esa-sceva/satcom-chunk-collection huggingface dataset:
# Manufacture of a Potential Antifungal Ingredient Using Lactic Acid Bacteria from Dry-Cured Sausages
## 3 Results and Discussion
### In Vitro Antifungal Study of the MB Formulated
These bacteria were previously selected due to their antifungal effect in the overlay method. growth on an agar plate is the consequence of a complex interaction of multiple chemicals and metabolites, all of which contribute to the overall antifungal activity. The microdilution assay demonstrated that the BFS of the fermented MRSb was the most active extract against all fungi tested, regardless of the bacteria isolated (Table 4). The MIC50 obtained ranged from 5-21 g/L; the MIC100 ranged from 21-85 g/L; and the MFC ranged from 21-170 g/L. However, for MB10 formulation, the MIC50, MIC100, and MFC parameters ranged from 11-43 g/L, 21-85 g/L, and 43-170 g/L, respectively. It is important to underline that the BFS of the MRSb and the MB10 fermented by _P. pentosaccus_ C15 showed lower concentrations required to achieve fungal growth against all the toxigenic strains tested compared to _P. pentosaccus_ C12 and _L. plantarum_ C60 fermented BFS. Although the BFS of the MRSb evidenced a higher antifungal effect, a poor difference was evidenced when compared to MB10. In some cases, there were no significant differences (\(p\leq 0.05\)) between the treatments, e.g., MB10 fermented by _P. pentosaccus_ C15 had no statistical difference to MRSb comparing the average values of MIC100 and MFC (Table 4). In other words, _P. pentosaccus_ C15 was able to ferment MB10 and produce antifungal compounds, which inhibited fungal growth at similar conditions to MRSb. In addition, MRSb fermented by _P. pentosaccus_ C15 obtained MIC50, MIC100, and MFC values lower than _L. plantarum_ C60 and _P. pentosaccus_ C12, regardless of the fungal strain essay. The MRSb was applied in both experiments as a control group since previous studies have demonstrated its antifungal activity while being fermented by LAB and producing antimicrobial substances. For instance, [PERSON] et al. (2019) identified _P. pentosaccus_ and _L. plantarum_ strains that showed good antifungal activity against _A. niger_ and _A. carbonarius._ In addition, the strains showed a high capacity for the degradation of OTA, one of the main toxins produced by these fungi. However, the authors could not identify the compounds responsible for the antifungal and antimicrobial activities of the strains. In other studies, the application of MRS fermented by _L. plantarum_ inhibited the _A. flavus and F.
## 4 Case Studies
### Probabilistic Forecasting of ALSC
#### 4.2.3 The Impact of Multi-Slack Buses on Probabilistic ALSC
Taking Method 3 and Method 4 as an example, Figure 11 shows the power changes of each generator during the increase of load under different allocation principles. From Figure 11, it can be found that Method 3 makes fuller use of the capacity of each generator, so the \(E_{\rm{ALSC}}\) calculated in Method 3 is greater than that in Method 4. This is because the principle of the second power imbalance allocation in Method 4 works according to the ratio of reciprocal of cost of each generator. The cost of generators is positively correlated with the power, as shown in (29). Therefore, generators with higher capacity, such as \(P_{38}\) and \(P_{39}\) have higher cost and get a smaller share in power allocation during the whole load increase process. Generators with lower capacity, such as \(P_{36}\) and \(P_{37}\), have lower cost and have a larger allocation ratio more quickly reach their output limits. On the contrary, the allocation principle of Method 3 is the ratio of the current power of generators. Therefore, \(P_{38}\) and \(P_{39}\), which have higher capacity, have a larger allocation ratio, while \(P_{36}\) and \(P_{37}\) with lower capacity have a smaller allocation ratio. Since the output limit of the high-capacity generator is correspondingly larger, this allocation principle can make full use of the capacity of each generator and obtain a larger \(E_{\rm{ALSC}}\). However, it should be emphasized that although the \(E_{\rm{ALSC}}\) of method with the first allocation principle is larger, the second allocation principle is more reasonable, because considering the economics in the face of sustained regular load growth is a common and indispensable practice in actual operation. Therefore, the evaluation results of the load supply capability obtained by the first allocation principle are more optimistic than the actual situation, which amplifies the risk of encountering security issues. Figure 11: _Cont._
## 2 Characteristic Analysis of the Iridium System
### The Epoch Level  
The Phenomenon of Epoch Redundancy  
In LEO SOP positioning, a large number of epochs are captured within each observation window. However, the limited number of orbital planes in LEO constellations results in many epochs originating from the same orbital plane or even the same satellite. This study makes a novel observation that introducing these similar epochs leads to a near-rank-deficient condition in the observation matrix, significantly degrading the noise resistance of positioning solutions. This phenomenon becomes particularly pronounced during short observation periods.  
