esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles of VSG and Low-Voltage Ride-Through Requirements  
The typical VSG model, as shown in Figure 2, simulates the inertia and damping of a synchronous generator through control methods. In this model: \(e_{\mathit{alk}}\) denotes the voltage at the midpoint of the inverter's leg, \(i_{\mathit{lake}}\) is the inverter output current, \(i_{\mathit{gabc}}\) is the grid-connected current. \(R_{f}\), \(L_{f}\), and \(C_{f}\) constitute the impedance of the LC filter circuit. \(v_{\mathit{gabc}}\) represents the voltage at the PCC. \(L_{g}\) and \(R_{g}\) are the grid-side impedances. D is the active damping coefficient. \(J\) is the virtual inertia. \(\omega\) is the virtual angular frequency. \(E_{m}\) is the voltage magnitude of the VSG. \(e_{\mathit{mabc}}\)* is the three-phase voltage generated by the VSG model. \(L_{v}=L_{f}+L_{g}\) and \(R=R_{f}+R_{g}\) represent the virtual impedance. \(i_{\mathit{LAB}}\)* is the reference value of the inverter output current. \(E_{m}\) is the modulation wave. \(d_{1}\)-\(d_{6}\) are the modulation signals.  
Equation (1) in the figure is as follows:  
\[e_{\mathit{mabc}}^{*}=\left[\begin{array}{c}E_{m}\cos(\theta)\\ E_{m}\cos(\theta-2\pi/3)\\ E_{m}\cos(\theta+2\pi/3)\end{array}\right] \tag{1}\]  
In accordance with national grid interconnection standards, the reactive current iqref injected into the grid by the inverter must meet the specified condition when the grid voltage magnitude decreases from \(V_{\mathit{gn}}\) to \(V_{\mathit{pcc}}\).  
\[i_{\mathit{qref}}=\left\{\begin{array}{l}0,V_{\mathit{pcc}}>0.9V_{\mathit{ SN}}\\ 1.5\times(0.9-V_{\mathit{pcc}}/V_{\mathit{SN}})I_{\mathit{N}},0.2\leq V_{ \mathit{pcc}}/V_{\mathit{SN}}\leq 0.9\\ 1.05I_{\mathit{N}},V_{\mathit{pcc}}/V_{\mathit{SN}}\leq 0.2\end{array}\right. \tag{2}\]  
where \(I_{\mathit{N}}\) is the rated current output by the VSG.  
Utilizing the instantaneous power formula while neglecting switching device and filter circuit losses, the grid-side voltage vector \(v_{g}\) aligns precisely with the d-axis component of the grid-side voltage, denoted as \(v_{\mathit{gd}}\). As a result, during the LVRT period, the reference  
Figure 2: VSG Topology Diagram.  
values for the active and reactive power outputs of the VSG can be calculated using the following equations [12; 13; 14]:  
\[\left\{\begin{array}{l}P^{*}=\frac{3}{2}v_{gd}i_{dref}\\ Q^{*}=-\frac{3}{2}v_{gq}i_{qref}\end{array}\right. \tag{3}\]  
In the equation, \(v_{gd}\) and \(v_{gq}\) represent the \(d\)-axis and \(q\)-axis components of the voltage at the PCC, respectively, where \(i_{dref}=\sqrt{2.25I_{N}^{2}-i_{qref}^{2}}\).  
The specific flowchart for the low-voltage ride-through based on VSG is shown in Figure 3:  
When the system detects a grid voltage sag signal, it disconnects the outer voltage loop and instead calculates the power reference values based on the reactive current. After the grid voltage recovers, the outer voltage loop is switched back on to provide the reference value for active power, completing the low-voltage ride-through process [15; 16].
## 4 Conclusions  
This paper presents a novel pair of FBG subsidence sensors, which are designed to measure left and right tilt and changes in water levels simultaneously. The sensors are uniquely skewed and configured in opposing directions, facilitating the collection of accurate data on slope variations and water level fluctuations. The sensing system is capable of resolving at least 0.424\({}^{\circ}\) of inclination and 0.5 cm of water level change, while also being able to detect variations in water temperature when either water level or inclination measurements are conducted independently. Furthermore, the proposed measurement system is compatible with FSO, streamlining the system by reducing the maintenance and installation costs typically associated with fiber optic cabling. This innovative FBG sensor system enables the measurement of multiple parameters with a reduced number of sensors, significantly enhancing sensing capabilities and maximizing cost efficiency.  
