esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
_Lactobacillus delbrueckii_ subsp. _bulgaricus_ and _Streptococcus thermophilus_ are lactic acid bacteria (LAB) that have been isolated from a variety of habitats, particularly traditionally fermented food [1; 2]. They play important roles in the production of dairy products, particularly yogurt, where they are the keys to final product quality. _Lactobacillus delbrueckii_ subsp. _bulgaricus_ and _S. thermophilus_ used in mixed culture have a symbiotic relationship in milk due to the exchange of metabolites [3]. For example, _L. delbrueckii_ subsp. _bulgaricus_ can easily utilize the pyruvic acid, formic acid, folic acid, and long-chain fatty acids produced by _S. thermophilus_, whereas the peptides, free amino acids, and putrescine produced by _L. delbrueckii_ subsp. _bulgaricus_ stimulate the growth of _S. thermophilus_[4; 5].  
Yogurt production is perhaps one of the most complex milk fermentation processes; milk fermentation with _L. delbrueckii_ subsp. _bulgaricus_ and _S. thermophilus_ produces yogurt with good flavor, acidity, and viscosity. Flavor is achieved by the integration of a variety of volatile compounds (VOCs), including acids, aldehydes, ketones, alcohols, esters, and hydrocarbons. These compounds can impart favorable flavors to yogurt [6; 7]. Using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS), [PERSON] et al. identified VOCs produced in milk by _L. delbrueckii_ subsp. _bulgaricus_ and _S. thermophilus_, including fatty acids, alcohols, and sulfur compounds [8]. [PERSON] et al. evaluated the VOCs produced by _L. delbrueckii_ subsp. _bulgaricus_ and _S. thermophilus_ from traditional fermented milk and reported similar results; these VOCs included acids, alcohols, aldehydes, ketones, and hydrocarbons [9].  
The introduction of GC-MS has accelerated the field of flavor chemistry, especially when linked to SPME as a pretreatment method [10; 11]. The main advantages of SPME are its simplicity, low cost, ease of automation, and in situ sampling [12]. SPME coupled with GC-MS has been used widely to evaluate the flavor chemical profiles of volatile aromas produced by a wide variety of substances, including fermented milk [13; 14], the fruit and sap of mango cultivars [15], grapes and wine [16], dry fermented sausage [17], and alcoholic beverages [18].  
_Lactobacillus delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03 were isolated from traditional fermented dairy products in China and Mongolia and selected based on their excellent processing properties, such as flavor, acidity, viscosity, and water-holding capacity [9; 19]. This study was performed to quantify variations in the profile of volatiles produced using different proportional combinations of _L. delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 6 Open Issues
### Security and Privacy Issues  
There is a strong need for advanced methods in combating ever evolving attack types against the reliability of communication [31] and the safety of critical infrastructures. Security concerns during the operation of UAV-based delivery systems include CP attacks targeted at hijacking the delivery UAV and damaging or stealing its load. Also, for multimedia systems relying on UAVs, disrupting the UAV's transmissions by forging their identities might have a severe impact especially in networks comprising a large number of UAVs. In such cases, UAV authentication and analysis of the received multimedia content by the BS is required, resulting in excessive delay. Then, in intelligent transportation systems, swarms of UAVs cooperate and exchange data to support specific tasks. Nonetheless, these swarms are vulnerable to attackers that joint the swarm and inject false data that might result in errors during UAV movement and possible damage due to collisions. To cope with the risk of adversarial ML during raw data transmission, federated learning is suggested, in which the training data of a specific ML task is stored in a distributed fashion across the UAVs in the swarm and the optimization problem is handled collectively [139]. In ML-enhanced PLS scenarios, there are still some challenges that are not tackled by current solutions. In Reference [48], the Q-learning-based algorithm adapts the transmission according to the attack type of the malicious UAV. However, in each time, knowledge is required to reconfigure the transmission parameters. In addition, the anti-jamming PHC-based algorithm of Reference [50] can be further developed to provide an anti-jamming mechanism for VANETs at the initial phase of the learning process, while achieving low-complexity RL solutions are necessary to improve PLS performance against smart jammers. Next, in GPS-spoofing scenarios [52], online learning can further improve the security provided by the NN, while unsupervised ML, capable of handling unlabeled data, can perform classification prior to processing, reducing the delay and increasing the accuracy. To further improve the performance of current ML-inspired detection methods, future work could be devoted to combining these methods with other conventional detection techniques, such as camera images and videos, radar echoes, and acoustic recordings, and to exploiting the advantages of each method. Moreover, future work may also focus on using different types of UAVs with varying velocities for the same experiments, on investigating the effect of interference and noise, and on collecting measured data with a greater variety of controllers.