Here, we formally define epoch redundancy for LEO SOP positioning for the first time. Consider a positioning scenario where the total number of received epochs is \(N_{total}\), with \(N_{1}\), \(N_{2}\), \(\ldots\), \(N_{k}\) representing the epoch counts from \(k\) individual satellites. Then the epoch redundancy for LEO SOP positioning can be defined as:  
\[R_{e}=\frac{\max\{N_{1},N_{2},\ldots,N_{k}\}}{\sum\limits_{i=1}^{k}N_{i}}=\frac {\max\{N_{1},N_{2},\ldots,N_{k}\}}{N_{total}} \tag{10}\]  
Figure 2 presents the result from 500 real-world Iridium signal positioning trials. As the epoch redundancy increases, both GDOP and positioning errors exhibit more severe fluctuations. This confirms that the observation matrix has become ill-conditioned, rendering the position solutions highly sensitive to noise.  
Figure 2: The variation curves of geometric dilution of precision (GDOP) and positioning errors versus epoch similarity derived from 500 real-world Iridium signal positioning trials.  
Therefore, in a positioning trial, the rank of an epoch within its parent satellite's observation sequence becomes a significant feature worthy of consideration. A higher rank value indicates that more similar epochs from the same satellite have been received prior to this epoch, consequently reducing its utility value for positioning.
## 5 Results and Discussion
### Results with Different Model-Updating Approaches  
The optimal results, including the minimum errors of the objective function and the natural frequencies, for the two approaches and the updated parameters are listed in Table 11. The objective function error of the original FE model, which was constructed according to the reference parameters in Table 3, was as high as 14.31%. The errors of the models updated by the first and second approaches were reduced to 1.40% and 1.05%, respectively. Since the weight coefficients of the four-order frequency errors were the same, the error gap was small. For the second approach, the maximum and minimum errors were 1.46% and 0.44%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Experiment** &
\begin{tabular}{c} **Original** \\ **FE Model** \\ \end{tabular} & **First Approach** & **Second Approach** \\ \hline \(k_{\mathrm{H}}(\mathrm{N}/\mathrm{m})\) & & \(\infty\) & \(\infty\) & \(10^{8}\) \\ \(k_{\mathrm{V}}(\mathrm{N}/\mathrm{m})\) & & \(\infty\) & \(\infty\) & \(10^{9}\) \\ Young’s & & & & \\ modulus (GPa) & - & 70.0 & 68.9 & 69.1 \\ Poisson’s ratio & & 0.33 & 0.3 & 0.3 \\ Density (kg/m\({}^{3}\)) & & 2720 & 2890 & 2890 \\ Length (mm) & & 300 & 303.1 & 302.9 \\ Thickness (mm) & & 2 & 1.89 & 1.89 \\ \(F_{\mathrm{obj}}\) (\%) & & 14.31 & 1.40 & 1.05 \\ First natural & 221.4 & 254.3 & 226.0 (+2.06\%) & 223.5 (+0.93\%) \\ frequency (Hz) & 429.8 & 489.9 & 434.4 (+1.08\%) & 427.9 (\(-\)0.44\%) \\ Third natural & 684.9 & 786.0 & 694.9 (+1.46\%) & 692.5 (+1.11\%) \\ frequency (Hz) & 1117.9 & 1270.4 & 1124.6 (+0.60\%) & 1101.6 (\(-\)1.46\%) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Optimal results and updated parameters.  
Figure 14: Mode tracking for the first and second approaches. (**a**) Mode tracking for the first approach and (**b**) mode tracking for the second approach.
