esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Photovoltaic Potential for Bus Shelters on a City-Scale: A Case Study in Lisbon
## 1 Introduction  
Green policies aim to decarbonize the energy supply. Recognizing cities' importance in mitigating future energy and CO2 emissions growth, the European Council has included in the 2030 climate and energy framework targets for increasing the use of renewable energies to at least 27% of energy consumption [1]. Additionally, and to assist cities and human settlements in achieving a better and more sustainable future, the United Nations' 2030 Agenda for Sustainable Development defined 17 goals for the coming years. Among these, two Sustainable Development Goals (SDG) aim to promote energy efficiency through renewable sources and to support sustainable urbanization. These are SDG7--affordable and clean energy--and SDG11--sustainable cities and communities [2].  
Solar technologies have evolved and are now commonly applied in the urban environment. Typical applications include electricity (photovoltaic--PV) and heat energy for residential use. However, other applications can also use PV technology. These include street lighting, charging stations for mobile devices or e-bikes, informative multimedia stands, carports, road signs and many others. In these PV-based urban applications, no external electrical power supply is needed, so there is no cabling or trenching required, thus keeping installation costs low and providing environmentally friendly energy with zero carbon emissions. It also means that there is minimal disturbance to paved surfaces in the city, making it possible to provide electricity in previously inaccessible areas or where the cost of providing grid-supply electricity is too high.
## 3 Experimental Results
### Frequency Fiber Channel Response Analysis
From Equation (1), the bandwidth of SMF in a DML based IM-DD system can be expressed in Equation (2) [24]:  
\[\text{BW}\ \approx\ \sqrt{\frac{\text{c}}{\text{D}\text{A}^{2}L}} \tag{2}\]  
The first frequency notches for \(L\) = 25 km, 75 km, and 118 km are at around 18 GHz, 10 GHz, and 8 GHz respectively. These values are consistent with the values calculated by using the Equation (2). The bandwidth of SMF in the DML based IM-DD system is more dependent on the frequencv of the first notch of the adiabatic chirp. since the first notch of  
Figure 4: _Cont._  
Figure 4: Decomposition of frequency response (transient chirp and adiabatic chirp) of SMFs with different fiber lengths L. (**a**) \(L=25\) km, (**b**) \(L=75\) km, and (**c**) \(L=118\) km.
## 4 Proposed Algorithm for Reliability Computing of Chain Routing Protocols in WSN
### Chain Head Selection  
* Chain head selection based on weight \(Q\): In single-chain formation, the BS chooses the chain head (CH) based on the weight \(Q\) assigned to each node (Q-Chain). Each node computes its weight \(Q\), by dividing its residual energy by its distance from the BS, as described in Equation (5). The BS compares the weights of all the nodes in the chain and judges the node with the highest weight as a CH. Each node \(i\), is transferring the data to parent nodes, and then the CH sends the collected data to the sink. In multi-chain single-hop routing, the BS selects one CH for each chain, and the CHs send the collected data to the BS. On the contrary, in multi-chain multi-hop routing, the leader node (LN) is selected from the CHs based on the highest \(Q\). The CHs send the collected data to the LN, and the LN sends the data to the BS. \[Q_{i}=E_{i}/D_{i}\] (5) where, \(E_{i}\) denotes the residual energy of sensor node \(i\), while \(D_{i}\) indicates the distance between sensor node \(i\) and the sink.
* Chain head selection based on distance \(D\): In single-chain formation, the BS chooses the CH based on distance \(D\) (D-Chain), where each node computes its distance from the BS. The BS compares the distances of all the nodes in the chain and the node that has the lowest distance is assigned as a CH. Each node \(i\), transfers the data to parent nodes, then the CH sends the collected data to the BS. In multi-chain single-hop routing, the BS selects one CH for each chain, and the CHs send the collected data to the BS. While in multi-chain multi-hop routing, the LN is selected from the CHs based on the lowest \(D\). The CHs send the collected data to the LN, and then the LN sends the data to the sink.
