esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Successive Approximation Using CS-WSNs
### Simulation Results  
#### 5.3.1 Rate-Distortion Evaluation  
We consider temperature, humidity, and illumination signals, gathered by a WSN in the Intel Berkeley Research Lab [7]. In this setup, we consider a block length of \(N=512\) samples and LASSO for reconstruction. From each \((M_{i},B_{i})\) point, we consider rates increments of 10%, i.e., \(d_{R}=0.1\). We also impose \(d_{N}=0.5\) (\(M\leq\frac{N}{2}\)) and \(c_{n}=10\). The Bjontegaard Delta metric (\(\Delta BD\)) is used to evaluate the RD performance of the successive approximation scheme against the exhaustively generated optimal RD curve. The dataset employed to obtain the table mapping the successive approximation setups is different from the one employed to evaluate the RD performance.  
Figure 8 presents the RD-optimal convex hull curve (as in Section 4.2) and the successive-approximation scheme rate-distortion curve for the reconstruction of the temperature (a), humidity (b), and illumination (c) signals. The Bjontegaard Delta between the exhaustively generated RD-optimal curves and the successive approximation RD performances for temperature, humidity, and illumination signals are \(\Delta BD=0.32\) dB,  
\(\Delta BD=0.72\) dB, and \(\Delta BD=0.67\) dB. The results indicate that the successive-approximation scheme operational-RD curve closely matches the RD-optimal convex hull for the three monitored signals. Nevertheless, for each one, the incremental-transmission scheme operational curves contain more RD points than the convex hull does, meaning that it allows a finer adjustment of the rate.  
Figure 8: RD-optimal convex hull and rate-distortion curves for the successive approximation scheme, for the reconstruction of the signals with LASSO. The block length \(N\) is equal to 512.
## 4 Discussion
Overall, the results of this study demonstrate that coordinate solutions derived from two independent GNSS processing software packages--GAMIT/GLOBK and Bernese--are highly consistent and reliable within the current national geodetic reference frame, KGD2002. Most stations show horizontal residuals on the order of a few millimeters, indicating that both solutions are well-aligned with the existing static datum. However, small systematic biases and increased dispersion at network peripheries are observed in certain regions, suggesting the need to update the KGD2002 frame, which has now remained unchanged for over 15 years since its adoption. These issues can be mitigated to a significant extent by transitioning to a time-dependent (dynamic) reference frame such as ITRF2020. A dynamic frame models not only linear station velocities but also nonlinear motions--including seasonal signals and post-seismic deformation--thereby ensuring that coordinates remain consistent over time with geophysical reality. Furthermore, ITRF2020 is closely aligned with global GNSS frameworks (e.g., PPP, WGS84), offering enhanced interoperability for geodetic and geospatial applications that demand international-level precision [3; 34]. This approach has already been adopted in several countries. For instance, Malaysia implemented a semi-kinematic geodetic reference frame based on ITRF2014, in which national control point coordinates are defined not only by their epoch positions but also by associated velocity vectors. This system enables the temporal propagation of coordinates without requiring repeated field surveys and provides a stable foundation for high-precision surveying, land administration, and long-term spatial data infrastructure [35]. Considering these international developments, the findings of this study support the growing domestic and global consensus on the need for adopting a time-aware, globally consistent reference frame to support the evolving demands of high-accuracy geodetic applications.
# Green Project Planning with Realistic Multi-Objective Consideration in Developing Sustainable Port
## 2 Literature Review
### Previous Studies in Project Planning and Scheduling  
Project planning and scheduling problems have been studied widely in the literature, which involve different industries including surgical scheduling problem [1; 2], construction project planning [3; 4; 5; 6], manufacturing resource planning [7; 8; 9; 10; 11], enterprise resource planning [12; 13], etc. Many papers have been published in this area and some researchers provided related comprehensive review papers [14; 15].  
