esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 5 Results
### Distance Error  
Figure 5 shows the ground truth (in blue) and the model prediction (in orange) for the following six different target satellite pairs: synthetic satellite pairs 0-5, 0-9, and 4-6 and Celestrak satellite pairs 0-1, 0-5, and 2-3. As explained in Section 2, the ground truth represents the time evolution of the normalized satellite-to-satellite distance between 1000 km and 3000 km. Satellite-to-satellite distances higher than 3000 km are denoted with 0, indicating an inability to establish communication, whereas values of 1 reflect distances below 1500 km, symbolizing the potential establishment of an ISL. Encounter values between 0 and 1 can be directly translated to satellite distances between 1000 km and 3000 km, and the decision on when to start ISL communication relies on the stakeholders. From visual inspection, we can say that the model can predict satellite-to-satellite distance with high performance, detecting all the communication opportunities, even when they are really short in duration, as in synthetic pair 4-6 and Celestrak pair 0-5.  
To quantify the error between the ground truth and the model prediction, Figure 6 shows the distribution of the mean absolute error (MAE), considering all satellite pairs in both the synthetic and Celestrak datasets. Note that the error computation only considers the time instances with a ground truth higher than 0 and lower than 1. In cases where the true value is either 0 or 1, the actual distance remains unknown, preventing the calculation of the true distance error. The results show average MAE values of 281 km and 239 km for the synthetic and Celestrak test sets, respectively. Based on these findings, it is evident that the model lacks the level of accuracy necessary to be employed to predict the distance between satellites, particularly in critical applications like collision avoidance.  
Figure 4: Weighted mean square error loss function with \(A=0.5\).
# Effects of Night Ventilation on Indoor Air Quality in Educational Buildings--A Field Study
## 2 Materials and Methods
### TVOC-Concentration
#### 3.2.2 TVOC, Night Ventilation, and Space Usage  
Figure 7 on the left shows the temporal variation of TVOC concentration during the normal occupant periods with the monitored airflow rates, and pressure difference over the building external wall on the left panel. Figure 7 on the right shows the effects of space usage on TVOC-concentrations. The measurements were carried out during a weekday morning in the classroom in primary school 1 representing the general trend in these measurements. In building external wall, the measured pressure differences were mainly below 5 Pa in the measurements. This reveals that the supply and exhaust airflow rates were in balance.  
The pre-started night ventilation method reduced the TVOC concentration to near the minimum level approximately two hours after the ventilation starts up. The continuous ventilation kept the TVOC concentration to a minimum at night. However, the TVOC concentration increased rather in a similar way in the morning than the pre-started ventilation method, although the TVOC level with the continuous night ventilation method was slightly lower at 7:00. The morning room cleaning was at 7:00-8:00 and the lessons began at 8:00-8:30. In intermittent ventilation, start-up, and shut-off the ventilation had a considerable effect on the TVOC concentration changes at night, such that the TVOC concentration decreased after the start-up and increased after the shut-off. This can be likely explained by material emissions or the different operation schedules of air handling units, allowing contaminants to spread between different indoor zones. This intermittent effect was observed in the other buildings as well. This means that the operation of the air-handling units in areas all operation modes must be adjusted and synchronized so that possible spread of pollutants does not occur.  
The results indicate that the TVOC concentration level increased mainly due to daily space usage because the changes of TVOC-concentrations correlate well with the corresponding CO\({}_{2}\) concentration changes. This can be seen clearly in Figure 7 (right), where the CO\({}_{2}\) peaks are rather the same time than the local TVOC maximums. The CO\({}_{2}\) levels measured were at an acceptable level during the occupied hours (below 1000 ppm).  
Figure 6: TVOC concentration [ppb] in the weekday mornings: (**a**) the classroom in primary school 1; (**b**) the playroom in kindergarten 1; (**c**) the classroom in secondary school (intermittent not available); and (**d**) the playroom in kindergarten 2. The line refers to average concentration. The vertical axis stands for concentration and the horizontal axis for early morning between 2:30 and 7:30.
# An Algorithm to Generate a Weighted Network Voronoi Diagram Based on Improved PCNN
## 4 Network Voronoi Diagram Construction Based on the Improved PCNN
### The Construction of the Weighted Network Voronoi Diagram  
As described above, the construction of the weighted network Voronoi diagram can be summarized as the following steps:  
1. Preprocess the network data: 1 All the nodes are found and regarded as the neurons in the improved PCNN, and the road network is divided by the nodes into road segments. 2 The point cluster is projected onto the road segments, and the projection points are set as initial neurons. 3 The connection relationship matrix between neurons is computed.
