esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Results of the Parametric Study
#### 4.6.3 Effect of the Stiffness of the Pipe and Backfill
In other words, the lateral earth pressure coefficient, which represents the horizontal effect of the vertical stresses, also increases with \(\mathrm{Fv}\). Figure 22 shows the stresses in the crown obtained using Equation (2) and the analyses. Figure 21: Stress and deformation variation ratios for different \(\mathrm{r_{s}}\) (**a**) \(\mathrm{r_{vd}}\) (**b**) \(\mathrm{r_{vs}}\). Figure 20: Variation of K concerning Ep for SP-1 (**a**) for different stresses (**b**) mean K for different \(\mathrm{r_{s}}\). When Es was raised, there was a notable reduction in both Ph and Pv. However, this resulted in only a marginal reduction of up to 10% in \(\sigma_{\rm h1}\), which extended to the tank wall. In contrast, \(\sigma_{\rm v1}\) increased by 30% due to the more efficient working of the arching, resulting in a decrease in K.  
The mean d\({}_{\rm r}\) values for stiffness ratios of 16, 12, 9, 6, and 4 were 0.79, 0.73, 0.69, 0.63, and 0.56, respectively. The corresponding c\({}_{\rm 1}\) coefficients for these values were 0.97, 1, 1.02, 1.05, and 1.07. Figure 23 illustrates the variation of the K values derived from both analyses and Equation (7). Increasing the stiffness of the backfill significantly reduces K at low stress levels, and this effect gradually diminishes with increasing stresses (Figure 23a).
# Electrochemical Removal of Chromium (VI) from Wastewater
## Materials and Methods
### Experimental Procedure  
Reduction experiments were carried out in a 250 mL beaker fixed in a thermostatic water bath with a temperature precision of \(\pm\)0.1 \({}^{\circ}\)C. In the batch experiments, 100 mL of solution containing 1.000 g/L chromium (VI) was prepared by dissolving a certain K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) in distilled water, and the acidic medium was prepared by adding different volumes of H\({}_{2}\)SO\({}_{4}\), then the current supplied by a DC power supply was applied as the solution was heated to a predetermined temperature. During the experiments, the samples were collected at different intervals (5 min), and analyzed for the residual concentration of chromium (VI) in the solution [10]. The electrode used in the experiments was a plate-like dimensionally stable anode (Baoji Zhiming Special Metal Co., Ltd., Shanxi, China) with a surface area of 1 cm\({}^{2}\) (1 \(\times\) 1 cm). A cathode with an identical surface area was fixed at a distance of 2 cm [21].  
The concentration of chromium (VI) in the solution was determined by inductive couple plasma-optical emission spectrometry (ICP-OES) [22]. The reduction efficiency (\(\eta\)) was calculated as Equation (1):  
\[\eta=\frac{\mathrm{C}_{1}\cdot\mathrm{V}_{1}-\mathrm{C}_{2}\cdot\mathrm{V}_{2 }}{\mathrm{C}_{1}\cdot\mathrm{V}_{1}}, \tag{1}\]  
where C\({}_{1}\) and C\({}_{2}\) are the concentrations of chromium in the solution before and after the experiment, in g/L; and V\({}_{1}\) and V\({}_{2}\) are the volumes before and after the experiment, in \(L\).
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry
#### 1.5.4 Navigation  
The navigation system aboard an aircraft contains accelerometers and gyroscopes. It is used with a flight computer to continuously calculate an aircraft's position, orientation, altitude, speed, and direction of movement without visual navigation.  
The paper [35] provides a detailed review of accelerometers, gyroscopes, and other sensors, all of which form the basis of inertial navigation aboard planes, drones, and UAVs, as well as different types of vehicles, such as self-driving cars.  
The book [36] is a detailed manual concerning the fundamentals of navigation and inertial sensors, with accelerometers and the signal processing of accelerometer data beginning as an essential part of the manuscript.  
The paper [37] presents a combination of an accelerometer and a laser ring gyroscope in designing an advanced navigation system for aircraft applications.
## 1 Introduction
This study takes a mechanistic modeling approach, utilizing the mechanical specific energy theory to predict ROP in deep wells within the Fukang Depression, while analyzing the reasons behind the lower drilling rates in deep formations. Subsequently, driven by data, a model suitable for predicting ROP in deep wells of the Fukang Depression is developed using the random forest algorithm, with engineering parameters like weight on bit, rotational speed, and drilling fluid equivalent density as the feature variables. The model's validity is assessed based on the correlation of influencing parameters with the mechanistic model. The analysis results indicate that, compared to traditional mechanical drilling rate prediction models, data-driven mechanical drilling rate prediction demonstrates higher accuracy and applicability. This offers valuable guidance for predicting ROP in complex deep formations.
