esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of IMUs and Optical Systems in Layup Shot Biomechanics
## 3 Results
### Statistical Parametric Mapping (SPM) Analysis
Figure 7: **(a)** An example of the difference between the shoulder joint angle output of the Vicon and Xsens, where the shoulder flexion-extension and external-internal joint angle trajectories did not exceed the dotted lines, which indicate the critical threshold for significance (**t*** = 3.007, **t*** = 3.027), while the shoulder’s abduction-adduction joint angle had supra-threshold clusters that crossed the significance threshold twice (**t*** = 3.047, \(p\) = 0.025, \(p\) = 0.044), but this occurred during consecutive time points not exceeding 6% of the task (0–3%, 70–76%). **(b)** Despite general waveforms agreement, significant differences were detected at separate instances for shoulder abduction-adduction and external-internal joint angles, evident by sizable supra-threshold clusters crossing the critical-\(t\) line (**t*** = 3.132, \(p\) = 0.022, \(p\) < 0.001; **t*** = 3.186, \(p\) < 0.001). Time window of clusters occurring roughly before and after take-off. The mean offset across all joint angles was 1.3\({}^{\circ}\), with the highest offset observed in the external-internal shoulder angle (4.9\({}^{\circ}\)), while the lowest offset was found in the flexion-extension knee angle (0.0\({}^{\circ}\)). Notably, the majority of joint angles showed differences of less than 1.0\({}^{\circ}\). SPM confirmed the consistency of Xsens in detecting condition-specific changes, with cluster sizes and locations showing strong similarity between defended and undefended conditions in both systems (Table 1). Figure 9: SPM plots of the between-condition differences detected by the Xsens (**left**) and Vicon (**right**) systems for the shoulder joint angles during the layup task under defended and undefended conditions. Significant condition-based differences, indicated by supra-threshold clusters exceeding the critical \(t\)-value, were consistently detected by both systems, occurring within similar time windows.
## 3 Results
### Tram Dwell Time and Passenger Volume Trend Analysis  
As the number of passengers has the greatest impact on the tram vehicle dwell time, a trend analysis was performed based on the numbers of counted passengers and tram vehicle dwell time (Figure 11). As expected, with the increase in the number of passengers, the dwell time grows. For the TMK301 tram, the regression line was steeper compared to the regression lines for the TMK2100 and TMK2200, i.e., for the same increase in the number of passengers, the increase in dwell time for the TMK301 was higher.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Percentile**} & \multicolumn{4}{c}{**Dwell Time (s)**} & \multicolumn{4}{c}{**Passenger Number**} \\ \cline{2-9}  & **Overall** & **TMK301** & **TMK2100** & **TMK2200** & **Overall** & **TMK301** & **TMK2100** & **TMK2200** \\ \hline Minimum & 5.0 & 5.0 & 9.0 & 11.0 & 1 & 1 & 1 & 1 \\
1 st Quarter & 11.0 & 10.0 & 13.0 & 14.0 & 4 & 4 & 5 & 4 \\ Median & 13.0 & 12.0 & 15.0 & 15.0 & 7 & 7 & 8 & 7 \\ Mean & 13.9 & 13.0 & 15.6 & 16.3 & 8.1 & 7.7 & 9.1 & 8.2 \\
3 rd Quarter & 16.0 & 15.0 & 17.0 & 18.0 & 11 & 10 & 12 & 11 \\ Maximum & 31.0 & 26.0 & 31.0 & 29.0 & 46 & 37 & 46 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tram dwell time and passenger volume five-number summaries.  
Figure 10: Distribution according to tram vehicle type and tram stop: (**a**) dwell time; (**b**) passenger volume.  
For small passenger volumes (less than 20 passengers), the TMK301 tram with four doors and the fastest opening/closing mechanism had the shortest dwell time, and the modern low-floor TMK2200 tram with six doors had the longest dwell time. For less than five passengers, the dwell time for TMK301 was as much as five seconds shorter. The regression line of TMK2100 showed the same trend as the line of TMK2200. TMK2100 had a faster door opening mechanism (Table 5), and, therefore, its dwell time was around one second shorter. As the number of passengers increased, the difference in dwell time decreased in favor of the longer tram vehicles, TMK2100 and TMK2200.
