esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 2 Fundamentals of Optical Wireless Communications
### Channel Model in VLC  
The optical channel components are the optical transmitter (LED), the photo detector (PD) and the transmission medium. For VLC links, intensity modulation (IM) is used, in which the waveform of the signal to be transmitted is modulated onto the instantaneous power of the optical carrier. The technique used in reception is direct detection (DD), in which a photodetector produces an electrical current proportional to the received optical instantaneous power. Usually, optical wireless systems based on IM/DD are modeled as a base band linear, time-invariant system [14], see Figure 2.  
Figure 1: Optical link configurations in VLC: (**a**) directed LOS; (**b**) non directed LOS; (**c**) diffuse.  
Figure 2: Optical channel modeled as a base band linear system.  
In Figure 2, \(X(t)\) is the instantaneous input power, \(Y(t)\) is the output current, and \(h(t)\) is the impulse response. \(N(t)\) is signal-independent additive noise and \(R\) is the receiver responsivity. This base band channel model can be expressed by Equation (1), where \(\otimes\) symbol denotes convolution.  
\[Y(t)=R\cdot X(t)\otimes h(t)+N(t) \tag{1}\]  
The impulsive response, \(h(t)\), is determined by transmitter and receiver characteristics but also depends on their position, orientation and optical signal reflections as well. Plenty of works have been published to characterize an optical wireless channel and its impulsive response \(h(t)\), such as [16] based on evaluating of measures, [17] which applies iterative algorithms, or [18] based on statistical methods. Others works have also been published focusing on studying the VLC channel, such as [19; 20; 21].
###### Abstract  
Recognizing dangerous situations in advance and determining priority is essential in vessel traffic surveillance. The traffic management priority is determined by the vessel traffic service operator (VTSO) employing the closest point of approach (CPA) and the time to CPA (TCPA) of the targets considering their current navigational data. Various environmental conditions influence CPA and TCPA, which affects the importance of surveillance. This study aims to support vessel traffic prioritization in the navigation surveillance of VTSO from the observer side. The vessel tracks were clustered based on density, and a priority index of the vessel surveillance was developed in the VTS area by reflecting regional navigation characteristics. Density-based spatial clustering of applications with noise (DBSCAN) was used for data clustering to classify the surveillance area. A fuzzy membership function was constructed based on the CPA and TCPA belonging to each cluster, and a dataset for determining priorities was constructed, yielding 17 clusters, fuzzy rules, and tables, with the priority index extracted for all vessel pairs to visualize the priority. The results indicated prior recognition of all dangerous situations. The proposed method facilitates vessel surveillance priority determination in high-density areas and predicts the risk in advance, thereby contributing to traffic management.  
vessel traffic service; data clustering; collision risk detection; fuzzy theory; machine learning; surveillance prioritization; decision-making support +
Footnote †: journal: Medical Informatics
## 3 Results  
Figure 4 shows the positional error at the bottom of the hole and the regression curve. The mean errors and the standard deviation were 0.44 (\(\pm\)0.22), 0.61 (\(\pm\)0.26), 0.41 (\(\pm\)0.21), and 0.25 (\(\pm\)0.15) mm for freehand, and the guides were 3.0 mm, 4.5 mm, and 6.0 mm in length. The regression coefficient \(\alpha\) was 2.13 and constant \(\beta\) was \(-\)0.08. As for the significance of the regression coefficient, the length of the guide had a statistically significant impact on the error and the \(p\)-value was 0.0153 (\(p<0.05\)).  
Figure 5 shows the positional error at the opening of the hole and the regression curve. The mean errors and standard deviations were 0.90 (\(\pm\)0.27), 0.16 (\(\pm\)0.10), 0.10 (\(\pm\)0.04), and 0.08 (\(\pm\)0.03) mm for freehand, and the guides were 3.0 mm, 4.5 mm, and 6.0 mm in length. The regression coefficient \(\alpha\) was 0.48 and the constant \(\beta\) was 0.00. For the significance of the regression coefficient, the length of the guide did not have a statistically significant impact on the error, and the \(p\)-value was 0.0709 (\(p>0.05\)).  
Figure 4: (**A**) A boxplot of the error at the bottom of the hole. The black dot represents each sample. (**B**) A regression curve of the error.  
Figure 6 shows the angular error between the ideal insertion vector, actual drilling vector, and regression curve. The mean errors and standard deviations were 5.46 (\(\pm\)2.43), 5.29 (\(\pm\)1.78), 4.38 (\(\pm\)1.72), and 2.83 (\(\pm\)1.47) degrees for freehand, and the guides were 3.0 mm, 4.5 mm, and 6.0 mm in length. The regression coefficient \(\alpha\) was 13.81 and the constant \(\beta\) was 0.84. For the significance of the regression coefficient, the length of the guide had a statistically significant impact on the error, and the \(p\)-value was 0.0415 (\(p<0.05\)).