# A New Road Damage Detection Baseline with Attention Learning
## 5 Experiment and Analysis
### Difficulty Analysis of Different Types of Damage  
The RDD2020 dataset provides four kinds of damage labels, while our CNRDD dataset provides eight kinds of damage annotations. Some types of damage in these datasets have obvious similarities. For example, longitudinal cracks, reverse cracks and complex cracks all have obvious crack characteristics. This makes the detection of damage more difficult than the detection of actual objects. In addition, subsidence, rutting and looseness are often close to healthy pavement under mild conditions. Even trained models have difficulty identifying them correctly. Based on this background, we compare and analyze the detection results of different damage and hope to find a targeted solution to promote the development of ADRD. The comparison results are shown in Figure 8 in the form of PRC. Since the RDD2020 test sets do not provide GT, the automatic evaluation system does not provide separate evaluation for each type of damage. Therefore, the experiment is implemented on the RDD2020 training set and the CNRDD test set.  
As shown in Figure 8, the experiments of the first line are performed on the RDD2020 training set, and the experiments in the second row are conducted on the CNRDD test set. By comparing the three subgraphs belonging to the same row, we find that the performance of different methods on different types of damage show the same order. On the RDD2020 training set, no matter which method, D20 damage (crack) has the best performance, and the area under curve of D10 damage (longitudinal crack) is the worst. Coincidentally, the area under the PR curve for different types of damage on the CNRDD dataset also shows similar ranking. The damage_1 damage (crack) performs best, and the results of damage_8 (pothole) and damage_9 (looseness) are poor. The main reason is that the cracks have obvious features, high frequency and rich training samples. However, the number data points for looseness and potholes collected in the CNRDD dataset is less than 1000, which results in poor detection. This gives us a hint that collecting and labeling diverse training samples is the core of improving road damage detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**CNRDD**} & \multicolumn{2}{c}{**RDD2020**} \\  & **[EMAIL_ADDRESS]** & **F1-Score** & **[EMAIL_ADDRESS]** & **F1-Score** \\ \hline Baseline & 24.8 & 32.25 & 47.14 & 50.68 \\ +AF+SFLwN & 25.3 & 32.92 & 48.4 & 51.34 \\ +SFLwN & 25.52 & 33.97 & 48.3 & 51.22 \\ +AF+SFLwN & 25.57 & 34.55 & 48.7 & 51.56 \\ +AF+SFLwN+Finetune & **26.6** & **34.73** & **50.9** & **51.72** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the effectiveness of each component. ‘AF’ refers to attention fusion module, ‘SFLwN’ represents salient feature learning module with NAM, ‘SFLwN’ is salient feature learning module without NAM, and ‘FT’ means fine-tuning strategy.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Infrastructure-Based ITS Sensing
#### 3.2.4 Parking Detection  
Alongside roadway monitoring, parking facility monitoring, as another typical scene in the urban area, plays a crucial role in infrastructure-based sensing. Infrastructure-based parking space detection can be divided into two categories from the sensor functionality perspective: the wireless sensor network (WSN) solution and camera-based solution. The WSN solution has one sensor for each parking space, and the sensors need to be low power, sturdy, and affordable [123; 124; 125; 126; 127; 128; 129; 130; 131; 132]. The WSN solution has some pros and cons: algorithm-wise, it is often straightforward; a thresholding method would work in most cases, but a relatively simple detection method may lead to a high false detection rate. A unique feature for the WSN is it is robust to sensor failure due to a large number of sensors. That means, even if a few stop working, the WSN still covers most of the spaces.  