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 3 Results
### Results for the General Review of Parameters' Sensitivity  
Next, the results from the first stage are presented, which presents the opportunity to check the importance of predefined parameters--the air gap, the coil thickness, and the number of rotor disks. Firstly, it is evident that the air gap needed revision, and it was decreased for the first simulation from 140 mm to 50 mm (simulation model I.1). Next, the coil thickness was decreased from 200 mm to 120 mm while maintaining the air gap of 50 mm (simulation model I.2), and the last simulation model I.3 has an additionally decreased number of rotor disks, from 5 to 3. The obtained results are summarized in Figure 6.  
The comments for this stage are as follows:  
* A significant improvement in efficiency is achieved directly in design variant I.1, where just the distance between magnets and coils is decreased from 140 mm to 50 mm.
* A further improvement is shown for the rest of the examined variants that led to the best variant, I.3, which also has approximately half as many components and is expected to be cheaper. Its efficiency reaches 90%.
* Further simulations are to be performed using the best chosen variant (as the performance of the twice-decreased power loss)--I.3.  
Figure 5: Power loss and efficiency variation over mechanical torque change.
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 2 Materials and Methods
### Optimized Regression Loss Function  
In target detection, the evaluation of the similarity between bounding box predictions is a crucial task. The commonly used IoU (Intersection over Union) metric measures the similarity between two sets of samples. However, it has been observed that the IoU metric is sensitive to position offsets, particularly for objects of different scales. Figure 5 illustrates this sensitivity, wherein a small position offset can result in a significant decrease in the IoU value for a tiny object (e.g., 5 \(\times\) 5 pixels), but only a relatively small change for a general object (e.g., 33 \(\times\) 33 pixels). This discrepancy in sensitivity affects the accuracy of label assignments. To address this issue, a new metric called NGWD is introduced as a regression loss [30], replacing the traditional IoU metric. NGWD offers several advantages, particularly in measuring the similarity between distributions even in cases where there is no overlapping region, or the overlapping region is negligible. This capability makes NGWD more suitable for measuring the similarity between tiny objects. The NGWD is defined in Equations (5)-(7) as follows:  
\[W_{2}^{2}(\mathcal{N}_{a},\mathcal{N}_{b})=\left\|\left(\left[c_{x_{a}},c_{y_{ a}},\frac{w_{a}}{2},\frac{h_{a}}{2}\right]^{\mathrm{T}},\ \left[c_{x_{b}},c_{y_{b}},\frac{w_{b}}{2},\frac{h_{b}}{2}\right]^{\mathrm{T}} \right)\right\|_{2}^{2} \tag{5}\]  
\[NGWD(\mathcal{N}_{a},\mathcal{N}_{b})=\exp\left(-\frac{\sqrt{W_{2}^{2}( \mathcal{N}_{a},\mathcal{N}_{b})}}{C}\right) \tag{6}\]  
\[\mathcal{L}_{NGWD}=1-NGWD(\mathcal{N}_{p},\mathcal{N}_{g}) \tag{7}\]  
where, \((c_{x_{p}},c_{y_{p}},w_{p},h_{p})\) denotes the center coordinates, width, and height of the prediction frame, \((c_{x_{g}},c_{y_{g}},w_{g},h_{g})\) denotes the center coordinates, width, and height of the real frame, \(\mathcal{N}_{p}\) denotes the Gaussian distribution model of the prediction frame, \(\mathcal{N}_{g}\) is the Gaussian distribution model of the real frame \(G\), \(W_{2}^{2}(\mathcal{N}_{p},\mathcal{N}_{g})\) is the distance metric, and \(C\) is the constant associated with the dataset, which is taken as 12.8 here.
## 3 Field Tests and Results
### Roadway Support Scheme Based on Long Flexible Bolts
#### 3.2.2 Short Cable Reinforcement Support  
The roof should be reinforced twice prior to mining the adjacent working face and constructing the blasting borehole. A row of high pre-stressed cables with a diameter of 21.8 mm and length of 6100 mm are arranged in the middle of the flexible bolts in the roof. Five cables are arranged in each row with a row spacing of 1600 mm. Each cable has a large tray (300 \(\times\) 300 \(\times\) 16 mm) and the preloading force is not less than 120 kN.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 2 LoRaWAN Main Features
### LoRaWAN Physical Layer  
LoRaWAN supports two types of modulations for physical transmission between an ED and a gateway: the LoRa modulation and the Gaussian Frequency Shift Keying (GFSK). The former is the most frequently used modulation in many scenarios. LoRa is based on a chirp spread spectrum [16]. The duration of a LoRa symbol depends on the Spreading Factor (SF) in use, as a LoRa symbol comprises 2 SF chirps [6]. Six SFs (ranging from seven to twelve) are defined, leading to six different corresponding Data Rates (DRs). The SFs are orthogonal, which contributes to spectral efficiency.  