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 3 Drug Delivery Systems  
In chemotherapy, the most important step is that the drug reaches the desired site of action successfully with minimal adverse reactions. A variety of drug delivery systems have been developed, trialled and used in order to deliver anticancer APIs to specific sites of action, i.e., the lungs, in order to avoid high-dose delivery and therefore reduce associated side effects (Figure 1). Drug delivery systems are also important because they avoid affecting the healthy cells or tissues surrounding the targeted area. The maximum efficacy with the minimum side effects of a drug delivery system can be achieved via controlling the physicochemical properties of the formulation; these include the particle size, shape and distribution, drug loading, entrapment efficiency, surface properties and charge as well as release profile of a drug [30].  
There are three major categories of these lipid-based drug delivery systems that have been widely trialled in cancer treatment: (a) emulsions, which include micro and nanoemulsion [31], and self-emulsifying drug delivery systems [32]; (b) vesicular systems, which include liposomes/proliposomes [33], niosomes/proniosomes [34], transfer-somes/protransfersomes [35]; and (c) lipid particulate systems, which include SLNs [36], NLCs [37] and lipid/drug conjugates (Figure 1).  
A comparison of the susceptibilities of cancer cell lines and normal cell lines is essential during assessment of any formulation in the early stages of development; this is completed ahead of any animal testing in order to identify whether the formulation is able to selectively target cancer cells to be an effective chemotherapeutic agent. Cytotoxicity studies are employed to assess the cell death that is caused by a test material and the parameter measures, and compare cell number in the absence of and following exposure to the specific material [38]. The following terms are used in Table 1.  
* Cell viability: This term refers to the number of healthy, alive cells [39].
* IC50: This term refers to the concentration of a tested material, i.e., active pharmaceutical ingredient or formulation, that is required for 50% inhibition [40].
* Antitumour/cytotoxic effect: This term refers to the ability of any tested material to stop abnormal cell growth or kill (cancer) cells via various mechanisms such as cell apoptosis [41].
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Data Preprocessing Based on the FLASH and RPC Methods  
Because the satellite imagery data in ARD format can be loaded and managed by ODC after being preprocessed, preprocessing steps such as radiometric correction and geometric correction of satellite imagery data are very important. Imagery data from different data distributors (e.g., GF-1, GF-2, GF-6, HJ1A, HJ1B, and JL-1) have different formats and varied data quality. For example, the GF-1 data are of L1A quality with only simple radiometric correction. Therefore, further processing such as high-precision radiometric correction and geometric correction is required to generate Analysis Ready Data with surface reflectance. In this paper, the absolute radiometric calibration coefficients were used for radiometric calibration of GF-1 satellite imagery data, and the FLASH method was used for atmospheric correction. The HighImgCorrect method was adopted for geometric normalization. Eventually, the quality of GF-1 satellite imagery data products was improved to Level 4, which met the requirements for quantitative remote sensing analysis of water body changes, vegetation changes, urbanization, and coastline changes.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 6 Discussion of Simulation Results  
This section analyzes the behavior of RBS Orchestra and our scheduler under different traffic loads. The two placements of nodes described in Section 5 are used for the performance analysis. The evaluation environment and the new scheduler parameters are as described in Table 3. We study the reliability of the network, average delay, and the stability of the network when there is a sudden increase in traffic. RBS Orchestra is a static scheduler. Thus, the allocated resources do not adapt to a sudden increase in traffic. Our approach allocates additional resources if the root's children detect a potential collision. Considering these, we expect our approach to perform better than RBS Orchestra during traffic bursts and perform equally well during low traffic. We also analyze the impact the number of nodes has on the performance of the new scheduler and compare it with a state of the art adaptive approach.
