esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Research on the Load Transfer Mechanism of Tie Plates for 400 km/h High-Speed Turnouts
## 2 Materials and Methods
### Design of Experiments  
The basic principle of the experiment is to load the tie plate and its components by loading equipment, test and record the applied loads and the displacement changing processes of the tie plate and cover the plate and other components. The main experiment facilities used includes vertical loading equipment, load sensors, displacement sensors and necessary tooling parts for fixing the plate and loading. The vertical loading equipment is shown in Figure 4a, and the maximum loading capacity is 300 kN. The load sensor is integrated into the loading equipment, and the load measuring accuracy is 0.001 kN. The displacement sensor is shown in Figure 4b. The measuring range is 20 mm and the accuracy is 0.001 mm.
## 3 Data Filtering for Short-Term Forecasting of Photovoltaic Power Plants Generation
### The K-Means Filtration Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **26 February** & **21 May 2018–1** & **11 September** & **28 January** & **For All** \\  & **2018** & **June 2018** & **2018–20 September** & **2019–3** & **Periods** \\  & **2018** & **June 2018** & **February 2019** & **February 2019** & **\\ \hline \(W_{\Sigma}\), \(\mathbf{\mathrm{kW}}\)-\(\mathbf{h}\) & 391,805.4 & 1,156,028.2 & 601,402.2 & 86,694.7 & 2,235,930.5 \\ \(E_{\Sigma}\), \(\mathbf{\mathrm{kW}}\)-\(\mathbf{h}\) & 113,424.0 & 75,093.5 & 184,327.4 & 45,661.1 & 417,287.7 \\ \(E_{avg}\), \(\mathbf{\mathrm{kW}}\)-\(\mathbf{h}\) & 691.6 & 377.4 & 1417.9 & 563.7 & 733.4 \\ \(\sigma_{E}\), \(\mathbf{\mathrm{kW}}\)-\(\mathbf{h}\) & 997.7 & 893.1 & 1724.2 & 733.3 & 1 214.6 \\ \(E_{\Sigma}^{\%}\), \(\%\) & 29.0 & 6.50 & 30.7 & 52.7 & 18.7 \\ \(\mathbf{\mathrm{R}}^{2}\) & & & & & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of calculating the parameters for estimating the PVPP generation forecasting accuracy using the k-means method for data filtering. Disadvantages of filtering the initial data using the k-means method: high costs of computing resources, the calculation time for the considered example reached almost two h; more complex algorithm compared to the empirical model.
# Fungal-Lactobacteria Consortia and Enzymatic Catalysis for Polylactic Acid Production
## 3 Results and Discussion
### Optimization of Growth Conditions for Interkingdom Consortia  
After selecting the fungal strains, the growth of the microorganisms involved in the study was tested in different minimal media with glucose as the carbon source (GPP, Czapek Dox, CDM and PMM5). The three selected fungi grew up in all of them, but LAB growth was very slow (not shown). Since LAB are frequently cultivated in MRS complete medium, the culture of the selected fungi in this medium was assayed, with good results. Therefore, MRS was selected for further experiments. In these trials, LA concentration was measured using the \"L-Lactate and d-Lactate Assay Kits\" (Sigma-Aldrich) in order to confirm that the selected growth conditions do not interfere with its production.  
The effect of agitation in the co-cultures was analyzed using only _T. amestolkiae_ as a representative of the three amylolytic fungi. Optimization of this parameter is especially relevant due to the different requirements of fungi and LAB, since the selected fungi need higher aeration, whereas the bacterial fermentations occur in low-oxygen cultures. Fortunately, lactic fermentation is one of the few fermentative processes that can take place in the presence of oxygen, although it is known that LA yield decreases in favor of biomass growth [38]. Different agitation conditions (static, 20 rpm and 200 rpm) were tested with both LAB strains in pure cultures and in co-cultures with _T. amestolkiae_. LA production was measured after 24 h incubation, and the co-culture was observed by light microscopy after 72 h (Figure 1).  
The production of l-lactate in all conditions (D-lactate was not detected) demonstrated that lactic fermentation occurred in each case, usually being higher in static cultures (not shown). The non-amyloidytic strain M9 MG6-B2 was a better l-LA producer than WCFS1, and the production observed in agitated pure bacterial cultures depended on the speed. These results were similar in co-cultures with the fungus, which indicated that the presence of _T. amestolkiae_ does not exert a negative influence on LA production by the bacterial partner.  