LoRaWAN has been defined to support operation in several world regions. In our study, we consider the LoRaWAN physical layer characteristics specified for the European Union (EU), including the use of the 868 MHz band, where three default radio channels are defined: 868.10 MHz, 868.30 MHz, and 868.50 MHz. These channels are characterized by a bandwidth of 125 kHz, use the LoRa modulation, and offer several data rates, from DR0 to DR5, which correspond to 0.3 kbps to 5 kbps, respectively (see Table 1). DR6 and DR7 are optional (the latter is the only one based on the GFSK). In order to provide robustness to the communication, frequency channel hopping is used.  
ETSI regulations establish spectrum access restrictions on the duty cycle, including a duty cycle limitation to less than 1% for the band between 868.0 MHz and 868.6 MHz. LoRaWAN complies with the mentioned duty cycle restriction by introducing an idle interval of suitable duration after the transmission of a message.  
In class A, message exchanges between an ED and the NS are initiated by the former. The NS is allowed to transmit only in one of two available receive time windows, called RX1 and RX2 (see Figure 2), which are available after a message transmission by the ED. RX1 and RX2 start after RECEIVE_DELAY1 and RECEIVE_DELAY2 from the end of the uplink transmission, with default values for these parameters of 1 s and 2 s, respectively. If the NS has to transmit a downlink message, such a transmission will only be possible when a new receive window is open. By default, the DR used in RX1 is the same as the DR of the last uplink message transmission. In RX2, DR0 (i.e., SF12) is used by default, on the 869.525 MHz frequency band [17]. This band is dedicated to the downlink, and it is subject to a duty cycle limitation of less than 10% [18].  
Figure 1: LoRaWAN protocol stack for each network element.
# Biochemistry of Terpenes and Recent Advances in Plant Protection
## 2 Biosynthesis of Terpenes  
Despite being widely accepted as the basic unit of terpenes, terpenes synthesis is not initiated via the isoprene unit. Two distinct molecules with a similar structural arrangement as the isoprene unit (isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP)) are the precursors for the synthesis of terpenes. These molecules are the products of two independent pathways. The mevalonate pathway (MVA), discovered in the 1950s by [PERSON], [PERSON], and [PERSON], occurs in animals, fungi, cytosols of plants, archaea, and a few bacteria. Furthermore, the 2C-methyl-D-erythitol-4-phosphate (MEP) pathway, discovered by [PERSON], [PERSON], [PERSON], and [PERSON] in the 1990s/2000s, occurs in the plastids of plants, green algae, and most bacteria. Both IPP and DMAPP subsequently undergo rearrangements, repetition, and cyclization reactions to yield various terpene classes [37; 38; 39]. Despite variations in the start-up molecules in the two pathways, the same IPP and DMAPP are formed (Figure 3).  
IPP and DMAPP undergo a condensation reaction, catalyzed by geranyl pyrophosphate synthase (GPPS) and farnesyl pyrophosphate synthase (FPPS) to produce GPP (C10_monoterpene) and FPP (C15_Sesquiterpenes). Geranylgeranyl pyrophosphate synthase (GGPPS) and farnesyl geranyl pyrophosphate synthase (FGPPS) catalyze a similar enzymatic reaction to produce GGPP (C20_diterpenes) and FGPP (C25_sesterpenes). The terpene synthase family further catalyzes GPP, FPP, GGPP, and FGPP precursors cyclization, rearrangement reactions to form various terpenoid classes (Figure 4) [40; 41; 42; 43; 44; 45].
### State of the Art  
The utilization of artificial intelligence (AI) equipped with deep learning capabilities has become prevalent throughout the pandemic for assessing chest X-ray pictures. The present study aims to develop a research design for the identification of anomalies in the lung pictures of patients during the initial screening process. Another researcher also utilized artificial intelligence to identify COVID-19 based on cough sounds. The researchers directed their attention towards the analysis of cough sounds obtained from several datasets, with the objective of detecting and diagnosing respiratory diseases. This was achieved via the utilization of a support vector machine (SVM) classifier and linear regression techniques. The utilization of artificial neural networks (ANNs) and the random forest (RF) classifier has also been observed in the analysis of cough patterns and the subsequent determination of the severity of respiratory diseases in patients [10]. Respiratory conditions, such as asthma and cough, can be detected via the application of Wigner distribution methodologies within a low-noise setting [10].  
The other study has focused on the problem of classifying COVID-19 patients from healthy people using a limited public dataset. They used two-step transfer learning models to train on small datasets [11]. They conducted a pre-trained deep residual network on a large pneumonia dataset in their first step. Satisfying results for COVID-19 detection in healthy and pneumonia-infected X-ray images were obtained. Their proposed model used two types of layers for pre-trained steps using ResNet. Afterwards, they used feature smoothing (FSL) based on 1 \(\times\) 1 convolution to maintain the shape of tensors. They continued by smoothing pre-trained tensors to acquire the characteristics of the new image input. FSL will then be used with residual blocks for further processing with a feature extraction layer (FEL).  
