esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Treated wastewater used for agricultural purposes has been proposed as a means of enhancing food security while addressing water scarcity issues related to growing water demands [1]. Most reclaimed water usage in the world comes from agricultural uses, with Mexico and Egypt reported to have the highest usage of treated wastewater for irrigation [2]. In North Carolina, high quality reclaimed water, designated Type 2, has been proposed for the irrigation of food crops [3; 4]. Despite this level of wastewater treatment, the actual microbial quality and potential health risks from pathogens in this type of reclaimed water are not known and therefore are still of concern.  
To evaluate the microbial risks of reclaimed water for agricultural use, the World Health Organization (WHO) has recommended the use of quantitative microbial risk assessment (QMRA) to assess the added disease burden [5]. QMRA is an analytical tool used to estimate the health effects resulting from exposure to microorganisms [5; 6]. There are four key steps to the QMRA process: (1) hazard identification, (2) exposure assessment, (3) dose-response assessment, and (4) risk characterization. Previous researchers conducting QMRAs on the agricultural reuse of reclaimed water have focused on the use of reclaimed water on either specific crops [7] or by specific irrigation methods [8]. These studies have also examined the use of secondary effluent, final effluent or reclaimed water treated by chlorine disinfection only. As the concentrations of pathogens and the associated risks from these pathogens are potentially different in the reclaimed waters that have been previously evaluated, it is necessary and important to evaluate the risks of using NC Type 2 reclaimed water for agricultural proposes.  
In this study, five representative pathogens from the three groups of microorganisms (bacteria, virus, and protozoan parasites) included in the NC legislation on reclaimed water were selected. Although the NC reclaimed water legislation is focused on fecal indicator microorganisms to specify water quality and treatment system performance requirements, the goal of this risk assessment was to select representative pathogens from each group to model the risk of exposure posed by reclaimed water via raw fruit and vegetables ingested by consumers under various exposure scenarios. The selected pathogens from each group include _Salmonella_ spp. bacteria, Adenovirus groups A-F, Norovirus GII (as representative enteric viruses) and _Cryptosporidium_ spp. and _Giardia_ spp. as protozoan parasites. The exposure scenarios evaluated in this analysis are focused on three irrigation water application types, specifically, spray, drip, and subsurface drip irrigation.
## 3 Related Work
DiffFlow [30] also uses packet sampling to perceive the existence of long flows in the network, but does not explore the sampling performance in detail. Other works include MircoTE [31], OpenSample [32], Sample&Pick [33], etc. These solutions focused on data center network load balancing, and did not have a good answer to how to obtain network long flow information. Freeway [34] proposed a dynamic path partitioning algorithm to adjust dynamically with varying traffic load the number of low latency and high throughput paths. While mice flows are transmitted over low latency paths using a simple equal cost multiple path (ECMP) scheduling, Freeway loaded balances elephant flows on different high-throughput paths. Freeway is actually a resource reservation solution. Similar to Freeway, PIAS [35] minimized the FCT by mimicking shortest job first (SJF) on the premise that flow size is not known a priori, then leveraged multiple priority queues available in existing commodity switches to implement a multiple level feedback queue. Based on PIAS [36], LinkGame applied two-sided matching decision in game theory to solving traffic scheduling problem in data center network. Considering the preference ordering, path-flow matching problem was formulated as a multiobjective optimization problem with the target to ensure the stability and satisfaction from the matching scheme. Some works had also considered the impact of switches on load balancing, such as SAB [37] and [PERSON] [38]. SAB utilized the characteristics of that the switch buffer pool, usually much larger than the round-trip delay bandwidth product in the data center network. SAB set the congestion window value of the flow by assigning the switch cache. This scheme reduced the short flow completion time and effectively solved the problems of TCP Incast and TCP Outcast. However, SAB sacrificed the performance of long flow to make the short flow completion time shorter, so it is not suitable for long flow. [PERSON] proposed that the mapping between data center traffic and switch memory is a many-to-one stable match problem and proved this problem is an NP-hard problem. [PERSON] considered the impact of switch resources on flow completion time, and did not consider long flow scheduling through multiple paths. There are also some studies that transfer long flow detection and scheduling from the switch to the server. [PERSON] [39] obviously had higher sampling accuracy but this was not compatible with existing networks. AuTO [40] developed a two-level deep reinforcement learning system, mimicking the Peripheral & Central Nervous System, to solve the scalability problem. Peripheral System resided on end-hosts, collect flow information, and make traffic optimization decisions locally with minimal delay for short flows. Central System aggregated and processed global traffic information and made individual traffic optimization decisions for long flows. However, the latency of deep reinforcement learning systems is the major obstacle to traffic optimization at the scale of data centers.