Conceptualization, C.-K.Y.; methodology, C.-K.Y., Y.-J.C. and Y.-C.X.; data curation, Y.-C.X. and C.-K.Y.; model validation, Y.-J.C. and C.-K.Y.; formal analysis, C.-K.Y., Y.-J.C. and P.K.; investigation, C.-K.Y., Y.-J.C., Y.-C.X., P.K. and P.-C.P.; visualization: C.-K.Y.; writing--original draft preparation: C.-K.Y.; writing--review and editing: C.-K.Y.; supervision, P.-C.P. All authors have read and agreed to the published version of the manuscript.  
Figure 4: The FBG wavelength shift results of the tilt measurements correspond to 1548 nm at different water levels. The temperature conditions for the water during the measurements were as follows: (**a**) 30 \({}^{\circ}\)C, (**b**) 40 \({}^{\circ}\)C, (**c**) 50 \({}^{\circ}\)C, and (**d**) 60 \({}^{\circ}\)C.  
**Funding:** This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Discussion  
### Grip Strength  
The literature has shown that wearing gloves could lead to a reduction of grip strength up to 23% [40]. It is well known that gender, handedness, and grip span all have significant effects on grip strength [41; 42; 43; 44]. It is also known that there is a grip span that allows the production of maximum grip strength for each individual [45; 46; 47; 48]. Grip strength declines when the object being handled is larger than this grip span. Our grip strength data were consistent with those in the literature.  
Our grip strength data showed that wearing gloves led to declines in grip strength under most of the experimental conditions. These declines are lower than those in the literature [38]. This was because the thickness of our gloves is much less than those in the latter. The cut resistance gloves (glove 3) showed the highest decline than the other gloves, and the knitted gloves with NBR coating showed the lowest decline. Based upon EMG assessments of forearm muscles, a literature [49] has found that declination of grip strength is linearly related to the thickness of the gloves. This was partially consistent with our findings. The grip strength decline of the thickest gloves (9.4%), or the cotton gloves, was less than that of the cut resistant glove (12.8%). The thinnest glove (glove 2) showed the lowest grip strength decline (6.8%). The reason for this may be that our glove thickness was in a narrower range (1.03 mm-2.17 mm) than that in [49] (1.2 mm-8.2 mm). This may also imply a decline of grip strength due to wearing gloves not only depending on glove thickness but also the stiffness of the gloves. The literature [31] has reported strong correlation (0.77-0.94) between a glove's longitudinal stiffness and grip strength. The glass fibers in the cut resistance gloves in our study increase the stiffness of the gloves and hence hamper the production of grip force. The stiffness angle of glove 2 (34.6\({}^{\circ}\)) was higher than that of glove 1 (28.8\({}^{\circ}\)) but the former had a significant (\(p<\) 0.05) lower RGS than the latter. This was because the effects of stiffness on the grip strength was impeded by glove thickness.  
The relationships between grip strength and grip span are shown in Table 5. Grip span also affected the decline of grip strength when wearing gloves especially for females. Table 4 shows that at a grip span of 65 mm, female participants experienced averages of 2.1% and 3.3% grip strength increase, instead of decrease, when wearing gloves 1 and 2, respectively, on their non-dominant hands. Their dominant hands, however, experienced 1% and 3.7% declines of grip strength on the same grip span and gloved conditions, respectively. These percentages are relatively small implying insignificance of wearing gloves on grip strength at a grip span of 65 mm for females. Declines of grip strength for female participants were more obvious at the grip spans other than 65 mm for those we had tested. For male participants, the grip strength reduction wearing glove 2 at both 45- and 55-mm grip spans on the dominant hand were also negligible.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
###### Abstract  
Celiac disease and gluten sensitivity affect a significant portion of the population and require adherence to a gluten-free diet. Dining in social settings, such as family events, workplace gatherings, or restaurants, makes it difficult to ensure that certain foods are gluten-free. Despite the availability of portable gluten testing devices, these instruments have high costs, disposable capsules, depend on user preparation and technique, and cannot analyze an entire meal or detect gluten levels below the legal thresholds, potentially leading to inaccurate results. In this study, we propose RGB (Recognition of Gluten in Bread), a novel deep learning-based method for automatically detecting gluten in bread images. RGB is a decision-support tool to help individuals with celiac disease make informed dietary choices. To develop this method, we curated and annotated three unique datasets of bread images collected from Pinterest, Instagram, and a custom dataset containing information about flour types. Fine-tuning pre-trained convolutional neural networks (CNNs) on the Pinterest dataset, our best-performing model, ResNet50V2, achieved 77% accuracy and recall. Transfer learning was subsequently applied to adapt the model to the Instagram dataset, resulting in 78% accuracy and 77% recall. Finally, further fine-tuning the model on a significantly different dataset, the custom bread dataset, significantly improved the performance, achieving an accuracy of 86%, precision of 87%, recall of 86%, and F1-score of 86%. Our analysis further revealed that the model performed better on gluten-free flours, achieving higher accuracy scores for these types. This study demonstrates the feasibility of image-based gluten detection in bread and highlights its potential to provide a cost-effective non-invasive alternative to traditional testing methods by allowing individuals with celiac disease to receive immediate feedback on potential gluten content in their meals through simple food photography.  
