esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical and Adaptive Approach to Predicting Indoor CO2
## 2 Related Work
Similarly, in [9], the authors used a gated recurrent unit (GRU) network as deep learning technique to predict fine dust, light amount, volatile organic compound (VOC), CO2, temperature and humidity by giving the past values of the same variables as input to the neural network. However, the architecture proved to be very heavy: it requires more than a day and a half to be trained, resulting in an inefficient solution to be used on edge devices. Moreover, data collected over more than six months and complex variables were considered. In this work we mainly refer to [10]. Unlike all the above studies, it provides a detailed overview of the prediction of indoor CO2, starting from the data collection architecture to the forecast results of the models, taking into account the challenges of using edge devices in such a problem. In this work, different artificial intelligence techniques (ridge, decision tree, random forest, multilayer perceptron) were analyzed from both a computational load and a prediction accuracy point of view. Moreover, the impacts of the input variables and the number of past values to use, along with the number of future values to predict, were evaluated. Due to their computational load, the authors underlined the infeasibility of applying neural networks on edge devices to address this issue. As a result, a less computationally demanding technique, such as a decision tree, was suggested. Despite the detailed analysis, the proposed approach may be difficult to be applied in a real scenario. Indeed, a large amount of data collected over a whole year from different rooms was taken into account to train models for predicting CO2 within the monitored rooms in the so-called \"hard sections\" (i.e., when CO2 has a significant variation). As a result, it is necessary to collect data for a long time period before training the AI models. Table 1 highlights the key points of each work presented in this Section. Compared to them, a major benefit of our approach is the automated updating mechanism, which is necessary to keep up with the changes in the environmental conditions, and hence to cope with the dynamics of real-world application scenarios.
## 2 Dynamic Coupled Equation  
### Model Overview  
The spatial vehicle-track-bridge interaction model is shown in Figure 1. The 3-D train-track-bridge coupled dynamic equation with normal supporting track is established based on the virtual work principle. The spatial interaction forces between the wheel-rail and the bridge-rail reflect the dynamic coupled relationship between the substructures. Besides, it is assumed that the track and bridge lose the interaction force to simulate the unsupported track. Each vehicle has multiple rigid bodies, composed of a carriage, trailing bogie, leading bogie, four wheelsets, primary suspension between carriage and bogie, and secondary suspension between bogie and wheelset. The train keeps a constant speed across the track line. The longitudinal connection and vibration between each vehicle are ignored.  
Figure 1: 3-D vehicle–track–bridge coupled model with the unsupported track: (**a**) front view and (**b**) lateral view.  
The spatial vehicle-track-bridge coupled dynamic model is based on the following assumptions:  
1. The 3-D finite element model adopted the Euler-Bernoulli beam, each of which has translation along X, Y, and Z directions and rotation around X, Y, and Z axes;
2. The bridge-rail coupled elements are connected by a single-layer discrete spring and dashpot. The rail and the sleeper are treated as a whole, and the position of each sleeper is used as a discrete support point;
3. The linearized Hertz contact theory is used to simulate the wheel-rail geometric relationship in the vertical and lateral directions;
4. Each vehicle has 27 degrees of freedom (DOFs). Each carriage and bogie have five DOFs including lateral, vertical, rolling, pitching, and yawing motion and each wheelset have three DOFs including lateral, vertical, and rolling motion;
5. Each track line consists of the left-side rail and the right-side rail distributed on both sides of the centerline of the bridge or subgrade symmetrically.
# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
## 6 Evaluation Methodology
### Evaluation Parameters  
The proposed mechanism is tested based on three different parameters which are as follows: Round trip time (rtt) in ms to measure the latency, calculated by Equation (1); Throughput (tp) in kB/s, calculated by Equation (2); and Memory usage (RAM), which examines the storage footprint of each instance of data retrieval, calculated in Equation (3). The test was loaded with 500 concurrent connections as a desired load.  
\[AvgLatency=\frac{RTTs1+RTTs2}{2}+\frac{RTTc1+RTTc2}{2} \tag{1}\]  
\[TH=\frac{I}{T}+\frac{RTTc1+RTTc2}{2} \tag{2}\]  
Figure 5: Testing scenario: (**a**) VM to VM with the same network; (**b**) VM to VM with a different network.  
\[Mem=100-(mf+b+c)\times\frac{100}{mv} \tag{3}\]  
where \(RTT\) is the round trip time in s; \(s\) is the server, and \(c\) is client. In throughput, \(I\) is inventory, and \(T\) is time. \(mf=\) memory free, \(b=\) buffer, \(c=\) memory cache, and \(mv=\) max value.
## 2 Theoretical Analysis
### Optimized PRBS Signal  
In a periodic binary sequence with period N, there are m ones and N-m zeros. If its autocorrelation function  
\[C(v)=\sum_{j=0}^{N-1}a_{j}a_{j+v} \tag{5}\]  
has only two values  
\[C(v)=\left\{\begin{array}{ll}m,&\text{if }v\equiv 0(\text{mod}N)\\ mc,&\text{otherwise}\end{array}\right.\text{and }c=\frac{m-1}{N-1} \tag{6}\]  
then this binary sequence becomes a PRBS. c is called the duty cycle of the PRBS. The following Figure 2 shows a sequence diagram of PRBS-9 with a clock rate \(f_{cr}\) of 1 GHz.  
It can be noticed in Figure 2 that the PRBS has a specific tour property, i.e., for a PRBS with pattern n, there is one single \"1\" run of length n and one single \"0\" run of length n \(-\) 1, 2\({}^{\rm n-2-i}\) runs of length i \"111...1\", and 2\({}^{\rm n-2-i}\) runs of length i \"000...0\", where i \(\leq\) n \(-\) 2. For example, a PRBS-9 period of a certain run length is shown in Figure 2, including one longest sequence (MLS) (11111111), one second longest sequence (0000000), two third longest sequences (111111) (000000), four fourth longest sequences, eight fifth longest sequences, and so on.  
In 2021, [PERSON] and collaborators [23] investigated how SBS responds instantaneously to binary sequence waveforms in optical fiber systems. They observed that long-period binary sequences, exhibiting damped oscillations akin to the SBS attenuation process, can effectively suppress SBS if the dwell time is less than the time constant required for SBS to develop and the sequence period is sufficiently long. In 2022, [PERSON] and his team [24] found that the occurrence of high peak pulses in the backward direction was related to the residence time of a PRBS. Our study theoretically examined how extended dwell times impact these backward high peak pulses and proposed an optimization for the longest PRBSs to prevent the formation of giant pulses.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Quantity** & **Symbol** & **Quantity** \\ \hline T & 293 K & \(\rho_{0}\) & 2201 kg/m\({}^{3}\) \\ n & 1.45 & \(A_{ao}\) & \(2.67\times 10^{-10}\) m\({}^{2}\) \\ \(l_{s}\) & 15 dB/km & \(\
u_{A}\) & 5900 m/s \\ \(\lambda_{s}\) & 1064 nm & \(\Omega_{B}-\Omega\) & set to 0 \\ \(n_{2}\) & 2.6 \(\times\) 10\({}^{-20}\) & \(w_{BGS}\) & 57.1 MHz \\ \(A_{eff}\) & 2.6 \(\times\) 10\({}^{-10}\) m\({}^{2}\) & L & 10 m \\ \(\gamma_{e}\) & 1.95 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and the values used for simulation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ CGR & Contact graph routing \\ CNC & Cognitive network controller \\ CSG & Cognizant space gateway \\ DTN & Delay-tolerant networking \\ EO & Earth observation \\ GS & Ground station \\ HDTN & High-rate delay-tolerant networking \\ ISL & Inter satellite link \\ IOL & Inter orbital link \\ SNN & Spiking neural network \\ NASA & National Aeronautics and Space Administration \\ QoS & Quality of service \\ \end{tabular}
## 1 Introduction  
Currently, the incidence and prevalence of excessive weight gain and fat accumulation followed by obesity has sharply increased throughout the world. A high-sucrose or high-fat diet can lead to the development of metabolic syndrome [1] by increasing blood levels of triglycerides (TG), continuously inducing visceral fat accumulation, and ultimately leading to insulin resistance [2], hyperglycemia [3], and obesity [4].  