# A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
## 1 Introduction
However, the authors of those system worked under LOS conditions and did not checked the performance of their systems under NLOS conditions. The further structure of this paper is as follows: the second chapter presents the method of data transmission developed by us, the third chapter describes the channel model, chapter four describes the test stand and presents the results of tests carried out in simulations and laboratory conditions, and chapter five contains conclusions from the research and proposes further research towards the development of the underwater acoustic communication system.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 3 Experiments
### Object Test Sets  
Four different sets of test objects were used. The first three sets of objects closely replicate the objects used in the evaluation of the following grasping algorithms in the literature: Grasp Point Prediction (GPP) [18], DexNet 2.0 [19], and GG-CNN [20]. This allows for a point of comparison. The fourth set of objects is a self-designed set of objects to illustrate the capabilities of the algorithm in a wide variety of scenarios. Figure 16 shows all these four sets of objects used for testing.  
Figure 15: From left to right: (**a**) arm, (**b**) gripper, and (**c**) camera.  
Figure 16a shows the objects tested by GPP [18], containing eight objects, with each grasped four times. The objects are tape, keys, marker, toothbrush, mug, translucent box, horn, and coiled wire. Figure 16b shows the objects tested by DexNet 2.0 [19], containing nine objects, with each grasped five times. The objects are spoon, marker, toy shark, clip, whistle, tape, mouse, glue gun, and a black triangular object. Figure 16c shows the objects tested by GG-CNN [20], containing 12 objects, with each grasped 10 times. The objects are mug, tape, clothes peg, sponge, coiled wire, marker, stuffed toy, toothbrush, screwdriver, die, ball, and rubber duck. To further illustrate the abilities of the proposed algorithm, 18 other objects, as shown in Figure 16d, were tested on top of those used by the three studies. These objects are stapler, scissors, glasses, flashlight, banana, bowl, canned drink, Swiss army knife, kitchen knife, circuit board, wine opener, remote control, spatula, battery pack, game console, jar, electrical component, and a slinky toy.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 3 System Design
### Messages in the Beacon Network
If it is a match, the Mobile App uses the discovered Major/Minor values to locate itself immediately. Once the locating process is successful, the Mobile App will send the mobile user's presence state by broadcasting a presence message (i.e., a Type 2 message). In the presence message, the Mobile App specifies the encrypted user ID and the Location (i.e., the received Major/Minor values), meaning that the user is nearby the location associated with the Major/Minor values. Once a Type 2 message is detected by any Mesh Beacon, the Mesh Beacon uses the embedded User ID and Location values to construct a Type 3 message, and then broadcasts the Type 3 message. For all other Mesh Beacons that discover the Type 3 message, they use the managed-flood-based approach to relay it. When the Type 3 message finally reaches the Mesh Router, the Mesh Router sends the encrypted user ID and the Major/Minor values to the Application Server over HTTP. At the same time, the Mesh Router uses the received Type 3 message to compose a Type 4 message by simply changing the Service ID to 0 xD124, and then broadcasts the message. With the help of Mesh Beacons, the Type 4 message is forwarded over the Bluetooth beacon network to the Mobile App. On receiving the Type 4 message with its own user ID, the Mobile App will show a message box indicating that the application server has been notified of the presence of the user. At the Application Server, the Major/Minor values are translated into the name of the associated location, and then the user's presence state is sent to the Cloud Database. Eventually, with the presence state of all the mobile users in the database, we are able to analyze the locating history and to create our own innovative applications.
## 5 Conclusions  
During spine surgery, HSIs of skin features can be used for noninvasive patient positioning and tracking. Skin feature tracking offers an extension and an improvement of current tracking systems, with the aim of optimal patient motion compensation and reliable surgical guidance.  
In this paper, we have performed a human subject study acquiring HS images from the back of the body. We presented a preliminary study in which we demonstrated, as a proof-of-concept, that HSI allows the detection of on-body salient feature points and the finding of reliable correspondences after skin displacements due to breathing. Optical markers were used as ground truth, and on average, we found an overall error of 0.25 mm in computing 2D feature locations when deep local features were detected, outperforming the handcrafted SURF method by slightly more than 5%.  
The preliminary results are promising and represent an initial investigation into the acquisition and detection of spectral features in human volunteers with a sub-millimeter error lower than 0.5 mm. Future development should strive towards real-time 3D applications for in-vivo surgical navigation. We are of the opinion that the presented approach has strong potential to improve workflow and reliability in navigated surgery and may serve as a reference for introducing HSI in a surgical environment.  
Conceptualization, investigation, methodology, software and writing, F.M.; supervision, conceptualization, review and editing, F.v.d.S., S.Z. and R.H.; project administration, review and editing, C.S.; review and editing, G.B., E.E., A.E.-T.; resources, R.J.M.H., M.L.; supervision, review and review, P.H.N.d.W. All authors have read and agreed to the published version of the manuscript.  