2. Perform transmission based on the improved PCNN: The auto-waves generated from the initial neurons expand along the corresponding road segments with the speed calculated by the Formula (9); they look for and travel along the shortest paths between neurons simultaneously. The auto-wave will not stop until all the neurons ahead have been fired.
3. Repeat (2) until all the neurons are fired and the transmitting auto-waves stop.
4. Road segments that are not used by any auto-wave are assigned to the corresponding initial neurons. So far, the road network and the corresponding space are assigned to the initial neurons and the weighted network Voronoi diagram has been constructed.  
Figure 8: The final state of auto-wave transmission.  
Figure 7: Transmission of auto-waves.  
As shown in Figure 8, the network space is distributed to different points according to the weights of the points and the attribution of the road segments by the shortest path principle.
# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System
### Soil Macronutrients (N, P and K)
[65] embarked on detecting soil phosphorus and potassium using a vast dataset comprising over 1500 soil samples. Opting for the 1100 to 2500 nm range within the VNIR spectral region, they aimed for reliable prediction outcomes. However, they observed that the optical estimation for accessible soil P and K might display inconsistencies, given its dependence on the covariation of nutrient concentrations with other soil properties, rendering prediction outcomes susceptible to perturbations. Gleaning insights from the aforementioned studies, it is evident that the precision of soil macronutrient detection oscillates based on the choice of training and testing data derived from soil samples. Incorporating a broader spectrum of soil samples bolsters the calibration model, especially when confronting unfamiliar soil types. Furthermore, there is a discernible correlation between macronutrients, particularly N and P, and SC content [66, 62]. This highlights a prospective avenue to segregate macronutrient detection from SC metrics. A noticeable trajectory in macronutrient detection methods also emerges, shifting from laboratory-based spectrometers [33, 35] to field-compatible Printed Circuit Boards (PCB), underscoring the potential for on-the-ground, cost-effective, real-time macronutrient detection systems. Table 3 provides a summary of traditional methods used for detecting soil TC, TOC, soil moisture, and soil macronutrients. The table reveals two notable trends in traditional soil sensing research. Firstly, calibration techniques have evolved from PCR to PLSR, and now LS-SVM and ANN are gaining prominence due to advancements in computational methods and neural networks. Secondly, there is a growing emphasis on the VNIR spectral range, as these wavelengths are more readily available and offer promising applications for field deployments. However, it is important to note that simply relying on the \(R^{2}\) value is not sufficient for assessing the efficacy of these systems, as some studies do not provide the scale or range of concentration levels. A higher concentration range is more likely to result in a higher \(R^{2}\) value. Additionally, all these studies involve preprocessing steps that, while improving generalizability and system stability, also hinder their direct application in real-world settings.
## Results and Discussion
### Organic Acids  
Table 4 lists the quantities of organic acids detected in the two varieties. In particular glutamic acid was the most abundant compound present in PdP, and the content of this compound ranged from 826.45 to 830.47 mg/100 g between the beginning of the storage time and after 6 months, respectively. Glutamic acid is a well-known compound that is able to influence tomato flavour. Additionally, many authors have highlighted the role of this compound in fruit acceptability and umami taste [32; 33].  
Considering the other organic acids in both tomato varieties, it is possible to underline that in PdP, the citric acid content increased from 404.14 to 441.66 mg/100 g (9%) during 6 months of storage. The variation of citric acid with storage time showed different trends for the CIL variety after 1 month, the amount of citric acid increased from 557.60 mg/100 g to 811.51 mg/100 g (31.3%). A reason for this can be the fermentative processes due to the natural microflora that characterize this food matrix.  
PdP samples were also characterized by a high content of malic acid, which is considered an index of freshness and its concentration levels ranged from 580.48 to 492.62 mg/100 g at the beginning of storage and after 6 months, respectively. The results evidenced in our study are also in accordance with those reported by other authors [32].  
The minor organic acids detected were succinic, oxalic and ascorbic acid. In particular, succinic acid is an important molecule involved in the citric acid cycle [34] and its amount ranged between 16.21 and 54.38 mg/100 g.  
The quantity of oxalic acid in PdP was significantly lower than in CIL (see Table 4). This bioactive compound is important from a nutritional point of view because it forms insoluble salts with calcium and other divalent cations that produce a decrease in the bioavailability of these minerals [35].  
Finally ascorbic acid showed a significant decrease during ripening in both PdP and CIL. The mechanisms of ascorbic acid losses could be due to multiple factors, such as oxidation, pH, moisture and temperature. However, ascorbic acid is relatively stable in tomatoes because of the acidic conditions found in this vegetable tissue [36].
