esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Collection
### Data Clustering Analysis
#### 4.1.1 Clustering Scenario, I (Tourist Nationalities)
From an economic point of view, reducing the operational costs of tourist agencies arranging the trips, as these tourists have close habits due to the similarity between the peoples of the region in social, food habits, and probably the language which facilitates their communication. Tourist companies can coordinate together in determining the number of trips and their destinations in proportion to the number of tourists coming from a particular area, and this in turn contributes to reducing the operational cost of offices, which is positively reflected in the decrease in the cost of the trip for tourists, which encourages tourism investment and increases the number of tourists. Following are some benefits of using Scenario I in the tourism sector:  
Figure 5: Clusters region distribution for January 2019 when N = 10. * Transportation costs will be reduced for each tourist because all nationalities that have similar routes will go on the same trip, so the number of transportations used will be decreased and each one of the transportations will reach its full capacity, thus the individual cost will be reduced, and the offices will benefit from this idea because it saves money. * The tourists for sure will feel psychological comfort because all people talk the same language, and have the same interests. * The number of tourists on the trip will be small at each site, so the social distancing will be excellent and safety protocols will be used. * This mechanism reduces the number of tourists on one route, so it will reduce the possibility of Coronavirus infection. * Instead of each agency take its tourists from different nationalities with different routes to all places, this mechanism will take all nationalities on the same route on the same tourism trip, which will reduce the lost time on-road and save the transportation cost and then the economic cost to each office. * It allows the creation of advanced tourist routes and protects the quality level of the services provided. * Reducing the travel trips using this approach will contribute achieving the goals of the UN sustainability 17 goals, such as goal number 8, 9 and 12.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs
### Scenario Description
#### 2.1.2 Sets and Parameters  
\begin{tabular}{l l} \(Q_{m}\) & Set of visible satellites for satellite \(m\). \\  & The node degree of satellite \(m\); \(2\leq\alpha_{m}\leq\mid\!\!Q_{m}\!\mid\) and \(\alpha_{m}\in N^{+}\). This implies that each satellite in the DSCNs should be connected by at least two and at most \(\mid\!\!Q_{m}\!\mid\) ISLs. \\  & Capacity of a wavelength and \(\Omega=\delta\,\Omega_{s}\), where \(\Omega_{s}\) is the capacity of a sub-wavelength \\ \(\Omega\) & and \(\delta\) is the number of sub-wavelength-level aggregated flows that a wavelength \\  & can carry. \\ \(\Delta_{m}^{agg}\) & Number of ports used for aggregating the low-speed traffic requests in satellite \(m\). \\ \(\Delta_{m}^{at}\) & Number of ports used for optical/electric conversion in satellite \(m\). \\ \(\
abla_{m}^{ro}\) & Total number of optical ports in satellite \(m\). \\ \(\
abla_{m}^{ro}\) & Total number of electric ports in satellite \(m\). \\ \(\
abla_{m}^{r}\) & Total number of ISLs from \(m\) to \(n\); If \(m\in Q_{n}\) or \(n\in Q_{m}\), \(I_{mn}=1\), otherwise, \(I_{mn}=0\). \\ \(I_{inj}\) & Number of virtual lightpaths from \(i\) to \(j\); \(L_{ij}=L_{ij}^{w}\), where \(L_{ij}^{w}\) is the number of lightpaths from \(i\) to \(j\) on wavelength \(w\). \\ \(\Gamma_{ij}^{sd}\) & Number of connection requests between node pair (\(s\), \(d\)) which passes through virtual lightpath (\(i\), \(j\)). \\ \(\Theta_{mn}^{jl}\) & Number of virtual lightpaths from \(i\) to \(j\) that traverse ISL (\(m\), \(n\)). \\  & Real capacity of lightpath (\(i\), \(j\)) which is occupied by the \(f\)-th connection request and \\ \(\Psi_{ij}^{f}\) & \(\Psi_{ij}^{f}\leq\Omega_{s}\), \(\forall 1\leq f\leq F_{s,d},\ i\,j\in V\). \\ \end{tabular}
###### Abstract  
Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability to send, receive, and process signals at IRS. Since most of the existing channel estimation methods are developed to obtain cascaded base station (BS)-IRS-user devices (UDs) channel, this paper studies the problem of computation and communication resource allocation of the IRS-assisted federated learning (FL) system based on the imperfect CSI. Specifically, we take the statistical CSI error model into consideration and formulate the training time minimization problem subject to the rate outage probability constraints. In order to solve this issue, the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) are invoked to transform it into a convex problem. Subsequently, a low-complexity algorithm is proposed to minimize the delay of the FL system. Numerical results show that the proposed algorithm effectively reduces the training time of the FL system base on imperfect CSI.  
