esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Spatial Interaction Between HDT Behaviors and Clustered Freight Zones
#### 5.4.4 Dump Truck (DT)  
Figure 12 shows a systematic analysis of the trip patterns of dump trucks. As the primary carrier for construction material and debris transportation, dump trucks exhibit complex spatial dynamics characterized by terminal agglomeration and construction site diffusion, along with multi-scenario adaptation. A Sankey diagram analysis shows that Cluster 1 (22.5%) and Cluster 2 (35.64%) account for over 58% of DT trips, concentrated in Shanghai's riverside sand and gravel terminals, which align closely with the bulk cargo transportation hubs and supporting the designation of Clusters 1 and 2 as industrial zones. Dump trucks' operations radiate from these terminals to surrounding construction sites but differ from the hub-and-spoke high-frequency shuttling of STs, instead displaying a fragmented radiation pattern. This includes terminal to single-site material transport, inter-site transfers, and terminal to multi-site dispersed deliveries, reflecting the dynamic and non-fixed nature of material demands in construction projects.  
Concentration metrics reveal an \(HHI_{E}\) of only 0.23 (the lowest) and a route \(HHI_{R}\) of 0.01 (the second lowest value), quantifying the highly dispersed nature of DT trips. Sand and gravel terminals act as material input hubs with high-frequency connections to construction sites, while construction waste transportation links to recycling facilities, forming a closed material-in, waste-out loop of infrastructure logistics, along with inter-site transfers. Notably, DTs have the highest trip share in Cluster 3 across all vehicle types, directly linked to the zone's construction waste transport and resource recovery functions. For example, the low ID entropy in the Pudong construction waste disposal area indicates that transportation demand is predominantly met by licensed fleet operators, consistent with the regulatory requirements for construction waste transport qualification control.  
Spatially, DT trip end hotspots are closer to urban centers, contrasting with the sub-urban industrial concentration of STs, a pattern closely tied to Shanghai's urban renewal processes. Overall, the DT spatial interaction model is shaped by the combined forces of infrastructure logistics demand, transport vehicle characteristics, and urban development imperatives: the agglomeration in Clusters 1 and 2 stems from bulk material transportation economies of scale, the high penetration in Cluster 3 reflects the specialized division of construction waste management, and the urban center activity is a response to the infrastructure-driven demands of stock renewal. This interplay underscores how DT mobility integrates industrial logistics with urban spatial transformation, serving as both a response to and a driver of evolving urban needs.
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
## 3 Model and Algorithm  
### Temperature Drift Model  
In this paper, in order to solve the temperature drift problem of HGMA mentioned in Section 2, a new MEMS temperature drift model is established. Three factors (temperature, temperature variation rate and temperature product term) are considered in this model. They can fully reflect the temperature condition of the MEMS accelerometer. Therefore, it is beneficial to improve the accuracy of the accelerometer. The three-factor matrix \(C\) is shown in Equation (1).  