###### Abstract  
Aiming at the problem of valve port erosion caused by contaminated particles, a three-dimensional prediction model of valve orifice erosion was established based on computational fluid dynamics (CFD) and erosion theory, considering the influence of the valve port eddy current on the oil flow. The discrete phase model (DPM) of fluent is used to obtain the orifice erosion location and erosion rate, and the causes of erosion of the high-speed on/off valve are analyzed. On this basis, several factors influencing valve port erosion are simulated to investigate the relationship between particle collision behavior and valve port erosion morphology under different factors, and the change rule of valve port erosion under different factors is analyzed. The results show that the high-speed on/off valve service life decreases with a larger cone angle of the spool, a larger pressure difference at the valve port, a larger proportion of large particle contaminants in the oil, a higher oil contamination level, and a larger filter aperture. In addition, the grey relational analysis method is used to clarify the pressure difference at the valve port, the particle size, and the filter aperture as the three most important factors affecting the erosion of the valve port of a high-speed on/off valve.  
high-speed on/off valve; erosion; analysis of influencing factors; grey relational analysis Article
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 2 Methodology
### Apparent Temperature  
The system net-loads are greatly affected by external factors such as temperature, humidity, wind speed and seasonal events that change over time. The apparent temperature index [21] equivalent to the temperature felt is used to evaluate its effect upon the load. The apparent temperature \(T_{a}\) is defined as:  
\[T_{a}=1.04*T_{c}+0.2*e-0.65V-2.7 \tag{1}\]  
where \(T_{c}\) is the temperature in Celsius, \(e=6.105*\left(\frac{\text{RH}}{\text{R0}}\right)*\text{Exp}\left[\frac{17.27T _{c}}{23.7+T_{c}}\right]\), \(e\) is the pressure in hPa, \(V\) is the wind speed in m/sec, and RH is the relative humidity in percent. The apparent temperature for the next 48 h (with a 3 h resolution) is provided by the TCWB. Throughout this work, references to \"temperature\" refer to the apparent temperature.  
Different regions have different weather patterns. The total system load is a combination of the loads from the north, central, south and east regions of Taiwan (whose average load proportions are 38%, 28%, 33% and 1%, respectively). Since the goal is to forecast the total system load, the temperatures of the four regions are merged into one value by taking the weighted average of their temperatures with weights equal to their load proportions; namely:  
\[\widetilde{T}_{a}=\sum_{i=1}^{4}\ell_{i}T_{a,i} \tag{2}\]  
where \(i=1,\ldots,4\) corresponds to the north, central, south and east regions respectively, \(T_{a,i}\) is the temperature in the \(i\)th region and \(\ell_{i}\) is the proportion of regional load in system total energy demand.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 2 Related Work
### TEG and e-TEG in LEO Satellite Networks  
TEG has emerged as a powerful tool for modeling the dynamic topology of satellite networks. By representing the network's state at different time points as a series of snapshots, TEG provides an effective means for analyzing and optimizing flow transmission in satellite networks [13]. The dynamic nature of satellite networks, characterized by time-varying topologies and dynamic bandwidth, poses challenges for traditional network analysis techniques. TEG addresses these challenges by capturing the network's evolution over time, allowing researchers to formulate and solve problems related to network capacity, routing strategies, and flow optimization. The application of TEG in satellite networks extends beyond flow optimization. It has been used for network capacity analysis, enabling researchers to determine the upper bound of transmission capacity in a given satellite network.  
While TEG provides a robust framework for analyzing satellite networks, it does not account for the limited energy resources of satellites, which are critical in satellite operations. To address this limitation, researchers have extended TEG to e-TEG [11], which incorporates energy constraints into the model, providing a more accurate representation of satellite networks with energy-limited satellites. In e-TEG, virtual arcs are introduced to model energy consumption, with capacity bounds representing available energy resources.  
The e-TEG model has enabled researchers to address the energy-limited maximum flow problem, where the objective is to maximize data throughput while ensuring that energy consumption remains within the satellite's limited resources. Algorithms leveraging temporal augmenting paths [14] have shown significant improvements in throughput and energy efficiency, making e-TEG a foundational tool for optimizing data transmission in LEO satellite networks. In addition to energy constraints, dynamic resource allocation strategies have been developed to address the high mobility and dynamic characteristics of LEO satellites, improving resource utilization and system throughput [15].
