esa-sceva/satcom-chunk-collection huggingface dataset:
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation  
In this section, we evaluate the performance of FLMPC-One and FLMPC-Two. In FLMPC-One, every sensor node looks up to two hop neighbors and finds \(PFN\). While, FLMPC-Two uses three hops neighbor information to forward data packet. The chance for a data packet to enter void hole region reduces. Void hole avoidance results in fewer number of drop packets. In the simulations, we implemented two different scenarios of LMPC, FLMPC-One and FLMPC-Two. In the first scenario, 150 sensor nodes are deployed in the network of 2000 m \(\times\) 2000 m. We consider two noise sources here i.e., wind and shipping noise. Wind speed is considered 5 m/s and shipping noise is 0.2 dB. Similarly, in scenario two, 500 sensor nodes are distributed over the area of 4000 m \(\times\) 4000 m. The wind speed and shipping noise are stronger in scenario two i.e., 20 m/s and 0.8 dB, respectively. The transmission power and the reception power of sensor nodes are 0.66 mW and 0.395 mW, respectively. Five gateways are deployed at the surface of water. All surface gateways relay data packets to a sink which combines them to generate original data packet. Simulations are run for 1000 s. The average of five simulation results is taken in order to compensate the difference of random deployment. The network dimensions and important performance parameters for simulations are listed in Table 4.  
Figure 8: Noises Feasible Region.
## 3 Methodology
### Data Imputation
#### 3.2.1 An Application of K-Nearest Neighbour Technique  
Regarding the machine learning theory, the KNN technique may be thought of as a kind of supervised machine learning strategy that is frequently used to substitute values in data sets that have been missing or not been gathered. The KNN concept is sensible: it simply substitutes the most exact or closest values for missing data. The k-value depends on the amount of complete data present in the data collection. For instance, when k is selected as 4 (k = 4) this means that four nearest data points will be used to calculate, via selected functions, a new value to substitute the uncollected value. In this study, the k value is set at 5 (k = 5).  
In contrast, a poor selection of the k-value may result in an inaccurate evaluation of the new value. The choice of k-value must be appropriate, since using one that is too small may result in missing the trend, while using one that is too big can create issues with other variables [90; 91; 92; 93]. Some researchers mentioned that the best match for the k-value is found by comparing multiple k-values to the data to minimise a specific inaccuracy [94]. This research applies the standard Euclidian distance form, as it can be seen in Equation (1).  
\[d(a,b)=\sqrt{(a_{1}-b_{1})^{2}+(a_{2}-b_{2})^{2}+\cdots+(a_{n}-b_{n})^{2}} \tag{1}\]  
where: \(d\) = Euclidean distance among the point \(a\) and \(b\), \(a\) = \(\{a_{1},a_{2},\ldots,a_{n}\}\) and \(b\) = \(\{b_{1},b_{2},\ldots,b_{n}\}\).
## 5 Conclusions  
In this paper, we used 60 data points from the Datong mining area for the model training and verification, and established a GA-BP model that is suitable for the Datong mining area to calculate the initial pressure strength and a step distance of the roof. The following conclusions can be obtained:  
1. A lot of influencing factors can be determined in the course of prediction of the first weighting of the working face roof utilizing artificial neural network methods, which can map the complicated nonlinear relation between them.
2. The gray correlation degree is used to calculate the relational degrees between the first weighting of the working face roof and various influencing factors in the Datong mining area. The input parameters of the BP prediction model include the width of the working face, mining height, advance speed, roof condition of the coal seam, burial depth, thickness and dip of the coal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{R^{2}}\) & **Iteration Times** & **Computation Time (s)** \\ \hline BP & 0.8561 & 53 & 27.32 \\ GA-BP & 0.9605 & 25 & 63.87 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the BP and GA-BP models.  
Figure 7: Comparison of the first roof weighting interval results: (**a**) the value of the first roof weighting interval predicted by the BP and GA-BP prediction models; (**b**) a comparison of the determination coefficients predicted by the BP and GA-BP models.  
seam, change rate of the inclination angle, burial depth, coal thickness, direct top thickness, and thickness of the main roof.
* Compared with traditional BP, the BP-GA model has stronger robustness, is available for parallel global searching, and can improve the accuracy of prediction results and the stability of the prediction model.