\[C=\left[\begin{array}{c}T\\ \dot{T}\\ T\times\Delta T\end{array}\right] \tag{1}\]  
where \(T\) is the temperature, \(\dot{T}\) is the rate of temperature variation rate, and \(T\times\Delta T\) is the product term. Then, the complex model is constructed by a neural network, and these three factors are used as inputs to the model, just as shown in Figure 4. Let \(F(\cdot)\) be the objective function to be trained. The objective function is shown in Equation (2):  
\[E=F(\mathbf{C})=F\{\left[\begin{array}{c}T\\ \dot{T}\\ T\times\Delta T\end{array}\right]\} \tag{2}\]
# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 1 Introduction  
Artificial intelligence (AI) is transforming healthcare, particularly from a Precision Medicine (PM) perspective, the branch of medicine that focuses on tailoring medical treatments to individual patients based on unique genetic, environmental, and lifestyle factors. This personalized approach represents a major shift from the traditional \"one-size-fits-all\" paradigm: the ability of AI to process and analyze vast and complex datasets, such as genomic data, medical histories, and patient health information, is critical to creating targeted diagnostic and therapeutic methods. The \"five Vs\" of big data--volume, velocity, variety, veracity, and value--highlight the complexities that healthcare data present, requiring sophisticated AI models to manage these dimensions effectively. As reported in [PERSON] et al. [1], the convergence of AI and PM is already demonstrating significant potential in areas such as early disease detection, treatment planning, and genomics-guided therapies. AI's power to integrate structured and unstructured data, along with its capabilities in natural language processing, computer vision, and conversational systems, enables it to generate actionable insights to support clinical decision-making. The societal impact of AI in healthcare is predicted to expand dramatically. According to a recent Elsevier report [2], 72% of researchers and physicians think AI will revolutionize healthcare within the next few years. However, this transformative potential comes with challenges, including concernsover data privacy, model reliability, and the risk of bias in AI outputs. As noted in both reports, data security and trust are critical for the successful integration of AI systems in healthcare. A key aspect of AI in this context is unsupervised learning (UL), which can identify hidden patterns within unlabeled data, generating new insights that are crucial for disease stratification, biomarker identification, and patient classification. But as these technologies become more integrated into healthcare, guaranteeing the fairness, transparency, and ethical application of AI systems continues to be a crucial priority. This review will explore the latest developments in UL, its applications in PM, and the future directions for integrating AI-driven solutions into personalized care. We will also address the ethical challenges associated with AI, the need for more robust data security frameworks, and the potential impact of these technologies on patient outcomes and healthcare costs.
# A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development
## 7 Discussions and Recommendations
### Rationality of Cell-Level Model Expansion  
The construction of a pack-level lifetime model from cell-level is certainly more logical but complex than studying real-life pack-level dataset. Field data from on-road vehicles often require challenges such as missing data points, noisy and/or partial data, limited boundary conditions, etc., to be tackled in a black-box approach. However, an extension of a cell-level aging model provides the opportunity to merge several interconnected models such as electrical, thermal, and mechanical. Hence, the 1D electro-aging model gives a clear understanding of the cell's performance and characteristics for any operating conditions that are required for other pack-level models leading to a merger of collective models.  
The cell-level models are usually based on numerous lab-based tests that are almost impossible to replicate at pack-level. The variations in the operating conditions, test setup, various performance tests, etc., give the flexibility to develop models with an extended reach. In situ measurements and/or cell autopsy can also verify the physical link to cell degradation if an appropriate cell-level campaign is designed.
## 2 Materials and Methods
### Physical Characterization  
Elemental analysis was achieved through an energy-dispersive X-ray spectrometer (EDX) with JEOL JED-2140, Japan. Powder X-ray diffraction (XRD) was performed on a Rigaku RINT 2200 (Japan) using CuK\(\alpha\) (filtered) radiation (\(\lambda\) = 0.154 nm) at 40 kV and 20 mA between 1.8 and 50\({}^{\circ}\). Scanning electron microscopy (SEM) was accomplished with a JEOL JSM-6330F, (15 kV/12 mA), Japan. Thermal analyses (Differential Thermal Analysis; DTA, Differential Thermal gravimetric; DTG, and Thermal gravimetric; TG) were carried out up to 800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min in a flow of gases using a Seiko SSC 5200 apparatus (Japan). Fourier Transform Infrared (FT-IR) spectroscopy was performed as KBr discs in the range of 425-4000 cm\({}^{-1}\) by Horiba FT-720 (Japan).  
The diffuse reflectance technique was used to measure the optical parameters of the nanomaterials by using a UV/VIS/NIR Shimadzu 3600 spectrophotometer (Shimadzu, Columbia, Maryland, United States). The spectrophotometer was equipped with an integrating sphere attachment (ISR-603) to measure solid materials, and the thickness of the sample was 3 mm. Barium sulfate was used as the reflectance standard.