# Rate of Penetration Estimation with Parameter Correction
## 4 ROP Estimation with Models  
### State-Space Model  
Since the dynamics of ROP is difficult to model, here we assume that there is no significant change between \(\text{ROP}(t+1)\) and \(\text{ROP}(t)\) in a short time interval \(\Delta t\). Equivalently, it can be expressed as  
\[\text{ROP}(t+1)=\text{ROP}(t)+\xi_{1}(t), \tag{30}\]  
where \(\xi_{1}(t)\) is the parameter to represent the model uncertainty. From Equation (28), the system can be easily rewritten as  
\[\Delta z_{\text{bit}}(t+1)=\Delta z_{\text{bit}}(t)+\text{ROP}(t)\Delta t+ \xi_{2}(t), \tag{31}\]  
where \(\xi_{2}(t)\) is also introduced as some model uncertainties. The model for the dynamic elongation (\(\Delta L_{\text{dynamic}}(t)\)) can be considered as  
\[\Delta L_{\text{dynamic}}(t+1)=\Delta L_{\text{dynamic}}(t)-\beta_{d}(t) \Delta t+\xi_{3}(t), \tag{32}\]  
where \(\beta_{d}(t)\) is the time constant which dictates the speed at which the system's dynamic response decays and \(\xi_{3}(t)\) is the parameter to represent the model uncertainty. However, since \(\beta_{d}(t)\) is also a parameter needed to be estimated, it is assumed that  
\[\beta_{d}(t+1)=\beta_{d}(t)+\xi_{4}(t). \tag{33}\]Here, let the state vector \(\mathbf{x}(t)\) be given by  
\[\mathbf{x}(t)=\begin{bmatrix}\text{ROP}(t)\\ \Delta z_{\text{bit}}(t)\\ \Delta L_{\text{dynamic}}(t)\\ \beta_{d}(t)\end{bmatrix}\]  
The state equation becomes  
\[\mathbf{x}(t+1)=\mathbf{A}\mathbf{x}(t)+\mathbf{B}\mathbf{u}(t)+\xi(t)\]  
where \(\mathbf{A}\) is the state transition matrix, \(\mathbf{B}\) is the input matrix, \(\mathbf{u}(t)\) represents external inputs (if any), and \(\xi(t)\) represents model uncertainties. The state transition matrix \(\mathbf{A}\) is  
\[\mathbf{A}=\begin{bmatrix}1&0&0&0\\ \Delta t&1&0&0\\ 0&0&1&-\Delta t\\ 0&0&0&1\end{bmatrix}.\]  
The input matrix \(\mathbf{B}\) is  
\[\mathbf{B}=\begin{bmatrix}0\\ 0\\ 0\\ 0\end{bmatrix}.\]  
Assume that the dynamic elongation is obtained from Equation (22) that can be treated as the measurement. The other available parameter is the MD (\(MD_{measured}\)). Given that  
\[\text{MD}_{measured}=\Delta z_{bit}(t)-\Delta L(t)+\text{MD}_{initial}.\]  
One new parameter could be defined as  
\[\widetilde{\text{MD}}(t)=\text{MD}_{measured}(t)-\text{MD}_{\text{initial}}+ \Delta L_{\text{static}}=\Delta z_{\text{bit}}(t)-\Delta L_{\text{dynamic}}(t).\]  
It can be used as the other measurement for such a control system. Then, the measurement equation is  
\[\mathbf{y}(t)=\begin{bmatrix}\widetilde{\text{MD}}(t)\\ \Delta L_{\text{dynamic}}(t)\end{bmatrix}=\begin{bmatrix}0&1&-1&0\\ 0&0&1&0\end{bmatrix}\mathbf{x}(t)+\mathbf{v}(t).\]  
It is written in general as  
\[\mathbf{y}(t)=\mathbf{C}\mathbf{x}(t)+\mathbf{v}(t)\]  
where \(\mathbf{C}\) is the measurement matrix and \(\mathbf{v}(t)\) is the measurement noise. The measurement matrix \(\mathbf{C}\) is  
\[\mathbf{C}=\begin{bmatrix}0&1&-1&0\\ 0&0&1&0\end{bmatrix}.\]
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)  
Human-centric manufacturing (HCM) is an approach to manufacturing that places the human operator at the center of the manufacturing process. It aims to create a work environment that is safe, healthy, and comfortable for workers, while also optimizing manufacturing efficiency and productivity. Human-machine interaction (HMI) is a key component of HCM, as it is essential for creating interfaces between humans and machines that are intuitive and easy to use. HMI plays a critical role in HCM by enabling workers to interact with machines in a way that is natural and efficient. This involves designing interfaces that are intuitive and user-friendly