* We can extend this method of calculating the initial compression strength and step distance to other mining areas. As long as we can collect enough input parameter data, we can train a prediction model that is suitable for each mining area.  
Conceptualization, T.T. and Z.Y.; methodology, T.T. and K.Z.; investigation, F.C.; data curation, F.C. and K.Z.; writing--original draft preparation, T.T.; writing--review and editing, Z.Y.  
This work was supported by the Fundamental Research Funds for the Central Universities [Grant No. 2017 CXNL01].  
The authors declare no conflict of interest.
## 1 Introduction
Moreover, their use, either combined with conventional physical therapy (CPT) [20; 21] or as a standalone intervention [22; 23], has demonstrated positive effects on self-reported functionality in patients with OA. However, their implementation is currently recommended primarily as a complementary therapy [24; 25]. Because AVGs are a form of exercise, their beneficial effects in patients with OA may be attributed to cellular mechanisms of protection and tissue maintenance [26]. Additionally, the cognitive demands inherent to AVGs could stimulate neuroplasticity through an increase in neurotrophin release, enhancing cognitive function and promoting structural adaptations in key brain areas [27]. Finally, through gamification elements, AVGs not only foster positive emotions and a sense of self-efficacy; they also increase engagement and motivation towards exercise, which is especially relevant in the rehabilitation of patients with chronic pain, such as OA [18]. Although AVGs have shown benefits across various health conditions, their application in OA management remains limited. Recent systematic reviews emphasize the need for studies evaluating their specific outcomes in knee and hip OA, underscoring the current lack of conclusive evidence [28; 29]. Furthermore, to the authors' knowledge, no studies have examined the impact of AVGs on clinically meaningful measures, such as the minimum clinically important difference (MCID) for pain and function. Additionally, no research has stratified WOMAC scores to assess patient progress following AVG interventions. Therefore, a clinical trial is warranted as an optimal strategy to provide evidence on AVG efficacy within this population. The present study aims to evaluate the effects of an AVG protocol as a complementary treatment to CPT on functional disability, measured by the Western Ontario and McMaster Osteoarthritis Index (WOMAC), in older adults with knee and/or hip OA. Additionally, the study introduces an innovative approach to assessing clinically meaningful changes by establishing MCID thresholds across each WOMAC dimension and the Global Rating of Change (GRoC) scale. Furthermore, it seeks to identify responders to the intervention and classify participants into quartiles based on their baseline functional disability levels to observe their clinical behavior post-intervention. This comprehensive approach enables a holistic analysis of clinical outcomes and subjective improvements, providing deeper insights into the perceived and measured impact of AVGs as a complement to CPT in OA management.
## 2 Materials and Methods
### Sediment Sampling and Analysis  
For sediment sampling, the first 20 cm of sediment depth were collected with a hand auger. The sediment sampling followed two strategies. First, five sediment samples were taken randomly per plot. This was done in May 2019 to obtain the initial state of sediment in the studied areas, i.e., before sowing the plots but after plowing. The second sampling strategy was carried out in September and October 2019, just before the harvest. Ten sediment samples associated with plant samples were taken per plot, leading to a total of 30 samples for each sorghum cultivar (10 samples * 3 areas). Sediment samples were dried at 40 \({}^{\circ}\)C in a forced-air oven until constant weight, ground (Retsch BB51), and sieved to  
Figure 1: Sorghum sowing design and pictures of the two cultivars on the three areas 99 days after the sowing.  
<2 mm. Sediment pH was measured following the International Standard Organisation (ISO) 10390 (2005). Five grams of sediment were mixed with 25 mL of distilled water and shaken for 2 h. After 1 h rest, pH was measured (pH meter Hanna edge). To estimate the plant available fraction of TE, a selective extraction was performed. Ten grams of sediment with 20 mL of ammonium nitrate (NH4 NO3, 1 M) were shaken for 2 h [42]. The mixture of sediment and NH4 NO3 was filtered (0.45 \(\upmu\)m, millipore) and acidified with nitric acid (69%) to obtain a pH of 2.  