## 2 Methods
### Electrical Resistivity Tomography (ERT)  
#### 2.2.1 Procedure  
For the resistivity measurements, we used the ERT measurement system, which is a combination of a resistivity meter (Syscal Pro, IRIS Instruments, Orleans, France) and a remote-control unit (Syscal Monitoring Unit, IRIS Instruments), and performed all measurements remotely using an independent power supply consisting of a solar panel and lithium-ion battery.  
The survey line of 23.5 m was established in a straight line on the ground surface of each landfill, and 48 stainless steel electrodes were driven in at 0.5 m intervals and connected to the instrument by a multicore coaxial cable. The measurement profile sequence was generated by the Electre II software (Version 05.06.00, IRIS Instruments), using the dipole-dipole method (a = 1, 2, 4 and n [depth level] = 1-14, 7-11, 21/4-26/4, respectively), and 872 raw data as an apparent resistivity were measured from the surface layer to a depth of  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameters** & **Site A** & **Site B** \\ \hline Location & 36\({}^{\circ}\)05\({}^{\prime}\)38.1\({}^{\prime\prime}\) N, 139\({}^{\circ}\)12\({}^{\prime}\)14.9\({}^{\prime\prime}\) E & 36\({}^{\circ}\)53\({}^{\prime}\)01.2\({}^{\prime\prime}\) N, 139\({}^{\circ}\)43\({}^{\prime}\)11.0\({}^{\prime\prime}\) E \\ Landfill type & Engineered semi-aerobic & Engineered semi-aerobic \\ Capacity (m\({}^{3}\)) & 620,000 & 370,000 \\ Surface area (m\({}^{2}\)) & 57,000 & 42,000 \\ Depth (m) & 15 & 9 \\ Cover soil & yes & no \\ Operation period & 2019–the present & 1988–the present \\  & MSW non-combustible residues & \\  & (38\%) & \\ Waste component (\%) & MSW incineration ash (24\%) & MSW incineration ash (100\%) \\  & Industrial plastic residues (30\%) & \\  & Other materials (8\%) & \\ \hline \end{tabular}
\end{table}
Table 1: Outline of two landfill sites using ERT monitoring.  
approximately 4 m. The current injection time is 500 ms. The measurement time of each run is approximately 20 min.  
Using the apparent resistivity data obtained from the measurements, we performed an inverse analysis using the finite element method and nonlinear least squares method to create a resistivity cross-section profile with the computer application of inversion (Res2 DInv, Version 3.71, Aarhus Geo Software, Aarhus C, Denmark). Negative values and other anomalous values are removed from all data to ensure the quality of the dataset before analysis. The statistical algorithms of the resistivity data processing software (Prosys II, Version 03.13.06, IRIS Instruments) are used to automatically remove anomalous values. Specifically, finite element analysis is used to determine the distribution of resistivity from an initial model created with the measured potential data. The resistivity model is iteratively corrected using a nonlinear least squares algorithm to arrive at the resistivity profile.
## 3 Principle of Particle Swarm Optimization
### Improved PSO (IPSO) Method  
The principle of the basic PSO method is relatively simple as it has few adjustable parameters. In order to expand the application of the PSO method, many researchers have expanded the range of PSO, in which introducing the inertia weight \(\omega\) is one of the widely used optimization algorithms. By introducing inertia weight, the global and local optimization capabilities of basic PSO are coordinated.  
We modified the velocity equation Equation (32) in the basic PSO algorithm into Equation (34), and the inertia weight \(\omega\) can then be used to determine the response of particle to the current velocity.  
\[v_{id}^{t+1}=\omega v_{id}^{t}+c_{1}r_{1}\big{(}p_{id}^{t}-x_{id}^{t}\big{)}+ c_{2}r_{2}\Big{(}p_{gd}^{t}-x_{gd}^{t}\big{)}, \tag{34}\]  
Therefore, whether IPSO has good exploration and development abilities depends on the selected value of inertia weight \(\omega\).  
