esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Feedstock
#### 3.1.4 Codigestion  
Codigesting multiple substrates can improve biogas yield during AD at various combinations and fractions. For instance, methane production increase of up to 65% [116] can be achieved by codigestion of cattle manure with organic wastes (C5 molasses) [117; 118], seaweed [47], energy crops [116], food wastes [119], agrowastes [120], and fruit and vegetable wastes [121]. A considerable increase in methane yield from codigestion of many other substrates has also been detected and documented in a comprehensive work by [PERSON] et al. [122].  
Generally, substrate in the AD process is degraded to biomethane by the synergistic interaction of a bacterial consortium, consisting of three functional group of microorganisms involved into the processing steps: hydrolysis, acidogenesis, and methanogenesis. Due to the insufficient buffering capacity, the products of each of these steps can ultimately cause inhibition of methanogenesis. For example, the reduction in pH due to the accumulation of VFAs from acidogenesis caused by the insufficient buffering capacity can result in substantial drop in production of biomethane and thereby loss in AD efficiency.  
By deploying codigestion, the digester can be designed for operating at an optimum C:N ratio [123] under which enhanced nutrient balance with improved pH buffering can be achieved. Theoretically, with a C:N ratio of about 25-30:1 [11; 124], methanogens use nitrogen to fulfil their protein requirements and optimize the production of methane. At higher ratios, the nitrogen availability depletes quicker, resulting in incomplete methanogenesis, leading to reduced biogas productivity. Conversely, lower C:N ratios give excess nitrogen causing ammonia formation together with an increase in pH and consequently drop in methane yield [125].  
Besides C:N ratio enhancement, codigestion can also improve overall macronutrients balance by adjusting the proportion of carbon, nitrogen, phosphorous and sulphur content. The requirement of macronutrients in AD is usually not so high, with a typical ratio of C:N:P:S as 600:15:5:1 [126]. Nonetheless, this depends on the characteristics of feedstock and an individual AD process. [PERSON] et al. [127] showed that substrates with simultaneous high concentrations of N and P, resulting in a combined C:(N + P) ratio between 53 and 40, have a pronounced effect in an increased methane content in the produced biogas. Many other combinations and proportions of macronutrient contents can also lead to possible conditions for enhanced biogas production for a codigestion process, and research into this area is still ongoing.
## 3 Materials and Methods
### Attention Mechanism  
Attention mechanisms are a powerful concept in neural networks that allow models to focus on specific parts of the input data when making predictions [11; 16]. By selectively focusing on relevant information, you can improve the performance of your model. At its core, the concept works by assigning different weights to different parts of the input data. These weights determine the importance of each part, allowing the model to prioritize certain elements over others. Figure 5 shows the attention mechanism. This process involves three main components: queries (\(Q\)), keys (\(K\)), and values (\(V\)). Queries represent the current state or parts of the input that need attention, keys represent potential parts of the input that can be attended to, and values represent the actual information that is retrieved based on the attention weights. These weights are applied to the values to create a weighted sum that represents the focused information.  
Self-attention, a specific form of attention, allows the model to weigh the importance of different words or features within the same input sequence when processing each element. Unlike traditional attention, where queries, keys, and values might come from different sources (e.g., encoder and decoder in sequence-to-sequence models), in self-attention, all three components originate from the same input sequence [11; 12; 16]. This enables the model to capture complex internal dependencies and relationships within the input itself.  
This approach allows the model to dynamically focus based on context, resulting in more accurate and context-aware predictions. In fall detection, the attention mechanism is particularly beneficial as it enables the model to pinpoint critical motion cues, such as rapid changes in posture or velocity, which are crucial for differentiating between falls and  
Figure 5: Attention mechanism.  
other activities that might have similar movement profiles. This selective focus enhances detection accuracy and reduces false positives, especially in noisy radar data or scenarios where falls resemble other rapid movements.
## 5 International Roadmap to Mandate Connectivity on Board
### When to Mandate Connected Vehicles in Asia  
According to \"Made In China 2025\" published by the Ministry of Industry and Information Technology, they are aiming to establish the key technology for intelligent driver support as \"Intelligent Connected Vehicles\" by 2020, and to possess the key technology for autonomous driving by 2025 [51]. There is tight collaboration between the government and industrial circles, promoting a broad and long-term range of proposals at the stage of international standardization, such as the international organization for standardization (ISO), ITU-R, ITU-T, and 3 GPP.  
Connected vehicles is a key topic also in Japan, since the Strategic Innovation Promotion Program (SIP) was launched in 2014. The item concerned is also under discussion in a collaborative trilateral meeting between the United States, Europe and Japan. A large-scale verification test began in 2017 to review various forms of data exchange centered on communications and security issues.  