For each zone, 50 g per sediment sample was taken and mixed to create a composite per studied zone on which agronomic sediment parameters were measured (Supplemental Table S1). These analyses were subcontracted (Laboratoire Departemental d'Analyses de la Mayenne, La Mayenne, France). Additionally, the composites' TE content was analyzed by digesting a dried (oven, 40 \({}^{\circ}\)C until constant weight) and homogenized sample (250 \(\upmu\)m, ball mill, Lavallab Pulverisette 6). The digestion consisted of 0.2 g of sediment mixed with 6 mL of hydrofluoric acid (HF) (48%) heated in a microwave digester (Mars Xpress CEM). HF was then neutralized with boric acid (5.5%). To obtain a 50 mL solution, the mixture was completed with Milli-Q water and filtered at 0.45 \(\upmu\)m (hydrophilic Teflon). TE concentrations in digestates were analyzed either by ICP-OES (Agilent 5100) or ICP-MS (Agilent 7500), depending on sediment sample concentrations. One standard reference material was used for analytical quality control (NIST SRM 2710, Montana soil). For all the digestion sets, recoveries were between 1.12 and 1.15 for As, 0.80 and 0.93 for Cu, 1.05 for Ni, 0.95 and 0.98 for Pb, and 0.96 and 1.04 for Zn in the Montana soil. For Cd, Cr, and Sb, no recoveries could be calculated; the results obtained from ICP-OES analysis were below the limit of quantification (LoQ).  
The extractable and total TE concentrations in the soils allowed us to calculate the % TE mobility, with % mobility = [TE] extractable sediment/[TE] total sediment * 100.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 1 Introduction
Our research aims to contribute to this gap by designing a power network for a T-UAV to support uninterrupted aerial communication services. Using continuous power delivery, we ensure extended operational times and robust data transmission, which makes it applicable to critical emergency response and IoT scenarios.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 1 Introduction
### Scope of the Study  
This survey focuses on the various methodologies applied in intrusion detection and prevention systems, provides a survey on fog and cloud computing technologies, and recommends how organizations can adopt them. The detailed evaluations of techniques and the advantages and disadvantages of each technology are studied. Moreover, there is an examination of the many network topologies used alongside potential cyber threats. The study provides an overview of the possibilities of these systems and the essential qualities needed for the efficient and effective technological solutions and functions of fog and cloud computing and intrusion detection and prevention for both.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 2 Background Knowledge  
### Stochastic Modeling of DER Generation  
Renewable energy-based DERs exhibit characteristics, such as variability and uncertainty; therefore, a stochastic approach is required to describe the generation output of these resources. For example, the PV output characteristics can be described by the beta distribution function in Equation (1) [8].  
\[f_{x}(x,\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^ {\alpha-1}(1-x)^{\beta-1} \tag{1}\]  
where \(\alpha\) and \(\beta>0\) are parameters of the beta distribution. In this study, we have adopted this probability density function (PDF) as the generation output of the DERs with the parameters \(\alpha=2\) and \(\beta=1.5\).
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Intelligent 5G Services  
#### 4.2.1 User-Centric Computing  
The user receives content after recognizing, interpreting and inferring on big data-based situational information collected through various sensors. Figure 6 shows examples of such services. Intelligent health services, such as personal health care, psychotherapy, de-stressing, business coaching, etc., are based on the big data analysis of life-logs. Future networks are expected to be more congested due to the increasing number of devices and data traffic. Consequently, this increases network delay and may hamper connectivity to cloud computing servers. The use of mobile edge computing is handy to reduce network delay and maximize efficiency when utilizing network resources [18]. Moreover, it is also useful for mobile services like smart cars, smart health care, industrial automobiles, augmented  
Figure 5: Massive content streaming services.  