Keywords:gluten detection in bread images; celiac disease; decision-support tool; machine learning; convolutional neural network +
Footnote †: journal: Applied sciences
## 3 Related Work
### The NBLA and Its Open-Loop Power Control Approaches  
The NB-IoT link Adaptation (NBLA) approach is mainly focused on an inner loop link adaptation aspect that is mainly focused on addressing the issue of rapid changes that are often observed with the transmission BLER in NB-IoT systems. The NBLA approach as proposed in [3] works as follows.  
* During the duration of a single period of \(T\), all transmission ACK/NACKs are computed to work out an estimated value of the BLER,
* Based on the obtained BLER value, the transmission repetition number is adjusted accordingly to cope with the variation in the channel's condition and to ensure that there is less probability of failed transmission.
* This is a way to save on energy consumption of the NB-IoT network.  
Figure 2: Illustration of data repetition during a single transmission.  
The main challenge faced by the NBLA approach resides in the fact that despite its effort in estimating the channel condition based on the computed BLER value, because of the reduced (narrow) bandwidth and considerably unstable channel conditions of the NB-IoT systems, the NBLA power control strategy often fails to ensure reliable uplink transmissions. This results in significant energy wastage due to repeated unsuccessful transmissions. The development of a new and more energy efficient approach that is capable of adaptively addressing the variations of channel conditions by looking at more than one-dimensional aspect of previous BLER performance but also considering the MCS level in selecting an appropriate repetition number is highly needed. This is expected to contribute towards adequate energy management within NB-IoT systems.
## 3 Results and Discussion
### Biocatalyst Selection Based on Economic Evaluations and Sustainability Indicators
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Cost (EUR/kg NPGDL)**} \\ \cline{2-4}  & **Cost** & **Novozym® 40086** & **Novozym® 435** \\ \hline Lauric acid 1 ** \\ Neopentyl glycol 2  & 1.5 EUR/kg & 0.49 & 0.45 \\ Biocatalyst 3  & & \\ Novozym® 40086 & 600 EUR/kg & 10.69 & \\ Novozym® 435 & 1300 EUR/kg & & 10.64 \\ Thermostatic bath & & & \\ Initial & 6.8 10\({}^{-3}\) EUR/min & 3.23 (8 min) & 5.19 (14 min) \\ Maintenance & 2 10\({}^{-4}\) EUR/min & 6.27 (8 h) & 13.10 (5 h) \\ Overhead stirrer & 10\({}^{-4}\) EUR/min & 3.18 & 1.71 \\ \hline Total direct cost & & 25.28 & 32.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Substrates, biocatalysts, and energy prices. NPGDL total direct production costs using Novozym® 40086 and Novozym® 435. For both immobilized lipases, the same value of Atom Economy (AE) is obtained since its calculation is based on the stoichiometry of the reaction and is higher than 90%, which shows the sustainability of the process [27]. The Environmental Factor (E-factor) quantifies the amount of waste produced in a process and, obviously, its value must be close to zero. The specialized literature suggests calculating two types of E-factor: the simple E-factor and the complete E-factor, which includes water as a residue [28; 29]. The very low values obtained for both E-factors indicate that the process studied produces a minimum quantity of residues, the main one being the water formed in the esterification reaction. Both values are significantly lower than those described for bulk chemical synthesis processes [28]. Finally, the sustainability of the processes is also evidenced by the Carbon Mass Efficiency (CME) [30] and Process Mass Intensity (PMI) [29] values, which, in both cases, present values above 90% and close to unity, respectively. In view of the above, it can be affirmed that, from the sustainability point of view, the use of either of the two biocatalysts tested in this paper is suitable to carry out the NPGDL synthesis reaction in accordance with the main green metrics and, therefore, the final choice of the biocatalyst is not conditioned by these indicators.
## 3 Materials and Methods
### AIS Data Preparation and Analysis  
#### 3.2.1 Data Preprocessing  
Due to satellite signal transmission and human factors, the AIS messages received by satellites may have problems such as data error and incomplete format that are not conducive to analysis in practical applications [5]. To address the adverse effects of these issues, data preprocessing such as data cleaning and calibration is required. Data preprocessing can provide reliable AIS data for subsequent ship classification and anomaly detection. AIS data preprocessing mainly consists of format error screening and data deduplication. Format error screening aims to delete the noise data in messages 1, 2, 3, and 5 that do not conform to AIS format specifications and omit important information. Duplicate data are defined as messages with the same MMSI and time. According to the statistics of the AIS database, such messages usually have the same longitude and latitude information, so only one duplicate AIS message is reserved. In addition, there is a very small number of \"duplicate data\" with different longitude and latitude information, which cannot be judged as correct or incorrect, so it will not be considered in this paper. Furthermore, the \"type\" attribute field for messages 1, 2, and 3 is created and filled according to the \"ship and cargo type\" attribute field of message 5. By preprocessing the AIS data, 62 million messages for various types of ships were obtained.
