esa-sceva/satcom-chunk-collection huggingface dataset:
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 9 Cyclospora cayetanensis
### Diseases Caused by C. cayetanensis  
Enterocyte invasion by _C. cayetanensis_ causes damage to the epithelium of the small intestine with disruption of the brush border, loss of membrane-bound digestive enzymes, and atrophy of villi. Consequently, water, nutrients, and electrolyte uptake in the small intestine are reduced, and symptoms manifest as watery diarrhea, abdominal cramps, bloating, nausea, fatigue, low-grade fever, anorexia, and weight loss [235; 239]. Prolonged diarrhea may cause death in rare cases involving infants and individuals immunocompromised or with co-morbidities [235]. Infection is often mild or asymptomatic in residents of countries in which _C. cayetanensis_ is endemic, while a more severe disease occurs in travelers from non-endemic countries [235; 239].  
Late identification of the etiology of _C. cayetanensis_ diarrhea can lead to complications such as acute kidney injury (AKI), which can turn into chronic kidney disease (CKD). A recent case of AKI involved a kidney-transplanted 35-year-old Mexican male who was most probably infected after eating fresh vegetables in a Dutch restaurant during a trip [240].  
This parasite is endemic in the Indian subcontinent. However, the first case of _C. cayetanensis_ infection was reported only recently in India and regarded a 30-year-old woman who was in complete clinical remission from [PERSON]'s lymphoma and was affected by diarrhea lasting two months that was unfruitfully treated with quinolones. _C. cayetanensis_ was isolated from stool and identified by PCR and DNA sequencing, so she was treated with tab nitazoxanide and recovered. The source of infection was suspected to be contaminated water or fruits and fresh vegetables available locally [239]. A 76-year-old Spanish man with diffuse large B cell lymphoma and long-lasting diarrhea was also treated with ciprofloxacin without success. _Campylobacter jejuni_ and _C. ayetanensis_ were detected by multiplex one-step real-time PCR on stool samples. After treatment with azithromycin and an initial improvement, the patient relapsed and _C. ayetanensis_ was still detected in stool. Therefore, he was treated with TMP-STX, and his diarrhea symptoms improved [241].  
TMP-STX treatment is effective against cyclosporiasis, and in immunocompromised patients, such as those with AIDS and transplant recipients, it should be administered for longer or at higher doses, and as prophylaxis for relapse prevention [235].  
Infection prevention measures that should be followed, especially by immunocompromised patients, include avoiding eating uncooked raw vegetables and unpeeled fruits, eating fully cooked foods since _C. ayetanensis_ oocysts are inactivated by cooking and avoiding consuming untreated well water and surface water [235].
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 3 Case Studies
### Global Surface Flux Estimates  
Fluxes at the land-atmosphere boundary play a key role in regulating water, carbon and energy cycles. These fluxes include latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP). While these fluxes cannot be measured directly from remote-sensing observations, other remotely sensed variables can be used to estimate these fluxes. Moreover, these three fluxes are highly coupled and, therefore, a coupled model is ideal.  
A fully connected neural network model was developed for this purpose [196], named water, energy, and carbon with artificial neural networks (WECANN). Inputs to WECANN are remotely sensed estimates of precipitation, soil moisture, net radiation, snow water equivalent, air temperature and solar induced fluorescence. The target variables for training the model were derived from outputs of global models. However, this presents the difficulty that the target variables are model outputs that can have substantial error, which will propagate in the WECANN model. To mitigate this problem, three independent estimates of each of the three fluxes (LE, H and GPP) were retrieved from the global models. Then a novel statistical approach, named triple collocation (TC, Figure S2, equation S1), was used to combine those estimates to a new dataset for training the WECANN model.  
Triple collocation (TC) is a technique for estimating the unknown error (measured with standard deviations or RMSEs) of three mutually independent measurement systems, without treating any one system as zero-error \"truth\" [197]. The three measurement systems estimate a variable collocated in space and time, hence the name triple collocation. Using these probabilities, at each pixel and at each time one of the three estimates of the target variable is randomly selected to generate the TD.  
