esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Cation Influx on the Viability of Freeze-Dried _Lactobacillus brevis_ WiKim0069
## 1 Introduction  
Lactic acid bacteria (LAB) play a major role in various fermented foods and beverages, such as kimchi, cheese, yoghurt, and rice wine [1]. Although the quality characteristics of fermented products mainly depend on the LAB species engaged in the fermentation process [2, 3], many fermented products depend on natural fermentation by various LAB present in the raw materials, often leading to inconsistent quality of the end products. The backsopping method, in which small amounts of a previous fermentation culture are inoculated into the new batch, has been used on a small scale to prevent fermentation failure. However, for large-scale production for commercialization, a large amount of LAB starters are required to improve sensory quality and guarantee uniform quality of the final products.  
An important consideration in the industrial use of LAB is to maintain their viability during the distribution process. Freeze-drying is widely used for long-term storage of LAB starters [4]. However,during the process, LAB cells are exposed to high levels of stress, such as ice crystallization, extreme pH, and low temperature [5; 6]. Moreover, the membrane structure of the LAB is often damaged during the process of freezing and thawing, resulting in reduced viability [7]. Protective agents are often employed to enhance the viability of LAB. Various substances have been used as a protective agent, including sugar, polyols, amino acids, and salts [8; 9; 10; 11]. However, the protective effects of the agents during the freeze-drying process and subsequent storage vary with the LAB species [12].  
There are many reports on the mechanisms of the protective agents with regard to microorganism viability. Different sugars, e.g., sucrose, trehalose, and lactose, have been reported to maintain the stability of the cell membrane through preferential exclusion and replacement of water during freezing and freeze-drying [13]. When glycerol is used as the agent, excessive formation of ice crystals is reduced. The reaction between the amino acid group of nitrogen sources and the carbonyl group of LAB stabilizes protein structure [14]. Both the content of the agents and their different mechanisms caused a difference in viability of LAB [15]. It is known that the osmotic balance inside and outside the cell, regulated by an influx of cations such as calcium or magnesium during freeze-drying, plays an important role in reducing the damage caused by dehydration [16; 17; 18; 19; 20; 21]. However, the underlying mechanisms remain largely unclear, as there are few studies because of the difficulty in measuring cation influx.  
In this study, the survival rate of _Lactobacillus brevis_ WiKim0069 in the presence or absence of cations, i.e., calcium and/or magnesium, was examined. In addition, the relationship between cation influx and the viability of _L. brevis_ WiKim0069 after freeze-drying was investigated by promoting cation influx.
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 1 Introduction
This paper presents results from a laboratory study where five different IBPs and four different biochars were used to stabilise three soft Norwegian soils: a soft, low-sensitive clay from Onsey [40], located in southern Norway; a quick clay; and a peat, both from Tiller-Flotten [41; 42], located in mid-Norway. All binders and soils were characterised by their composition and mineralogy, and the potential for soil stabilisation was investigated by strength and stiffness testing. The purpose of this study was to investigate if and how the stabilisation effects of the IBPs and biochars differed for the different soil types and whether the stabilisation effect could be correlated to the binder and soil compositions. As few studies compare several types of IBPs and biochars in both clay and peat, the results presented herein give new insights into how their effectiveness can vary. This study shows that the differences can be considerable and highlights the importance of taking both the type of binder and the type of soil into consideration. It is believed that the results can also provide valuable guidance for further testing.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 4 Proposed Algorithm
### Cluster Size Optimization in Clustered Corona Based WSNs
#### 4.2.2 Determining the Maximum Angle and Exact Angle from the Interval by Two-Stage GA  
First Stage (Maximum Angle)  
The maximum value of the interval is estimated at the first stage of the proposed GA. In our strategy, solutions are represented in binary as strings of 0s and 1s. The sum of the decision of variables in each chromosome denotes the number of sectors. Since the energy consumption of nodes depends on their data transmission range, the objective is to minimize the differences between the total transmission range of nodes in SCWSN and the total distance between member nodes and local CHs in CCWSN as follows:  
\[f(x)=((M\cdot r)-\sum_{s=1}^{k}\sum_{i=1}^{x}\sum_{j=1}^{\frac{N_{i}}{\pi}}d_{ toCH}(j))^{2}\text{Subject to : }_{Min}<x \tag{21}\]  
where \(M\) denotes the number of node in the network and \(r\) denotes the thickness of coronas or the data transmission range of sensor nodes in SCWSN. \(d_{toCH}\) is the distance between sensor nodes and local CH and Ni is the number of nodes in \(i\)th corona. \(x\) is the number of sectors which is generated by the GA.  