Some researchers investigated the project planning and scheduling problems from other perspectives. [PERSON] et al. [16] addressed the resource constrained project scheduling problem with uncertain activity durations. [PERSON] et al. [17] considered a generalization of the resource-constrained project scheduling problem with flexible resource profiles in discrete time periods. [PERSON] et al. [18] proposed two different integer linear programming (ILP) models to solve resource-constrained project scheduling problem. To better balance environmental issues and economic goals, some researchers investigated the deconstruction project scheduling [19; 15], which could have major impacts on the local environment in terms of noise, dust and vibrations. However, few studies have focused on realistic environmental or green objectives in project planning and scheduling problems so far. [PERSON] et al. [20] proposed an attributional life cycle assessment of the secondary environmental impacts of a site remediation. [PERSON] et al. explored the use of project planning methods for integrating sustainability into project management practices of construction engineering projects [21]. A life cycle assessment model, namely the environmental model of construction, was developed by [PERSON] and [PERSON] [3] to help decision-makers assess the environmental performance of building construction projects in Hong Kong from cradle to the end of construction. However, their study only pointed out the importance of environmental factors in project planning and how to evaluate environmental performance in project planning.  
As mentioned above, the GPP in a port has not been investigated so far, although studies on various project planning problems have been reported in the literature. In real-world port operations, it is usual that multiple objectives need to be considered simultaneously. It is thus crucial to address a multi-objective GPP problem in a port.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 5 Experimental Result
### Comparison with State of the Arts  
This experiment compares our method with state-of-the-art techniques in different sets of sensors. First, we pick some recent mobile-based authentication methods based on semi-supervision and full supervision, which comply with the HMOG and BBMAS datasets. Then, we train and test those models with the same procedure of Section 4.2.  
We reproduce the models in [10; 13; 14] for the touch screen, accelerometer, and gyroscope. The AUToSen [10] is an end-to-end learning-based model, while the others are based on hand-crafted features. The DAKOTA [13] first extracts hand-crafted features and verifies users by the SVM. [PERSON] et al. [14] train a three-layers MLP on hand-crafted features, which has 128 neurons in each layer.  
The competing models for accelerometer and gyroscope [3; 4; 6; 9] are all end-to-end models. The CNNAuth [4] and DeFFusion [6] are semi-supervised models, while the others are fully supervised models.  
Table 4 shows the averaged EERs of SOTA methods. As illustrated in Table 4, our method achieves the lowest EERs for both sets of sensors. For the touch screen, accelerometer, and gyroscope, our model showed the EER 10.95% and 12.85% in the HMOG and BBMAS datasets, outperforming SOTA methods with margins of 0.76% and 1.21%, respectively. For the accelerometer and gyroscope, the proposed model achieves an EER of 18.32% and 22.98% in each dataset, outperforming others with margins of 8.04% and 0.30%, respectively.  
We observe that the non-end-to-end methods, such as the DAKOTA, were superior to the end-to-end AUToSen. This is because deep features are sensitive to the intra-class variance of touch screen data. Even though our method is end-to-end, it significantly outperformed the AUToSen by 11.18% and 2.49% in each dataset. This is because LFP-TCN learns fine-grained representations of impostors from noisy data by aggregating global and local feature information.  
Overall, our proposed method is superior to other methods in three aspects. The first is the use of scarce impostor data. Table 4 shows that the impostor-informed models, such as ours and [PERSON] et al., significantly outperformed the semi-supervised models for the accelerometer and gyroscope data. Another advantage is model architecture; a few competing models, such as AUToSen and DeFFusion, are based on LSTM and CNN. In addition, the LFP-TCN is a more robust architecture designed to characterize detailed impostor patterns. Finally, our model learns diverse impostor patterns with random pattern mixing, which generates synthetic impostor instances not present in seen impostors.  