## 3 Materials
### SigLIP 2
#### 3.2.1 Architecture and Key Differences  
SigLIP 2, similar to SigLIP and CLIP, employs a dual-encoder architecture, comprising an image encoder (typically a vision transformer) and a text encoder (typically a transformer) [17]. However, SigLIP 2 incorporates several key architectural and training innovations that contribute to its superior performance. These advancements can be categorized as follows:  
1. SigLIP 2 employs a sigmoid loss during pre-training. This encourages the model to independently assess image-text relevance, improving semantic understanding and producing more discriminative feature representations.
2. Neighborhood Attention Flex (NAFlex) allows SigLIP 2 to process images at varying resolutions and aspect ratios with a localized attention mechanism, improving computational efficiency and scalability, especially for high-resolution images.
3. SigLIP 2 captures semantic relationships across multiple languages, offering an advantage over models like BiomedCLIP, which is primarily trained on English text.
4. SigLIP 2 benefits from larger batch sizes, extended training schedules, and advanced data augmentation, allowing for more generalizable feature representations and reduced overfitting.
5. SigLIP 2 offers superior performance in capturing fine-grained details and nuanced relationships, making it ideal for tasks like analyzing anatomical variations in ultrasound images of CL/P.
## 1 Introduction
In [10], a multiscale edge-feature-based perceptual hashing scheme is proposed for HRRS image, which has good sensitivity to detect subtle illegal tampering, while it adopts the traditional feature extraction method and cannot distinguish false edge information. In the last few years, the deep learning represented by end-to-end learning of feature representation has achieved well performance in several image hash applications [11; 12; 13; 14; 15; 16], but from the perspective of authentication for HRRS images, many challenges remain. 1. One of the most crucial challenges for applying deep learning to perceptual hash is that it requires a large number of training samples, otherwise it is easy to cause overfitting. However, there are only a few samples are available for each type of HRRS images to meet the authentication accuracy. 2. Existing deep-learning-based hash algorithms mainly focusing on the field of image retrieval [11; 12; 13; 14; 15; 16], while image retrieval and image authentication are two different research areas, and the requirements of them are very different. 3. Interpretability has been identified as a potential weakness of deep neural networks [17], while it is necessary to illustrate the tampering of the images during the authentication process. Given the complexity, modern Earth system models are often also not easily traceable back to their assumptions in practice [18], which limits the interpretability of remote sensing image authentication. 4. The accuracy of the label of the training sample has a significant effect on the accuracy [19], while most of the existing research adopts the method of manually labeling samples, making the accuracy of sample information have a negative impact on image authentication. In this paper, we proposed an improved perceptual hash scheme based on deep learning for the authentication of HRRS images by making use of the advantages of U-net model. U-net [20], which is an improved fully convolutional neural network (FCN) model, has a strong capability to automatically learn the high-level representations for images. We use U-net to extract the robust edge features of the HRRS image. The contributions of our work were elaborated as follows:  
1. To the best of our knowledge, this is the first attempt in the GIS field to explore the application of U-net to the integrity authentication of HRRS images. 2. As high-precision training sample is an important guarantee for ensuring the accuracy of the authentication algorithm for HRRS images, a training sample generation method combining Canny operator and artificial processing was proposed. 3. A modified U-net model is studied to extract robust edge feature of HRRS images, which is the key step of perceptual hash. The rest of this paper is organized as follows: Section 2 outlines the progress of related research. Section 3 presents our proposed method. Section 4 analyzes and discusses the experiment result. A discussion and a conclusion of our study are presented respectively in Sections 5 and 6.