Second Stage (Exact Angle)  
In the second stage of the two-stage GA, the optimal angle is derived from \([Min,Max]\) where min and max denote the lower and upper bound of variables, respectively. The objective is to minimize the total energy consumption of clusters:  
\[g(y)=(\sum_{s=1}^{k}\sum_{i=1}^{y}(E_{CH}(s,i)+\sum_{j=1}^{\frac{N_{i}}{\pi}}E _{MN}(j)))\text{Subject to : }_{Min}\leq y\leq f(x) \tag{22}\]  
where \(y\) is a randomly generated number between \([Min\,\ Max]\) which denotes the number of sectors. \(E_{MNi}\) and \(E_{CH}\) denote the energy usage of member nodes and CHs, respectively.
# Train Scheduling with Deep Q-Network: A Feasibility Test
## 5 Experiments
### Q-Learning  
To train the agent, represented by a single neural network explained in the next subsection, we randomly generate an instance and this instance is solved by the agent. Random instances of a particular number of trains can vary according to the departure interval at their initial station. This process is called an _episode_. The experiences of the agent, consisting of the state, action, and the next state, will be used to train the neural network. The size of the experience memory is set to 200,000. To accumulate enough experiences, the update of the behavior network is postponed until 4000 episodes were explored. When the train begins, we randomly choose 8 experiences, and the weights of the neural network are updated whenever the agent takes an action. In addition, the parameter \(\epsilon\), initially set to 1, is diminished according to the factor of 0.9999 after each update of weights of the neural network. When \(\epsilon\) is less than or equal to 0.01, the entire process of train ends. The target network is updated to the behavior network whenever an episode ends.  
As explained earlier, for a given state \(s\) and the action \(a\) taken by the agent, the new state \(s^{\prime}\) is obtained by solving the corresponding LP. LP is solved by PuLP. The optimal objective value is the absolute difference between the target schedule and the current schedule (the optimal solution of LP).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(From, To)** & **Local Train** & **Express Train** & **(From, To)** & **Local Train** & **Express Train** \\ \hline
1–2 & 291 & 291 & 16–17 & 112 & 83 \\
2–3 & 91 & 60 & 17–18 & 100 & 76 \\
3–4 & 85 & 57 & 18–19 & 102 & 78 \\
4–5 & 102 & 68 & 19–20 & 137 & 105 \\
5–6 & 132 & 88 & 20–21 & 100 & 66 \\
6–7 & 127 & 84 & 21–22 & 84 & 56 \\
7–8 & 93 & 66 & 22–23 & 88 & 58 \\
8–9 & 203 & 173 & 23–24 & 111 & 82 \\
9–10 & 99 & 67 & 24–25 & 104 & 77 \\
10–11 & 94 & 67 & 25–26 & 89 & 66 \\
11–12 & 118 & 84 & 26–27 & 87 & 65 \\
12–13 & 103 & 74 & 27–28 & 100 & 70 \\
13–14 & 121 & 104 & 28–29 & 98 & 69 \\
14–15 & 89 & 75 & 29–30 & 124 & 124 \\
15–16 & 91 & 67 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum transit times (seconds) of local and express trains.  
Thus, the performance measure used in our experiments is the objective value of LP defined by the final state, i.e., the final schedule.
## 3 Thermal Balances of Overhead Electrical Lines Designed According to Standards  
The International Council on Large Electric Systems (CIGRE) and IEEE address standards that explain algorithms for estimating ampacity and conductor temperature. Both techniques (CIGRE and IEEE) are based on the thermal balance of heat gained and lost in the conductor, according to the load and environmental factors [16].  