In South Korea, several test pilots are ongoing, but still neither mandatory rules nor timelines have been fixed.
# A 20 W, Less-Than-1-kHz linewidth linearly Polarized All-Fiber Laser  
[PERSON]  
1 State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China; [EMAIL_ADDRESS] (M.X.); [EMAIL_ADDRESS] (L.N.); [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (H.H.)  
2  
Footnote 1: email: {M.X.X.  
gain bandwidth, the stimulated Brillouin scattering (SBS), becomes the predominant limitation to achieve higher output power. In order to increase the SBS threshold, the application of a strain to the active fiber [16], a temperature distribution along the active fiber [17], a longitudinal variation in the dopant concentrations [18], and a change in the core radius [19] was used. However, the most basic approach is to increase the effective mode area and reduce the length of the fiber. The SBS threshold in the gain fiber is related to the effective mode field area of the fiber and the actual length of the fiber. When the mode area of the gain fiber is larger, the SBS threshold is higher; when the fiber length is greater, the SBS threshold is smaller.  
In this paper, we report a 1550 nm high-power narrow-linewidth all-fiber linearly polarized laser with the MOPA technology. By adopting three cascaded erbium ytterbium co-doping amplifiers, we obtained an output power of 20 W laser source with a linewidth of 0.88 kHz, a power stability less than 2%, and an optical SNR over 40 dB. The amplifiers consisted of core-pumped signal-mode Er\({}^{3+}\)-doped polarization-maintaining fibers, cladding-pumped Er\({}^{3+}\):Yb\({}^{3+}\)-co-doped double-clad polarization-maintaining fibers, and cladding-pumped Er\({}^{3+}\):Yb\({}^{3+}\) -co-doped LMA polarization-maintaining fibers, which avoided nonlinear effects, in particular SBS.
## 3 Experiments and Results
### Experiment Datasets  
In order to evaluate each module in the MHGSA system in a more comprehensive way, we introduced VGGSound [32] datasets in the experimental part to evaluate different modules. It is worth mentioning that the datasets typically have only one granularity of event labeling. In order to fit the multi-branching structure, we classified the labels of the dataset with coarse granularity. VGGSound [32], an audio-visual dataset containing more than 300 classes, consists of more than 200 k ten-second video clips, totaling about 560 h of memory. We extracted 12 disaster-related classes from the dataset and categorized them into three coarse-grained categories. The extracted dataset contains a total of 9289 video clips, with 50 video clips in each class for testing.
# Propulsion Performance of the Full-Active Flapping Foil in Time-Varying Freestream
## 3 Results and Discussions
### The Influence of the Time Varying Freestream  
In the previous section, we discuss the propulsion performance of the full-active flapping foil at uniform freestream and analyze the reduced frequency and pitching amplitude on the foil. Based on the above study, the oscillating amplitude and oscillating frequency coefficient of the time-varying freestream on the hydrodynamic performance of the flapping foil is investigated in this section. First, we analyze the effect of the time-varying flow velocity on the flapping foil in one period, which is the period of the heaving and pitching motion of the flapping foil. Then, we study the influence of the time-varying freestream on the mean propulsion of the foil at a different period. Finally, we recalculate the mean propulsion power coefficient of the flapping foil over 10 periods and analyze the influence of the time-varying flow on the propulsion performance of the flapping foil.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 1 Introduction
To circumvent such issues, as a second contribution we design a novel two-step algorithm with reduced complexity to solve the joint TOA/RSS range-based localization problem; more specifically, we split the original problem into two separated phases:  
* a first _calibration_ phase carried out by the different nodes in the network to estimate all the unknown RSS and TOA model parameters, including the synchronization offsets;
* a second _localization_ step that leverages the combination of hybrid TOA/RSS ranging and iterative least squares (ILS) approach to estimate the unknown target position. It is worth highlighting that the proposed approach, in each of the two separated phases involved in the localization task, exploits _cooperation_ between nodes with known positions (anchors) and the node to be localized (blind node). More precisely, anchors need to exchange their collected TOA/RSS measurements and to communicate the time instants they start to transmit to the blind node. In this respect, we assume that all nodes in the network are equipped with low-power radio technologies (e.g., based on IEEE 802.15.4 standard, such as ZigBee, 6 LoWPAN, etc.) that enable communications in a range of a few meters, while still preserving the required power efficiency. The rest of the paper is organized as follows. In Section 2 we provide the problem formulation and discuss the resolution of the joint estimation problem using both TOA and RSS measurements. In Section 3 we derive in details the proposed two-step localization approach with self-calibration. Section 4 is devoted to the performance assessment of the proposed approach also in comparison to natural competitors. We conclude the paper in Section 5.