reality and gaming. Mobile edge computing and accurate big data analysis of data coming from sensors are essential to provide prompt and timely response in the case of any disaster. They are also helpful to counteract climate change and industrial accidents. The increasing demand of data traffic requires future 5G technology to increase the data rate and decrease latency. Energy efficient actuators and sensors are necessary to support green communication. Moreover, low-power telecommunication technology is another desirable 5G feature. In short, 5G must provide efficient big data processing and rapid big data transmission with minimum latency. Additionally, accurate localization of mobile terminals is required to provide these services in a timely manner.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 3 Stability Analysis of GPS-BDS DISBs
### Stability Analysis of DISBs
The amplitudes relative to the mean value are all within 0.05 cycles, and the standard deviations (stdev) of the phase DISB estimators for both L1-B1 and L2-B2 are all within 0.008 cycles. The stdev of code DISBs estimators is much larger than those of the phase due to the pseudorange  
Figures 2 and 4 depict the dual-frequency code and phase DISB series, respectively, for the three baselines at Curtin University with different receiver types. Figure 5 depicts the results for the baseline at Southeast University with receiver types of ComNav and Unicore. From these figures, we can see the code DISBs still appear generally stable with time. For the phase DISB series, besides the random terms, there are also small low-frequency variations (except Septentrio-Javad), even for the zero-baseline 'CUT0-CUT1' where no multipath effect exists. The standard deviations of the phase DISB estimators are obvious larger than those with the same receiver types. These low-frequency variations may be caused by the influence of different frequencies, since the inter-system differencing suffers not only the inter-system bias but the inter-frequency bias [33; 34]. Fortunately, these variations are very slow and smooth. The amplitudes of the wave are all within about 0.1 cycle for tens of hours to several days. In the real-time estimation of GPS-BDS phase DISB, we can use the random walk process  
Figure 4: L1-B1 and L2-B2 code DISB time series for the three baselines at Curtin University with different receiver types. Figure 3: L1-B1 and L2-B2 code DISB time series for the three baselines at Curtin University with the same receiver types. Figure 2: L1-B1 and L2-B2 phase DISB time series for the three baselines at Curtin University with different receiver types. with a secure spectrum density (e.g., \(0.05\times 0.05\) cycle\({}^{2}\)/h) to model this slow variation. This will also be utilized in the positioning experiments of next section.
## 4 Conclusions  
A low-energy oxygen plasma etching of commercially polished Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystals was performed using different etching depths of 50, 100, 250 and 400 nm. Shorter plasma treatments deepened surface scratches, which increased the initial \(R_{q}\) = 0.73 nm to \(R_{q}\) = 1.4 nm for the 100 nm etching case. However, deeper plasma etchings resulted in much smoother surfaces with \(R_{q}\) = 0.84 nm, which was almost equal to the initial value. TEM images of nonetched crystal showed a continuous damaged subsurface layer, having extended defect agglomerations down to a depth of 250-300 nm. These damages were completely removed in the 250 nm and 400 nm etching cases. All performed plasma treatments did not made any changes to the initial crystalline structure below the surface. The R(1)-on-1 laser-induced damage threshold for 1540 nm 3.5 ns laser pulses was 55 \(\pm\) 6 J/cm\({}^{2}\) for the plasma-unprocessed crystal surface. A substantial LIDT improvement up to 303 \(\pm\) 40 J/cm\({}^{2}\) was obtained after 100 nm removal by plasma. The laser radiation resistance did not change within the error bars for deeper etchings of 250 nm and 400 nm. The damage probability curves demonstrated two types of defects with corresponding low and high LIDTs. The extracted defect densities by fitting damage probability data allowed the calculation of an initial total defect density of 10\({}^{18}\)/m\({}^{2}\), which dropped to 5.012 \(\times\) 10\({}^{8}\)/m\({}^{2}\) after 100 nm of removal by plasma. A plasma etching of 400 nm was optimal for a huge increase of the LIDT, the removal of subsurface damage and for almost preserving the initial surface quality and roughness. The presented results demonstrate a good potential of oxygen plasma etching for obtaining highly laser-damage-resistant Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystals for high-power laser applications.  
Figure 8: Index of cumulative defect density for nonetched and plasma-etched to different depths Co:MALO crystals.  
Author Contributions:G.A.: conceptualization, supervision, project administration, resources, methodology, validations, formal analysis, investigation, writing--original draft, review and editing, funding acquisition. M.S.: formal analysis, methodology, investigation, visualization, writing--review and editing. R.J.: methodology, validation, formal analysis, investigation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research has received funding from European Social Fund (project no. 09.3.3-LMT-K-712-19-0180) under a grant agreement with the Research Council of Lithuania (LMTLT).  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
We are grateful to [PERSON] for her assistance with the AFM measurements and sample preparation, [PERSON] for the TEM specimen preparation using FIB milling and [PERSON] for supporting the LIDT data interpretation.  
The authors declare no conflict of interest.