The first CIGRE method calculates the conductor temperature using steady-state conditions, whereas the second method estimates its temperature using dynamic equilibrium, which considers the thermal inertia of the conductor. In steady-state conditions, the basic thermal balance is defined as follows [16]:  
\[P_{\text{c}}+P_{\text{r}}=P_{\text{S}}+P_{\text{j}}+P_{\text{m}}, \tag{1}\]  
where  
* \(P_{\text{c}}\) represents cooling due to convection (W/m),
* \(P_{\text{r}}\) represents cooling due to radiation to the surroundings (W/m),
* \(P_{\text{S}}\) represents heating due to solar radiation (W/m),
* \(P_{\text{j}}\) represents heating due to Joule effect (W/m),
* \(P_{\text{m}}\) represents heating due to magnetic effect (W/m).  
If the thermal inertia of the conductor is considered, the following dynamic thermal balance is applied instead of equation [16]:  
\[m\cdot c\cdot\frac{\text{d}T_{c}}{\text{d}t}=P_{\text{s}}+P_{\text{j}}+P_{m}-P_ {\text{c}}-P_{r}, \tag{2}\]  
where  
* \(m\) is the mass per unit length of the conductor (kg/m),
* \(c\) is the specific heat capacity of the conductor (J/(kg\cdot\text{K})),
* \(T_{\text{c}}\) is the conductor temperature of the conductor (\({}^{\circ}\)C).  
The equation for the steady-state thermal balance in IEEE Standard 738 [17] is represented by [18]  
\[P_{\text{r}}+P_{\text{c}}=P_{\text{S}}+P_{\text{J}}, \tag{3}\]  
where [18]  
* \(P_{\text{r}}\) represents cooling due to radiation to the surroundings (W/m),
* \(P_{\text{c}}\) represents cooling due to convection (W/m),
* \(P_{\text{S}}\) represents heating due to solar radiation (W/m),
* \(P_{\text{J}}\) represents heating due to the Joule effect (W/m).  
Accordingly, the thermal balance Equation (3) can be graphically depicted in Figure 2 [18].  
The differences between the methodologies mentioned in the standards CIGRE and IEEE are as follows [18; 19]:  
Figure 2: Thermal balance of an overhead conductor according to IEEE 738.  
* Both methods consider meteorological factors such as wind speed and direction, ambient temperature, and solar radiation, but they calculate the thermal balance differently.
* Solar heating (solar radiation) is calculated by considering the position of the sun in different seasons. CIGRE employs a more complex algorithm that considers direct, diffuse, and reflected radiation.
* CIGRE approaches convective cooling using Morgan correlations based on the Nusselt number, whereas IEEE utilizes McAdams correlations based on the Reynolds number.
# Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
## 3 Experimental Setup
### Method Comparison  
For comparison purposes, we considered the following representative unsupervised and supervised feature selection methods: (i) variance-based relevance analysis (VRA), as an unsupervised method that generates a feature ranking based on a variability criterion (PCA) [68]. (ii) Self-weight ranking (SW), as an unsupervised technique, coding the feature relevance in terms of a self-similarity measure [45]. (iii) Laplacian score (LS), as an unsupervised approach that computes a relevance ranking from input samples to highlight the importance of each provided characteristic from graph edges [25]. (iv) Distance weight (DW) as a supervised method quantifying the relevance of the distance between samples from different clusters [69]. It is worth noting that whole provided feature selection algorithms are employed to rank the multi-domain parameters to build an HMM classifier performance curve by adding them one-by-one under a nested 10-fold cross-validation strategy. Furthermore, the achieved discrimination accuracy of relevant state-of-the-art approaches devoted to the Case Western Reserve University database classification, are also included in Section 4 for comparison purposes.
# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 2 YOLOv8
YOLOv8's detection head decouples classification and regression branches, independently predicting target categories based on probabilities and bounding box dimensions based on geometric coordinates. This decoupling addresses optimization conflicts, enhancing positioning accuracy, even in complex backgrounds. For instance, on highways, it robustly detects densely distributed vehicles of various sizes. YOLOv8 refines classification and regression using specific loss functions. To address the imbalance where background pixels exceed 95%, varifocal loss (VFL) is applied in classification. VFL is used to adjust sample weights based on detection difficulty, effectively identifying sparse positive features of small objects. For regression, distribution focal loss (DFL) estimates bounding box coordinates using a continuous probability distribution, rather than traditional discrete estimations. This makes the regression robust to small targets. This regression is sensitive to small coordinate shifts of tiny targets. Furthermore, using centroid distance and aspect ratio, pixel-level localization accuracy is enhanced in UAV photography. The application of loss functions also reduces the missed detection rate by 18%. Considering the need for lightweight and real-time algorithms, YOLOv8n was selected as the base model in this study.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 6 Perspectives  
Thanks to the rapid development of satellite sensors and the outstanding contributions of many researchers, the use of remote sensing in the monitoring of agricultural drought has been dramatically expanded ever since the 1960s. This paper introduced the physical mechanisms behind agricultural drought monitoring via optical and thermal remote sensing, summarized classical and state-of-the-art models and methods, and explained how sensor advancement has contributed to better monitoring of agricultural drought. Reviewing the past and looking to the future, we suggest that in future research onthe remote sensing of agricultural drought, the following aspects should be prioritized.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Optical Spectrum Processing  
To adjust the offset of the power spectra, an intermediate step is necessary. OSA processing methods cause a discrepancy between the total power, indicated by \(TOT\_Power\_1N\) and \(TOT\_Power\_OUT\), and the power evaluated from the corresponding power spectra. To reconcile these values, a normalization factor is needed. The total signal power, expressed in dBm, can be calculated from the power spectral density using the following equation:  
\[P_{spectrum}=10\log_{10}\left(\sum_{n=1}^{N}p_{n}|_{\mathrm{HW}}\cdot\frac{ \Delta f}{RBW}\right) \tag{10}\]  
Here, \(p_{n}\) represents the power spectral density at each frequency point, \(f_{n}\), \(RBW\) is the resolution bandwidth of the spectrum analyzer, and \(\Delta f\) is the frequency step, which is the spacing between each pair of frequency points. If the spacing is uniform, \(\Delta f\) is given by:  
\[\Delta f|_{\mathrm{GHz}}=\frac{f_{N}-f_{1}}{N} \tag{11}\]  
Once the total signal power is calculated from the spectrum, the offset can be determined using:\[P_{offset}|_{\text{dB}}=P_{tot}|_{\text{dBm}}-P_{spectrum}|_{\text{dBm}} \tag{12}\]  
In this equation, \(P_{tot}\) is the signal power indicated in the measurements (stored in the parameters \(TOT\_Power\_IN\) and \(TOT\_Power\_OUT\)). Finally, the offset is applied to each element of the transmitted and received spectrum power arrays, namely \(spectrum\_TX\_power\) and \(spectrum\_RX\_power\) arrays. Note that there is a single offset for the input power spectrum, while for the output power there is a distinct offset for each unique pair of target gain and tilt.  
The first step in processing the measurement data is to identify the central frequency of each active channel in the spectrum. This is done by using a filtered version of the power spectrum to minimize data fluctuations and eliminate erroneous samples. The filtering process involves clamping the spectrum values within a reasonable range to remove outliers and convolving the spectrum with a constant function. The convolution parameters are fine-tuned to ensure they work well across all devices. The central frequencies of the active channels are identified using the \(find\_peaks\) function from \(SciPy\), which locates the indices of all local maxima in the spectrum [27].  
