esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Nerolidol Renal Dysfunction following Ischemia-Reperfusion Injury in the Rat
## 1 Introduction  
Renal ischemia-reperfusion injury (IRI) occurs during many procedures and conditions such as renal transplantation, partial nephrectomy and recovery following systemic hypotension [1]. It leads to several renal alterations, including reduction in the glomerular filtration rate (GFR) [2]. Efforts to reduce the impact of IRI are ongoing, and several agents have been shown to attenuate this effect [3; 4; 5; 6; 7; 8; 9].  
Nerolidol (3,7,11-trimethyl-1,6,10-dodecatri-3-ol), also known as peruviol, is a naturally occurring sesquiterpene alcohol that is present in the essential oils of various plants with floral odors, including _buccharis dracunculifolia_[10; 11]. Leaves are the main source of nerolidol, although it also is found in other parts of plants, such as flowers, seeds, fruits, roots and stems. Numerous methods have been used to extract nerolidol from plants, the most common of which is the hydrodistillation method using the Clevenger-type apparatus [12]. Nerolidol was shown to have protective effects under different conditions and in different organs. For instance, nerolidol has a protective effect against myocardial infarction in rats [13] Nerolidol was also found to be neuroprotective against Parkinson'sdisease due to its antioxidant properties and lipid peroxidation inhibition activity [14; 15]. Moreover, nerolidol exhibits antimicrobial activity against serval microbial agents, such as _Staphylococcus Aureus_, _Candida Albicans_ and _Escherichia Coli_[16]. In the kidney, nerolidol was reported to have a reoprotective effect in lipopolysaccharide-ancharide-induced acute kidney injury in rats, as it attenuated the rise in blood urea nitrogen (BUN) and creatinine and inhibited an increase in TNF-\(\alpha\) and IL-1B [17]. In a thioacetamide-induced kidney injury model, nerolidol was also shown to ameliorate oxidative damage in kidney tissues [18]. However, the effect of nerolidol on renal dysfunction following IRI has not been studied yet; therefore, the aim of this study was to investigate this effect in a rat model of bilateral warm renal IRI.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 4 Adjustment Approaches of IPCB Geometry Configuration  
As discussed in Section 2, IPBs have little actuation capability since they are not equipped with propellers. The primary actuation they can implement is to control their valves and fans, which cannot change the IPCB geometry configuration directly. Therefore, this paper adopts an indirect adjustment approach.  
In the vertical direction, IPBs can actively change their masses and flight altitudes by switching their valves and fans. Specific adjustments of each IPB can be managed by its balloon controller. In the horizontal direction, IPBs can change their trajectories with the help of local winds at different altitudes [38; 39; 40]. By combining the adjustments in the two directions, IPBs can change their flight trajectories, and the IPCB can modify its geometry configuration [9; 28; 41; 42].  
Subsequent detailed analyses will be presented based on the vertical adjustment and the horizontal adjustment of individual IPBs, respectively. The following assumptions are made to simplify the analysis.  
1. The flight altitude of an IPB is fully controllable, and its variation depends on a rise rate and a sink rate, represented by \(v_{\text{rise}}\) and \(v_{\text{sink}}\), respectively.
2. The horizontal velocity of an IPB is proportional to the local horizontal wind velocity.  
3. Wind velocities vary with altitude but not with horizontal location. The wind velocities and atmosphere environment are steady during IPCB service time.
4. The influences of balloon volume variation and thermal effect on IPB motions are ignored.
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 6 Limitations and Ethical Risks of Using GenAI
### Legal and Workflow Integration Challenges  
For evidentiary admissibility, the \"black-box\" nature of AI is a primary obstacle [42]. Since it is often impossible to retrace an algorithm's decision-making process, using its output as direct evidence in court is problematic [42]. The EUCPN paper recommends that AI results should be treated as \"conjecture in the realm of probability\", not as conclusive facts [42]. Therefore, workflows must ensure that humans are always the ultimate decision-makers regarding any intervention [42]. Furthermore, the proposed EU AI Act classifies predictive policing systems as \"high-risk\", subjecting them to strict transparency and human oversight requirements, which are prerequisites for legal acceptance [42].  
