esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Significance of the Problem  
Two major operating schemes were observed from data in a recent study by [PERSON] [7] of wildlife tracking data due to a fixed rate of data collection of 15 minutes. Firstly, available energy from the battery dropped below a minimum charge level and data collection stopped until adequate energy had been harvested and stored. Secondly, batteries remained mostly or fully charged due to harvested energy being greater than the sensor usage energy. The two cases described are opposites in that there were either data gaps or the data collection rate was consistent. Since both devices were deployed at the same location and the animals migrated to different regions of North America, the performance was clearly tied to the potential for energy harvesting. In addition, had the data collection rates been adjusted to reflect the energy harvesting potential, gaps in data or more data may have been achieved. The devices' data collection rates were not adjusted during the study, which is something that must occur manually on a remote system. Fixed data collection rates are typical of wildlife behavior devices due to simplicity of operation [7]. Movement data collected by animal behavior monitors are typically desired at fixed intervals for ease of integration into statistical behavior models, but are rarely achieved due to sometimes aggressive data collection rates.  
In addition to the previous study, devices with consistently low Li-ion battery levels have been shown to have significantly shorter life cycles than devices with consistently full batteries and continually charging a Li-ion battery to 100% may reduce the cycle life [6]. Therefore, maintaining a fully charged battery may not be beneficial to maintaining the operational lifetime of a remotely-deployed wildlife behavior monitor. Energy usage of a wildlife behavior monitoring device manufactured by Cellular Track Technologies (CTT) shown in Figure 1, was measured in a fixed setting using a programmable power supply to mimic solar energy in order to illustrate the problem with a fixed data collection rate that exceeds the harvested energy. The selected rate was too aggressive for the amount of harvestable energy (shown in Figure 2), and data collection had to be stopped after three days to allow a four-day charging cycle. If left alone, the charge and discharge cycle would continue to oscillate, leaving gaps in data collection. Solutions were sought that would automatically adjust a behavior monitor's data collection rate based on the availability of solar energy such that (1) a fully charged battery (\(>\)80% capacity) is maintained and (2) variation in data collection rate is reduced.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 3 Proposed Methods
### Network Structure  
Figure 2 displays the network structure of our improved YOLOX model. We added the ECA modules to the back of the backbone network to strengthen the learning of the captured features and the ASFF to the back of the neck network to enhance the feature fusion. The VFL function is used in the decoupling heads as the loss function in training.  
Finally, to improve the detection performance of the model for high-resolution remote sensing images, we combined the improved YOLOX model with the SAHI framework. We named the improved model RS-YOLOX. Figure 3 displays the detection process of this model. After the SAHI framework slices the high-resolution image, it is input into our target detection model. After detection is complete, all the slices are merged into a complete detection result map.  
Figure 2: Structure of our improved YOLOX model.
# Food Processing with UHP Waterjets
## 3 Waterjet Food Systems  
There are several platforms in a food waterjet cutting system; some of these platforms are common to most food cutting systems such as UHP pumps, while others depend on the type of food being cut. These platforms can be divided into the following subsystems:  
* UHP pumps and lines: This is the most upstream component in a waterjet system where the water enters the pump at ambient pressure and exits the pump into the plumbing system at a higher pressure. The UHP plumbing is used to transport pressurized water to the jet-forming nozzle(s). This plumbing system may consist of tubing, hoses, fittings, swivel joints, and rotary swivels
* Cutting heads: An on/off valve is used upstream from the nozzle to start and stop the UHP water flow to the jet-forming orifice. The orifice is a most critical component that transforms the pressure energy into kinetic energy. If abrasives are used, then a mixing tube is used to mix and accelerate the abrasives using the high-velocity jet.
* Motion System: The motion system in a food system depends on the food product to be cut. General examples are the following: \(\odot\) Cross cutting table: In this example, the waterjet(s) are traversed in one direction (X direction) and the food product is traversed on a feed screen in the Y direction. \(\odot\) Robotic arm: A robotic arm is used to manipulate the jet over a traveling or indexed screen carrying the food product, mainly meat such as chicken beef, fish, and pork.