## 2 Materials and Methods
### Data Collection  
Subjective data: the staff collected subjective wellness data ([PERSON] questionnaire) every morning. Players were to respond to four questions: \"sleep quality\" (SLEEP), \"muscle soreness\" (SORENESS), \"fatigue\" (FATIGUE) and \"stress\" (STRESS), rating their responses on a Likert scale (1-7). After the training, subjective score of perceived exertion (RPE) was collected using a 0 to 10 scale [7,9]. This value then was multiplied by the training duration to quantify the subjective training load (s-RPE) [9]. External load data: during trainings and games, the GPEXE global positioning system (18 Hz, GPEXE, Exelio srl, Udine, Italy) was used to collect the external load data. The players wore a sports vest with a pocket on the back (between the scapulae) where the devices were placed. This equipment was used in the past by other authors and validated as reliable to determine movement patterns [37]. The following variables were assessed: number of accelerations of 2-3 m/s\({}^{2}\) (ACC2), number of high accelerations \(>\)3 m/s\({}^{2}\) (ACC3), number of decelerations of \(-\)2--3 m/s\({}^{2}\) (DEC2), number of high decelerations \(<\)\(-\)3 m/s\({}^{2}\), total distance (DIST), distance in speed zone of 14.4-19.7 km/h (DIS4), distance in speed zone of 19.8-25.2 km/h (DIS5), distance in speed zone \(>\)25.2 km/h (DIS6), number of speed efforts at 19.8 km/h (SPEF), metabolic power distance at 15-25.5 w/kg (MPW4), metabolic power distance at 25.5-50 w/kg (MPW5), metabolic power distance at \(>\)50 w/kg (MPW6), number of metabolic power efforts at \(>\)25.5 w/kg (MPWEF).
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**City** & **Country** & **Köppen–Geiger Classification** & **CDD\({}_{\mathbf{18}}\)**: & **HDD\({}_{\mathbf{18}}\)**: \\ \hline Larnaca & Cyprus & BSh & 1259 & 759 \\ Seville & Spain & Casa & 1063 & 916 \\ Athens & Greece & Casa & 1076 & 1112 \\ Brindisi & Italy & Casa & 834 & 1151 \\ Santander & Spain & Cfb & 209 & 1369 \\ Rome & Italy & Casa & 649 & 1444 \\ Porto & Portugal & Csb & 146 & 1491 \\ Madrid & Spain & Casa & 628 & 1965 \\ Plovdiv & Bulgaria & Cfa & 543 & 2471 \\ Milan & Italy & Cfa & 380 & 2639 \\ Paris & France & Cfb & 142 & 2644 \\ London & England & Cfb & 32 & 2866 \\ Timisoara & Romania & Dfa & 365 & 2896 \\ Brussels & Belgium & Cfb & 96 & 2912 \\ Geneva & Switzerland & Dfb & 193 & 2965 \\ Ankara & Turkey & BSk & 253 & 3307 \\ Ljubljana & Slovenia & Dfc & 168 & 3383 \\ Copenhagen & Denmark & Dfb & 29 & 3563 \\ Prague & Czech Republic & Dfb & 84 & 3703 \\ Munich & Germany & Dfb & 79 & 3738 \\ Bergen & Norway & Cfb & 21 & 3996 \\ Moscow & Russia & Dfb & 99 & 4655 \\ Helsinki & Finland & Dfb & 33 & 4712 \\ Reykjavik & Iceland & Dfc & 0 & 4917 \\ Kiruna & Sweden & Dfc & 0 & 6967 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the 25 European cities considered (data from epw weather file). Figure 2: Individuation of the sample of 25 cities used for the analyses. To understand energy demand variations, an ideal-load HVAC system was considered for active strategies, while, for passive strategies, no HVAC system was implemented and natural ventilation strategies were adopted. HVAC operational schedules were kept as standard, and setpoints of 21 \({}^{\circ}\)C in heating operational mode and 24 \({}^{\circ}\)C in cooling mode were considered. With regard to passive strategies, a supplementary EMS program was developed in order to calculate the adaptive thermal comfort optimal range, in accordance with current European recommendations (EN 16798-1:2019 [71]). This EMS program calculates, starting from the temperature of the previous 7 days (\(\theta_{ed-i}\)), the running mean outdoor temperature (\(\theta_{m}\), Equation (6)) and the optimal operative temperature (\(\theta_{c}\), Equation (7)). Then, considering the first comfort class, the program calculates the operative temperature (\(T_{OP}\)) comfort range (Equation (8)), as described in the following equations:  
\[\theta_{m}=\frac{(\theta_{ed-1}+0.8\ \theta_{ed-2}+0.6\ \theta_{ed-3}+0.5\ \theta_{ed-4}+0.4\ \theta_{ed-5}+0.3\ \theta_{ed-6}+0.2\ \theta_{ed-7})}{3.8}\left[{}^{\circ}\text{C} \right], \tag{6}\]  