However, a large number of sensors do require a high cost of labor and maintenance in large-scale installation. Magnetic nodes, infrared sensors, ultrasonic sensors, light sensors, and inductance loops are the most popular sensors. For example, [PERSON] et al. [131] developed a cost-effective parking space detection algorithm based on magnetic nodes, which integrates a wake-up function with optical sensors. The camera-based solution has been increasingly popular with advances in video sensing, machine learning, and data communication technologies [8; 123; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144]. Compared to the WSN, one camera covers multiple parking spaces; thus, the cost per space is reduced. It is also more manageable regarding maintenance, since the installation of camera systems is non-intrusive. Additionally, as aforementioned, video contains more information than other sensors, which has the potential to apply to more complicated tasks in parking. [PERSON] et al. proposed to use background subtraction and SVM for street parking detection, which achieved a promising performance and was not sensitive to occlusion [133]. [PERSON] et al. designed a pipeline with a unique design of dilated convolutional neural network (CNN) structure. The design was validated to be robust and suitable for parking detection [136].
# Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
###### Abstract  
The performance of the coded generalized frequency division multiplexing (GFDM) transceiver has been evaluated in a shallow underwater acoustic channel (UAC). Acoustic transmission is the scheme of choice for communication in UAC since radio waves suffer from absorption and light waves scatter. Although orthogonal frequency division multiplexing (OFDM) has found its ground for multicarrier acoustic underwater communication, it suffers from high peak to average power ratio (PAPR) and out of band (OOB) emissions. We propose a coded-GFDM based multicarrier system since GFDM has a higher spectral efficiency compared to a traditional OFDM system. In doing so, we assess two block codes, namely Bose, Chaudari, and Hocquenghem (BCH) codes, Reed-Solomon (RS) codes, and several convolutional codes. We present the error performances of these codes when used with GFDM. Furthermore, we evaluate the performance of the proposed system using two equalizers: Matched Filter (MF) and Zero-Forcing (ZF). Simulation results show that among the various block coding schemes that we tested, BCH (31,6) and RS (15,3) give the best error performance. Among the convolutional codes that we tested, rate \(1/4\) convolutional codes give the best performance. However, the performance of BCH and RS codes is much better than the convolutional codes. Moreover, the performance of the ZF equalizer is marginally better than the MF equalizer. In conclusion, using the channel coding schemes with GFDM improves error performance manifolds thereby increasing the reliability of the GFDM system despite slightly higher complexity.  