Regarding fungal growth, in static cultures only conidiospores were observed (Figure 1A), whereas there was development of hyphae in shaken cultures that was more abundant at 200 rpm (Figure 1C). Therefore, we selected 200 rpm as the best agitation for  
Figure 1: Light microscopy pictures (\(\times\)40) of _T. amestolkiae–L. plantarum_ M9 MG6-B2 co-cultures after 72 h incubation. (**A**) Static conditions; (**B**) 20 rpm; (**C**) 200 rpm.  
consortia to favor fungal growth and still produce high levels of LA, since fungal spores are metabolically inactive [39].
###### Abstract  
Lingenonberry pomace (LP) is a by-product rich in valuable bioactive compounds and can be used in the food industry after various treatments and property characterization. This study aimed to evaluate the impact of commercially available enzymes (Viscozyme(r) L, Pectinex(r) Ultra Tropical, and Celluclast(r) 1.5 L) and supercritical carbon dioxide (SFE-CO2) extraction technology on the chemical composition and technological properties of treated LP products. The Megazyme kit was used to determine the soluble dietary fiber (SDS) and insoluble dietary fiber (IDF) contents, while the changes in mono-, disaccharide, and oligosaccharides were analyzed by applying high-pressure liquid chromatography with a refractive index detector. The analyzed properties were as follows: the water swelling capacity (WSC), water retention capacity (WRC), water solubility index (WSI), oil retention capacity (ORC), bulk density (BD), and emulsion stability of modified LP. The tested LP contained 8.49 g/100 g of SDF and 65.36 g/100 g of IDF (in dry matter). The partial separation of lipophilic substances during SFE-CO2 extraction did not significantly affect the enzymatic hydrolysis efficiency. The amount of oligosaccharidesarides in the LP increased using enzymes with pectinolytic activity (Viscozyme(r) L and Pectinex(r) Ultra Tropical), while cellulolytic enzymes (Celluclast(r) 1.5 L) increased the amount of SDF and improved the IDF/SDF ratio. Enzymatic hydrolysis increased the SI, WRC, and ORC of LP powder. Emulsions with LP hydrolyzed with Pectinex(r) Ultra Tropical demonstrated the highest stability during storage. This study demonstrates that the modification of LP powders provides diverse technological properties, which could expand the application of such products for further food production.  
Keywords:lingonberry pomace; enzymatic hydrolysis; technological properties; dietary fiber; oligosaccharides
## 1 Introduction
### Inventory Management Methods with Value-Based RL  
Value-based RL methods update the value function during the interaction with the environment and make decisions based on the value function. [PERSON] and [PERSON] [11] were the first to apply RL to optimize supply chain and inventory management in 2002. In recent years, the RL methods are applied to address supply chain inventory management problems due to its advantages in handling sequence decision-making problems such as inventory management. RL-based supply chain inventory management methods mainly involve agents learning how to effectively manage supply chain inventory through feedback from the rewards obtained by interacting with the environment. The objective is to optimize policies to maximize profits [11], minimize costs [13; 21], and maintain a specific target inventory level [14]. [PERSON] et al. [12] discussed several reward function definition patterns based on Q-Learning algorithm for RL-based inventory management method. A significant portion of research only relies on simple RL methods [22], such as Q-learning [23], a classic value-based RL method. However, these simple methods face the significant challenge of the curse of dimensionality when dealing with high-dimensional real-world state spaces.  
The Deep Q-Network (DQN) algorithm is another classic value-based RL method. This approach is a natural extension of the Q-learning method in the field of deep learning. [PERSON] [15] attempted to use Deep Q-Network (DQN ) DQN to solve the beer game problem, a fourth-level linear supply chain environment considering both deterministic and stochastic demands. In the experiment, only one supply chain participant made decisions using DQN, while the other participants followed specific heuristic algorithms. The environment state comprised inventory levels, demands, incoming orders, and incoming products over the past m (hyperparameter) periods. The experiment showed that from the perspective of a single node in the supply chain, using RL methods to make decisions on supply chain inventory management was more efficient than using heuristic methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contributors** & **Contributed Content** \\ \hline [PERSON] [11] & Applied RL in supply chain inventory management \\ [PERSON] [12] & Discussed reward function definition based on Q-Learning \\ [PERSON] [13] & Setted minimizing costs as the optimize target \\ [PERSON] [14] & Setted maintaining a specific target inventory level \\ [PERSON] [15] & Combined DQN and heuristic algorithms in the beer game \\ [PERSON] [16] & Used SARSA in the multi-node single-item mode supply chain inventory \\ [PERSON] [17] & Amaded the lead time in delivery \\ [PERSON] [18] & Constructed a four-level single material supply chain environment \\ [PERSON] [19] & Compared performance of some RL algorithms \\ [PERSON] [20] & Researched aviation inventory management of multiple materials under a single node \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main related works about inventory management with RL.