Regarding GDPR compliance, any implementation must adhere to the EU's legal framework, including the GDPR and the Law Enforcement Directive (LED) [42]. The LED mandates that authorities conduct a Data Protection Impact Assessment (DPIA) before deployment to identify and mitigate risks to citizens' fundamental rights [42]. This is a critical step in any legally compliant workflow.  
Jurisdictional challenges arise from the fragmented landscape of predictive policing within the EU. Different Member States, such as the Netherlands, Germany, and Estonia, use disparate systems based on unique national data sources and methodologies [42]. For instance, the Dutch Crime Anticipation System (CAS) utilizes demographic data, whereas Estonia's model incorporates border crossing information for person-based predictions [42]. This lack of a standardized approach creates significant challenges for developing harmonized, cross-border operational workflows, as an AI-generated risk score in one country may not be legally or methodologically compatible with those in another.
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
## 2 Materials and Methods  
### IoT Connectivity  
Several wireless standards for IoT applications have been proposed during the last two decades [13]. For local and personal area networks, the Wi-Fi, Bluetooth low energy (BLE), Zigbee and thread standards [14], Z-wave protocol [15], radio-frequency identification (RFID) [16; 17], and near field communication (NFC) [18] can be highlighted. For wide areas, the long-term evolution (LTE) [19], Neul's ultra narrow-band (UNB), Sigfox and LoRa are broadly used [14; 20].  
Wireless is 'de facto' the connectivity technology for IoT. In part, this is because of its agility and scalability. There is virtually no cost and no waiting time for setting up and tearing down connectivity facilities for IoT elements. However, there are well known issues with wireless communications, especially for wide areas, which are only partially addressed by current technologies. Spectrum usage, propagation environments and topography are relevant factors that may severely degrade a signal's quality. In addition, weather conditions and multipath fading effects may induce signal-to-noise fluctuations that might be incompatible with reliability requirements for providing connectivity for critical IoT applications. The broadcasting nature of wireless connectivity has evidently fundamental privacy and security vulnerabilities; and they may carry more weight than other aspects when deciding the right IoT connectivity solution. Finally, time delay is another drawback for wireless communication systems. [PERSON] et al. proposed a reconfigurable network model to find a trade-off between the security, performance and time delays for wireless network sensors. A developed smart grid model is used to identify the optimal positions of direct connections minimizing transmission delay, energy consumption and financial cost of sensors' arrangement [PERSON] et al. (2019).
###### Abstract  
Background--Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method--This paper compares the performance of human operators with image processing, artificial intelligence software and 3D scanning for different types of inspection. The results were statistically analysed in terms of inspection accuracy, consistency and time. Additionally, other factors relevant to operations were assessed using a SWOT and weighted factor analysis. Results--The results show that operators' performance in screen-based inspection tasks was superior to inspection software due to their strong cognitive abilities, decision-making capabilities, versatility and adaptability to changing conditions. In part-based inspection however, 3D scanning outperformed the operator while being significantly slower. Overall, the strength of technological systems lies in their consistency, availability and unbiasedness. Conclusions--The performance of inspection software should improve to be reliably used in blade inspection. While 3D scanning showed the best results, it is not always technically feasible (e.g., in a borescope inspection) nor economically viable. This work provides a list of evaluation criteria beyond solely inspection performance that could be considered when comparing different inspection systems.  
Keywords:human technology comparison; human versus machine; visual inspection; automated defect detection; aircraft engine maintenance; MRO; emerging technologies; Industry 4.0
## 3 Reviews of UPC System and Three Applications
### UPC System  
First, we review the _UPC_ system. The UPC system can provide computational powers efficiently for members in a group, such as engineers in a company or students in a laboratory, by using idling computing resources of their PCs. To allow various application programs to run on different PC environments, the UPC system adopts _Docker_. _Docker_ is a popular software tool that has been designed to create, deploy, and execute various application programs on various platforms by packaging the necessary dependencies of the application [18].  
Figure 1 shows the overview of the UPC system. The UPC system adopts the _master-worker model_. Users submit computing jobs to the _UPC master_ through the _UPC web server_. After synchronizations of the jobs, the _UPC master_ assigns the submitted jobs to the appropriate _UPC workers_. Each worker computes its assigned jobs and returns the results to the master upon completion. Users can access the results at the web browser.  