Figure 6: Averaged EERs (%) against only unseen impostors for different data augmentation methods.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 4 Software Design of RFID Reader System
### Design of Communication Protocol for RFID Reader System  
In the whole system, ZigBee terminal node controls MF RC522 RF card collection of data, and then transmits to other wireless or wired network through the ZigBee protocol transmission. The system communication protocol frame is shown in Figure 7.  
Figure 6: System main program flow chart.  
Figure 7: System communication protocol architecture.  
In the RFID reader system, the communication protocol between RF chip of MF RC522 and electronic tags follows ISO/IEC 14443A. The master chip controls the communication between MF RC522 and electronic tag whose type is consistent with the range type of the antenna through the SPI interface. It reads the electronic tag information and processes the information to packets according to ZigBee protocol [16]. It then sends the data packet to the ZigBee coordinator. The communication protocol between them is IEEE 802.15.4. Coordinator connects with the host computer through the UART and eventually transmits data to the host computer for processing. It can also modify the electronic tags information through the host computer sending control instructions.
## 4 JFML-IoT-Based Energy Management System
### Implemented Sensors and Actuators  
The JFML-IoT library includes a wide and extensible set of sensors and actuators designed to support the development of fuzzy logic-based IoT applications. Its modular architecture allows the integration of a broad variety of devices, enabling seamless connectivity with low-power embedded systems and real-time communication through protocols such as MQTT.  
Although JFML-IoT provides implementations for a large number of devices, this section highlights a representative selection of sensors and actuators particularly suitable for the energy management scenario addressed in this paper. These devices cover essential variables such as temperature, humidity, CO\({}_{2}\) concentration, ambient light, and human presence, as well as control interfaces for lighting, ventilation, and HVAC systems.  
The precision and measurement error of the selected sensors were considered during the system design, based on the specifications provided in their technical data sheets. The DHT22 sensors for temperature and humidity offer a typical accuracy of \(\pm\)0.5\({}^{\circ}\)C and \(\pm\)2% RH, respectively. The BH1750 light sensors provide an illuminance resolution of 1 lux with an accuracy of \(\pm\)20%. The MH-Z19 CO\({}_{2}\) sensor has a stated accuracy of \(\pm\)50 ppm or \(\pm\)5%, whichever is greater. The HC-SR501 PIR occupancy sensors are binary devices with a detection range of up to 6 m, but no quantified precision is defined due to their nature. These specifications are aligned with the tolerances commonly acceptable for indoor environmental monitoring and have been considered in the fuzzy rule design to absorb small fluctuations and ensure robust control.  
These components provide direct integration with the fuzzy inference engine by transforming raw data into fuzzy linguistic variables and applying actuator decisions in response to rule-based outputs. Each device is encapsulated within a reusable class that adheres to the abstract interfaces defined in the library, ensuring consistency and maintainability across diverse implementations.  
It is important to note that the list presented in Table 1 is not exhaustive. The JFML-IoT library supports many more sensor and actuator models beyond those shown here, and it has been designed to simplify the addition of new devices as needed. This extensibility is particularly valuable in smart building environments where sensing and actuation requirements evolve with the scale and specificity of the deployment.  
These devices play an important role in validating the proposed fuzzy energy management system, serving as representative hardware for demonstrating the framework's interoperability and modularity. Rather than focusing solely on hardware capabilities, the emphasis lies on how seamlessly these components can be integrated within the JFML-IoTecosystem to support real-time, interpretable, and adaptable energy control strategies in indoor environments.
# A Novel Bearing Lubricating Device Based on the Piezoelectric Micro-Jet
## 1 Introduction  
The drop-on-demand (DOD) injection technology, which was first proposed in ink-jet printing technology, has the advantages of higher injection precision, better control flexibility and greater efficiency than continuous injection [1]. As the primary structure forms to realize DOD injection, the piezoelectric micro-jet brings about high speed development and widespread applications in 2-D or 3-D printing [2], bio-medical field [3; 4; 5], material science [6], MEMS [7], electric fuel injection technology [8; 9]_etc_.  