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
###### Abstract  
Air pollution in urban street canyons presents a serious health risk, especially in densely populated areas. While previous research has explored airflow characteristics in these canyons, it often lacks detailed data on pollutant dispersion and the effects of wind speed on airflow patterns and vortex formation. This study uses Computational Fluid Dynamics (CFD) to deliver quantitative measurements of pollutant dispersion rates and qualitative insights into airflow patterns across various street canyon morphologies. The analysis examines a range of aspect ratios (ARs), from wide (AR = 0.75) to narrow (AR = 4.5), and different wind speeds to evaluate their effects on pollutant dispersion. Findings indicate that purging flow rates decline as the AR increases, with a more pronounced decrease at lower AR values. In narrower streets, airflow patterns are particularly sensitive to wind velocity, leading to unexpected vortices that hinder effective pollutant dispersion. By incorporating these insights into urban design strategies, cities can enhance street ventilation, thereby reducing pollutant concentrations and improving public health. This study also tests a specific street layout in Seville to predict pollutant accumulation under various conditions, assessing health risks based on World Health Organization guidelines. Ultimately, this research aids in developing healthier, more sustainable urban environments.  
p urging flow rate; pollutant dispersion; street canyon; urban ventilation; urban design; sustainable cities; CFD +
Footnote †: journal: Applied sciences  
Article
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 5 Extensions of Merging-Squeeze-Excitation Feature Fusion
### MSE Feature Fusion with Global Channel Attention  
In the proposed MSE feature fusion shown in Figure 1, the channel-weighted and merged feature maps \(\mathbf{Y}^{msc}\) are obtained from \(\mathbb{F}_{Merge}(\mathbf{P}^{(1)},\mathbf{P}^{(2)},\dots,\mathbf{P}^{(N)})\). In addition, we may compute a different set of channel-weighted feature maps based on the channel dependency of \(\mathbf{B}\) (the output of the pre-merging step) directly. Figure 3 shows the MSE feature fusion with a global channel attention, where we create an additional set of channel-weighted feature maps \(\mathbf{R}\in\mathbb{R}^{1\times W\times C}\) according to \(\mathbf{B}\). The set of feature maps \(\mathbf{R}\) is denoted as \(\mathbf{R}=[\mathbf{r}_{1};\mathbf{r}_{2};\dots;\mathbf{r}_{C}]\), \(\mathbf{r}_{c}=[r_{1,c},r_{2,c},\dots,r_{W,c}]\), and \(r_{w,c}\) is a value. Similar to the previous calculation, we can obtain \(\mathbf{R}\) according to the following steps. We find the channel weights \(\mathbf{v}\in\mathbb{R}^{C}\), where \(\mathbf{v}=[v_{1},v_{2},\dots,v_{C}]^{T}\) and \(v_{c}\) is a value, from  
\[\mathbf{v}=\sigma\big{(}\mathbf{W}_{2}^{\intercal}\delta(\mathbf{W}_{1}^{ \intercal}\mathbf{u})\big{)}, \tag{18}\]  
where \(\mathbf{u}\) is the channel-wise statistics as shown in Section 4.2.2, \(\mathbf{W}_{1}^{\intercal}\in\mathbb{R}^{\frac{C}{7}\times C}\) is the weight matrix of the first FC layer, and \(\mathbf{W}_{2}^{\intercal}\in\mathbb{R}^{C\times\frac{C}{7}}\) is the weight matrix of the second FC layer. The \(c\)-th 1D feature map \(\mathbf{r}_{c}\) is equal to the feature map \(\mathbf{b}_{c}\) weighted by \(v_{c}\):  
\[\mathbf{r}_{c}=v_{c}\mathbf{b}_{c}. \tag{19}\]  
Figure 2: MSE feature fusion architectures with skip connections. There are two types: (a) MSE feature fusion with local skip connections. A skip connection is added to each branch. As a result, we combine the feature maps \(\mathbf{Q}^{(n)}=\mathbf{P}^{(n)}+\mathbf{A}^{(n)}\) instead of \(\mathbf{P}^{(n)}\). The output \(\mathbf{Y}^{lsc}\) still contains the feature maps directly obtained from 1D CNN models and (b) MSE feature fusion with a global skip connection. The pre-merged feature maps \(\mathbf{B}\) are added to the feature maps \(\mathbf{Y}^{msc}\). As a result, the output feature maps \(\mathbf{Y}^{gsc}\) contain both pre-merged feature maps (no channels weighted) and channel-weighted feature maps.  