Currently, there are two strategies to determine the inertia weight \(\omega\). One is the nonlinear method, in which the inertia weight \(\omega\) first increases and then decreases, written as follows:  
\[\omega(t)=\left\{\begin{array}{ll}1\times\frac{t}{T}+0.4&0\leq\frac{t}{T} \leq 0.5\\ -1\times\frac{t}{T}+1.4&0\leq\frac{t}{T}\leq 0.5\end{array}\right., \tag{35}\]  
Figure 4: Flowchart of the PSO method.  
This strategy combines the advantages of decreasing and increasing strategies to improve the performance of the algorithm.  
The other one is the linear method proposed by [PERSON] and [PERSON] (1998) [30], and the inertia weight \(\omega\) can be obtained by  
\[\omega=\omega_{\max}-\frac{t}{t_{\max}}(\omega_{\max}-\omega_{\min}), \tag{36}\]  
where \(\omega_{\max}\) and \(\omega_{\min}\) are the maximum and minimum values of the inertia weight \(\omega\). \(t\) and \(t_{\max}\) are the \(t\)-step iteration and maximum step iterations.  
The IPSO method with added inertia weight value is widely used, and the inertia weight value would also have different effects on the calculation results. When the inertia weight \(\omega\) is large, it benefits the global search. Although the convergence speed is fast, it is not easy to obtain an exact solution. On the other side, when the inertia weight \(\omega\) is small, it is conducive to local searches and obtains more accurate values, but it becomes easy to be trapped in a local search. In this paper, the IPSO method with inertia weight is used as the algorithm for solving the optimization equations. Due to the stochastic nature of the IPSO algorithm, no safe conclusions can be derived through one optimization run. Thus, 50 independent runs should be performed and the mean value should be derived. Compared with the standard PSO method, the IPSO method has faster optimization times and fewer iterations, indicating that IPSO has faster convergence speed and better performance.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Discussion  
An overview of all the discussed schedulers is presented in Table 1.  
Alice and Escalator are static approaches, meaning that the nodes allocate the same number of cells regardless of the traffic requirements. Not adapting to traffic can lead to packet loss, increased latency, and network instability. In contrast, our scheduler exploits an adaptive approach. Like in OSCAR, its design principles are based on the assumption that, in case of convergecast traffic, the nodes that suffer most are the children of the root. In low traffic conditions, OSCAR and our scheduler behave like RBS Orchestra. The traffic load is measured for the children of the root, and additional cells are allocated if it is high. Instead of using the rank to compute the number of needed additional cells, as done in OSCAR, we consider the subtree size. For unbalanced networks, in which the children of the root might have a significant difference in number of descendants, the subtree size is a relevant parameter for determining how much loaded the children of the root can get in case of high traffic. The problem of unbalanced networks is discussed in [16]. In our solution, the subtree size, which indicates how many cells should be added, is passed between nodes using an existing field of the DAO message. A similar approach is used in PAAS. The latter sends the information needed to adapt the schedule via D using an existing field, the authors add another field to the DIO message, increasing the control traffic overhead. Moreover, in PAAS, Tx cells are allocated before Rx cells, which might lead to packet loss if the nodes start sending messages on Tx cells that do not have a corresponding Rx cell on the receiver side. We tackled this problem by waiting for the Rx cells to be allocated before adding the Tx cells. The subtree size of the root's children indicates how many additional cells should be allocated but not when. The root's children monitor their queues to anticipate congestion and adapt their schedule. Additional cells are allocated if the number of remaining packets exceeds a threshold, denoted activation_threshold. If the number of remaining packets in the queue drops below the deactivation_threshold, the additional cells are removed, and our scheduler will again behave like RBS Orchestra. The authors of ATAS also determine congestion by looking at the remaining packets in the queue. The nodes react and allocate consecutively a number of cells equal to the number of remaining packets in the queue. The congestion is dealt with immediately, but allocating cells consecutively might lead to unwanted collision with the schedule of the neighboring nodes. The receiver node needs to allocate/deallocate the corresponding communication cells. In our implementation, a flag for activating/deactivating the scheduler is piggybacked on the next unicast data packets. At this point, our approach is not fully autonomous anymore. Still, it ensures a higher average PDR, lower average delays, higher stability, and