To address issues caused by slight variations in channel central frequencies, these frequencies are determined exclusively from the input power spectrum and then applied to the output power spectra. Although it is possible to use prior knowledge of channel central frequencies, which is typically available in test setups, this program is designed to be flexible. It aims to function with minimal dependence on external information. Consequently, the program does not require any input regarding the number of active channels or their central frequencies.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 5 Experiment
### Separation Time Compare  
In addition, the calculation time of each frame of the Stacked-LSTM time domain mask method and time-frequency domain mask method was calculated, as shown in Table 4. In most signal separation studies of time-frequency domain masking methods, the window length of STFT was at least 256 points [40; 49; 54; 55], so the single frame duration in this experiment was 0.256 ms, and the calculation time is 1.24 ms. A longer time window and calculation time increased the minimum delay of the system. On the other hand, the Stacked-LSTM time-domain mask method could reduce the length of a single frame to 0.032 ms without reducing the separation accuracy, and its calculation time was only ms. To successfully separate the source signal from the time-frequency representation, the time-frequency domain masking method required high-resolution frequency decomposition of the mixed signal. This required a long STFT time window. This requirement increased the minimum delay of the system, which limited its applicability in real-time and low-latency applications, therefore, more and more research has begun to turn to time-domain methods [12; 23; 24; 26; 27; 29].
# Simulation Tests of the Passing Distance of Ships on a Two-Way Fairway
## Materials and Methods  
* Simulation studies of three passing, identically loaded ships (bulk carrier, tanker, and sea ferry) with lengths of 220 m, 103.6 m and 228 m, respectively, were carried out. The research was conducted on four sections of the Swinoujscic-Szczecin waterway with different parameters and different navigational (optical) markings. The results of these studies were used to determine the distance between the passing ships on the fairway considering the accuracy of position determination.
* The distance between the passing vessels determined by a simulation method considers the accuracy of determining the position of passing vessels. This allows a modification of the deterministic-probabilistic MTE method that determines the width of the safe maneuvering area on the fairway for two-way traffic.
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Current AI Technologies for Agriculture
#### 2.2.1 Cost Benefits of AI Technologies for Agriculture  
The focus on the role of AI systems in smart greenhouses and the agricultural sector, in general, was validated by the unique challenges and the enormous contribution in terms of Gross Value Added (GVA). Agriculture provided direct employment to approximately 4.4% of the population in Europe, and its GVA exceeded \(\copyright\)181 billion [67]. The scenario was not specific to the EU; at least 70% of the Indian population depended on agriculture for employment and sustenance [68]. Despite the enormous contribution, the future of agriculture in Europe and across the world is threatened by population dynamics; most European farmers are older (\(>\)80%), while a minority are below 40 years [67]. In contrast to the European Economic and Social Committee's report [67] on the impact of demographics on agricultural technology adoption, [PERSON] et al. [69] argued that the primary barriers were time and costs.  
The average cost of installing AI systems on farms was enormous and beyond the reach of smallholder farmers [70]. The contrary was true for large commercial farms with a higher capacity to absorb costs and risks and a higher tendency to allocate resources for capital-intensive investments [71]. The concerns raised by [PERSON] et al. [70] were supported by the Cisco and the International Telecommunication Union (ITU) report [72], which noted that agricultural sensors for AI systems with greater functionality are expensive ($150-$1000 per sensor). The initial capital outlay was a key impediment to technology adoption, considering that multiple sensors are required for monitoring greenhouse microclimate (humidity, temperature, soil nutrient, water levels, and pH). From another dimension, the expenses were relative, given that local production of smart greenhouse components may offset costs [73]; this view was corroborated by [PERSON]'s study [73] on South Korean companies Hyundai Metal Farming Company, Sewoon, DaeSungHione, and Dae Ryun and local production of precision farming systems, such as sensors, actuators, fans, heaters, dehumidifiers, and window motors. In contrast, there was insufficient production capacity in developing countries.  
The cost-centric worldview advanced by [PERSON] et al. [70] and [PERSON] et al. [69] negated the role of externalities. [PERSON] and [PERSON] [18] argued that the effectiveness of AI systems was largely dependent on the willingness of the local stakeholders to \"modify, generate, and extend their operational capabilities to improve efficiency\" (p. 5209). Additionally, the processes, AI resources, and tools (including drones, sensors, and robots) employed by commercial farms profoundly impacted the effectiveness of AI systems. Drawing from [PERSON] and [PERSON]'s study [18], the cost was not a primary barrier to AI technology adoption.