\[\theta_{c}=0.33\ \theta_{rm}+18.8\ [{}^{\circ}\text{C}], \tag{7}\]  
\[\theta_{c}-3\ \leq\ T_{OP}\ \leq\ \theta_{c}+2\ [{}^{\circ}\text{C}]. \tag{8}\]  
Hence, the results of these calculations were used for both the definition of the number of discomfort hours--comparing the operative temperature with the optimal range in Equation (6)--and the definition of a natural ventilation schedule. Indeed, in the passive
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 3 Adaptive Error Tracking and Equalization
### Decision Feedback Equalization  
The first scheme considers the problem as recovering a signal affected by frequency-selective, fading-causing Inter-Symbol Interference (ISI). After downsampling from symbol synchronization, the \(l-m_{b}\)th sampling decision symbol can be expressed as (\(l\) represents current symbol)  
\[y_{l-m_{b}}=s_{r}(t-m_{b}T)=s_{r}(lT_{s}-m_{b}T), \tag{4}\]  
where \(T_{S}\) is the sampling interval, \(T\) is the symbol length, and \(m_{b}\) is the symbol index.  
Figure 3: Schematic diagram of the signal processing flow in the DSDT system.  
Next, an adaptive equalizer can be operated to improve the detection performance by minimizing the joint error of multiple consecutive symbols. The objective function is as follows:  
\[\min_{g\in S}E\left[\left|\hat{s}_{l}-\left(\sum_{m_{b}=0}^{M_{b-1}}w_{m_{b}}y_{l- mb}-\sum_{n_{f}=1}^{N_{f}-1}w_{nf}\hat{s}_{l-nf}\right)\right|^{2}\right], \tag{5}\]  
In Equation (3), \(W_{mb}\) represents the weights of the inverse filter, \(W_{nf}\) represents the weights of the feedforward filter,\(M_{b}\) and \(N_{f-1}\) represent the taps of the inverse filter and feed-forward filter, \(y_{l}\) represents the lth symbol of the combined signal, and \(\widehat{s}_{l}\) represents the ideal constellation after the hard decision of the lth symbol. By minimizing the objective function mentioned above, the filter coefficients can be obtained through iterative optimization.  
Although the DFE algorithm can mitigate the ISI caused by channel characteristics, it does not fully utilize the channel information carried by all sub-spectra. Moreover, as a traditional DFE algorithm, it has the drawbacks of slow convergence speed and high bit error rate (BER) at low signal-to-noise ratio (SNR) levels.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 4 Linearization of the Scanning Function  
The scanning functions of both GSs and PMs are non-linear (Table 1), while their scanning velocity is variable (Figures 3 and 7). The scanning functions could be considered with a linear expression, but only for a limited FOV. Furthermore, to properly make such an assumption, the distance \(L\) (Figures 1 and 2) must be increased, as concluded in Section 3.4.
## 3 Study Area and Research Method
### Research Method  
#### 3.2.1 Data Processing  
Patent data from the CNIPA with application addresses in the YRD are extracted and collated. Over 8.6 million patent records applied from 2000 to 2018 are identified and the YRD and its 41 cities are selected as the spatial units for analysis. The applicants' addresses were geocoded for accurate statistics of patents quantity in each city. This  
Figure 2: Study area of Yangtze River Delta, China. Source: author.  
research also applies kinds of classification strategies based on the information from the patent dataset. The examination by time section has unfolded an upsurge in the number of patent applications, from less than 27 thousand in 2000 to more than 118 thousand in 2018, with the ratio of valid patents simultaneously increasing by years. The ratios of three types of patents, however, show contrasting trends during the 19 years: the ratio of patents classified as an invention has slightly increased but the ratio of valid patents classified as an invention has greatly decreased, ratios of valid patents classified as a design and utility model have been increasing (Figure 3), and their proportion to inventions became stable around 2010. As invention patents require a higher standard of innovation, the research has distinguished this type of valid patent since 2010. It computes and sorts 35 fields defined by World Intellectual Property Organization [63] to prepare the data for the following analysis.