* A CNC controller is typically used to control the interpolating motion of the machine. A robotic arm system may also be used as a manipulator.
* Controls, Software, and Sensors: A CNC controller is typically used to control the interpolating motion of the machine. The software is used to enable operators to interface with the machine controller and is specifically tailored for the application. Sensors are used to guide the motion based on certain portioning criteria.
* Other: There are other subsystems for food processing such as sorting, packaging, and labeling but these are outside the scope of this paper.
# Quality and Safety of Dried Mushrooms Available at Retail Level
###### Abstract  
Pathogenic microorganisms surviving in dry products have regularly led to recalls and foodborne disease outbreaks. Therefore, the microbiological quality of 61 dried mushrooms samples purchased online and in supermarkets were analyzed. Counts of aerobic mesophiles (AMCs), _Enterobacteriaceae_ (EB), yeasts and molds, presumptive _Bacillus cereus_ (pBC), the presence of _Salmonella_ spp., and _L. monocytogenes_ were investigated. Isolates of pBC were screened for their partial _pamC_ gene sequences and their toxin genes' profiles. The microbiological quality of the dried mushrooms investigated in this study was generally found to be acceptable. Average AMCs, EB, yeasts, and molds were 3.9 log, 1.1 log, 1.6 log, and 1.5 log cfu/g, respectively. All mushroom samples tested negative for _Salmonella_ spp. and _L. monocytogenes_. Presumptive BC were detected in 59.0% of the samples, but the contamination level was low (1.0 to 3.4 log cfu/g). None of the isolates were positive for the _ccs_ gene. Incomplete labeling was found in 45.9% of the samples, mainly in the form of missing heating instructions (31.1%) and/or country of origin (16.3%). Contamination by pathogens can occur in dried mushrooms. Adequate information on home cooking practices is essential to reduce the risk of foodborne illness to the consumer and to provide a safe food product.  
microbiology contamination; _Listeria monocytogenes; Salmonella_; presumptive _Bacillus cereus; Bacillus cereus_ toxin profile; adequate labeling +
Footnote †: journal: _Applied Sciences_  
of the presence of _Listeria_ (_L._) _monocytogenes_ have been associated with fresh cultivated and wild mushrooms and have led to notifications and recalls in Europe and the United States [11; 12; 13; 14]. Recalls of sunflower seeds, several nut species, and dried fruits that were contaminated with _L. monocytogenes_ in the USA and Canada indicate that this pathogen is regularly present in dried food [15; 16]. Few studies have reported on the microbial status and presumptive _B. cereus_ (pBC) in dried mushrooms.  
However, no data are available that focus on the prevalence of food-borne pathogens (_Salmonella_ spp., _L. monocytogenes_, and pBC) after the common rehydration procedure before mushrooms are used in the kitchen. Therefore, the aim of the present study was to investigate the potential risk of humans consuming dried mushrooms that are marketed online and in Asian supermarkets in Vienna, Austria, (i) focusing on the microbiological safety after soaking the mushrooms in water and (ii) reviewing the mandatory labeling requirements of the European Union (EU) [17]. Additionally, _Salmonella_, _L. monocytogenes_, and pBC isolates were confirmed by specific PCR methods. _B. cereus_ were screened for their toxin gene profile.
## 2 Materials and Methods
### Implementation
#### 2.2.2 Routine for Picking a Container from the Table  
This routine consists of detecting and picking up a requested container placed on a table. This process is illustrated in the flowchart shown in Figure 5 and in the sequence of frames shown in Figure 6. It is supposed to start with the robot positioned in front of the table where the container is located, and the execution steps are as follows:  
Figure 4: Scanned images from cameras with detected ArUco markers plotted.  
1. Look for the container: The coordinator initiates the process by searching for the specified ArUco marker ID received by the user's request. The process is represented by the finite state machine shown in Figure 7. It begins with a simultaneous tilt and scan of the camera using vision and head node services, which continue until the marker is detected. Once detected, the image is centered on the marker, and the coordinates are computed. If the marker is not found during centering, the head moves slightly to re-detect the marker, addressing any issues caused by delays or occlusions in the initial position computation. 2. Correct base position: The optimal distance for container pick-up has been empirically determined to be between 0.8 and 0.9 m. Once the marker position is obtained, the base is moved until it falls within this range.  