## 1 Introduction
Researchers have devoted a lot of time to numerous Z-number MCDM approaches, such as the Z-number based CFAHP-CoCoSo, FAHP, DEA, ELECTRE III, TOPSIS, DEMATEL, COPRAS, and CODAS methods [17; 18; 19; 20; 21; 12]. The sustainable circular economy's enablers of the additive manufacturing (AM) paradigm have identified mass customization as one of the vital enablers for sustainability, with social, economic, and environmental impacts, through DEMATEL [22]. However, only some studies have examined mass-customized CE with 3D printing or AM. Further study is needed to explore different industrial sectors. This work is significant, considering it attempts to bridge this gap by reviewing the highly sustainable packaging industry within a mass-customized environment suitable for circular economy practices. The goal is to achieve sustainable value-based SMEs, recognizing their integral role in the circular economy, with the active participation of circular economy experts from academic institutions as decision-makers. Research questions:  
* How can mass-customized circular economy initiatives in the packaging sector help SMEs build a highly sustainable packaging model for value-driven performance? * What are the main challenges SMEs face when implementing mass-customized circular economy practices in the packaging sector and creating sustainable circular supply chain operations? To address this study's research question, we employ the Group Multi-Criteria Decision-Making (GMCDM) technique to determine which circular packaging model has the most sustainable impact on the circular model in the context of the SMEs-based mass customization packaging industry. This technique, which involves assessing a collection of options in view among a selection of decision criteria, is based on expert decision-maker inputs. To address the challenges in the closed-loop mass-customized packaging industry, we propose a Z-number-based fuzzy DEMATEL with TOPSIS (ZF-DEMATEL-TOPSIS) model. Based on prior literature investigations, this model is designed to solve an illustrated mass-customized sustainable circular model with a Z-number-based MCDM model. The goal is to select a highly sustainable packaging circular model known as the first level. Five significant objectives make up the second level. Based on the previous literature on circular models, the third level was divided into 15 sub-criteria as challenges. The three circular models make up the fourth level. We evaluate the suggested technique by ranking the circular model needed to accomplish the goal with a validity test. The remainder of this article is structured as follows: Section 2 provides an overview of related works. Section 3 presents the proposed framework model-solving method, which has practical applications in the circular model selection process. Section 4 offers an application of our framework in the context of significant challenges in the circular model selection. Finally, in Section 5, we conclude and outline future directions, inviting further engagement with our research.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 5 Results and Discussions
### Resource Utilization  
\[\text{resource\_utilization}=\{r:[u(r,t)\mid t\in T]\mid r\in R\}\]  
* **resource_utilization**: A mapping of each resource to its utilization across all time slots.
* \(R\): The set of resources, \(\{1,2,\ldots,15\}\).
* \(T\): The set of time slots.
* \(r\): A specific resource from the set \(R\).
* \(t\): A specific time slot from the set \(T\).
* \(u(r,t)\): The utilization of resource \(r\) at time slot \(t\), representing how much of that resource is being used or allocated at that specific time.  
Figure 10: Actual and maximum allowed working hours for the second model procedure.  
Figure 9: Actual and maximum allowed working hours for the first model procedure.  
**Patient Utilization:**  
\[\text{patient\_utilization}=\left[\sum_{p\in P}u(p,t)\mid t\in T\right]\]  
* patient_utilization: Total number of assignments for all patients at each time slot.
* \(p\): A specific patient within the set \(P\).
* \(P\): Refers to the collection of all patients.
* \(t\): A specific time interval or slot within the set \(T\).
* \(u(p,t)\): Number of assignments for a particular patient at a given time slot.
* \(T\): The collection of all time intervals or slots considered.  
**Staff Utilization:**  
\[\text{staff\_utilization}=\left[\sum_{i\in N}u(i,t)\mid t\in T\right]\]  
* staff_utilization: Total number of assignments for all staff members at each time slot.
* \(i\): A specific staff member within the set \(N\).
* \(t\): A specific time interval or slot within the set \(T\).
* \(u(i,t)\): Number of assignments for a particular staff member at a given time slot.
* \(N\): The collection of all staff members.  