Finally, the set of channel-weighted and merged feature maps \(\mathbf{Y}^{sca}\) entering the classifier is from  
\[\mathbf{Y}^{sca}=\mathbf{Y}^{mse}+\mathbf{R}, \tag{20}\]  
where \(\mathbf{Y}^{mse}\) is defined in (14). As a result, the prediction will be computed from both local-channel-attention and global-channel-attention feature maps.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Underwater Communication  
Underwater data transmission commonly uses four main methods: acoustic waves, optical communication, magnetic induction (MI), and radio-frequency (RF) methods [84, 85]. Each modality operates within specific frequency bands and ranges, with trade-offs between bandwidth, latency, and environmental adaptability. Table 7 summarizes these characteristics, including sonar frequencies for SLAM and vision-based systems in varying water conditions.
## 2 Train Dynamical Model and Problem Statement  
### Description of Train Dynamical Model without Actuator Faults and Actuator Saturation  
The single-point-mass model, which treats the train as a single particle, can describe the complete characteristics of the train, simplify the train modeling, and achieve good real-time performance, so it has been widely applied in the tracking control of HSTs [24; 25; 26; 4; 8; 11; 16; 2]. Considering train dynamics in the longitudinal direction, the force diagram of a single-point-mass model of HSTs is depicted in Figure 1.  
By [PERSON]'s second law, the single-point-mass train dynamical model can be described as follows [22]:  
\[\dot{x}=v, \tag{1}\]  
\[m\dot{v}=F-f-d, \tag{2}\]  
where \(m\) is the train total mass; \(x\) and \(v\) represent the position and the velocity, respectively; \(F\) denotes the traction force (traction phase) or braking force (braking phase); \(d\) means the additional disturbance from rail conditions, which is mainly composed of ramp resistance, tunnel resistance, curve resistance  
Figure 1: Force diagram of longitudinal train dynamical model.  
and other resistances. In practice, \(d\) is unknown and varies with the corresponding environment. \(f\) models the basic resistance, which is usually expressed by the Davis equation:  
\[f=a_{0}+a_{1}v+a_{2}v^{2}, \tag{3}\]  
where \(a_{0}\), \(a_{1}\), \(a_{2}\) are coefficients of the Davis equation.  
**Assumption 1**.: _The unknown and time-varying additional disturbance \(d\) satisfies \(|d|\leq\xi<\infty\), where \(\xi\) is a positive but unknown constant._
## Methodology
### Electromagnetic Properties Measurement  
The EM parameters of all samples were measured by the transmission line and waveguide method using the WR90 and WR62 rectangular waveguides. A sample denoted as M0 was prepared by homogeneously mixing 50 wt.% of the C-Fe\({}_{3}\)O\({}_{4}\) in paraffin wax solution. While, the C-Fe\({}_{3}\)O\({}_{4}\)/PVDF nanocomposites (M10, M20, and M40) were compressed by hot pressing at 180 \({}^{\circ}\)C. All samples were molded into a rectangular shape with the dimensions of \(10.16\times 22.86\) mm\({}^{3}\) for measurements at X-band frequency and \(7.90\times 15.80\) mm\({}^{3}\) for Ku-band frequency (Figure 1c). All samples were tightly fitted onto a WR-90 and WR62 waveguides for the EM parameters measurement with Keysight E5071C vector network analyzer (Keysight, USA) in the studied frequency ranges. The VNA was calibrated  
Figure 1: Digital image of **(a)** C-Fe\({}_{3}\)O\({}_{4}\) **(b)** C-Fe\({}_{3}\)O\({}_{4}\)/PVDF (M40) **(c)** rectangular shaped M40.  
using a two-port through reflection line (TRL) calibration procedure to eliminate possible systematic experimental errors which could arise due to system imperfections [15]. To avoid EM wave leakage the samples were snugly fitted such that no gap exists between the sample holder and the material as shown in Figure S1.