Figure 5: Flowchart of the routine of detecting and picking up the container from a table.  
Figure 6: Sequence of frames showing the routine for picking a container from a table.  
Figure 7: Finite state machine for the process of searching for the ArUco marker.  
3. Gripper centering: The marker position obtained may have some errors, which can cause the gripper to miss the container handle, resulting in a failed pick-up. To avoid this, the camera on the gripper is used. This process follows the finite state machine shown in Figure 8. Once the gripper is positioned in front of the marker, the end-effector moves along the Cartesian axis proportionally to the error between the detected centroid and the image center. If the marker is not detected, a spiral movement in that plane is executed to locate the marker. Once the image is centered, the pose of the end-effector provides a more accurate position of the marker in height and width. The distance to the marker is still obtained from the RGB-D camera.
4. Pick up the container: Once the pose of the container is determined, the pick-up process is executed. The goal in this step is to achieve the grasping position shown in the scheme of Figure 9. This involves moving the end-effector in front of the container handle with the gripper open. The end-effector then approaches the grasping frame to the determined ArUco position and closes the gripper. After securing the handle, the end-effector is elevated and retracted to a distance of 0.6 m from the base. The pick-up process concludes by moving the base backward 0.2 m.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 6 Experiment
### Model Variations
#### 6.5.3 The influence of Different Sliding Window Widths  
The width of the sliding window (i.e., length of a sub-sequence) is sensitive for the model to capture the hidden information in time series. In this experiment, different window widths, \(t_{w}\in\{10\times\mathrm{i},\mathrm{i}=1,2,3,4,5,6\dots,10\}\), are used to observe the influence of different sliding window widths on the performance of anomaly detection. Here, \(\alpha\) is set to 0.2, and the number of Transformer layers is set to four. For each sub-sequence length, the TGAN-AD model was trained recursively for 100 iterations. We depict the box-plots of the  
Figure 7: The variation of _Precision_, _Recall_, and \(F1\) with different Transformer Layers of two representative datasets, i.e., SWaT, WADI and KDDCUP99. Note that all other parameters are fixed, with the exception of the number of encoder and decoder layers, which are empirically set at 2, 4, 6, and 8.  
metrics values of TGAN-AD at each of the training iterations over different sub-sequence lengths in Figure 8.  
As shown in Figure 8, the impact of sequence length of our model is given as follows:  
1. **SWaT dataset:** When the sequence length was set at 60, our TGAN-AD achieved the best performance in _Precision_ and \(F1\), while the value of _Recall_ was close to 0.9. When the sequence length was 100 and 40, the model showed relatively bad performance.
2. **WADI dataset:** In the experiment, \(F1\) and _Precision_ showed bad performance, since the model predicted some false positive results. However, in the scenario of anomaly detection, the wrong alarm of non-anomalous samples is permissible. When the length was 20, the model performed best.  
Figure 8: The variation of _Precision_, _Recall_, and \(F1\) with different sequence lengths in the representative dataset, i.e., SWaT. The experiments are conducted with 100 iterations. The median values of each box are shown linked together with a red line, and the mean values of each box are shown linked together with a blue line.  
**KDDCUP99 dataset:** TGAN-AD was not stable in testing on the KDD dataset but had good overall average metric values. When the sequence length was 50, it had an excellent metric value (_Precision_, _Recall_, and \(F\)1) close to 0.9.
## 2 Overview of Retrieval-Augmented Generation
### Augmentation
#### 2.3.2 Adaptive Retrieval  
Adaptive retrieval represents a dynamic approach. The RAG-based LLMs' retrieval process is continuously tailored to specific needs of a task, as it evolves in response to the context provided by the model's ongoing outputs. Adaptive retrieval primarily aims to optimize the relevance and utility of retrieved information, thereby ensuring that generated responses are correct and contextually appropriate.  