For further details, the usage flow of the UPC system will be described:  
1. Job reception: A user submits jobs from the web browser and requests to compute them in the UPC system.
2. Worker assignment: The UPC master selects an appropriate active worker to compute each job using a job-worker assignment algorithm.
3. Docker image generation: The UPC master generates the _Docker image_ to execute the job on the assigned worker.
4. Docker image transmission: The master sends the _Docker image_ to the assigned worker.
5. Job execution: The worker generates the _Docker container_ from the image and executes the job there.
6. Result transmission: The worker returns the result to the master upon completion.
7. Result response: The master shows the computing results of the jobs to the user through the web server.
## 4 Results
### Models Evaluation and Mapping of AGB
#### 4.3.2 Mapping of Four AGB Models  
The predicted values of AGB from the four models ranged from 11.80 to 324.12 Mg\(\cdot\)ha\({}^{-1}\). For a better comparison, the values were divided into five levels by equal intervals of 62.46 Mg\(\cdot\)ha\({}^{-1}\) (Figure 6). Maps show the various spatial distributions of AGB. All of the six maps show that the western part of Lushuihe town was a high AGB region, with values ranging from 199.19 to 324.12 Mg\(\cdot\)ha\({}^{-1}\), while low AGB regions were located near the highway connecting Lushuihe and Yangjiang towns, with values ranging from 11.80 to 136.72 Mg\(\cdot\)ha\({}^{-1}\). The map resulting from ANN was characterized by more explicit spatial variation than the others. Comparing the estimated and measured AGB (14.64\(\sim\)317.40 Mg\(\cdot\)ha\({}^{-1}\), mainly ranging from 11.80 to 136.72 Mg\(\cdot\)ha\({}^{-1}\) with 87.5%) resulted in the following performance ranking for the four algorithms from strong to weak: SVR, ANN, RF, and GWR, meaning that ML performed better than GWR. These maps can guide resource allocation for carbon sequestration and forest management. The evaluation of the forest AGB mapping results by the four models was insufficient for this study as we were limited by the sample size. Future verification work should be conducted; this is conventionally done by independent sample sets or by acknowledged high-accuracy results such as airborne data, especially unmanned aerial vehicle LiDAR data [17; 97].  
Figure 5: Scatterplots of the measured and estimated above-ground biomass (AGB, Mg\(\cdot\)ha\({}^{-1}\)).
## 5 Results and Discussion
In addition, the statistical values for compressive strength values found from training and testing in PSOANFIS and GAANFIS models (RMSE, MAE, mean error, and standard deviation (SD)) are also given in Figures 8 and 9 and Table 5. While the statistical values of RMSE and MAE from training in the PSOANFIS model were found as 3.269 and 2.236, respectively, these values were found in testing as 2.251 and 1.847, respectively. Similarly, while the statistical values RMSE and MAE from training in the GAANFIS model were found as 3.115 and 2.293, respectively, these values were found in testing as 2.531 and 2.115, respectively. A small perceptible deviation was observed for the calculated values (Figure 9). This result demonstrates that the proposed PSOANFIS and GAANFIS models are suitable and can predict the compressive strength values, which were very close to experimental values. In general, the compressive strength of GPC containing waste steel slag could be predicted using PSOANFIS and GAANFIS without conducting too many experiments. Therefore, it can be reasonably stated that the PSOANFIS and GAANFIS algorithms are very promising AI tools for the prediction of compressive strength of GPC using entirely waste-sourced steel slag. These tools could provide useful information that could help engineers save time and costs, and increase work effectiveness.
## 2 Basic Theory
### Basic Concept of the Statistical Filter  
Although fault diagnosis of sliding bearings is often artificially carried out with frequency analysis of signals, there is a need for a reliable, automated diagnosis method thereof [21]. However, the effect of the background noise in the signal is serious, and the symptom of five kinds of faults is not evident. Therefore, the purpose of statistical filter is removing the negative effect from the noise and extracts the sensitive information by the statistical method, which can save the abnormal information by comparing the general statistical indicator between the normal signal and abnormal signal, as shown in Figure 1.  