while also providing feedback to the user to help them understand the status of the machine and the manufacturing process. Collaborative intelligence is enabled by empathic understanding between humans and machines [47; 48]. In traditional system-centric manufacturing control strategies, humans are viewed the same as any other manufacturing resources, prioritizing system productivity and reliability over the human factor, which is insufficient for future manufacturing. Concerns have been raised that AI advancements could lead to job loss on the shop floor [49]. However, research shows that better system performance is achieved when humans and AI systems collaborate to form a partnership, creating collaborative intelligence [50]. Interactive collaboration between humans and empathic machines can determine the most effective collaboration mechanisms and action plans in a dynamic setting. Consequently, manufacturing control must evolve to incorporate learning, reasoning, and control mechanisms. The goal of HCM is to create a manufacturing environment that is optimized for human workers, rather than simply automating tasks and replacing human labor with machines. This requires a deep understanding of the needs and capabilities of human workers, as well as the capabilities and limitations of machines. HMI is an essential component of this understanding, as it enables designers to create interfaces that are tailored to the needs of human workers. In summary, HCM and HMI are closely intertwined, as they both seek to optimize the relationship between humans and machines in the manufacturing environment. By creating interfaces that are intuitive and user-friendly, HMI can help create a manufacturingenvironment that is safe, healthy, and comfortable for workers, while also optimizing manufacturing efficiency and productivity (Figure 8).
## 3 Results
### Implant Stability  
The results from implant stability measurements are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Case** & **Hp (mm)** & **Ha (mm)** & **A (\({}^{\circ}\))** & **V (mm)** \\ \hline
1 & 1.85 & 2.82 & 6.39 & 0.96 \\
2 & 1.62 & 2.18 & 5.45 & 1.53 \\
3 & 0.87 & 1.41 & 4.71 & 0.54 \\
4 & 0.75 & 0.97 & 2.49 & 0.31 \\ Mean \(\pm\) SD & 1.27 \(\pm\) 0.47 & 1.85 \(\pm\) 0.71 & 4.76 \(\pm\) 1.44 & 0.84 \(\pm\) 0.46 \\ Median (Min, Max) & 1.25 (0.75, 1.85) & 1.80 (0.97, 2.82) & 5.08 (2.49, 6.39) & 0.75 (0.31, 1.53) \\ \hline \end{tabular}  
Hp: horizontal deviation at implant platform (mm). Ha: horizontal deviation at implant apex (mm). A: Angular deviation. V: Vertical deviation at the apex (mm).  
\end{table}
Table 2: Parameters showing deviation between virtually planned and actually placed implants.  
Figure 4: Periapical radiographs taken at baseline and 3 months. (**A**, **B**, **C**, and **D**) represent Cases 1, 2, 3, and 4, respectively, at baseline. (**E**, **F**, **G**, and **H**) represent cases 1, 2, 3, and 4, respectively, at 3 months postoperative.  
All implants were placed with sufficient initial torque and stability for the connection of healing abutments. The initial placement torque ranged from 30 to 50 N in all cases. ISQ values showed a pattern of increase from the initial to the 3-month postoperative period. Case 1 exhibited the lowest stability at immediate and 3-month postoperative, represented by ISQ values of 59 and 64, respectively. These values indicate the borderline between low and medium stability of the implant fixture. Medium to high stability was achieved in the other cases. Case 4 exhibited the highest initial stability represented by ISQ values of 70. All of the ISQ values were acceptable for the application of conventional loading protocol in this study.
# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
###### Abstract  
Urban rail transit passenger flow forecasting often relies on the traditional \"four-step\" method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents' actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization.  
intelligent transportation; traffic analysis zone division; malleable area unit problem; smart-card data; multiobjective optimization +
Footnote †: journal: Applied sciences  
Article
## 4 Failure Characteristics and Mechanisms  
### Comparative Analysis of Failure Modes  
To investigate the failure process and characteristics of coal under quasi-static loading, acoustic emission (AE) equipment was employed to localize internal crack propagation during the experiment. Figure 8 illustrates the evolution of the AE signals during quasi-static loading. The results demonstrated that coal samples under quasi-static loading predominantly failed via diagonal shear or conjugate shear modes. In the initial loading phase (low cycle numbers), no AE signals were detected. AE activity only initiated when N  
Figure 7: Comparison of peak stress under quasi-static and dynamic loading.  
reached 10, indicating the onset of internal crack propagation, with initial cracks occurring near the sample end regions. As N increased further, the AE signals near the original end regions gradually proliferated, and new AE sources propagated toward the sample interior. At N = 10, cracks began to form, yet AE signal growth remained slow. For example, under a confining pressure of 20 MPa, the AE counts only increased by 46 as N rose from 10 to 16, corresponding to the early phase of unstable crack propagation. A dramatic surge in AE activity (455 counts) occurred between N = 16 and N = 17, marking the advanced phase of unstable crack propagation and subsequent macroscopic fracture formation. This AE evolution aligns with the characteristic trend of \"gradual increase-rapid increase-abrupt surge\" in AE counts [37].  
Under dynamic loading, AE signal acquisition was not feasible due to the opaque confining pressure system (see Figure 2). Consequently, surface crack evolution was analyzed to characterize dynamic failure. Figure 9 presents the surface crack progression in coal specimens under varying confining pressures. Under unconfined dynamic impacts, a single transverse penetrating crack dominated the surface. At confining pressures of 10 MPa and 20 MPa, multiple interconnected cracks emerged, exhibiting tensile splitting failure modes [38]. This behavior arises from the synergistic effects of (1) the continuous propagation of pre-existing fractures, (2) new crack generation under cyclic impacts, and (3) the coalescence of original and induced fractures, ultimately forming interconnected fracture networks and macroscopic failure surfaces.
## 2 Detection System and Data
### Worldwide Lightning Location Network  
The WWLLN initiated and established by the University of Washington in 2004 has its central station situated on the university's campus, with sensors strategically positioned across the globe to facilitate real-time and ongoing surveillance of lightning activity worldwide.  
Operating within the very low-frequency band ranging from 3 to 30 kHz, the WWLLN's sensors are capable of capturing EMPs from lightning strokes that occur thousands of kilometers away. The system employs the time-of-group-arrival method for lightning location, with a global average location error of 3.4 km, as reported by [19]. By 2017, the number of WWLLN's ground stations had expanded significantly from 11 in 2003 to over 65, according to [20]. The network is adept at detecting both intra-cloud lightning and cloud-to-groundlightning, achieving a global detection efficiency of 15% for total flash occurrences, as documented by [21].  
The dataset furnished by the WWLLN includes comprehensive parameters for each lightning event, such as the precise timestamp, geographical longitude, and latitude. Since 2009, the network has been enhanced by integrating energy-related metrics for lightning occurrences. Furthermore, the temporal precision of the recorded data has been refined to an accuracy of 1 us, as confirmed in the studies by [20; 21].  
While the detection efficiency of the WWLLN may not match that of terrestrial-based systems within land areas, its capacity to monitor global lightning phenomena, particularly over the vast oceanic regions where ground-based detection is sparse or absent, offers unique and invaluable advantages in the field of atmospheric electricity and weather monitoring.