## 3 Methodology
### Embeddings Generation  
To mitigate the problem of the curse of dimensionality, we represent each location with a low-dimensional dense vector (embedding) instead of using traditional location representations such as one-hot. Similar to word embeddings in NLP [30; 31; 32], we generate location embeddings \(\theta_{i}\in\mathbb{R}^{d}\) (\(d\) is the dimensionality of the embedding space) according to the motion behavior of people traveling over the territory, whereby behaviorally related locations share similar representations in mathematical terms. These vectors rely on the concept of \"behavioral proximity\" between places based on people's trajectories, not on locations' geography: two locations are behaviorally similar if they often belong to the same trajectories, they often share the same neighbor locations along the trace [33].  
In order to construct location embeddings, we apply Word2 vec [26], one of the most efficient techniques to define word embeddings in NLP, on the previously pre-processed trajectories. Based on co-occurrences in large training corpora, each element is represented as a vector with multiple activations, whereby elements occurring in similar contexts have similar vectors.  
More specifically, we associate each location with a random initial vector of pre-defined size: the whole list of places refers to a lookup table where each location corresponds to a particular unique row of an embedding matrix of size \(num\_locations\times vector\_size\). To update the matrix, we move a sliding window through every trace, identifying at each step the current focus location and its neighboring context locations along the trajectory. Although we are dealing with an unsupervised model, an internal auxiliary prediction task is defined: each instance is a prediction problem whose goal is to predict the current location with the help of its context (or vice-versa). The task is performed by a neural network model made of a single linear projection layer between the input and the output layers. In our implementation, we adopted the Skip-gram approach, consisting of maximizing the probability of observing the correct context locations \(cL_{1},\ldots,cL_{j}\) given the focus location \(L_{t}\), with regard to its current embedding \(\theta_{t}\). The cost function \(C\) is the negative log probability of the correct answer, as reported in Equation (1):  
\[C=\ -\sum_{i=1}^{j}\log(p(cL_{i}|L_{t})). \tag{1}\]  
The outcome of the prediction, through backpropagation, determines in what direction the location vectors are updated: the gradient of the loss is derived with respect to the embedding parameters \(\theta\), i.e., \(\partial C/\partial\theta\), and the embeddings are updated consequently by taking a small step in the direction of the gradient. Prediction here is therefore not an aim in itself, but just a proxy to learn vector representations. The model updates the embedding matrix according to locations' contexts along the traces using mini-batch stochastic training, until embeddings converge to optimal values.
## 2 Materials and Methods
### Electron Paramagnetic Resonance (EPR) Spectroscopy  
Nine GHz EPR (X-band) spectra were recorded on a Bruker Elexys E-500 spectrometer (Bruker, Rheinstetten, Germany). Capillaries containing the samples were placed in a standard 4 mm quartz sample tube containing light silicone oil for thermal stability. The temperature of the sample was regulated at 25 \({}^{\circ}\)C and maintained constant during the measurement by blowing thermostated nitrogen gas through a quartz Dewar. The protocol of EPR testing is sketched in Figure 2. The instrumental settings were as follows: sweep width, 120 G; resolution, 1024 points; modulation frequency, 100 kHz; modulation amplitude, 1.0 G; time constant, 20.5 ms. From preliminary power saturation tests, r.f. power levels 0.203 and 0.40 mW selected, respectively, for asphaltene and maltene fractions, were found to be sufficiently low to avoid saturation effects. Several scans, typically 4-16, were accumulated to improve the signal-to-noise ratio. Linewidths were measured from first-derivative curves (peak-to-peak). The spin concentration could be estimated by taking the ratio of area of the sample to that of a standard containing a known number of radicals. Therefore, the concentration of paramagnetic centers in both the asphaltene and maltene fractions of bituminous specimens were obtained by double integration of the experimental first-derivative spectrum and compared with EPR spectrum of a known amount of a MgO-MnO solid solution used as standard [36; 37] (spin density = 6.83 \(\times\) 10\({}^{15}\) spin/g). The \(g\)-value (Lande factor) was determined from the condition \(h\
u=g\beta B_{r}=g_{s}\beta B_{s}\), from which \(g=g_{s}B_{s}/B_{r}\) where \(\
u\) is the frequency of the used microwave radiation, \(\beta\) the Bohr magneton for electron, \(g_{s}=1.9810\) is the \(g\)-value for the standard [38; 39], \(B_{s}\) and \(B_{r}\) are the magnetic field values of standard and sample, respectively.