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 3 Proposed Method
### System Framework  
Figure 2 shows a flowchart of the PM2.5 value forecast. The PM2.5 data, meteorology data, and weather forecast data were included for PM2.5 value forecasting. It was considered that meteorology data are highly correlated with air quality. The PM2.5 data and meteorology data were combined for each hour and were regarded as historical data. Then, the PM2.5 value was forecasted using the weather forecast data and the spatial data.  
There were some pre-processing tasks for PM2.5 data. Some unreasonable air monitoring devices, which are considered as being installed indoors, needed to be removed. Some weather features, such as temperature and humidity, were more stable than outdoor weather features [38]. The most important factor was that indoor air quality was less impacted by meteorology. Then, the PM2.5 data of an air quality sensor and the meteorology data of the nearest weather station were combined for each hour as historical data. Additionally, the weather forecast data were regarded as future data. There were two parts to the methodology: temporal and spatial parts. In terms of the temporal part, the preliminary PM2.5 value was forecasted using the similarity between historical meteorology data and weather forecast data. Air quality was associated with time. In addition to the data with similar meteorology features, recent data were also referred to. Therefore, recent data and data with similar weather features were detected to forecast a PM2.5 value. The preliminary forecasted PM2.5 value was evaluated using the weighted-average formula. The degree of similarity of weather features was considered to be the weight.  
In terms of the spatial part, it was found that the PM2.5 values were highly correlated with distance. The PM2.5 value trends of devices near to each other are similar. Therefore, the preliminary forecasted PM2.5 value was smooth with other forecast PM2.5 from nearby devices from each forecast hour. In addition to distance, wind speed and wind direction were both taken into consideration. It was considered that the forecasted distribution per hour was a credible trend and resulted in the forecast trend being similar due to normalization. The value after smoothing was the final result of the forecast.
## 3 System Concept and Hardware
### Concept  
In our previous work, we identified five essential requirements for a new micromobility counting system [2]:  
1. **Noninvasive**;
2. **Location-independent**;
3. **Easy set-up**;
4. **Low power consumption**;
5. **Enables classification**.  
In order to meet these requirements, we proposed and evaluated a concept using a low-resolution LWIR sensor combined with a neuromorphic processor for classification. To help with the classification, we estimated that the system should be mounted on the side of the road at about 3 m in height and pointing toward the road, as shown in Figure 1. Preliminary testing showed that higher mounting positions were better for counting, as the occlusion of one object by another object was minimized. However, for classification, we observed better results when using profile views from lower mounting positions. As easy installation was one of our goals, we decided on the compromise of 3 m, as this was also a height which could easily be reached by using a ladder while conforming to official safety regulations. To keep the power consumption low, we proposed the use of low-resolution sensors with a resolution of less than 20,000 pixels, drastically reducing the computational load compared with a high-resolution camera. To further reduce the power consumption, we evaluated the use of a low-power neuromorphic processor for classification, while preprocessing was performed by the main processor.  
We evaluated the concept in multiple series of indoor and outdoor tests. The tests showed that by using the low-resolution thermal images, the system was capable of monitoring different scenes independent of the lighting conditions, fulfilling requirements 1 and 2. In our test, the prototype was mounted on the side of the road using a tripod for mobile lighting installations and mounting clamps for consumer cameras as seen in Figure 1b,c. The prototype was powered by a consumer-grade powerbank. Even with the sensors and optics detached for transport, the system could be set up by a single person within 10-15 min.  
Based on a series of field tests, we concluded that positioning the LWIR camera at a height of 3 m and angled toward the ground yielded the most accurate results (see Figure 1 for details). With this position, objects in the front do not fully obstruct objects in the back. Thus, people walking or driving side by side can be distinguished easier. At the same time, a partial profile view leads to better classification results than a full frontal view.  
Based on our observations during our field tests, we extended our concept with an additional low-power radar sensor. Figure 2 shows a rendering of the extended prototype with an additional radar module. The prototype was designed as an extension board compatible with the Raspberry Pi platform. It comprises integrated voltage regulators that allow the use of a 12 V lead acid battery as a power supply. The purpose of the additional radar is to work as a trigger for the other sensors. This allows one to power down most components during periods of no activity to extend the battery life.