The research activity leading to the results of this paper was funded by the H2020-ECSEL Joint Undertaking under Grant Agreement No. 692470 (ASTONISH Project).  
We acknowledge the NVIDIA Corporation for their donation of the Titan Xp GPU used for this research and Quest Medical Imaging BV for setting the camera used for the experiments.  
The authors who are affiliated with clinical academic institutions (FM, FVDS, SZ, AET, GB, EE, PHNW) have no financial interests in the subject matter, materials, or equipment or with any competing materials and did not receive any payments as part of the study. The authors affiliated with Philips Healthcare (RH, CS, ML) are employees of Philips and thereby have financial interests in the subject matter. The author affiliated with Quest Medical Imaging BV (RIMH) is an employee of Quest Medical Imaging BV and thereby has financial interests in the materials and equipment. The extent of influence on the data, manuscript structure and manuscript conclusions by these authors was limited to technical knowledge and input. Authors without conflicts
## 2 Materials and Methods
### The Proposed BO-Stacking-TabNet Model  
The BO-Stacking-TabNet Model is an enhanced deep learning architecture that integrates Bayesian Optimization (BO) with Stacking ensemble learning into the original TabNet framework for improved tabular data classification. The model employs a two-tier hierarchical structure consisting of base learners and a meta-learner, both utilizing TabNet encoders with optimized hyperparameters determined through Bayesian Optimization.
## 5 Experiments
### Hyper-Parameters  
For the hyper-parameters in the models for clause segmentation and clause parsing subtasks, we list their layer, name, and value in Table 9. Note that both models are not trained until reaching the maximum epochs, where the model of clause segmentation is trained in about thirteen epochs for five hours and the model of clause parsing is finished in about seven epochs for ten hours with the experimental environments introduced in Section 5.2. For the batch size, we tested several candidates and found that there was no significant performance gaps. For the optimizer, we tested AdamW and SGD in the hyper-parameter-tuning experiments of both clause segmentation and parsing subtasks. The results showed that AdamW outperformed SGD and converged faster.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **Value** \\ \hline \multicolumn{3}{c}{**Hardware**} \\ \hline CPU & Intel i9-10900K @ 3.7 GHz (10-core) \\  & (Intel Corporation, Santa Clara, CA, USA) \\  & NVIDIA RTX 3090 Ti (24 G) \\  & (Nvidia Corporation, Santa Clara, CA, USA) \\ Memory & 64 GB \\ \hline \multicolumn{3}{c}{**Software**} \\ \hline Python & v3.8.16 \\ Pytorch & v1.12.1 \\ Anaconda & v4.10.1 \\ CUDA & v11.3 \\ IDE & PyCharm v2022.2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hardware and software used in our experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Hyper-Parameter** & **Value** \\ \hline \multicolumn{3}{c}{**Clause Segmentation Model**} \\ \hline \multirow{3}{*}{Character Embedding (Bi-LSTM)} & layer & 1 \\  & hidden\_size & 64 \\  & dropout & 0.2 \\ \hline \multirow{3}{*}{Word Embedding} & fastText & 300 \\  & Electra & 1024 (large) \\ \hline Feature Embedding & POS/Lemma/DP & 100 \\ \hline \multirow{3}{*}{Bi-LSTM} & layer & 1 \\  & hidden\_size & 512 \\  & dropout & 0.1 \\ \hline \multirow{3}{*}{Trainer} & optimizer & AdamW \\  & learning rate & 5e-4, 1e-4 \\ \cline{1-1}  & \# epochs & 60 \\ \cline{1-1}  & patience of early stopping & 10 \\ \cline{1-1}  & validation criteria & +span\_f1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Final hyper-parameters’ configuration of the clause segmentation model. Note that “#” represents the number of the subsequent item.
## 2 Materials and Methods
### Division of Dataset and Spectra Preprocessing  
The dataset obtained from 10 farms was partitioned using data recorded from 7 buffalo farms for model training, while data from 3 randomly selected farms were used as an independent dataset. This resulted in a calibration dataset comprising 168 samples and a validation dataset consisting of 72 samples. This partitioning ensures that the two datasets remain independent.  
A basic statistical analysis was conducted on these datasets for each dependent variable. The mean, standard deviation (S.D.), minimum and maximum range, and coefficient of variation (C.V.) were calculated.  
Spectral data were preprocessed using techniques such as data normalisation and baseline correction. To optimise the model, scattering correction methods such as Standard Normal Variate (SNV) and detrending were used. Additionally, a derivative as a mathematical transformation was applied to enhance the accuracy of the Partial Least Squares Regression (PLSR) model [36; 37; 38].
# Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions
###### Abstract  
This paper describes an experimental test campaign while using an Unmanned Aerial Vehicle (UAV) and measuring the obtained UAV positions during different flight tasks and in different operative conditions. A new test procedure has been presented and tested for different devices in various weather conditions. This paper describes and analyses the measurements of the flight trajectory of the UAV that was performed with the use of a robotic total station (RTS), as compared to the design data and the data recorded in the internal memory of the UAV. Five different test tasks have been conducted. The obtained results have allowed for the assessment of the correctness of task performance as compared to the design and to determine the flying accuracy of the entire UAV set. The proposed set of tasks can be successfully utilised to control the correctness of operation of various types of UAVs and it may be implemented as a universal test to verify the algorithms optimising take-offs and landings, test flights of the objects, as well as flight planning in various terrain and weather conditions, which will increase the safety of the flights while using UAVs.  
unmanned aerial vehicle; UAV navigation; path planning; flight trajectory; positioning accuracy; barometric altitude +
Footnote †: journal: Applied Sciences
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 2 Related Work  
### Dense Visual SLAM  
Dense visual SLAM is considered a pivotal approach to solving problems related to scene understanding and autonomous goal navigation. DTAM [24] and KinectFusion [12] were the first systems to realize dense scene reconstruction and are highly inspiring for subsequent research. Unlike systems that rely on feature extraction, DTAM implements camera tracking directly through whole-image alignment, using multi-view stereo constraints to update the global point cloud map. KinectFusion uses ICP for pose estimation and uses aligned depth maps to incrementally update the TSDF and eventually estimate normal maps. Some recent works [25; 26; 27] have used deep learning networks to improve pose estimation and scene reconstruction accuracy, but the scene representation and overall framework still follow traditional SLAM.
###### Abstract  
The objective of the present report is the dissemination of information acquired during the application of ISO 6579-1:2017 for the isolation of _Salmonella_ spp. from swine samples. ISO 6579-1:2017 is the protocol officially recommended by the EU for the isolation of _Salmonella_ spp., aiming for the harmonization of effective control of _Salmonella_ infection in food-producing animals. Successful control of animal salmonellosis is highly dependent on the sensitivity and reliability of the biochemical methods used to detect the presence of the pathogen in various stages of food production. Application of ISO 6579-1:2017 resulted in the isolation of twelve _Salmonella_ spp. and eighteen other members of the family of Enterobacteriaceae biochemically and other bacteria antigenically resembling salmonellae. An evaluation of the culture media was conducted, including xylose-lysine-desoxycholate agar (XLD), Salmonella-Shigella Agar (SS), Brilliant Green Agar (BGA), Salmonella Chromogenic Agar (SCA), Triple Sugar Iron (TSI), and modified semi-solid Rappaport-Vassiliadis (MSRV) agar. The evaluation showed that these culture-selective media differed significantly in their performance for the isolation of _Salmonella_ from swine samples. It was concluded that the presence of atypical _Salmonella_ strains negatively affects the prevalence of _Salmonella_, thus affecting the identification of carrier pigs and eventually affecting the efficiency of control programs. Thus, doubtful results require additional biochemical testing to confirm the accuracy of such universally recommended isolation methods.  
Salmonella; isolation; ISO 6579-1:2017; pre-enrichment; selective media; culture methods; swine +
Footnote †: journal: Applied  
1 Laboratory of Hygiene of Foods of Animal Origin, Faculty of Veterinary Medicine, University of Thessaly, 224 Trikalon Street, 43100 Karditsa, Greece; [EMAIL_ADDRESS]  
2 Department of Nursing, University of Peloponnese, 23100 Tripoli, Greece; [EMAIL_ADDRESS]
## 4 Applications of GenAI-Driven CV in Smart Agriculture  
Smart agriculture is undergoing a significant transformation, largely driven by advancements in CV. While traditional CV and early deep learning models have laid a foundational framework for automating agricultural tasks, their performance is often hampered by two persistent challenges: data scarcity and domain shift. Data scarcity refers to the difficulty and expense of collecting and annotating large, diverse datasets required to train robust models, especially for rare conditions or specific crop varieties. Domain shift occurs when a model trained in one environment (e.g., a specific farm, lighting condition, or season) performs poorly in another, limiting its generalisability.  
The emergence of GenAI has introduced a paradigm shift, offering powerful tools to directly address these limitations. By synthetically generating realistic data and enabling models to adapt across different domains, GenAI is unlocking unprecedented innovations in smart agriculture.  
To systematically synthesise the literature and highlight this paradigm shift, this section is organised thematically around key agricultural applications, such as crop and field management, drone and remote sensing analysis, livestock and aquaculture monitoring, and product quality control (see Figure 3). Within each theme, we first provide an overview of the limitations of traditional CV techniques and early deep learning models, establishing the specific data and generalisation challenges inherent to that application context. We then analyse how GenAI-driven solutions, functioning as a distinct methodological category, directly tackle these challenges--focusing on two primary mechanisms: synthetic data generation to overcome data scarcity, and domain adaptation techniques to mitigate domain shift.