federated learning; intelligent reflector surfaces; imperfect channel state information; outage probability +
Footnote †: journal:  
0000-0001-9363-3300][PERSON]  
1 Wuhan Maritime Communication Research Institute (WMCRI), Wuhan 430079, China; w0 [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (Z.H.)1
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 4 Results and Discussion
### End-User Experience  
The UAV flight is managed by a service provider company in charge of a turnkey solution. The services include the preparation of the flight plan and the permits for flights, the actual flight, and the data delivery. For the data delivery, the company has a cloud service and a client application that connects to the data channel of the client. The client application has two versions: the desktop and the mobile device, with slightly different interfaces to best output to the screen of the device. However, both versions share the same kernel consisting of a light web browser. This web browser opens the fixed Internet address where the cloud server channel is connected. A screenshot of the two versions can be seen in Figure 12. The calculated geolocation of the trees cannot be further validated due to the lack of the ground-truth coordinates of any tree.  
Before the flight, the application shows a map of the area with an overlay of the UAV flight plan and the estimated take-off time. Once the UAV takes off, the screen shows the partial count number of trees inventoried to the moment. Every two seconds, a new image is captured and processed, and the new detected trees are added to the counter. In the desktop application, the partial count of the new image is also shown, together with the geolocation of these last trees. As demonstrated in [42], the delay of the mqtt protocol is lower than using the httprotocol, consumes less power, and is able to send a message to one thousand clients with only one operation.  
On the map, the application sets a place mark on each location where a tree is detected. Clicking on the place mark provides information about the geolocation of the tree and about the pixel locations of  
Figure 12: User application interface on different devices.  
all the images where this tree can be seen. Clicking on the row of the coordinates of a tree will have the same effects.
# A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots
## 3 Neural Network with Error Minimization  
### Neural Network  
The neural network is an algorithm mimicking the operations of a human's brain to explore the underlying pattern among sets of data. It has been proven that, with a suitable amount of neurons, a two hidden layers network has the ability to approximate any non-linear function with an arbitrary error, regardless of the order of the problem theoretically [31]. For our IKP problem, MLP is selected by virtue of computational robustness and low computational cost. A brief introduction to the mathematical model of MLP is given as below.  
A MLP is composed of an input layer, an output layer, and several hidden layers. Figure 2 demonstrates two examples of MLP with five hidden layers. Each layer contains certain amounts of basic computational units called neurons. For each neuron, there exists an activation function \(g(\cdot)\), the relationship between the input and output of the neuron being represented as:  
\[a=g(z) \tag{5}\]  
where \(a\) and \(z\) are the output and input of the neuron, respectively.  
Then, the relationship between the outputs of two adjacent layers can be modeled as:  
\[\mathbf{a}^{i+1}=g(\mathbf{\Theta}^{i}\mathbf{a}^{i}) \tag{6}\]  
where \(\mathbf{a}^{i}\) is the output of layer \(i\), \(\mathbf{\Theta}^{i}\) being the weight matrix between layers \(i\) and \(i+1\). By using Equation (6), the prediction or hypothesis of MLP can be calculated with a chain process.  
To seek the optimum set of weights fitting the proposed inverse kinematics function, the cost function is defined as:  
\[J(\mathbf{\Theta})=\frac{1}{m}\sum_{i=1}^{m}\sum_{k=1}^{n}[Y_{k}^{i}(p_{i})-y_ {k}^{i}(p_{i})]^{2} \tag{7}\]  
where \(m\) is the amount of the training data, \(n\) is the number of the outputs, \(\gamma_{k}^{i}(p_{i})\) is the \(k\)th output in the \(i\)th set of training data, \(y_{k}^{i}(p_{i})\) is the \(k\)th prediction of the network for the \(i\)th set of input. The training method applied in our study is Levenberg-Marquardt (L-M) and Adam for the prediction and classification system, respectively, with more details found in [32; 33; 34].
## 4 Optimization Methods
### Hybrid Optimization Method (HOM)  
The hybrid optimization method is developed to overcome the limitations of GBA and GA in the parameter estimation process (Figure 6). By combining the strengths of both algorithms, the HOM aims to improve the model fitting and reduce the randomness and exhaustive search for initial values involved in the optimization process. The first major step of HOM eliminates the exhaustive search encountered in GBA by using a typical random initial population-based GA. The computation parameters include an initial population size of 180, 18 elites, a crossover probability of 0.85, and the predefined parameters in the previous GA process. A mean _FVAL_ of 0.0381 was returned after 1100 s in the GA process. In the second major step, GBA is used to explore the local optima of the near-global optimal solutions obtained by GA in the first step. We set the maximum iteration to 50, the maximum function evaluation to 2700, the step tolerance to 1 \(\times\) 10\({}^{-15}\), and the function tolerance to 1 \(\times\) 10\({}^{-12}\), with the optimized parameters from the first GA step as initial values.  