# Study of Force Changes Based on Orthotic Elements Under the First Ray
## 2 Materials and Methods
### Gait Data Collection  
Each participant walked in a straight line on a dynamometric platform (FP4060-07, Bertec, Columbus, OH, USA), which recorded the forces in all three spatial axes during the gait of the evaluated foot. The data were sampled at 500 Hz and filtered using a fourth-order low-pass Butterworth filter with a cutoff frequency of 20 Hz, following the recommendations for biomechanical gait data processing [22]. The Bertec FP4060-07 force platform is widely used in gait-analysis studies due to its high precision (sampling at 500 Hz) and low noise levels [22]. Although test-retest reliability data specific to this model are not publicly available in the peer-reviewed literature, force platforms with analogous designs have reported intraclass correlation coefficients (ICCs) > 0.90 for repeated gait measurements [23; 24]. In our study, we followed strict protocols: calibration before each session, three practice trials per participant, and the averaging of five measurements per condition to ensure data consistency.  
The stance phase of the gait was defined according to standard biomechanical descriptions found in the literature, which divide the gait cycle into distinct phases based on functional and kinematic events [4; 25]. Specifically, the stance phase encompasses approximately 60% of the gait cycle, beginning at the initial contact (heel strike) and ending at toe-off. Within this phase, we focused on two sub phases: mid-stance (28% to 66% of the stance phase) and propulsion (67% to 100% of the stance phase), as these reflect the involvement of the first ray during the foot support phase. To evaluate the impact of orthotic interventions on the force distribution, for each spatial axis (medio-lateral, antero-posterior, and vertical), the greatest (G) and lowest (L) ground reaction force values were extracted  
Figure 1: Orthotic elements added to the custom plantar supports: (**B**) metatarsal elevation plus hallux, (**C**) Morton’s extension, (**D**) proprioceptive stimulation, (**E**) cut-out, and (**F**) kinetic wedge.  
during these two periods of the stance phase: mid-stance (period 1) and propulsion (period 2). Accordingly, the variables were labeled as FxG1, FxL1, FyG1, FyL1, FzG1, FzL1, FxG2, FxL2, FyG2, FyL2, FzG2, and FzL2, where \"Fx\", \"Fy\", and \"Fz\" refer to the spatial axes; \"G\" and \"L\" indicate the greatest and lowest recorded forces; and \"1\" and \"2\" correspond to the mid-stance and propulsion periods, respectively.  
The raw force data collected from the platform were processed using a custom Excel macro, in which each participant's body weight (in kilograms) was entered manually. The macro then converted the weight to Newtons (kg \(\times\) 9.8) and applied the following normalization formula: (force in N/body weight in N) \(\times\) 100. This allowed all ground reaction force values to be expressed as a percentage of each participant's body weight.
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 1 Introduction
In [29] a learning algorithm has been modeled to adapt the control policies for the household energy storage systems combining with the use of electric vehicles and heating, ventilating and air conditioning systems. In [30] an optimal sizing and energy management system has been developed to add a battery energy storage system and a photovoltaic power generation to maximize the home economy. In [31] a reduction of the power consumption is elaborated within the household by adapting DC appliances using a battery and a photovoltaic system. In [32] an energy management system for residential demand response is proposed using reinforced learning and fuzzy reasoning. In [33] a particle swarm optimization algorithm is proposed to schedule the household appliances in the off-grid energy system (photovoltaic, wind turbine, batteries and diesel generator) with the objective of saving energy and reducing the energy consumption cost. In [34] a multi objective optimization model is designed to minimize the electricity costs for the homeowner as well as the CO\({}_{2}\) emissions from the operation of a battery energy storage system under dynamic prices. In [35] a performance of a small household scale battery energy system with a lithium-ion battery pack and a single-phase DC/AC inverter. It is possible to charge the battery directly from the electrical network. However, there are some alternatives to this. In this paper, some possibilities are commented regarding the use of the workout devices and the electric vehicle. With workout devices, one type of environmentally friendly power plant can be obtained, consisting of a generator which is harnessing human-generated energy when exercising [36, 37], being important to recognize energy expenditure during activity with physical tests [38] or using electronic devices, i.e., smart watches [39]. On the other hand, to achieve the emissions' reduction objectives, the EU establishes, in Roadmap 2050 [18], that the impact of combustion cars will decrease slightly until 2030 and, in the 2040 decade, there will be a significant replacement. In turn, it is important to optimize the house-electric vehicle connection to manage the energy demand [40, 41] in the near future. This paper is organized as follows: Section 2 presents a description of the three analyzed systems; a description of household battery types is introduced and a discussion is included about how they can affect greenhouse gas emissions as well as some possible improvements for these systems. A specific case is shown in Section 3; the mathematical model employed for the resolution of the discussedscenarios is explained in Section 4; a simulation of the specific case is presented in Section 5; the results are discussed in Section 6 and the conclusions are outlined in Section 7.