FLARE (Forward-Looking Active Retrieval Augmented Generation) [11] advances RAG systems by introducing a forward-looking, active retrieval mechanism. Beginning with user input and initial retrieval results, FLARE iteratively generates a provisional next sentence. If this sentence contains low-probability tokens, the system retrieves additional relevant documents, using the sentence as a query before regenerating yet another sentence. It continues this process until a task is complete. This forward-looking strategy enhances the system's ability to maintain coherence and relevance throughout multi-step generation tasks, particularly in complex scenarios requiring multifaceted reasoning and strategic planning.  
SELF-RAG (Self-Learning Enhanced Retrieval-Augmented Generation) [27] advances RAG systems by incorporating a feedback-driven, self-learning mechanism. This process starts with standard retrieval and generation, where relevant documents are retrieved and used to produce an initial response. Following generation, the system evaluates its output and uses this feedback to refine subsequent retrieval strategies. Through iterative improvement, SELF-RAG adapts its retrieval process to retrieve increasingly relevant information, thereby continuously enhancing generated responses' accuracy and contextual relevance. This self-learning capability makes the system progressively more effective over time, especially in dynamic or specialized environments.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 2 Approaches for Inference Time Reduction
### Early Downsampling  
In ENet [8] model architecture, it is stated that since processing large input frames is very expensive, a good way to solve this is to downsample these frames in the early stages of the network, resulting in the use of only a small set of feature maps.  
Figure 1: (a) Standard convolution. (b) Depthwise separable convolution (adapted from [3]).  
Figure 2: Channel shuffling.  
The primary role of the initial network layers should be feature extraction and the preprocessing of the input data for the following parts of the architecture, rather than contributing to the classification stage [8].  
This approach has been employed by the ENet model architecture so that spatial information loss due to downsampling is prevented. ENet relied on the SegNet [9] approach of saving indices of elements chosen in max-pooling layers and using them to produce sparse upsampled maps in the decoder, as shown in Figure 3. This approach allows reducing memory requirements while recovering spatial information. Overall, since the proposed approach relies upon spatial downsampling, it is not recommended for applications where the initial image contains fine image details that have the potential to disappear after the corresponding max-pooling operation.
## 3 Results
### Response of Vegetation to Drought in Xinjiang
#### 3.3.1 Spatial Distribution of Vegetation Condition Response to Climate Change in Xinjiang
The NDVI-SPEI correlation coefficients for natural forests and grasslands in Xinjiang were mostly negative, with 71.6% of the area showing a negative correlation. Among the different vegetation types, moderate-coverage grasslands exhibited the highest correlation coefficient of approximately 0.3. This indicates that using solely NDVI to represent vegetation conditions in Xinjiang is insufficient, as vegetation may also be influenced by other environmental factors, and the vegetation structure (NDVI) in arid regions may not directly reflect vegetation health. In terms of the vegetation response to climate change, as indicated by the SIF, the SIF-SPEI correlation coefficients for natural forests and grasslands were generally higher than those of the NDVI-SPEI, with 92.7% of the area displaying positive correlations. Low-coverage natural grasslands showed the highest SIF-SPEI correlation (up to 0.6), whereas natural forests showed the lowest. This suggests that the physiological responses of vegetation to climate change are more pronounced than the structural re  
Figure 10: Spatial distribution of maximum correlation coefficients (Rmax) between vegetation indices and SPEI in Xinjiang. (**a**) The spatial distribution of the maximum correlation coefficients (Rmax) between NDVI and SPEI across Xinjiang, with values ranging from \(-\)0.372 to 0.745, indicating varying vegetation responses to climatic changes in different ecological regions. (**b**) The spatial distribution of the maximum correlation coefficients (Rmax) between SIF and SPEI, with values from \(-\)0.8 to 0.9. This figure highlights the spatial variation in vegetation sensitivity to drought stress across different regions. sponses. The presence of negative correlations further implies that vegetation is influenced by factors other than droughs.