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 2 Mathematical Basis
### Coherence  
One of the key measures to quantify neuronal synchrony is event-related coherence [39; 46; 47]. It examines the frequency-domain relationship between two signals, reflecting the extent to which their spectral components are synchronized. Specifically, it assesses the consistency of the relative amplitude and phase between two signals within a given frequency range. Mathematically, it is a linear method that generates a symmetrical matrix, which lacks directional information. When two signals are identical, the coherence value is 1, whereas it approaches 0 as the signals become increasingly dissimilar. Since its introduction, coherence has been widely employed in brain connectivity studies involving both patients and healthy individuals. These studies span a diverse range of applications, including working memory [48], brain lesions [49], hemiparesis [50], resting-state networks [51], schizophrenia [52; 53], responses to panic medications [54], and motor imagery [55]. Due to the inherent variability in human brains, distinct patterns of coherent neuronal activity have been observed across individuals. For instance, when exposed to flickering visual stimuli, subjects exhibit coherent responses in the visual cortex at the flicker frequency and its harmonics, with varying sizes of coherent neural networks [43; 56].  
Coherence between two signals (\(X(t)\) and \(Y(t)\)) is defined as  
\[Coh_{XY}(f)=\frac{P_{XY}(f)^{2}}{P_{XX}(f)P_{YY}(f)}, \tag{9}\]where \(P_{XX}(f)\) and \(P_{YY}(f)\) are the auto spectral densities of \(X(t)\) and \(Y(t)\) signals, respectively, and \(P_{XY}(f)\) is the cross-spectral density. The coherence function estimates the extent to which \(Y(t)\) may be predicted from \(X(t)\) by an optimum linear least squares function.  
Coherence values always satisfy \(Coh_{XY}(f)\in[0,1]\). In our study, since we focus on the changes in coherence induced by attention (\(\Delta Coh\)), these differences can be either positive or negative, thus satisfying \(\Delta Coh\in[-1,1]\). In the subsequent analysis, we will refer to an increase in coherence (\(\Delta Coh>0\)) as _coherence_ and a decrease in coherence (\(\Delta Coh<0\)) as _anticolherence_. This distinction allows us to better characterize the dynamic shifts in neural connectivity associated with attentional processes.
## 2 Materials and Methods
### Outcome Measures  
A healthcare professional blinded to group allocation administered the instruments the week before the intervention (pre-test), after 10 sessions (post-test 1), after 20 sessions (post-test 2), after 30 sessions (post-test 3), and four weeks after the intervention ended (follow-up). The tests applied were the following.  
Functional disability: This primary outcome was measured using the Spanish version of the WOMAC index [35]. The questionnaire consists of 24 items scored on a Likert scale from 0 to 4 points (0 = none, 1 = mild, 2 = moderate, 3 = severe, and 4 = extreme), with a total score of 98 points (higher scores indicate greater self-perceived functional disability). The items are grouped into three subscales, pain (5 items; 20 points), stiffness (2 items; 8 points), and physical function (17 items; 68 points), which are considered core outcomes in patients with OA [36].  
Perceived functional change: This outcome is assessed only at post-test 3 using a GROC scale. This scale has been used as an outcome measure and an external anchor for comparing other outcomes [37,38]. The GROC is a 15-point scale ranging from +7 (\"a very great deal better\") to 0 (\"about the same\") to -7 (\"a very great deal worse\"), with magnitude categories defined as 1-3 points = small, 4-5 points = moderate, and 6-7 points = large [38].
# Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
## 3 Results
### Synthesis of Findings  
Emerging technologies demonstrate potential for sports injury detection, with field-based tools offering sideline practicality, biomarkers providing high precision, sensors enabling real-time monitoring, and imaging delivering detailed diagnostics.