## 3 Results
### Co\({}_{2}\) Outgassing Estimates  
Estimated rates of CO\({}_{2}\) outgassing averaged 7.2 mmol m\({}^{-2}\) h\({}^{-1}\) (\(\pm\)4.7) during the study period. Outgassing rates were significantly (\(p\) < 0.0001) higher in the spring than summer, averaging 11.7 (\(\pm\)1.1) and 2.7 (\(\pm\)0.7) mmol m\({}^{-2}\) h\({}^{-1}\), respectively. There was no significant difference in CO\({}_{2}\) outgassing between samples in the same season. Similar to \(p\)CO\({}_{2}\), diel variability in CO\({}_{2}\) outgassing reflected a clear trend in both seasons (Figure 3c,d), decreasing from 6:00 to 15:00 and gradually increasing from 15:00 to 6:00 the following day (Table 1). In spring, outgassing rates gradually decreased by 13-24% from 6:00 to 15:00, followed by an increase of 12-46% until 6:00 the following day. Spring rates ranged by 1.5-4.4 mmol m\({}^{-2}\) h\({}^{-1}\) over a 24-h period. Much sharper diel increases and decreases occurred in the summer samples. CO\({}_{2}\) outgassing rates decreased by 54% from 6:00 to 12:00 on 13 August and by 55% from 6:00 to 15:00 on 15 August. Summer afternoon outgassing rates increased by 65-82% (1.4-2.1 mmol m\({}^{-2}\) h\({}^{-1}\)) from their respective minimums until 6:00 the following morning. This trend resulted in a summer diel range of 1.9-2.2 mmol m\({}^{-2}\) h\({}^{-1}\). Consequently, CO\({}_{2}\) outgassing rates during hours of darkness were significantly (\(p\) < 0.0001) greater than daylight in both seasons (Figure 4b). Spring outgassing rates during darkness were on average 10% (std. \(\pm\)8%) higher than during daylight hours, while darkness rates in summer were on average 25% (\(\pm\)10%) greater. Calculating daily outgassing (mmol m\({}^{-2}\) d\({}^{-1}\)) for sample days using linear interpolation between the 3-h intervals, rather than extrapolating the 15:00 measurement across the entire 24-h period, resulted in significantly higher daily outgassing rates in a paired t-test (\(p\) = 0.0261), ranging between 15.3 and 43.6 (mean 26.2, std. \(\pm\)12.7 mmol m\({}^{-2}\) d\({}^{-1}\)) higher than the single measurement calculation (Figure 5). These results strongly support our initial hypothesis.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 4 Insights: The (DTLS Handshake over 6 TiSCH
### Fragmentation
Since the segments are smaller, the receiving side needs to store fewer 6 LoWPAN fragments before it can reassemble the original packet. Next, the node quickly prepares several short TCP segments (maximum size is 356 B), containing the _ClientCertificate_, _ClientKeyExchange_, _CertificateVerify_, _ChangeCipherSpec_, and _Finished_ messages. When Nagle is not active, not all TCP segments are used at maximum capacity, which leads to more buffer spaces being occupied. With Nagle activated, fewer segments are created. The DTLS handshake behaves similarly to a TLS connection that uses a large IPv6 MTU and Nagle's algorithm. DTLS sends entire flights at once, automatically causing many 6 LoWPAN fragments in transit. A constrained endpoint must correctly receive all fragments before reassembly can take place. Forcing a lower handshake message size with the MFL extension reduces the buffer pressure for incoming messages, but does nothing to alleviate buffer pressure for outgoing messages.
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 2 Methodology for Orbit and Communication Resources Selection
### Determination of Suitable Frequencies for Communication Link Evaluation  
The link budget between devices on the moon can be evaluated according to the link budget equation as  
\[P_{R}=P_{Tx}+G_{Tx}+G_{Rx}-L_{FS}-A_{Att} \tag{4}\]  
where \(P_{R}\) is the received power in dBW, \(G_{Tx}\) and \(G_{Rx}\) are the gain of the transmitter and receiver antenna in dBi, respectively, and \(L_{FS}\) in dB is the free space path loss, which is defined in function of the link distance, \(r\), and the wavelength of the carrier frequency, \(\lambda\), as  
\[L_{FS}=20\log_{10}\left(\frac{2\pi r}{\lambda}\right) \tag{5}\]  
Using the link budget equation, we can determine the required sensibility of surface sensors for receiving commands from the satellites and the minimum transmitting power of those to be able to communicate to the satellites. A suitable carrier frequency can be determined based on the line of sight (LOS) availability of the ground devices, and the receiver sensibility.  
In addition to the link budgets, channel models for the Moon and Mars need to consider the local environment and quickly adapt to changing conditions. As mentioned in [9; 10], future space missions will require autonomous decision making, and possible, self channel determination (without expensive and time consuming measurement campaigns as in Earth) based on observed real time conditions and previous training, and power by artificial intelligence (AI). For the Moon, the absence of atmosphere have allowed solar rays to erode the surface and leave a rocky land, which is known by their reflective behavior.