## 3 Results and Discussion
### Adhesion Evaluation  
Table 3 presents the results of the tensile adhesion test for TBC coatings produced by the EB-PVD process on samples V-XII. The highest recorded adhesion strength was 105 MPa. However, all adhesion tests resulted in cohesive failure within the adhesive. The sets after the adhesion test, consisting of a TBC-coated sample and a counterpart sample, are shown in Figure 7. Each of the tested coatings exhibited greater adhesion than the tensile strength of the adhesive used. This is confirmed by the analysis of the tensile stress-strain curves obtained during the tests, shown in Figure 8. The behaviour of the curves and the mode of failure are characteristic of epoxy materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Bond Coat Preparation** & **Maximal Load [N]** & **Adhesion Strength [MPa]** \\ \hline V & As-coated & 33,305.2 & 65.7 \\ VI & Rough grinding & 46,368.4 & 91.5 \\ VII & Grinding & 46,410.6 & 91.6 \\ VIII & Polishing & 53,185.2 & 105.0 \\ IX & As coated & 39,892.1 & 78.7 \\ X & Rough grinding & 41,785.7 & 82.5 \\ XI & Grinding & 33,559.1 & 66.2 \\ XII & Polishing & 37,795.3 & 74.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of tensile adhesion strength tests.  
Figure 7: The coated samples: (**a**) VI; (**b**) VII; and their counterparts after the tensile adhesion strength test.  
Based on the results obtained, it cannot be determined whether the surface roughness of the bond coat affects the adhesion of the 7 YSZ top coat, as cohesive failure within the adhesive occurred in all cases. However, it can be unequivocally stated that the adhesion strength of the thermal barrier coatings produced by the EB-PVD process exceeds 90 MPa, demonstrating excellent mechanical properties. The adhesion of the TBC produced on a polished bond coat exceeds 105 MPa. Considering that the roughness of the bond coat significantly influences the microstructure of the deposited top coat, as demonstrated in this article, it can be assumed that it also affects the adhesion of the TBC coating. Unfortunately, currently, no adhesives available on the market have a tensile strength that significantly exceeds 100 MPa, making it impossible to obtain adhesion strength values of TBC coatings produced by the EB-PVD process according to ASTM-C633. Therefore, it is necessary to develop a new adhesive or use a different testing method to determine the adhesion of these coatings. This issue will be continued in future work. In the meantime, it can only be assumed that the adhesion of EB-PVD TBC coatings significantly exceeds the 105 MPa obtained in this study.  
Figure 8: Tensile stress–strain curves obtained for adhesion strength of (**a**) 65.7 MPa; (**b**) 91.5 MPa; (**c**) 91.6 MPa; (**d**) 105.0 MPa; (**e**) 78.7 MPa; (**f**) 82.5 MPa; (**g**) 66.2 MPa; and (**h**) 74.6 MPa.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 3 Mechanisms-Related Approaches
### Parallelism-Based Optimization  
This category is based on the concept of parallelism, where multiple controller solutions exploit parallelism in form of multithreading. This optimization method aims at improving the throughput in terms of traffic by increasing the number of used threads within the controller. Examples for these kind of controllers are NOX-MT [23], Beacon [24] and Maestro [25].  
As mentioned previously, the NOX operating system was initially implemented to work in a single-threaded environment. Recently, NOX-MT [23] has been introduced as a successor to work in a multithreaded environment. Aiming at improving the performance of the controller, NOX-MT exploits some optimization methods like multithreading and I/O batching. Leading to increasing the performance of NOX-MT by the factor 33 compared to NOX. NOX-MT operates with tables, for example, for the allocation of MAC addresses to the switches port numbers. Therefore, it is guarantees that packets will be sent on the right path across the network. MAC addresses of new sources will be updated in the table data structure.  
Beacon [24] is an open-source controller based on the Java programming language. Beacon has high potential for developers since Java has the advantage of sufficient memory management as well as a clear handling for memory leaks. IBeaconProvider is a widely used Java interface in Beacon to interconnect with the OpenFlow protocol. There are various libraries that enables for instant creating applications to design routing mechanisms on the application plane. It supports several platforms and the high performance complement this mentioned aspect. It improves the network performance through different event handling. Compared to other methods, Beacon uses a shared queue of all packets from the switches. Due to exploiting parallelism in the controller, multiple threads have the ability to perform the buffered requests in the queue. This helps in increasing the amount of handled requests by the controller.  
Another parallelism-based optimization approach was Maestro [25]. In order to improve optimization while using multithreading structure, Maestro utilizes batching of packets to individual destinations. Different requests are grouped together in a batch. Multiple threads may then be used to execute the pending requests. As a result, Maestro prioritizes the different requests. In terms of performance, Maestro can handle up to 600.000 requests/sec.  