underwater acoustic; generalized frequency division multiplexing; block codes; convolutional codes +
Footnote †: journal: Sensors  
doi:10.3390/s22072639  
Footnote †: journal: Sensors  
doi:10.3390/s22072639
## 5 Analysis and Outcomes
### Analysis of Performances by Section and Type of Activity
The average individual versus the average team performance under a statistical similitude assumption was associated with a dispersion plot, presented in Figure 5. It compares gender performance in each section (red triangle markers joined by the peach line for the female gender and square red markers joined by the blue line for the male gender) together with the general population (similarly using circular red markers joined by a green line). The lines are used only to mark each group represented. The corresponding data are included in Table 2 as a reference. There, the horizontal axis measures the average individual performance and the vertical axis measures the teamwork average. Red markers show each corresponding comparison by sections and gender, as explained. Note the large gap in performance between Computational Sciences and Challenge versus Math and Physics in the individual domain. This could be due to the teacher's style and/or the orientation of section contents. However, the design included rubrics for each activity. Each rubric sweeps grade ranges from 0 to 100 based on the elements  
Figure 5: Comparison between Individual and Teamwork average grades by genders (Peach—Female, Blue—Male, Green—General) and section (Math, Physics, Computation and Challenge). absent/present in each deliverable activity (including the examinations). In this sense, the teacher style for grading becomes limited as a factor of that difference. Another more feasible explanation (more acceptable as a cause) is that the Computational Sciences section is more oriented to practice than theory, making a sensible difference from the Math and Physics sections. Another comparative difference is that in Computation Sciences, the higher individual grades went to the female gender; for Math and Physics, the male gender. Furthermore, for the Challenge performance, the individual and team grades were almost equal, while for the content sections, teams generally performed better than the individuals. For the overall grade averages (shown in green), the standard deviation is reported as thin, black, vertical and horizontal error lines. The ranges comprehend the gender average grades; Figure 5 shows how, in the Math section's individual domain, the deep dispersion is explained by gender (almost located in the lower limit). Otherwise, in the teamwork domain, the Math and Physics sections' dispersion is deeper but not necessarily explained by gender: both sections' teamwork averages for each gender are not within the standard dispersion bar extremes. The Discussion section comments more on gender behavior. Did the female gender boost the team performance? Was the individual performance meaningfully higher for the male gender? Were the differences in performance meaningful by gender in each type of evaluation and section?
## 2 Materials and Methods
### Data
#### 2.2.4 Auxiliary Data  
The following data for environmental factors was used in the assessment.  
1. Mean elevations within the FY-4A grids were calculated using a 30 m resolution elevation dataset from the NASA Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 3 ([[https://doi.org/10.5067/ASTER/AST14](https://doi.org/10.5067/ASTER/AST14) DEM.003]([https://doi.org/10.5067/ASTER/AST14](https://doi.org/10.5067/ASTER/AST14) DEM.003); accessed on 20 January 2021). Slopes were calculated by the arcGIS planar method using this dataset.  
2. Land cover type and information entropy within the FY-4A grids were derived from the 30 m resolution land cover dataset of the Ministry of Natural Resources of the People's Republic of China, GlobalLand30 version 2020 ([[http://globallandcover.com](http://globallandcover.com)]([http://globallandcover.com](http://globallandcover.com)); accessed on 2 April 2021). Information entropy is a measure of system uncertainty and indicates surface heterogeneity [37; 38]. The probability distribution of a random variable \(X\) with \(n\) possible outcomes is P(\(X=x_{i}\)) = \(p_{i}\) for \(i\) = 1, 2,..., \(n\). Information entropy \(H(X)\) is calculated as:  
\[H(X)=-\sum_{i=1}^{n}p_{i}\log p_{i} \tag{1}\]  
3. The landform type for FY-4A grids was derived from the 1:1,000,000 dataset of the spatial distribution of landforms in China from the Institute of Geographic Sciences andNatural Resources Research, Chinese Academy of Sciences ([[https://www.resdc.cn/data.aspx?DATAID=124](https://www.resdc.cn/data.aspx?DATAID=124)]([https://www.resdc.cn/data.aspx?DATAID=124](https://www.resdc.cn/data.aspx?DATAID=124)); accessed on 30 March 2022).  
4. The mean value of soil moisture within the FY-4A grids was calculated using the Soil Moisture Active Passive (SMAP) L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture dataset ([[https://nsidc.org/](https://nsidc.org/)]([https://nsidc.org/](https://nsidc.org/)); accessed on 15 April 2022) from 1 October 2019 to 30 September 2021.  
5. The mean value of the normalized difference vegetation index (NDVI) in the FY-4A grids was calculated using an NDVI dataset with a 16 d acquisition period at 1 km resolution from 22 September 2019 to 30 September 2021. The data were obtained from the Suomi National Polar-Orbiting Partnership NASA Visible Infrared Imaging Radiometer Suite Vegetation Indices (VNP13A2) Version 1 ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)); accessed on 4 April 2022).