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 4 Result and Discussion
### Comparison of Different Network Layers  
We compare the performance of the local refinement module under different network layers. Table 4 shows the CD value and F-score value of local patch refinement under MLP, GCN, and FAGCN. As can be seen from the table, the structure using FAGCN performs the best, followed by GCN, and the worst using MLP.  
The worst performance of the network using the MLP layer is due to the fact that the MLP does not use the structural information of the mesh, ignores the connection between mesh points, and is not good at recovering mesh details. The effect of GCN is slightly better, but since GCN is only equivalent to a low-pass filter, although it can effectively filter out high-frequency noise, it cannot effectively utilize the high-frequency detail features of the mesh. On the other hand, FAGCN can learn the filters of different frequency bands from the data to adaptively utilize different frequency features, so it performs best.
## 2 Related Works
Moreover, based on the residual energies of all of the nodes in the network, a directed acyclic graph is proposed in [18] to guarantee the WSN lifetime. A distributed approach is used in [19] to schedule nodes to increase the network lifetime and to decrease overall energy consumption by turning off some redundant nodes. Even though all of these approaches evaluated the guaranteed lifetime by a heuristic solution using simulation, they failed to concede balanced energy consumption among sensor nodes. Moreover, most of them generated a profusion of communication overhead. In addition, all existing schemes ignored real deployment for different applications, but targeted only one direction, _i.e._, guaranteeing a pre-configured network lifetime or maximizing network lifetime. When it comes to applications, real-time communication becomes one of the essential components for real deployment. Most sensor applications are very sensitive in terms of delay; many WSN applications need to resolve the inherent conflict between energy-efficient communication and desired end-to-end communication delay [20; 21; 22; 23]. Both SPEED [20] and Multipath Multi-SPEED (MMSPEED) [21] are more promising QoS-based routing protocols that provide a soft end-to-end deadline for real-time packets in sensor networks. These protocols use a geographic forwarding mechanism to route packets to the sink. To guarantee a real-time packet, SPEED ensures a network-wide speed of packet delivery, while MMSPEED uses multiple speed layers. The soft real-time power-aware RACE protocol [22] effectively balances the load by using non-deterministic forwarding. A multi-path routing algorithm, QoS and Energy aware Multi-PAth Routing (QEMPAR) [23], addresses QoS in terms of timeliness and energy efficiency for real-time applications in wireless sensor networks. However, these protocols have the following two shortcomings: (1) the time delay caused by the channel is ignored in almost all existing real-time schemes in WSNs; and (2) high time complexity in these schemes makes them unsuitable for computing on resource-constrained environments. As a result, as far as the authors know, there is no particular answer for guaranteeing the pre-configured network lifetime \(\alpha\) while maintaining real-time packet requirements at the same time in WSNs, with the exception of our previous work.
## 4 The Effect of Error on the Meshing Force in Star Gear Train  
In order to simplify engineering problems, the mechanical model of gear train can be divided into an external meshing pair element and an internal meshing pair element. The external meshing pair element is composed of a sun gear and five star gears, and the internal meshing pair element is composed of five star gears and an inner ring gear. Taking the meshing pair unit as the research object, the error is transformed into the meshing line of each meshing pair equally, and then analyzed from the calculation of the meshing force and the distribution of error on the meshing force.  