Figure 1: Process of the statistical filter.  
The statistical filter removes noise with calculating the mean and standard deviation of each part (totally \(M\) parts), and select the useful information with the distinction index (DI) [22].  
\[DI_{i}=\frac{\left|\mu_{1,i}-\mu_{2,i}\right|}{\sqrt{\sigma_{1,i}^{2}+\sigma_{2, i}^{2}}}\;i=1,2,\ldots,M \tag{1}\]  
where \(\mu_{1,i}\) and \(\mu_{2,i}\) are the mean value of the ith spectrum part calculated by the sliding bearing signal at normal state and abnormal state, respectively. \(\sigma_{1,i}\) and \(\sigma_{2,i}\) are standard deviations of normal state and abnormal state, respectively. Moreover, in order to facilitate the calculation of the inverse fast Fourier transformation (FFT), we introduce a binary string inspired with [23]. Therefore, if \(DI_{i}\) is bigger than the synthetic detection index (SDI), the according spectrum data is saved and assigned to a unit vector that has the same length with the according spectrum part. Otherwise, it can be deleted as the useless information, and all value is set 0 that also has same length with the according part. Finally, the elements in new vector according to abnormal signal (the value are 0 and 1) are multiplied by the elements in the raw signal at according abnormal state. Therefore, the new signal after statistical filtered can be further mined by the SAE.
## 3 Optimization of the NeuMF Model
### The NeuMF-p Model  
NeuMF-p(pre-training) refers to the model that initializes NeuMF. NeuMF is initialized by pre-training GMF and MLP. The experiment in paper [1] shows that NeuMF-p is better than NeuMF. The specific initialization process of NeuMF is described in the following.  
Firstly, GMF and MLP are separately pre-trained. Pre-training at this time means that the GMF(MLP) model is first randomly initialized, and the model is then trained by the loss function until the model converges. Secondly, the parameters of the pre-trained GMF and the pre-trained MLP are the initialization parameters of NeuMF-p. Finally, \(h\) is the weight of the \"NeuMF layer\", and the initialized value is as follows:  
\[\mathbf{h}\leftarrow\left[\begin{array}{c}\gamma\mathbf{h}^{\textit{GMF}}\\ (1-\gamma)\mathbf{h}^{\textit{MLP}}\end{array}\right] \tag{15}\]  
where \(\mathbf{h}^{\textit{GMF}}\) is the weight of the prediction layer of GMF and \(\mathbf{h}^{\textit{MLP}}\) is the weight of the prediction layer of MLP. The \(\gamma\) is a hyper-parameter and the value of \(\gamma\) is set to 0.5 [1].  
Considering that the experiment [1] shows that NeuMF-p is better than NeuMF, this paper chooses to optimize the training process of NeuMF-p directly. Here is a brief introduction to the loss function of NeuMF-p and the original training process. The loss function of NeuMF-p is as follows:  
\[\begin{array}{c}L=-\sum\limits_{(u,v)\in y}\log\hat{y}_{uv}-\sum\limits_{(u,v )\in y^{-}}\log(1-\hat{y}_{uv})\\ =-\sum\limits_{(u,v)\in y,y^{-}}y_{uv}\log\hat{y}_{uv}+(1-y_{uv})\log(1-\hat{y} _{uv})\end{array} \tag{16}\]  
This is the loss function of NeuMF-p, also known as the log loss or binary cross-entropy loss, which can be optimized by the SGD (Stochastic Gradient Descent) algorithm. The value of the target \(y_{uv}\) is 0 or 1, which can be regarded as a label. If \(y_{uv}=1\), it means that there is an interaction between user \(u\) and item \(v\). If \(y_{uv}=0\), it means that there is no interaction between user \(u\) and item \(v\). By using the sigmoid function as the activation function of the output layer, \(\hat{y}_{uv}\) can be controlled to output in the range of \((0,1)\), where \(y\) represents the set of observed interactions in dataset \(Y\) and \(y^{-}\) represents the set of counterexamples in dataset \(Y\). The counterexample in the original paper [1] refers to the data of unobserved interactions in dataset \(Y\).