## 5 Conclusions  
In this study, a 3D discrete element simulation model for rock breaking using disc cutters was developed and validated by comparing the simulation results with the experimental and finite element data. The effects of key tunneling parameters--including disc-cutter diameter, disc-cutter spacing, and penetration--on rock-breaking performance were systematically analyzed. The main conclusions are as follows:  
1. An increase in the disc-cutter diameter led to a higher rolling force, while the rock-breaking specific energy initially rose and then declined. The 19-inch disc cutter produced a larger rock fragmentation volume, resulting in lower rock-breaking specific energy and slightly higher efficiency, making it particularly suitable for conventional tunneling operations.
2. As the disc-cutter spacing increased, the rolling force initially rose, peaking at 60 mm, and then decreased. Similarly, the rock-breaking specific energy decreased initially, reaching its minimum at 60 mm, before increasing. For tunneling in granite formations, a disc-cutter spacing of 60 mm provided the highest rock-breaking efficiency.
3. Increasing penetration depth allowed the disc cutter to penetrate deeper into the rock mass, resulting in a greater rolling force and a larger rock fragmentation volume. This, in turn, reduced the rock-breaking specific energy and improved efficiency. Thus, appropriately increasing penetration enhances the rock-breaking efficiency.  
This research provides valuable insights into optimizing tunneling parameters for disc cutters, offering practical guidance for improving tunneling efficiency in various geological conditions.  
Writing--original draft preparation, F.L.; Methodology, Project administration, C.P.; Writing--reviewing and editing, W.L.; Writing--original draft preparation, M.Y.; Investigation, F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42277165.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request.  
Authors [PERSON] and [PERSON] were employed by the company Xinjiang Water Conservancy and Hydropower Survey Design Institute Co., Ltd. Author [PERSON] was employed by the company Xinjiang Shuifa Construction Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Simulations and Experiments  
A few simulations were conducted to evaluate the Meshtastic protocol's performance along with the modified reputation model in the context of IoT trust. Our experiments simulated an IoT network scenario where devices equipped with temperature sensors communicate over a Meshtastic-enabled network. To this end, we used the Meshtastic simulator known as Meshtasticor [25], which allowed us to emulate the Mesh networks, enabling the assessment of Meshtastic's dynamic adaptability and the influence of the reputation model on trust relationships.  
Despite the existence of many IoT environments, due to the heterogeneity of the IoT devices, in our manuscript, we focus on combining a specific communication protocol and a trust/reputation scheme. In this sense, our solution can be adopted in a wide range of IoT application scenarios without taking into account the specific requirements of the single IoT environment.  
In particular, we simulated a scenario involving ten IoT devices, each with a temperature sensor. In our simulated scenario, nine of these devices function as normal nodes, while the tenth device acts as a potential threat, reporting anomalous temperature values. The abnormal temperature data coming from this malicious or damaged device triggers dynamic adaptations within the Meshtastic protocol based on the reputation model.  
We depict, in Figure 2, a simulated topology where a few nodes are mutually connected with high degree, and the node marked malicious/damaged is connected with a single node as a bridge to reach all the remaining nodes. To implement our modified version of the reputation model described in Section 4 within the Meshtasticator simulator's MeshNodeclass, we had to integrate the reputation score computation and to update mechanisms based on the interactions between nodes.  
While in the following part of this section, we explain the main behaviour of the node, the reader can examine Appendix A, which contains a Python code snippet that outlines the behaviour of the MeshNode class. The entire behaviour of the MeshNode has been adapted to include the features defined in the reputation model described in this section: each node holds a list of interactions with nodes and the ability to calculate the new reputation and send their feedback to the network. In our study, the parameter \(\alpha\) for the computation of the reputation assumes the value 0.5. The feedback is computed simply as follows: the value of the feedback will depend on the difference between the temperature locally detected and the temperature sent by a neighbour node. The Python code in Appendix A proves that we performed a very simple and linear integration of the reputation model into the Meshtastic protocol. Such a prototype can be considered a very effective blueprint for the future integration of similar and further features.