Recently, many researchers are paying attention to the rare sugar that are defined as \"monosaccharides and their derivatives that are rare in nature\" by the International Society of Rare Sugars. Among the various rare sugars, D-allulose, a C-3 cipher of D-fructose with a molecular formula C\({}_{6}\)H\({}_{12}\)O\({}_{6}\) that has 70% of the sweetness of sugar but nearly zero calories, is not commonly found in nature [5]. It is only present in small quantities in D-glucose and D-fructose obtained from the hydrolysis of sucrose or D-glucose isomerization, as a result of the heating process during manufacturing [6]. However, currently, it can be mass-produced by chemical synthesis and is widely available as a sugar substitute at a much lower cost. D-allulose has health benefits with the calorie-reduction effect, such as improving insulin resistance, antioxidant capacity, and hypoglycemic control [5], and helps control diabetes and obesity [6,7]. This sugar substitute has been reported to increase the excretion of dietary TG, cholesterol, and fatty acids under isocaloric diet conditions in obese animal models [7].  
We investigated whether D-allulose has lipid-lowering effects on the consumption of a high-fat diet (HFD) that is further enriched with non-dairy creamer in mice. Non-dairy creamers, generally used as milk substitutes for coffee, often contain hydrogenated fat, which is artificially produced by converting unsaturated fatty acids to saturated fatty acids. In general, a high saturated fatty acid intake induces dyslipidemia, which is associated with the risk of cardiovascular disease via abnormal regulation of blood lipid levels, by elevating total cholesterol, TG, and low-density lipoprotein (LDL) cholesterol, and decreasing high-density lipoprotein (HDL) cholesterol [8].  
Hydrogenated palm oil was used as a non-dairy creamer in this study. Saturated fatty acids can promote cholesterol synthesis, and their content is higher in hydrogenated palm oil than in other vegetable oils [9]. This study is the first to investigate the effect of supplementation of hydrogenated non-dairy creamer-enriched HFD with D-allulose for preventing obesity in mice. We hypothesized that dietary supplementation of HFD enriched with non-dairy creamer and 5% D-allulose could overcome the undesirable effects of hydrogenated fats and high-fat levels in the diet on lipid metabolism in obese mice.
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
## 2 Materials and Methods  
### Study Design  
This monocentric, observational, pilot study was conducted in a private dermato-cosmetology practice and was performed during the autumn and winter season, from September 2023 to January 2024. Twenty subjects (sixteen women and four men) from eighteen to sixty-nine years of age, with Fitzpatrick skin type I-III and facial melasma ranging from mild to severe, were enrolled in this study. Subjects with the following conditions were excluded: skin marks in the experimental area (scars, hypertrichosis, nevi, tattoo), current pregnancy or lactation, history of allergy or reactivity to topical products, and any previous or current topical or systemic therapies for melasma. All the subjects gave their consent before entering the study.