According to the mechanical model of the meshing pair, the calculation formula of the elastic meshing force between the meshing pairs is shown as follows:  
\[\left\{\begin{array}{l}F_{\text{spi}}=KS_{\text{spi}}\\ F_{\text{rp}i}=KS_{\text{rp}i}\end{array}\right. \tag{5}\]  
Here, \(F_{\text{spi}}\) and \(F_{\text{rp}i}\) represent the meshing force of external meshing pair and internal meshing pair, respectively, \(K\) represents the average meshing stiffness, and \(S_{\text{spi}}\) and \(S_{\text{rp}i}\) represent the relative displacement of the meshing pair on the meshing line considering the error, respectively.  
The relative displacement on the meshing line considering the error can be expressed as:  
\[\left\{\begin{array}{l}S_{\text{spi}}=w_{\text{spi}}-e_{\text{spi}}\\ S_{\text{rp}i}=w_{\text{rp}i}-e_{\text{rp}i}\end{array}\right. \tag{6}\]  
Here, \(w_{\text{spi}}\) and \(w_{\text{rp}i}\) respectively represent the ideal displacement of the meshing pair on the meshing line during external meshing and internal meshing, and \(e_{\text{spi}}\) and \(e_{\text{rp}i}\) respectively represent the relative displacement of the meshing line caused by the error during external meshing and internal meshing.
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 4 Case Study
### Dynamic Responses  
The above road model, road surface roughness, and the bus-human model were combined to form the human-bus-road coupled vibration system, and the self-developed program was used to solve the equations of motion to obtain the dynamic response of the system. The results are detailed as follows.  
Figure 9 is the vertical acceleration time-history curves of the wheel, the bus body, and the human body in the case of B-level road surface roughness. It can be seen that the vertical acceleration amplitude of the wheel is the largest, the bus body is the second, and the human body is the smallest. The values are 9.662, 3.006, and 0.9966 m/s\({}^{2}\), respectively. Compared with the acceleration amplitude of the wheel, the acceleration amplitudes of the bus body and the human body were reduced by 68.89% and 89.68%, respectively, which is due to the spring-damping systems between the wheel and the bus body, and between the bus body and the human body. The spring-damping system is beneficial to the improvement of passenger comfort.  
Figures 10-12 show the time-history curves of the vertical, pitch, and roll accelerations of the human body under B- and D-level road surface roughness. Figures 13 and 14 show the maximum acceleration amplitude of the A-, B-, C-, and D-level road surface conditions.  
Figure 11: Time history of human body pitch acceleration with B- and D-level road roughness.  
Figure 12: Time history of human body roll acceleration with B- and D-level road roughness.  
Figure 13: The vertical acceleration amplitude with four levels of road roughness.  
It is found in Figures 10-14 that the acceleration of the human body in all directions increases with the deterioration of the road surface condition. Corresponding to B-level and D-level roads, the vertical acceleration amplitude is 0.996 and 4.887 m/s\({}^{2}\), the pitch acceleration amplitude is 2.192 and 6.928 rad/s\({}^{2}\), and the roll acceleration amplitude is 5.434 and 13.566 rad/s\({}^{2}\), respectively. It is observed that the vertical acceleration of the human body is more sensitive to the change of road surface roughness than the pitch and roll accelerations. However, it should be noted that the amplitudes of pitch and roll accelerations are still large, and their impact on human comfort cannot be ignored.
## 3 Results
### Scaling and Reliability
#### 3.4.4 Versatile Mode: Performance on Consumer GPUs  
The results of running 20 different prompts and evaluating the seconds and standard deviation elapsed are shown in (Table 2).  
The performance metrics presented in Table 1 illustrate JAVIS's capability to efficiently handle a range of language and vision-language models on a consumer-grade NVIDIA GeForce RTX 3060 12 GB GPU. Notably, the response times increase with model size within the Llama GPTQ series, from an average of 3.36 s for the Llama-1B model to 9.21 s for the Llama-8B model. This trend highlights the expected scalability challenges as model complexity grows; however, the performance remains within practical limits for many hospital-related applications.  