Due to the development of the electronic technology, the main factor that restricts the life of space system has gradually turned into the bearing lubrication failure [10]. Researchers are mainly engaged in the research of new additives for lubricating oil in order to improve its effective lifetime [11; 12; 13] or design new friction structures [14]. To some extent, these solutions increase the longevity of the friction structure, but we do not think it can fundamentally solve the problem of deterioration, leakage and pollution of lubricating oil.  
To achieve controllable, quantitative and long-acting lubrication of the gyroscope's bearing system, a novel bearing lubricating device, based on the theory of the piezoelectric micro-jet, was proposed for this study. Piezoelectric micro-jets are embedded in the bearing system so as not to increase the overall quality and space. For this study, we only designed one kind of structure of the micro-jet to do the research, and the influences of different structure forms and the number of micro-jets in the results will be carriedout in future works. The injection performances of the micro-jet with different parameters of voltage excitation were studied here, and the relation curves of the performance parameter with related influencing parameters were determined by the results of our numerical simulations and experiments. The results indicate that the piezoelectric micro-jet lubricating device possesses excellent feasibility and effectiveness, which proves that the device can be used as an effective method for the lubrication of bearing systems.
## 2 Literature Review
### Photovoltaic Systems in the Agricultural Sector in Mexico  
The use of photovoltaic systems in the agricultural sector shows the importance of considering that the energy consumption in this sector was 189.27 PJ, increasing by 4.04% in 2018 compared to the previous year. Diesel is the most important fuel used in this sector, which accounted for 74%, followed by electricity with 23.54% of the total energy consumed [45].  
The main applications in the agricultural sector focus on pumping water in small irrigation areas for home gardens and greenhouses. On the other hand, in regions where connection to the CFE is possible, On Grid-PV systems have been used in agribusinesses seeking to mitigate production costs, including slaughterhouses, processing plants, packing plants, dariies, poultry farms, and agrotourism developments. Thus, through the Shared Irrigation Trust (FIRCO) program, from 2008 to 2016, more than 600 systems with loads less than 15 kilo Watt panel (kWP) were installed in the country [45].  
An analysis of the data concludes that the application to aquaculture farms has not been developed; this gap motivates the performance of the present work, with the expectation of a specific but not limited solution proposal, which allows replication in other similar aquaculture facilities. The 4623 Tilapia farms in the country are small-scale, with conditions equivalent to those of the study site, where the main problem lies in feed and energy costs.
Finally, the LDPI ionization method effectively avoids interference effects, like those caused by matrix additives in MALDI methods, which is advantageous in detecting relatively small molecules such as CBZ, as discussed in this paper [36]. In addition, this research method can be used in imaging mode to study the in situ distribution of compounds across the entire sample area. Previously, it has been widely used in biomedical research, drug metabolism research, proteomics, lipidomics, materials science, and other fields. It is particularly suitable for applications that require high sensitivity and rapid spatial imaging of small-molecule compounds, such as the distribution of functional molecules in cell tissues and the distribution of drugs in organisms [37]. Keeping in mind the aforementioned elaboration, we propose here a novel method for the in situ detection of CBZ pesticide residues in plant roots and stems. The method is based on LDPI combined with TOF-MSI, and it offers advantages such as not requiring sample preprocessing, enabling single-step in-situ imaging, and performing simultaneous analyses of multiple datasets, outperforming the existing methods for pesticide residue analysis.