The above three mentioned approaches can be simply be implemented as a single controller in SDN. These approaches aims to scale the network through performance improvements rather than manipulating the network topology. The absence of inter-controllers synchronization mechanisms also decreases the amount of the traffic passes through the network. However, there are some drawbacks, the single controller design may result in having bottleneck around the controller. Moreover, the single controller is susceptible to the single point of failure problem.
## 3 Results and Discussion
### Validation of Vaporisation Enthalpies Using Structure-Property Relationships
#### 3.3.4 Experimental Enthalpies of Formation of Iso-Propyl- and Tert-Butyl-Benzenes  
The thermochemistry of iso-propyl- and tert-butyl-benzenes is a long-standing interest of our laboratory [9; 10; 11; 12]. The available data on the condensed state enthalpies of formation are compiled in Table 8.  
Most of the entries in Table 8 were measured in our laboratory using combustion calorimetry and we took this opportunity to update the uncertainties of the enthalpies of formation reported in our previous work [9; 10] following [PERSON]'s [58] recommendation. These recalculated values are now given in Table S8. Furthermore, for this work, we re-evaluated the liquid-phase enthalpies of formation derived from chemical equilibrium studies reported in literature [10; 59]. The reactions and the liquid-phase reaction enthalpies, \(\Delta_{\rm F}H^{\rm o}_{\rm m}(\rm liq)\), are given in Table 9.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**  
These reaction enthalpies were used to re-calculate the \(\Delta_{\text{f}}H_{\text{m}}^{\text{o}}\)(liq)-values of 1,4-di-iso-propyl-benzene (from R1), 3-methyl-tert-butyl-benzene (from R2), 1,3-di-tert-butyl-benzene (from R3), 1,3-di-tert-butyl-benzene (from R4) and 1,3,5-tri-tert-butyl-benzene (from R5). The re-calculated data are given in Tables S9-S11. The re-evaluated and updated experimental data on \(\Delta_{\text{f}}H_{\text{m}}^{\text{o}}\)(cr, liq) of iso-propyl- and tert-butyl-benzenes were used together with the vaporisation and sublimation enthalpies evaluated in Tables 1-3 to calculate the experimental gas-phase enthalpies of formation of alkylbenzenes (see Table 8 column 4). These values will be utilised for validation of quantum-chemical methods as follows.
## 4 Implementation Details
### Hyperparameter Tuning  
As well as depending on the feature selection process described in Section 4.3, the performance of the forecasting model is highly dependent on the selection of the hyperparameters that are used for tuning the learning rate. Moreover, the hyperparameter selection also affects the computation and memory factors. Therefore, optimization of the hyperparameters is always a point of debate among researchers. Nevertheless, some rules have been defined by scientists and researchers for setting the values. In the present study, the following hyperparameters were considered for tuning the model: feature scaling, learning rate, optimization solver, number of epochs, dropout rate, number of hidden units, and number of hidden layers.  
As the computation significantly depends on the learning rate, this is the most important hyperparameter. For the optimization solver, the adaptive moment estimation (ADAM) [61], stochastic gradient descent with momentum (SGDM) [62], and RMSprop [63; 64] algorithms were tested, and SGDM was found to be the best solver. For hyperparameter optimization, search space is defined based on the trial and error method. Based on the defined search space, the hyperparameters were finally optimized. Table 3 shows the hyperparameters with the chosen optimal value for the LSTM-RNN model from the search space. In addition, hyperparameters were optimized using only the Golden dataset and applied these same parameters to other datasets. It avoids the over tuning of the model.  
The number of hidden layers was optimized as 3 and the hidden units were optimized as 24. Feature scaling showed the best results with a standard scaler method. The dropout rate was set to 0.1, which also works to avoid the overfitting problem. Finally, the number of epochs was tuned to 1500.  
By contrast, the hyperparameters used for the FFNN network were the same as in a previous study [44], i.e., the learning rate was 0.001, the number of hidden units was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Value** & **Search Space** \\ \hline Feature scaling & Standard & Min-max and Standard scaler \\ Number of layers & 2 & 1, 3, 5, 7, 10, 12, 15 \\ Learning rate & 0.005 & 0.0005, 0.1, 0.01 and 0.001–0.005 \\ Optimization solver & SGDM & ADAM, SGDM, RMSprop \\ Number of epochs & 1500 & 500–4000 \\ Dropout rate & 0.1 & 0.05–0.8 \\ Hidden units & 24 & 12, 24, 48, 96, 192 \\ Hidden layers & 3 & 2–6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter optimization.  
10, and 2 hidden layers were set. Full batch gradient descent was used, along with L2 regularization, standard scalar, and 1000 epochs.  
For the SVM algorithm, standard feature scaling and a gaussian kernel were used with a sequential minimal optimization (SMO).