FEL has double channels and three operations to be combined with a feature map into a single dimension [11]. The input of the X-ray images is 224 \(\times\) 224 pixels, using three channels in ResNet34. Then, it is fed into three residual blocks inside ResNet34. FSL is inserted before and after the second residual block. While FEL is implanted after the third residual block, the final training uses a 512 \(\times\) k layer (k refers to the number of classes) with limited X-ray images of COVID-19. The images are cropped using a ratio of 0.8-1.0. Then, it is rotated with a random rotation angle [-20, 20]. The result is an initial cross-entropy loss that is relatively high at around 0.67, with an average loss of 0.73. The significant decrease in entropy loss was sighted in the 100 th batch at 0.47. Then, after the 1635 th batch, cross-entropy loss can be pressed until 0.072; this number is very substantial since the initial loss is around 0.67. State-of-the-art COVID-19 automatic detection using chest X-rays varies depending on the data's characteristics and the convolutional network's structure. Table 2 summarizes related works on COVID-19 detection using chest X-ray images.
## 2 Related Work
### Supervised-Based Techniques  
This paper [9]introduces a density peaks nearest neighbors(DPNN) classifier based on the core ideas of k-nearest neighbors (KNN) and density peaks clustering (DPC) to improve the accuracy of intrusion detection. Although the results of the experiment indicate that DPNN can perform better than other classifiers in term of average accuracy and efficiency, it cannot detect U2R attacks. Furthermore, in [10], k-nearest neighbor (KNN) [1] has been proven to be efficient in dealing with the feature selection difficulty issues when utilized as its evaluation function. Their proposed algorithm was superior to others in terms of classification accuracy; however, they could also make improvements in the initialization phase. Among the machine learning techniques, researchers have shown that the support vector machine (SVM) can be considered as an efficient algorithm. As an example, Ref. [11] built a robust intrusion detection model with high performance in terms of accuracy, training speed, false alarm rate, and detection rate; however, the authors in this study only worked with the binary case of IDS problems which does not give a realistic model. In addition, a novel intrusion detection technique, named the optimum allocation-based least square support vector machine (OA-LSSVM), was introduced in [12]. This is based on sampling with the least square support vector machine (LS-SVM). Moreover, the experiments in [12] were conducted using KDD 99, which is a standard database considered as the \"de facto\" benchmark for evaluating performance. The algorithm was suitable for both static and incremental data and obtains a realistic performance. Another research work in [13] presents DoS attacks intelligent detection/defense scheme which improved the detection accuracy to 99.998%, but the main limitation of this algorithm, in addition to the high computational complexity, was that the performance evaluation of the proposed techniques was reported for a typical computer network simulation environment with a sorted dataset, while the effect of the system needs to be tested in the real environment.  
Although supervised techniques are usually considered simple, fast, highly accurate, and reliable algorithms, the main drawback is in classifying and labeling Big Data which is expensive or time-consuming due to requiring expert knowledge. On the other hand, noisy or incorrect data labels will certainly reduce the effectiveness of your model [5].
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.6 Differential Privacy  
Differential privacy is a mathematical framework that allows organizations to share aggregate information about a dataset while ensuring that any individual's data cannot be inferred from the shared information. This is particularly important in the context of smart cities, where large amounts of data are collected from a wide range of sources, including sensors, cameras and mobile devices.  
[PERSON] (2017) proposes a method for privacy-preserving medical data collection that considers many missing values for more accurate data analysis. The patient data is anonymized and processed by a data collection server using a generative model and a contingency table based on expectation maximization and Gaussian copula methods. The method is evaluated using differential privacy and results show improved accuracy compared to existing methods that do not consider missing values.  
Smart cities rely on this data to provide efficient and effective services to citizens, such as transportation, energy and public health. However, this data often contains sensitive information about individuals, such as their location, movement patterns and personal health information. If these data are not protected, they could be used to infer sensitive information about individuals, such as their whereabouts or medical conditions.  
In the context of smart cities, Aircloak Insights--that was described in Section 3.4--could be used to protect the privacy of individuals when sharing data about various urban services, such as transportation, energy usage or public health. For example, an organization could use Aircloak Insights to share aggregate information about transportation usage patterns in the city, such as the most popular routes and times of day, without revealing any information about individual travelers.  
Aircloak Insights could also be used to share data about other smart city services, such as parking, waste management and air quality, while still protecting the privacy of individuals. It allows multiple parties to access data without revealing the identity of the individual. Overall, Aircloak Insights can be seen as a powerful tool for smart city applications, as it allows organizations to share valuable data while also complying with data privacy regulations and protecting the privacy of citizens.