For GBA. Interestingly, this step yielded a reduced _FVAL_ of 0.0293 and a better fit of the transmissibility plots in the GBA process. Furthermore, we noticed that the values of some parameters obtained at different body segments varied only slightly while some remained unchanged.  
In the third major step, we executed a modified GA (denoted as mGA) to address the randomness in optimized values exhibited by the typical GA, such as the one used in the first step. We defined an initial population matrix in the mGA and assigned values of the optimized parameters obtained by GBA as the elements of the population matrix. During this step, if _FVAL_ by mGA is less than that by GBA in the previous step, a parameter sensitivity score is applied to update the bounds, and the mGA process is reinitiated. The final optimized parameters obtained in the mGA step are adapted as the HOM parameters. The HOM's advantages over GBA and GA include improved model fitting, reduced randomness, and lower values of the objective function, making it a promising optimization method for parameter estimation in various applications.
## 4 Discussion
Consistent with the results of a previous study, free phosphate ions in SBF decreased with the addition of BSA in a concentration-dependent manner in the present study (Figure 2). This effect of albumin may have also caused the small amount of apatite formation on F-NBG aged in 10-g/L BSA-SBF. During root canal therapy or vital pulp therapy, NBGs come into contact with the interstitial tissue fluid in the dental pulp or periodontal ligament. The interstitial tissue fluid contains albumin at a concentration of around 10-g/L [27; 28]. Considering the apparent apatite formation on P-NBG and the limited apatite formation on F-NBG in 10-g/L BSA-SBF, possibly only P-NBG produces sufficient apatite in clinical settings to prevent bacterial leakage [7] and support tissue healing [9]. A recent study also suggested that the AFA of F-NBG may be clinically insufficient, showing no apatite precipitation on F-NBG in rat subcutaneous tissue [29]. P-NBG was specifically designed for vital pulp therapy. At present, calcium silicate cements are the gold standard for vital pulp therapy because they are highly biocompatible [30; 31] and associated with favorable clinical outcomes [32]. One study reported that F-NBG induced a favorable quality of reparative dentin formation in rat molars compared with a representative calcium silicate cement, namely ProRoot MTA [10]. Although the clinical performance of P-NBG has not yet been evaluated, this study has shown that the AFA level--a key feature of vital pulp therapy agents--of P-NBG was superior to that of F-NBG; therefore, P-NBG may be a good option for vital pulp therapy. A limitation of this study was that the AFA levels of NBGs were compared only in vitro. Unlike the media used in this study, real body fluids contain various proteins in addition to albumin. The minor serum proteins have been reported to have positive [33; 34] or negative effects [35] on apatite formation. Moreover, [PERSON] et al. reported that lysozyme, a serum protein, decreased the inhibitory effect of albumin on apatite formation [36]. In addition, inflammatory reactions negatively affect apatite formation by reducing the local pH [37]; therefore, the in vivo AFA levels of NBGs may slightly differ from the in vitro AFA levels. As such, the in vivo AFA levels of NBGs should be compared in a future study. The present study has demonstrated that BG-based powder not only changes the consistency of NBG but also improves the AFA level. Moreover, a previous study reported that BG-based powder did not affect the solubility or cytocompatibility of NBG [11]. Based on these findings, it can be speculated that BG-based powder may be helpful in changing the consistency of other BG-based or non-BG-based endodontic materials, improving the AFA level. This possibility should be examined in future studies.
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 2 Background
### Basic-RRT  
The Basic-RRT algorithm is described in Algorithm 1 and the Basic-RRT is the basis of RJ-RRT, an underlying tree data structure is maintained in Basic-RRT. As shown in Figure 1. Firstly, Basic-RRT creates a tree rooted at the \(x_{init}\) (Line 1 in Algorithm 1). In each subsequent iteration, \(x_{rand}\) is obtained by randomly sampling in the configuration space \(X\), traversing the existing nodes in the tree, selecting the node \(x_{near}\) closest to \(x_{rand}\), and generating \(x_{new}\) through the steering function with a certain expand size (Lines 3-5 in Algorithm 1). If the edge \(\{x_{near},x_{new}\}\) is not located in \(X_{obs}\), then \(x_{new}\) is added to the tree with \(x_{near}\) as the parent node, and the edge \(\{x_{near},x_{new}\}\) will be recorded (Lines 6-8 in Algorithm 1). When \(x_{new}\) is within the range of \(X_{goal}\), the planning is ended, and the tree is returned. The return fails when the time-out or the number of iterations is exceeded (Lines 9-11 in Algorithm 1).  