_Applied_  
_sciences_  
_Article_  
**Adjustment of an Integrated Geodetic Network Composed of GNSS Vectors and Classical Terrestrial Linear Pseudo-Observations**  
**[PERSON]**  
Department of Land Surveying, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in Krakow, Balicka 253a Street, 30-149 Krakow, Poland; [EMAIL_ADDRESS]  
**Abstract: The paper proposes a new method for adjusting classical terrestrial observations (total station) together with satellite (GNSS-Global Navigation Satellite Systems) vectors. All the observations are adjusted in a single common three-dimensional system of reference. The proposed method does not require the observations to be projected onto an ellipsoid or converted between reference systems. The adjustment process follows the transformation of a classical geodetic network (distances and horizontal and vertical angles) into a spatial linear (distance) network. This step facilitates easy integration with GNSS vectors when results are numerically processed. The paper offers detailed formulas for calculating pseudo-observations (spatial distances) from input terrestrial observations (horizontal and vertical angles), horizontal distances, height of instrument and height of target). The next stage was to set observation equations and transform them into a linear form (functional adjustment model of geodetic observations). A method was provided as well for determining the mean errors of the pseudo-observations, necessary to assess the accuracy of the values following the adjustment (point coordinates). The proposed algorithm was verified in practice whereby an integrated network made up of a GNSS vector network and a classical linear-angular network was adjusted.**  
**Keywords: GNSS vector network; classical terrestrial measurements; linear pseudo-observations; adjustment of observations; method of least squares**
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 3 Methodology
### Midpoint Offset Loss Function
An anchor box is classified as a positive sample if either of the following conditions is met: the intersection over union (IoU) between the anchor box and any ground-truth box exceeds 0.7; or the anchor box achieves the highest IoU with a ground-truth box among all anchor boxes and this IoU value is greater than 0.3. Anchor boxes with IoU values lower than 0.3 relative to all ground-truth boxes are labeled as negative samples. Samples that do not fall into either category are treated as invalid and excluded from the training process. The subsequent definition of the \(L_{1}\) loss function is as follows:  
Figure 3: Illustration of midpoint displacement representation. \[L_{1}=\frac{1}{N}\sum_{i=1}^{N}F_{cls}(p_{i},p_{i}^{*})+\frac{1}{N}p_{i}^{*}\sum_{ i=1}^{N}F_{reg}(\delta_{i},t_{i}^{*}) \tag{13}\]  
where \(i\) denotes the index of the anchor box, \(N\) (defaulting to \(N=256\)) represents the total number of samples within a mini-batch. \(p_{i}^{*}\) signifies the ground-truth label for the \(i\)-th anchor box, \(p_{i}\) is the output from the classification branch, quantifying the probability of the candidate box belonging to the foreground, and \(t_{i}^{*}\) represents the supervised offset of the ground-truth box relative to the \(i\)-th anchor box, which is a parameterized six-dimensional vector \(t_{i}^{*}=[t_{x}^{*},t_{y}^{*},t_{w}^{*},t_{h}^{*},t_{x}^{*},t_{g}^{*}]\) characterizing the displacement of the predicted candidate box from the \(i\)-th anchor box:  
\[ \begin{array}{ll}\delta_{a}=\Delta\alpha/w&\delta_{g}=\Delta\beta/h\\ \delta_{w}=\log(w/w_{a})&\delta_{h}=\log(h/h_{a})\\ \delta_{x}=(x-x_{a})/w_{a}&\delta_{y}=(y-y_{a})/h_{a}\\ t_{a}^{*}=\Delta\alpha_{g}/w_{g}&t_{g}^{*}=\Delta\beta_{g}/h_{g}\\ t_{w}^{*}=\log(w_{g}/w_{a})&t_{h}^{*}=\log(h_{g}/h_{a})\\ t_{x}^{*}=(x_{g}-x_{a})/w_{a}&t_{y}^{*}=(x_{g}-x_{a})/h_{a}\end{array}  \tag{14}\]  
where \((x_{g},y_{g})\), \(w_{g}\), and \(h_{g}\) respectively define the bounding rectangle's central coordinates, width, and height, while \(\Delta\alpha_{g}\) and \(\Delta\beta_{g}\) respectively quantify the top/right vertex offsets relative to the top-edge midpoint and left-edge midpoint.