Conversely, vision-language models (VLMs) exhibit generally faster response times, with the Qwen2-2B model achieving an average of 1.72 s. Even larger Qwen2-7B and Llava-1.5 AWQ models maintain reasonable response times of 4.68 and 2.42 s, respectively, despite increased variability. These results demonstrate that JAVIS can effectively manage both smaller and moderately large models on consumer-grade hardware, ensuring versatility and reliability in diverse hospital environments where high-end computational resources may not be readily available. The **minimum average time** is **1.72 s** for Qwen 7B, whereas the **maximum time** is **9.21 s** for Llama 8B. In **all cases**, the **response time** is always **below 10 s**, concluding JAVIS's adaptability and robustness, making it a viable solution for a wide range of medical and research applications under typical laboratory constraints.  
In addition to the findings, the same experiment was performed in an A6000 50 Gb GPU with a single-instance model in order to test the performance. The analysis showed that even using the full capacity of a big card, response times do not vary greatly, reinforcing that the versatile mode container is efficient for consumer-grade GPUs.
## 5 Numerical Simulation Analysis  
### Numerical Modeling  
To verify the above theoretical analysis and provide a feasible basis for the optimized support scheme, the numerical simulation software FLAC3D (FLAC3D 6.0) was used to simulate the support scheme. The numerical model was established according to geological field information, as shown in Figure 5. The size of the model is 50 \(\times\) 10 \(\times\) 50, and the mechanical parameters of the layers are shown in Table 1.  
Figure 4: (**a**) U-shaped steel bracket; (**b**) anti-bottom arch interlocking beams; (**c**) support program (unmarked numbers are measured in mm).
## 6 Current Challenges and Future Prospects
### Technical and Operational Challenges
_Lightweighting_: AM focuses on lightweighting through topology optimization and advanced simulation tools, enabling the creation of intricate shapes and features not possible with traditional methods. This improves the performance-to-weight ratio in industries such as the automotive, cycling, aerospace, and medical industries. AM's precision allows for complex inner shapes and lattices, saving materials and potentially enhancing part strength and heat management [100; 101]. _AM and IoT optimization_: In Industry 4.0, IoT integration allows connected devices to process information and adapt operations, enhancing production quality. Quick wireless technologies like Bluetooth, WiFi, and 5G enable real-time system adaptation from design to end-user feedback. This process involves customers at every step, improving product development, reducing expenses and waste, and optimizing the supply chain [100; 101]. _Bionic and generative design opportunities:_ Additive manufacturing expands the possibilities for generative design by integrating design and production processes. This combined approach improves quality and reduces production time for essential structural components, such as joints that connect elements and transfer loads in structural systems. Using this methodology, generative design powered by machine learning algorithms can rapidly produce multiple optimized configurations of a component based on design objectives and cost constraints. Selected models undergo analysis for structural performance before being produced via 3D printing, ensuring precision and high product quality. Therefore, integrating generative design with 3D printing enables the creation of innovative and efficient solutions for complex structures.
## 4 Methods
### Building Artificialized Patches to Monitor Land Conversion
#### 4.2.2 Determining the Artificial Patch Envelope: Choosing a Continuity Distance between Buildings  
Current research on the choice of a building continuity distance is generally based on a building gap threshold of 200 m (a 100 m radius), which is internationally used for the definition of urban areas. This definition results from the development of a urban nomenclature that is shared worldwide and corresponds to the distance that a person can comfortably walk between two houses [38]. However, it may not be suitable for the definition of an artificial patch, whose accuracy and characterization depend on the mode of detection and on the spatial resolution of the data used in the analysis.  
We tested several distances and evaluated the results obtained by laying the resulting artificial patches on aerial photographs. The results showed that a 100-m radius (used to define an urban area) tends to include too many non-urbanized areas, while a 25-m median radius overexpresses sprawl in densely artificialized areas.  
Given these results, we decided that a 50-m median radius was sufficient to monitor land conversion in rural, peri-urban and urban areas. A study conducted by the Certu Institute (Research institute for the study of networks, transport, urban planning and state building projects) and the CETE Institute (Research institute for the development of infrastructure) supported our choice [43]. The artificial patch generated did not significantly encroach on surrounding rural areas and allowed us to retain a 1 to 2 ha minimum data collection unit.