# Creep Characteristics of Soil in the Sliding Zone of Huangtupo Landslide
## 2 Materials and Methods  
### Test Materials  
The Huangtupo landslide is mainly composed of Riverside Slump #1, Riverside Slump #2, the Substation Landslide, and the Garden Spot Landslide (Figure 3b); its area is \(1.35\times 10^{6}\) m\({}^{2}\), and the volume is 6.934 \(\times\) 10\({}^{7}\) m\({}^{3}\). The regional bedrock is the Middle Triassic Badong Formation (T\({}_{2}\)b).  
After long-term monitoring of the landslide, we found that the degree of deformation of Riverside Slump #1 was the most serious, and its sliding mass was creeping at a steady rate [32]. The plan view is shown in Figure 3a. In order to further understand the sliding zone soil of Riverside Slump #1, the tunnel group of the Badong field test site crossing Riverside Slump #1 was completed in 2013. Many researchers have conducted related studies, as well as monitoring efforts. The soil samples used in this experiment were from Riverside Slump #1 (see Figure 4) and were sampled from branch tunnel no. 3, as shown in Figure 3c.  
Figure 3: (**a**) The plan view of the Riverside Slump #1. (**b**) Composition of Huangtupo landslide. (**c**) Sampling site.  
The laboratory geotechnical tests of the sliding zone soil in the Huangtopo landslide show that the natural moisture content was 13-14%, the average density was 2.05 g/cm\({}^{3}\), the average liquid limit was 36.02%, the average plastic limit was 17.51%, and the average plastic index was 18.51. The average particle size distribution of the samples is shown in Figure 5. Since the original sample contained gravel, the sliding zone soil needed to be reshaped. The sample was screened with a standard sieve of 2 mm, and the soil particles less than 2 mm in size were taken to prepare the sample. The moisture content of the remolded sample was controlled at 13.87%, and the density was 2.05 g/cm\({}^{3}\). The size of the prepared ring-knife sample was 61.8 mm (diameter) \(\times\) 20 mm (height). In order to avoid excess air in the specimen to the greatest extent possible, the ring-knife sample was put into a vacuum-saturated cylinder and saturated for 4 h by pumping, and then left to stand in a vacuum for 20 h.
## 2 Overview of Retrieval-Augmented Generation
### Augmentation
#### 2.3.1 Iterative Retrieval  
RAG-based LLMs' iterative retrieval involves retrieving relevant information and generating responses in multiple cycles. Iterative retrieval is particularly effective in tasks that require detailed contextual understanding and precise information, as it allows a model to progressively refine its outputs by continually integrating more accurate and contextually relevant data.  
ITER-RETGEN [12] introduces an iterative methodology to enhance RAG systems' performance by creating a synergistic loop between the retrieval and generation processes. Following initial retrieval and generation, a model refines its prompts based on its output, which then guides a new round of retrieval aligned with the refined query. This iterative cycle continues, progressively improving the relevance and quality of both retrieved information and generated responses. By employing this process, the model achieves greater accuracy in tasks requiring detailed contextual understanding and precise information.  
IRCoT [16] presents an advanced framework that integrates retrieval with chain-of-thought (CoT) reasoning, enabling models to effectively address knowledge-intensive, multi-step questions. A model initially retrieves a base set of paragraphs using a question as the query, and then iteratively interleaves retrieval steps with reasoning steps until termination criterion is met. During its retrieval-guided reasoning phase, the model generates another CoT sentence using the earlier mentioned question, previously collectedparagraphs, and prior CoT sentences. In its CoT-guided retrieval phase, the model uses its last generated CoT sentence as a query to retrieve additional paragraphs, which are then incorporated into the reasoning process. Integrating retrieval with CoT reasoning significantly boosts the model's ability to tackle complex tasks involving both deep knowledge and logical reasoning.  
ToC [42] constructs a tree of clarifications to address ambiguous questions. A model first retrieves relevant passages for an ambiguous query and then generates potential interpretations by producing clarification questions or responses for each branch of that tree. Using these passages, disambiguated questions are recursively generated via few-shot prompting, before being pruned as necessary. Finally, the model generates a comprehensive long-form answer addressing all disambiguated questions. This approach markedly improves the model's ability to manage ambiguous queries, resulting in more accurate and contextually appropriate responses.