{pz}cos\theta +\varepsilon_{x}+\varepsilon_{ybc}sin\theta+\varepsilon_{xb}cos\theta- \varepsilon_{pz(t=0)}-\varepsilon_{skc}\right)\end{array} \tag{13}\]  
The main factors affecting the radial motion error along the Y axis include the following: the installation errors \(\delta_{xbc}\), \(\delta_{ybc}\), \(\varepsilon_{xbc}\), and \(\varepsilon_{ybc}\); the perpendicularity error \(\varepsilon_{py}\); the working angle manufacturing error \(\varepsilon_{pz}\); the position coordinates of the laser exit point \(U_{x}+U_{x}^{7}\) and \(U_{z}+U_{z}^{7}\). The error compensation method is the same as the radial motion error along the X axis.
## 2 Pressure Inversion Model for Gob Areas Driven by Optical Fiber Monitoring Data
### Distributed Optical Fiber Representation and Calculation Method for Subsidence of Key Strata
Taking the key strata model with a masonry beam structure formed by three collapses as an example, we consider the middle-collapsed rock block to be parallel to the initial position of the key strata. The simplified model is shown in Figure 2. The lengths \(L_{1}{}^{\prime}\), \(L_{2}{}^{\prime}\), and \(L_{3}{}^{\prime}\) of the optical fibers after fracture at different positions of the rock block can be obtained from the initial lengths \(L_{1}\), \(L_{2}\), and \(L_{3}\), along with the strains measured by the optical fibers, using the following expressions:  
\[L_{1}{}^{\prime}=L_{1}(1+\epsilon_{1}) \tag{4}\]  
Figure 1: Fiber characterization model before critical layer breakage. where \(\epsilon_{1}\) represents the tensile strain experienced by the segment \(L_{1}\). The expression for the subsidence \(H_{1}\) of the optical fiber at this location is:  
\[H_{1}=\sqrt{{L_{1}}^{2}-{L_{1}}^{2}}\approx L_{1}\sqrt{2\epsilon_{1}} \tag{5}\]  
Similarly, for the second exposed segment of the optical fiber, the calculation can be performed using the following formula:  
\[H_{3}-H_{2}\approx{L_{3}}^{\prime}\sqrt{2\epsilon_{2}} \tag{6}\]  
The relationship between the optical fiber, which changes position as the rock strata fracture, and its initial position can be regarded as a similar triangle with sides of \(L_{1}\), \(L_{1}\)', \(H_{1}\) and another triangle with sides of \({L_{1}}^{\prime}+{L_{2}}^{\prime}\), \(H_{2}\), and \(L_{1}+L_{2}\). The expression for this similarity is:  
\[H_{2}=({L_{1}}^{\prime}+{L_{2}}^{\prime})\frac{H_{1}}{{L_{1}}^{\prime}} \tag{7}\]  
Substituting Formula (4) into the relevant equation, we obtain:  
\[H_{2}=L_{1}\sqrt{2\epsilon_{1}}+L_{2}^{\prime}\frac{\sqrt{2\epsilon_{1}}}{1+ \epsilon_{1}} \tag{8}\]  
Next, we obtain the expression for \(H_{3}\), which represents the subsidence \(S_{x}\) after the fracture of the key strata. The integrated expression is as follows:  
\[H_{3}=L_{1}\sqrt{2\epsilon_{1}}+L_{2}^{\prime}\frac{\sqrt{2\epsilon_{1}}}{1+ \epsilon_{1}}+{L_{3}}^{\prime}\sqrt{2\epsilon_{2}} \tag{9}\]  
Based on the principles of Equations (4)-(9), the settlement at each fracture point of the key strata, which exhibit a masonry beam fracture pattern, can be inferred from the changes in the strain of the optical fibers embedded within the rock blocks. The tensile strain experienced by the optical fibers can indicate this settlement and the degree of key strata collapse. The specific expressions are as follows:  
\[S_{x}^{\prime}=x_{\rm N}\sqrt{2\sum_{N=1}^{N=x/l}\epsilon_{N}} \tag{10}\]  
where \(H_{N}\) represents the distance between each sampling point and the coal wall and \({S_{x}}^{\prime}\) denotes the subsidence at different positions after the fracture of the key strata. Figure 2.0 Fiber characterization model after critical layer breakage.
## 3 Free-Space Distribution
### Experimental Results  
The transmission test has been performed at different times of the day and under various weather conditions. Except very windy days, where the laser beam suffers strong beam wander, the noise measurement has given consistent results. Compared to fiber-optic distribution, the magnitude of the excess phase noise for free-space transfer is much greater. This fact can be clearly seen by turning off the SSBM and compare the recovered 80 MHz beat signal directly with the original 80 MHz clock signal driving the AOM. Figure 5a inset shows a time-domain trace of the IF output of the mixer (upon low-pass filtering). It is apparent that the phase fluctuation due to atmospheric propagation is much greater than 2\(\upmu\). This is attributed to the much greater refractive index fluctuation in the air due to turbulence. For such large phase modulations, the direct phase noise measurement scheme used in  
Figure 4: Schematic of the outdoor optical frequency transmission test system. AMP: microwave amplifiers, AOM: acousto-optic modulator, BS: beam splitter, EDFA: erbium-doped fiber amplifier, M: silver mirrors, PD: photodetector, R: retro-reflector, and SSBM: single-side band modulator.  
fiber-optic distribution is no longer valid. Instead, one has to look at the entire noise sideband of the clock signal to gain a proper evaluation of the phase noise. For this purpose, we directly measured the spectrum of the 500 kHz beat note. Figure 5a shows a typical spectral trace centered at the nominal frequency. The trace is averaged over 1 s. It shows that the optical clock signal is broadened to the scale of hundreds of Hz through the 60 m atmospheric transmission. Such a significant line broadening is believed to be caused by the fluctuation of the refractive index of the air over the transmission path due to wind and turbulence, which works effectively as a phase modulator.  
A typical result of the Allan deviation measurement is shown in Figure 5b. The fractional frequency instability due to transmission is about \(3\times 10^{-14}\) at 1 s. Considering the difference of the total transmission distance, our results approximately agree with a recent report on optical frequency transfer over a 100 m atmospheric link using a single-frequency laser [15]. The power law of the Allan deviation appears to be close to \(\tau^{-1}\), showing possible influence from a white phase or Flicker phase modulation.