## 1 Introduction
[10], organic filter beds, such as peat, soil or compost, are commonly characterized by a low adsorption capacity. Thus, compost is commonly used associated with a bulking agent (such as vermiculite, perlite, or woodchips) necessary to extend its lifetime and improve the structural stability of biofilters; or even mixed with another material with higher adsorption potential [6; 11]. During operation, compost media also deteriorates due to the activity of microbial populations, constant wetting and drying, volatilization and leaching of nutrients, among other factors [12]. Several authors have associated this premature deterioration of compost media with a decrease in biofilter performance, since it results in acidification, compaction, excessive dryness, loss of structure, short circuiting, channel formation, and/or clogging. Thus, compost media offers a reduced lifetime compared with other materials, and must be replaced every two to five years [12; 13]. On the contrary, the lifespan of inert carriers such as polyurethane foam, activated carbon, plastic rings or lava rock outperforms that of organic materials, representing the preferred packing in biotrickling filters [14]. Thus, their higher manufacture costs are outweighed by their longer lifetime and high performance. The properties of inorganic packing materials significantly vary from one to another: density, void fraction, roughness of the surface, etc. In general, inert packing materials are characterized by a higher physical strength, a much more regular shape and suffer less (physical, chemical and/or biological) deterioration over time, significantly reducing channeling problems, bed compaction and pressure drop. However, biomass growth on conventional inert carriers is usually hinderedby the absence of nutrients, whose addition is required to ensure efficient biodegradation performance, the challenging adhesion of microorganisms (increasing the start-up period) and their low water retention capacity [15,16]. Therefore, the development of new tailored-made packing materials that exhibit the merits of both organic and inorganic carriers (i.e., by providing a good specific surface area, water retention capacity and structural stability) while being competitive in the market, is of paramount importance to ensure a cost-effective operation of biofilters and biotrickling filters for odour control. In this context, the company SAINT GOBAIN has developed two novel clay-based packing materials, namely Filtralite(r) AIR 10-20 mm and Filtralite(r) Air AC, that meet the necessary requirements for odorants biofiltration. Both materials have been conceived at an industrial level in such a way that they can be produced in the different expanded clay plants that Saint Gobain has in Europe. This study comparatively assessed the performance of conventional plastic rings and both materials from the company SAINT GOBAIN for the abatement of an odorous emission in biofilters (BFs) and biotrickling filters (BTFs) operated at multiple gas residence times. The influence of the packing material and operation mode on the structure of the microbial community was also assessed.
## 3 Fine-Grained Image-Recognition Method for Fruit Flies
### Fruit Fly Fine-Grained Image Bottom Feature Extraction  
Bottom level feature extraction is an important task in image processing, which can help transform the original image into a more meaningful and usable representation. In the fine-grained image processing of fruit flies, the function of low-level feature extraction is to extract relevant feature information from the original image for subsequent processing and analysis. In this section, by using LSTM to extract low-level features, the memory and adaptability of LSTM can be fully utilized to better capture feature information in fine-grained images of fruit flies. The results of such low-level feature extraction will serve as inputs for the subsequent multi-channel self-attention mechanism module, laying a solid foundation for the implementation of fine-grained image-recognition tasks. LSTM is an improved RNN by adding input gates to neurons \(h\), the forget gate \(f\), output gate \(o\), and internal memory unit \(c\). This makes it more advantageous in processing fruit fly fine-grained images, and alleviates the occurrence of gradient disappearance and explosion [15]. Compared with RNNs, it can extract the feature information of fruit fly fine-grained images more effectively [16]. The input gate \(h\) controls how much of the input \(X_{t}\),\(t\) the current moment network is saved to the unit state \(Z_{t}\), the forget gate \(f\) determines how much of the unit state \(Z_{t-1}\) of the upper moment remains in the current moment \(Z_{t}\), and the output gate \(o\) controls how much of the unit state \(Z_{t}\) is output to the current output value \(F_{t}\) of LSTM.  