The results of WECANN model outputs were evaluated against ground measurements from global FLUXNET towers from 2007 to 2015 (Figure 10), using both the coefficient of determination and RMSE to evaluate accuracy. These show that WECANN's correlation was on average 17% higher (range 8-51%) than that of any one of the three individual inputs, while the RMSE was 21% lower (range 4-54%). These differences provide a partial quantification of the error inherent in any one of these training inputs and show that by combining them using the TC technique, we can reduce error in  
Figure 9: (**a**) Labels generated by experts for validation. (**b**) Predictions from the Khartoum model. (**c**) Predictions from Kumasi model. (**d**) Predictions from Khartoum model retrained in Kumasi with 10 K steps.  
an ML model for predicting the fluxes at global scale. This case study illustrates a means of assessing and accounting for error in TD for cases in which these data are not created specifically for the project, but rather are pre-existing data products with potentially quite different characteristics and potentially unknown error.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 2 Fast Charging Techniques for Electric Vehicles
### Ultra-Fast Charging (UFC)  
Ultra-fast charging (UFC) is a charging technology that significantly reduces EV charging times compared to traditional methods [57; 58]. With power levels typically exceeding 350 kW, UFC delivers a lot of energy to the vehicle's battery quickly [57]. This approach addresses concerns about long charging times, making EV charging more comparable to fuwelling a conventional vehicle and alleviating range anxiety [59; 60]. UFC systems employ control strategies for efficient power delivery and battery management, including Constant Current (CC) and Constant Voltage (CV) modes and temperature monitoring. Although UFC stations are still in the early deployment stages and are less widespread than conventional infrastructure, their numbers are increasing, especially along major highways [61]. Both the charging infrastructure and EVs must be compatible with higher power levels, requiring station design and in-station and battery technology advancements. Automakers and charging providers are developing UFC technologies, introducing new standards like CCS and CHAdeMO 3.0 to support higher power levels [62; 63]. Ongoing research aims to further improve charging speeds, infrastructure, and battery technologies in the dynamic field of UFC [61]. Ultra-fast charging offers unparalleled convenience with rapid charging times and minimal user interactions being required. However, it is important to acknowledge potential safety concerns, notably the risk of overheating. While generally safe, precautions should be taken to prevent overheating incidents. In terms of durability, ultra-fast charging stations are moderately weatherproof, ensuring reliable operation in various conditions. High-power charging stations facilitate integration with existing infrastructure, although their availability is not yet widespread. Compatibility primarily extends to newer electric vehicle (EV) models, though it is not guaranteed for  
Figure 2: Inductive charging: (**a**) static charging. (**b**) dynamic charging.  
all makes and models. Additionally, the high charging currents associated with ultra-fast charging may contribute to slightly accelerated battery degradation over time. Nonetheless, the scalability and upgradability of these stations are notable advantages, as they can be modified to accommodate even faster charging speeds. Although ultra-fast charging systems boast high efficiency, some energy loss still occurs during charging.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 3 Solar Interference Assessment Methodology  
Leveraging opportune solar hits collected during standard-volume scanning provides the most accessible option for ongoing monitoring of the pointing of the NXPol-1 radar system and is the only option for assessing historic campaigns. To assess this historic campaign, we implement the multi-step methodology described in the following sections.