## 2 Average Power Scaling of Modelocked Thin-Disk Lasers: Challenges and Milestones
### SESAMs for High-Power Femtosecond Modelocking  
#### 2.3.1 SESAMs in High-Power Ultrafast Oscillators  
As we mentioned in the previous paragraph, SESAMs with high-damage threshold and appropriate macroscopic parameters are one of the key points for power scaling of ultrafast oscillators. A specific investigation of damage and lifetime of SESAMs designed for high-power oscillators is therefore crucial. Previous investigations on optimized SESAM designs mostly focused on the realization of low saturation fluences [40,41], which is key for high repetition rates and stable modelocking of semiconductor lasers [34]. A recent investigation carried out in the context of high-energy modelocked TDLs focused on studying the influence of nitrogen incorporation on the carrier dynamics of SESAMs, but damage was not investigated [42].  
SESAMs for high-power ultrafast TDLs operate in a regime in which pulse energies and average power levels are several orders of magnitude higher than in standard low-power femtosecond oscillators. In Table 2, we present typical SESAM operation parameters in recently demonstrated high-power thin-disk oscillators. We notice that SESAMs in such oscillators operate at kilowatt intracavity power levels, peak intensities of gigawatts per square centimeter, and fluences above the millijoule per square centimeter level. The saturation parameter (\(S\)-parameter, \(S=F/F_{\mathrm{sat}}\)) is a useful parameter to describe operation of a SESAM in a laser cavity. Typical SESAM modelocked lasers operate at \(S=3\)-\(10\)[29], but, as we can see from Table 2, stable operation with \(S\) parameters larger than \(20\) is common in high-energy oscillators. Operation at high \(S\)-parameters makes multi-pulsing instabilities a critical issue in TDLs. In particular, the additional absorption observed at high fluences caused by induced absorption (IA) causes a \"rollover\" in reflectivity. Operating the SESAM close to this rollover can lead to multi-pulsing instabilities [43, 44, 45] because in this case multiple pulses with lower pulse energy can have a gain advantage compared to single pulses.  
Therefore, the most crucial parameters of SESAMs for high-power oscillators are:  
* large saturation fluences to operate at moderate saturation parameters and with small spot sizes, which relaxes cavity sensitivity to alignment and possible thermal lensing,
* high damage thresholds,
* low nonsaturable losses to avoid thermal effects,
* reduced induced absorption (IA), which is responsible for the reflectivity rollover at high fluences and can lead to multi-pulsing instabilities [43, 44, 45].  
The study presented in [19] focuses on how to tailor and combine suitable parameters for high-power operation and high-damage thresholds. As we have mentioned in paragraph 2.2, the latest average-power scaling step was made possible thanks to the guidelines established during this investigation.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Amino-Acids neurotransmitters  
#### 1.1.1 Glutamate  
Glutamate is the major excitatory neurotransmitter in the central nervous system [PERSON] et al. (2015). It is present in 90% of the excitatory synapses [PERSON] et al. (2015) and plays a key role in the plasticity of the nervous system (defined as the ability of neuronal circuits to change their connectivity) [PERSON] et al. (2015); [PERSON] et al. (2015); [PERSON] et al. (2015); [PERSON] et al. (2015). Recent studies  
Figure 1: Regions of the brain where neurotransmitters are mainly produced.  
have shown that glutamate also plays a role in glia-neuron signalling, along with two other neurotransmitters: D-serine and glycine [21; 22].  
Glutamate (and gamma-aminobutyric acid (GABA), defined further below) dysfunctions may be involved in brain disorders such as epilepsy [23; 24; 25]. Indeed, seizure was observed in mice lacking the glutamate transporter GLT-1 [24]. Besides that, the use of microdialysis and electroencephalography to measure extracellular glutamate in patients with epilepsy has shown a hippocampal increase of this neurotransmitter [25]. Recently, a glutamate dysfunction model of schizophrenia has emerged in addition to the widely known hypothesis involving dopamine. Schizophrenia is characterized by the disruption of sensory processing, leading to hallucination, paranoia, cognitive deficits in attention, short-term memory and movement abnormality [26].