When the input vector matrix of the original fine-grained image of fruit flies is \(X=[x_{1},x_{2},\cdots,x_{k}]\), the update formula of LSTM is:  
\[h_{t} =\sigma(W_{h}\cdot[F_{t-1},x_{t}]+b_{h}) \tag{1}\] \[o_{t} =\sigma(W_{o}\cdot[F_{t-1},x_{t}]+b_{o}) \tag{2}\]\[f_{t}=\sigma\Big{(}W_{f}\cdot[F_{t-1},x_{t}]+b_{f}\Big{)} \tag{3}\] \[Z_{t}=f_{t}\otimes Z_{t-1}+h_{t}\otimes\tanh(W_{z}\cdot[F_{t-1},x_{t}]+b_{z})\] (4) \[F_{t}=o_{t}\otimes\tanh(Z_{t}) \tag{5}\]  
where \(\sigma(\cdot)\) is the sigmoid activation function, \(\tanh(\cdot)\) is a hyperbolic tangent function, \(W\) is the corresponding weights, \(b\) is bias, and \(F_{t}\) is the bottom feature of the final output fruit fly fine-grained image. LSTM extracts low-level features as the input of the multi-channel self-attention mechanism module [17].
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.1 Proactive Power Update  
In [17], the proactive power update (PAPU) algorithm is presented as a new interference mitigation scheme to minimize power consumption and maximize throughput at each WBASN. At each WBASN's coordinator, PAPU is applied by game theory to optimize the tradeoff between network utility and power efficiency. Game theory is applied as follows: A player is defined as a transmission link in one time slot, and an action of each player represents the transmission power and has a payoff function. The payoff function describes the tradeoff between the network utility and the cost function, with the cost function consisting of transmission power and power price. In this game theory, each WBASN maximizes its own payoff function to obtain the best response for each player. The best response is the transmission power of a player that maximizes its own payoff function.  
Game theory applies to PAPU as in the following steps: At first, each player in a WBASN initializes its transmission power and broadcasts this information to its neighbor. Next, the player updates its neighbor's transmission power and SINR. If the player detects the updated power from its neighbors, it calculates its best response and exchanges this information to the others. This algorithm is evaluated based on the unique Nash equilibrium.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
###### Abstract  
Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced.  
Keywords: opportunistic networks; routing; trajectory prediction; node mobility model +
Footnote †: journal: Information
# Enhancing Asphalt Binder Performance and Storage Stability with Trinidad Lake Asphalt (TLA)
## 3 Results and Discussion
### Rutting Property (G*/sin \(\delta\))  
Higher values of G*/sin \(\delta\) obtained from the DSR test indicate increased resistance to rutting or permanent deformation at elevated pavement temperatures. In this study, the G*/sin \(\delta\) values of binders were evaluated both in their original state, immediately after modification, and after Rolling Thin Film Oven (RTFO) aging at a temperature of 64 \({}^{\circ}\)C. The evaluations were conducted before conditioning and after conditioning for 48 \(\pm\) 2 h, considering only the top and bottom parts of the sample for testing.  
The results, illustrated with standard errors and linear exponential equations in Figure 5, demonstrate that binders modified before conditioning, in both their original and RTFO-aged states, exhibit increased resistance to rutting as the TLA replacement percentage increases. This indicates that higher TLA content enhances the binder's ability to resist permanent deformation under load.  
After conditioning, when the top and bottom portions of the sample were considered separately for testing, the bottom portion exhibited higher resistance to rutting compared to the top portion. This could be attributed to the possible segregation of heavier components at the bottom. Additionally, samples tested before conditioning (i.e., in their original state) exhibited higher resistance to rutting compared to those tested after conditioning, with standard errors and linear exponential equations demonstrating this, as shown in Figure 6. This suggests that the conditioning process may lead to some degradation or softening of the binder, reducing its rutting resistance.  
Figure 6: G\({}^{*}\)/sin \(\delta\) at 64 \({}^{\circ}\)C of the TLA modified binders after conditioning considering top and bottom parts.  
The statistical analysis, presented in Tables 5 and 6, further clarifies these results. The increased G*/sin \(\delta\) values in the original state, particularly for the bottom portion of the conditioned samples, indicate better performance in terms of rutting resistance. This improved rutting resistance contributes positively to pavement life by reducing the likelihood of permanent deformation under repeated traffic loads, thereby enhancing the durability and longevity of the pavement.