In Figures 13 and 14, utilization refers to how many assignments each patient or staff member had in each time slot. These figures employ line graphs to depict staff assignments and the dynamics of task allocation over time. Red and blue lines correspond to specific roles or tasks, with intersections and divergences illustrating collaborative or independent work phases. We assessed these utilizations over time for both patients and staff. The graphs map each time slot to the model's total number of patient and staff assignments for the two model scenarios. The line graphs display assignment changes over time. Furthermore, the patients have red lines, while the staff have blue ones. These visual distinctions make comparing the two utilizations easy. The graphs also reveal assignment patterns across time and between patients and staff. They also identify peak times and bottlenecks. The two processes optimize assignment allocation and boost healthcare operational efficiency. These line graphs synthesize complex scheduling data into a comprehensible format, allowing for in-depth analysis of staff deployment patterns, task synchronization, and inter-staff coordination.
## 2 Materials and Methods  
### Mining Subsidence Dynamic Process Analysis  
Based on a substantial amount of measured data [41; 42; 43; 44; 45; 21; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55], it was evident that during a complete subsidence process, a ground point typically undergoes three phases as depicted in Figure 1.  
Figure 1: The process of moving a point on the surface.  
* Stage 1: Initial Phase  
The initial phase corresponds to the time when the surface begins subsiding until the subsidence reaches 10 mm or the subsidence velocity reaches 1.67 mm/d. During this interval, at the initial moment within the mining area (\(t=0\)) before any mining activities commence, the subsidence at a specific point on the surface above the coal seam is considered as \(W_{0}=0\) mm. Alternatively, due to factors like measurement errors, it may be assigned a relatively small value. In this phase, both the initial subsidence velocity and acceleration should be 0. Subsequently, as time \(t\) progresses, they gradually increase, the overlying rock strata progressively experience collapse and deformation, intensifying with the passage of time, and transmitting gradually to the surface.
* Stage 2: Active Phase  
The active phase corresponds to the period when the subsidence velocity exceeds 1.67 mm/d. During this phase, the subsidence velocity experiences an initial increase followed by a subsequent decrease. Simultaneously, the subsidence acceleration rises initially, then decreases until it reaches 0. Subsequently, it continues to decrease to a negative minimum and then gradually rises back to 0. Surface subsidence is notably intense during this phase, with the deformation magnitude continually increasing and transmitting to the surface.
* Stage 3: Decline Phase  
The decline phase, in other words, is the stage during which the subsidence velocity falls below 1.67 mm/d. In this phase, the subsidence velocity gradually diminishes and approaches zero. Concurrently, the subsidence acceleration shifts from negative values to gradually increase until it reaches 0. Surface subsidence gradually stabilizes during this phase, surface deformation in the short term is minimal. Furthermore, when the cumulative subsidence over the surface remains less than or equal to 30 mm for a continuous six-month period, the entire surface movement process concludes.
## 5 Differential Diagnosis  
### Typical Symptoms of Hamstring Strain Injuries  
At present, there is no completely unified consensus on the definitions and classifications for muscle injuries [87; 88]. The Munich Consensus Statement may be known as a highly comprehensive guideline for studying muscle injury [57; 87]. The approach of the Munich Consensus Statement deals with muscle injuries in a comprehensive manner, which covers the incorporation of acute, overuse, direct, and indirect injury descriptors [57]. Usually, it is not difficult to classify one as an acute injury or overuse injury based on its onset process and pathological features. An acute muscle injury in sport is characterized as a closed trauma with a clearly defined cause or sudden occurrence. The stresses and strains generated by force applied to tissue are greater than the tissue can withstand [89]. Either internal forces cause the macro-trauma of the tissue as tensile ruptures (strains/laceration) or by external forces from direct contacts, such as by contusions by collision [9; 89]. Noncontact acute muscle injury caused by excessive internal tension usually manifests as muscle fibers tear/rupture [90]. Injuries mostly occur in muscles that suffer from high active and passive tensions, which cause muscle fibers and their surrounding connective tissue to be damaged. Researchers considered that explosive and paroxysmal active tension is produced by the muscle contracting strongly instantly. And passive tension is caused by a stretch of the connective tissue elements between different muscle fibers [91].  
The most severe acute injury of hamstrings is avulsion, which usually involves the entheses in adults. This injury pattern occurs more commonly at the ischial tuberosity than at the distal ligamentous insertion [18]. In such a case, avulsion almost always involves the conjoint tendon, such as the biceps femoris and semitendinosus muscles. It often results in either complete or incomplete tearing of the semimembranosus due to retraction of the avulsion parts. This is the most common form of proximal avulsion [18]. Oblique coronal magnetic resonance imaging demonstrates a large hematoma, usually accompanied by retraction of the semitendinos muscle and the long head of the biceps femoris tendon [24]. It is reported that avulsion usually occurs in the setting of prior or chronic injury, with abnormal entheses morphologic features or degeneration being the most likely predisposing factors [18].  