## 2 Literature Review
Its primary aim is to enhance the usability and performance of the network and functional features needed for vehicular safety applications, that is, low delay and high data rate. In the present work, the proposed intrusion detection system (IDS) makes use of 5G technology for the prevention of flooding attacks in vehicular networks. Moreover, 5G technology facilitates vehicle coordination with measures for quick detection of possible cyber threats like DDoS [46]. The advancements in DDoS attack detection within Internet of Vehicles (IoV) systems highlight significant progress in both detection accuracy and computational efficiency. The application of Federated Learning (FL), particularly through innovations such as the FLAD algorithm, has proven effective in managing non-IID data and adapting to varying attack signatures. When combined with advanced feature selection techniques like the Gini index, these approaches offer substantial improvements in refining detection models. The CICDDOS2019 dataset, which includes 25 pivotal security features, is crucial in addressing the specific challenges associated with DDoS attacks in interconnected vehicular networks. This paper aims to leverage these advancements by investigating the efficacy of the Gini index feature selection method and federated learning in enhancing DDoS attack detection within IoV environments. This contribution is expected to advance the development of more robust and secure transportation networks. The insights derived from prior research underscore the importance of both feature selection and decentralized learning in enhancing DDoS detection. This study leverages the Gini Index for feature selection and Federated Learning to address the limitations in existing DDoS detection methods to enhance detection accuracy, scalability, and privacy preservation in IoV environments. DDoS attacks in IoV can take many forms, from simple flooding to sophisticated methods using emulation dictionaries, which make detection challenging by mimicking legitimate traffic patterns. Our approach aims to address these challenges by incorporating advanced feature selection and decentralized learning, with future enhancements planned to cover more sophisticated attack types.
## 4 Discussion
For the case of the architecture with DCP, a comparison can be made between the theoretical absorption of the active electric power input (16.06 kW) and the absorption actually measured on the system in one cycle (14.85 kW). An overestimation of the actual value of about 8% is found. This value is quite low. Therefore, it can be assumed that the performed analysis reflects the reality sufficiently well.
## 2 Materials and Methods  
### Principle of Transfer Learning in TPCCNN  
Several features, vibration frequencies, abnormal noise, etc., can be derived from the fault of a rotary machinery. TPCCNN (Transfer PCCNN) proposed in this paper focuses on monitoring vibration frequencies which can be featured by FFT, trained, and transferred in PCCNN model for further fault diagnosis. Due to different operational conditions and environments, subtle bias happens between machines with the same model and among different machines which affect the accuracy of model evaluations. Therefore, a method of TPCCNN combined with transfer learning and the PCCNN [16] models is developed for fault diagnosis of rotary machinery. The architecture diagram of TPCCNN is shown in Figure 1. The TPCCNN model derives from PCCNN that consists of four convolutional layers, four pooling layers, and three fully connected layers. We fine-tuned the preceding model. While adjusting, we used a learning rate that was equal to or less than the one used in the initial training model. We rarely did adjustment on the defined weights, for we had been highly confident in the pre-trained network.  
The transfer learning model in this study adopts parameter transfer, which fixes the features of the lower convolutional layers, pooling layers, and batch normalization before retraining the weights and parameters of the higher fully connected layers, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{4}{c}{**TPCCNN Parameter**} \\ \cline{2-5}  & **Parameter Size** & **Activation Function** & **Batch Normalization (BN)** & **Freeze/Fine-Tune** \\ \hline Input & / & / & / & / \\ \hline Convl-1 & \(32\times 64\times 1\times 1\times 4\) & ReLU & Yes & Freeze \\ \hline Pooling-2 & \(2\times 1\times 2\) & / & No & Freeze \\ \hline Convl-3 & \(64\times 3\times 1\times 32\times 1\) & ReLU & Yes & Freeze \\ \hline Pooling-4 & \(2\times 1\times 2\) & / & No & Freeze \\ \hline Convl-5 & \(96\times 3\times 1\times 61\times 1\) & ReLU & Yes & Freeze \\ \hline Pooling-6 & \(2\times 1\times 2\) & / & No & Freeze \\ \hline Convl-7 & \(128\times 3\times 1\times 96\times 1\) & ReLU & Yes & Freeze \\ \hline Pooling-8 & \(2\times 1\times 2\) & / & No & Freeze \\ \hline FullContd-9 & / & / & No & Fine-tune \\ \hline FullContd-10 & M \(\times\) 512 & ReLU & Yes & Fine-tune \\ \hline FullContd-11 & \(512\times\) N & / & Yes & Fine-tune \\ \hline SoftMaxPlus-12 & N & / & No & Fine-tune \\ \hline Output & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of Structural Parameter of the TPCCNN Model.