## 2 Scenario of Interference Source Moving along the Satellite Trajectory  
### LEO Satellite Downlink Scenario and J/S Ratio Calculation  
Figure 1 shows a conceptual figure of an LEO satellite downlink scenario in an interference situation where the ground station is exposed to strong EM waves incoming from an airborne source that moves along a path parallel to the LEO satellite trajectory. The LEO satellite moves along a trajectory with an altitude of \(h_{s}\) and transmits image data to the ground station through an X-band downlink. When the ground station is assumed to be the center of the coordinates, \(\theta_{s}\) is the elevation angle between the LEO satellite and the Earth's surface, and \(d_{s}\) is the slant distance from the ground station to the satellite. As the satellite transmits image data to the ground station, the airborne interference source moves parallel to the trajectory of the LEO satellite at a distance \(d_{p}\) from the ground station. The elevation angle between the interference source and the Earth's surface is \(\theta_{i}\), and the slant distance to the interference source is \(d_{i}\). The satellite transmits image data between \(t_{1}\) and \(t_{n}\) to the ground station located at a specific latitude and longitude on the Earth' surface. The  
ideal data communication time is about 600 s, during which the relative angle difference between the LEO satellite and the airborne interference source is \(\psi\). To analyze the link budget under these conditions, the \(J/S\) ratio can be calculated using Equation (1):  
\[\frac{J}{S}=\frac{P_{t}^{i}+G_{t}^{i}+G_{r}^{g}(\xi=\psi)-L_{i}}{P_{t}^{s}+G_{t} ^{s}+G_{r}^{g}(\xi=0^{\circ})-L_{s}}, \tag{1}\]  
where \(S\) is the power received by the ground station from the LEO satellite. \(P_{t}^{s}\) is the transmission power from the satellite, and \(G_{t}^{s}\) is the bore-sight gain of the data transmission antenna in the satellite. \(G_{r}^{g}\) (\(\xi\)) is the gain pattern of the ground station antenna according to the steering angle \(\xi\), and \(G_{r}^{g}\) (\(\xi=0^{\circ}\)) is the bore-sight gain of the ground station antenna. Here, it is assumed that the ground station antenna is precisely tracking in the direction of the satellite, and thus the ground station's movements toward the satellite are always \(G_{r}^{g}\) (\(\xi=0^{\circ}\)). \(L_{s}\) is the path loss between the LEO satellite and the ground station, calculated assuming free space. \(J\) is the power received at the ground station from the airborne interference source and is calculated in a similar way to \(S\). \(P_{t}^{i}\) is the transmission power from the airborne interference source, and \(G_{t}^{i}\) is the bore-sight gain of the airborne interference source antenna. \(G_{r}^{g}\) (\(\xi=\psi\)) is the sidelobe gain of the ground station antenna to the direction where the ground station is exposed to strong incoming EM interference waves. \(L_{i}\) is the path loss from the airborne interference source to the ground station.
## 3 New Compact Models
### The Complete Model
#### 3.1.1 The New Precedence Constraints  
The approach proposed in this work to deal with precedences is similar to the idea originally introduced in Dell'Amico et al. [42] for the _Precedence-Constrained Minimum-Cost Arborescence_ (PCMCA) problem. Constraints (4) and (5) impose the values of \(u_{s}^{t}\) and \(u_{t}^{t}\) to be 0 and 1, respectively, for all \((s,t)\in R\), and \(t\in V:\exists(s,t)\in R\). On the other hand, the set of constraints (6) enforces that \(u_{j}^{t}\geq u_{i}^{t}\) whenever \((i,j)\in A\) is selected to be part of the solution. These concepts together forbid any violation of precedence constraints along a same path of the solution arborescence \(T\).  
Figure 2 shows an example of how a precedence-violating path is detected using the set of constraints (6). In the figure, the range/value of variable \(u_{j}^{t}\) is written on the left of each vertex, and black arcs show the arcs that are part of the solution, while the red arcs show a precedence relationship \((s,t)\in R\). In the figure, constraints (6) enforce that \(u_{1}^{t}\) and \(u_{2}^{t}\) have to be greater than or equal to 1. However, once \(u_{s}^{t}\geq 1\) is imposed through this logic, the constraint (4) relative to variable \(u_{s}^{t}\) is violated, rendering therefore the solution infeasible.