Overuse injuries are caused by repetitive microtrauma to tissue, usually accompanied by progressively increasing pain [92]. An overuse injury is often associated with underlying pathology which is accumulated by a long-term inappropriate load. Our study will not elaborate on it in detail due to its complex pathology. As knowledge of hamstring injuries continues to grow, researchers suggested that not all hamstring injuries are the same [93]. For instance, the stretch type of muscle fibers injury and the injuries involving the tendon are totally different in hamstrings.
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 4 Discussion
### Future Research
If it were possible to estimate density with minimal variance from satellite imagery, then walrus abundance could be more precisely estimated using satellite imagery alone. Further research is needed to assess whether high resolution (<1 m) optical or SAR images contain sufficient information to accurately estimate walrus densities across fine spatial scales. Given that haulouts can shift in size over the course of just hours, field validation data (such as UAS survey imagery) collected coincident with prearranged satellite acquisitions would be required to robustly quantify relationships between the in-situ densities and those estimated with satellite data.
###### Abstract  
Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.  
chaos; signal detection; chaotic synchronization system; power spectrum; Lorenz system +
Footnote †: journal: Applied Sciences
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
###### Abstract  
This paper studies an adaptive coding scheme for B5G (beyond 5 th generation) mobile system-enhanced transmission technology. Different from the existing works, the authors develop a class of rate-compatible, non-binary, low-density parity check (RC-NB-LDPC) codes, which expresses the strong connection between the algebra-based and graph-theoretic-based constructions. The constructed codes can not only express rate-compatible (RC) features, but also possess a quasi-cyclic (QC) structure that facilitates the encoding implementation. Further, in order to achieve the code rate-adaptive allocation scheme, the authors propose using the K-means++ clustering algorithm to cluster different channel environments, considering various factors that affect channel characteristics. Finally, in order to present the advantages of the adaptive coding scheme, the authors construct a coding scheme for image transmission. The numerical results demonstrate that the developed code can obtain better waterfall performance in a larger code rate range, which is more suitable for data transmission; the adaptive coding transmission scheme can obtain higher reconstructed image quality compared to the fixed code rate-coding scheme. Moreover, when considering unequal error protection (UEP), the proposed scheme can further improve the reconstructed image quality.  
B5G; adaptive coding scheme; channel clustering; k-means++ algorithm; rate-compatible LDPC +
Footnote †: journal: Sensors
# Landsat Surface Product Validation Instrumentation:
## 1 Big MAC Overview
### Purpose
#### 1.1.3 Field Conditions  
The large natural target, shown in Figure 2, was an alfalfa field that was harvested approximately one week before August 30 and 31, the field collection period. The alfalfa had grown back to approximately 4 inches (10.16 cm) in height.  
Weather conditions varied throughout 30 August 2021. Temperatures remained in the upper 70 \({}^{\circ}\)F throughout most of the day, reaching a maximum temperature of 80 \({}^{\circ}\)F. The 10:00 AM (15:00 UTC) and 12:00 PM (17:00 UTC) field collection periods were free from clouds (see Figure 7). Broken altocumulus clouds formed over the Brookings area in the early afternoon, providing varied cloud cover during the 2:00 PM (19:00 UTC) field collection period, as shown in the upper right image of Figure 7. Wind speed data were taken from the Brookings Airport ASOS. Winds remained calm throughout the 10:00 AM collection period, with winds around 5 mph, as shown in the lower plot of Figure 7. Winds began to increase at the 12:00 PM field collection period, fluctuating around 9 mph \(\pm\) 2 mph.  
Figure 7: Weather conditions on 30 August 2021. All-sky photos are shown on the top row for approximately 10:30 AM (15:30 UTC), 12:30 PM (17:30 UTC), and 2:30 PM (19:30 UTC), respectively. A timeline of the wind speed recorded by the Brookings airport ASOS station is shown in the lower figure.  
Temperatures on 31 August were once again around 70 \({}^{\circ}\)F, with a maximum temperature of 78 \({}^{\circ}\)F. Cloud cover on 31 August 2021, remained clear throughout the majority of the field collection periods, as shown in Figure 8. Small cumulus clouds began to develop during the final field collection period, as shown in the top right image of Figure 8. Winds were higher throughout the day, with winds of 12 mph at the start of the 10:00 AM field collection period, continuing to increase throughout the hour. The 12:00 PM field collection period winds varied from 15 mph to 20 mph. Therefore, it was decided that the UAS would not be used at the final 2:00 PM collection time. While there was a very brief moment of the winds dropping below 15 mph from 2:00 PM-3:00 PM, winds remained at 15 mph-20 mph throughout the rest of the hour.