# Design of Conical Foundations with Increased Bearing Capacity in Areas of Undermined Soils
## 4 Discussion
Field tests were limited to a horizontal soil movement of 2.6 mm, although they also confirmed the higher load-bearing capacity of conical foundations that are effective in areas of possible seismic activity and in mined areas. In this case, the horizontal forces caused by horizontal tensile strains of the soil mass can be eliminated by installing a special rolling joint located between the conical foundations and the plinth structure. Analysis of the load-settlement graph in Figure 16 shows that the penetration of foundations with a conical base downwards into the soil increases as the horizontal tensile deformations increase. The dependence of settlement during undermining or tensile deformations in the soil according to [18; 20] is equal to:  
\[\text{Su}=\text{S}_{0}\cdot(1+\text{D}\cdot\varepsilon), \tag{7}\]  
Figure 15: Comparison of results of numerical and laboratory methods for conical (with 90\({}^{\circ}\) aperture angle) and isolated shallow foundations: 1, 2—curves obtained for isolated shallow foundation with horizontal tensile strains \(\varepsilon=0\), 3 mm/m; 3, 4, 5, 6—curves obtained for conical foundation with horizontal tensile strains \(\varepsilon=0\), 3, 6 and 9 mm/m;....—numerical modeling results. where D--is an empirical coefficient;  
\(\varepsilon\)--horizontal tensile strains of the surface soils (tensile deformation), \(0\leq\varepsilon\leq 9\times 10^{-3}\) m;  
Su--settlement during undermining (with horizontal displacement of soils);  
S\({}_{0}\)--settlement without undermining (without horizontal displacement of soils). Thus, to determine the expected settlement after horizontal tensile strains and soil displacement, the coefficient D was calculated equal to 56.5 for a conical foundation with an aperture angle of 90\({}^{\circ}\), and 50 for conical foundations with an aperture angle of 80\({}^{\circ}\). It should be noted that this parameter is determined for the equivalent soil used for the laboratory testing. For soils at the Kostenko mine (Kazakhstan), coefficient D was determined as 44.5 for conical foundations with an aperture angle of 90\({}^{\circ}\) and 39 for conical foundations with an aperture angle of 80\({}^{\circ}\).
## 3 Results & Discussion
### Electrochemical and PANI@CNT/CNC SPME MN-Coupled GC-MS Analysis of Kiwi, Tea, and Coffee Samples
For example, integration of molecularly imprinted polymers has been demonstrated using similar platforms by imprinting neon-icotinoids, and thus the platform can be highly selective and versatile with additional analyte specific molecular receptor [27]. The PANI@CNT/CNC SPME MN was further coupled with GC-MS and used to determine the caffeine content in green tea and earl grey tea samples using the external calibration curves in Figure 6c. Overlaid EICs for both green and earl grey tea samples are shown in Figure S2. The caffeine content of green and earl grey tea samples were found to be 0.111 \(\pm\) 0.007 and 0.132 \(\pm\) 0.007 mg/mL, respectively. These values align well with the caffeine content of 1 min steeped green and earl grey teas determined by [PERSON] et al., 2008 [38]. These results confirm the ability of PANI@CNT/CNC SPME MN for use in electrochemical sensing and GC-MS analysis of food and drink samples. To further evaluate the reproducibility of the PANI@CNT/CNC SPME MN under hot temperature GC/MS conditions, 400 mg/L of caffeine standard analyzed for 15 times over a period of one month using triplicate SPME MN platform, yielding an average of 2.5% RSD for triplicate platforms over this time, indicative of robustness even in high temperatures [25; 26]. This is not surprising as CNT/CNC and PANI are known to have excellent thermal stability. The fact that the PANI@CNT/CNC is entrained within the stainless-steel needle, adds to the robustness. Figure 8: (**a**) Camera image of the experimental setup for in situ electrochemical kiwi analysis; (**b**) Overlaid CVs obtained from analysis of 0.10 M phosphate buffer blank, fresh (0-day), and 3-day ripened kiwi fruit skin and core using the PANI@CNT/CNC SPME MN. Figure 10: **(a)** Overlaid CVs obtained from the analysis of 0.10 M phosphate buffer blank, coffee (in 0.1 M phosphate buffer), and 330 mg/L 3-caffeoylquinic acid (3-CQA) (in 0.10 M phosphate buffer) taken using the PANI@CNT/CNC SPME MN. **(b)** Associated 3-CQA concentration vs. \(\triangle\)Current standard addition plot. Figure 9: Total ion chromatograms (TICs) of GC-MS analysis of **(a)** kiwi surface and **(b)** kiwi core taken following SPME using a PANI@CNT/CNC SPME MN. Associated list of compounds found within **(c)** kiwi surface and **(d)** kiwi core deduced using the NIST library database.
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 8 Proposed Framework to Design Energy-Aware WSN for SEEB
### Data Link Layer Protocol  
The network topology affects the choice of the data link layer protocol. A robust network topology requires the selection of the data link protocols, which can go with the RF modules and provide the best coordination with the network layer protocol, along with low power consumption.
## 4 Methodology
### Information Extraction
#### 4.3.3 Named Entity Recognition  
Named entity recognition (NER) is an information extraction task, which identifies mentions of various named entities in unstructured text, and classifies them into predetermined categories, such as people's names, organizations, locations, date/time, monetary values, and so on.  
Terms that represent specific entities are more informative and have unique contexts. Furthermore, they represent real world objects, like people, places, organizations, etc., which are often proper names. Thus, NER is a prominent factor in information extraction that identifies named entities and segmenting them into appropriate classes.  
Based on this, we can define the task of NER in these three steps: Detect a named entity, extract the entity, and categorize the entity.  
In the case of NER, several implementations can be used. 'Lexicon approach' relies on a knowledge base called ontology, and contains all terms related to a particular topic, grouped in different categories. The system looks for matches with named entities. 'Rule-based systems' are a series of grammatical rules hand-crafted by computational linguists. We can gain results of high precision, but low recall. Last but not least, 'Machine learning based systems' build an entity extractor and feed the model with a large volume of annotated training data. Here, we need tagged and clean training data.  
For named entity recognition, we used'spacy' library and 'displayer' visualizer.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 2 Agricultural Concrete and Metallic Structures
### Specific Impact of Corrosive Agents on Steel Reinforced Concrete Structures
The risk of corrosion by ammonia and ammonium solutions is dependent on temperature-- a limited amount of corrosion has been reported following the exposure of zinc to ammonia at higher temperature. However, the risk of elevated temperature does not apply in farm environments [2; 7]. Higher temperatures apply in industrial settings. However, copper-containing Al alloys are an exception. Iron and steel structures are susceptible to corrosion by ammonium salts--at a rate of 0.27 mm/yr in about 50 g/L of ammonium chloride [2; 7]. Stainless chromium steel is resistant to NH\({}_{4}\)NO\({}_{3}\) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) solutions. Austenitic chromium and nickel steel are resistant to NH\({}_{4}\)NO\({}_{3}\) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) solutions. Zinc only exhibits resistance to corrosion in dilute solutions of ammonium chloride. Aluminum structures are resistant to attack by ammonium nitrate--which is a common ingredient in agricultural fertilizer. Similar to other corrosive agents, sulphuric acid corrodes steel. The risk of corrosion is most pronounced in 60-100% acid solutions at low temperatures. Austenitic Cr-Ni steel can only be used at low concentrations or at high concentration and low temperature; corrosion. The rate of corrosion of about 1 mm/yr has been reported in 20-70% H\({}_{2}\)SO\({}_{4}\)[2; 7]. Zinc is severely corroded with increasing acid concentration and temperature.