```
1:\(Tree\gets x_{init}\)
2:for\(i=1\) to \(n\)do
3:\(x_{rand}\leftarrow Sample()\)
4:\(x_{near}\gets Nearest()\)
5:\(x_{new}\gets Seter(x_{rand},x_{near})\)
6:if\(FreeCollision(x_{rand},x_{near})\)then
7:\(Tree.addEdge(x_{near},x_{new})\)
8:\(Tree.addNode(x_{new})\)
9:if\(x_{new}\in X_{goal}\)then
10: return Tree
11:endif
12:endif
13:endfor
```  
**Algorithm 1** Basic-RRT  
RRT-Biased [29] is an efficient method to improve RRT. By the premise of a certain probability, the \(x_{goal}\) is set as the sampling point so that the tree can expands faster to the goal area. In Algorithm 2, given a brief description of RRT-Biased.  
Figure 1: Expand of Basic-RRT.  
```
1:\(\mathit{prand}\gets RandomNumber()\)
2:if\(\mathit{prand}\leq K\)then
3:\(x_{rand}=x_{goal}\)
4:else
5:\(x_{rand}\gets Sample()\)
6:endif
```  
**Algorithm 2** Biased-RRT  
In this paper, the tree is rooted at \(x_{init}\) denoted as main tree. The rest of the trees denoted as subtrees.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Review of the Physical and Visual Condition of Components at Different Ages
#### 3.2.2 Standard Condition Performance (SCP)  
Standard condition performance (SCP) represents the deterioration in the performance of a partition under normal wear conditions without the impact of failure conditions (such as poor planning and construction, use of inappropriate or low-quality materials, etc.). Improper maintenance or exceptional occupancy conditions can accelerate the deterioration rate of the partition function. The SCP value is determined based on an equal weighting of all the phenomena listed above. The SCP of each partition is determined on the basis of the following general expression (2):  
\[SCP=\sum_{i=1}^{n}CP_{i}/n \tag{2}\]  
In this equation, n equals 6, and CP\({}_{1}\) represents the ratings for each criterion that characterizes the partition's deterioration under standard wear conditions. The SCP assesses the partition's decline in performance by referring to various indicators of wear and tear, including:  
* Surface spalling or flaking.
* Peeling of plaster or paint.
* Localized breakages.
* Scratches, holes, or indentations.
* Stains on the partition surface.
* Accumulation of dirt on the skirting, especially when it significantly protrudes from the partition surface.
# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 3 Methodology
### Bilingual Keystroke Dynamics Dataset (BKSD)
#### 3.1.2 Data Cleaning and Preparation  
The data collected contained raw system timings for each keydown-keyup sequence, and thus is unsuitable for use as is. First, we convert the data from raw system time into the actual time required: for each row, we subtract the first keydown time from all columns in the row. Next, we extract the hold time, the keydown-keydown time, and the keyup-keydown time (flight time) for each row. We also calculate the total time required for the entire phrase or password. The user ID is finally used as the last column for each user as the class label.  
The dataset is composed of four files, two for the password and two for the phrase, with one file in Arabic and one file in English for each. We removed any rows where the user made a mistake during typing, and so, the final number of rows for each user is 400 or less. The numbers of users who completed the data entry process were 50 and 35 for the password and phrase data, respectively.
# A Control-Oriented Linear Parameter-Varying Model of a Commercial Vehicle Air Brake System
## 3 Simulation and Experimental Validation
### Model Calibration Using Experimental Data  
Based on the developed model, with the supply pressure levels of 4.5 and 5.8 bar and reference brake chamber pressure levels of 2, 3 and 4 bar, the PID control gains are manually tuned to optimize the step responses, and the resulting gains are fixed at \(K_{P}=0.8\), \(K_{I}=0.08\), and \(K_{D}=0.068\), respectively. To ensure that the supply and exhaust solenoid valves do not open at the same time, a hysteresis logic block is required to form a dead-zone to avoid overlapped opening of supply and exhaust valves by adjusting the PWM (pulse width modulated) duty-cycle. During the model development and system identification, the fixed step is used. This is mainly due to the switching nonlinearity of the valve system; for example, the intake (\(V_{1}\)) and exhaust (\(V_{2}\)) are switched based on the PID control output, which makes ode45 challenging to be used. In the simulation study, the PWM frequency is selected to be 60 Hz, and the simulations were conducted with a fixed-step of 0.001 s using the \"ode4\" Runge-Kutta solver in the Simulink.  
Figure 5(a),b compare the experimental results from the test bench (see Figure 5) and Matlab/Simulink simulation results for a pulse pressure reference, where the simulation results are in dashed-lines and experimental ones are in solid-lines. Note that for both experimental and simulation study, the brake chamber pressure is regulated by a PID controller based on the given reference pressure. It can be observed that for both supply pressure levels of 4.5 bar (see Figure 5(a)) and 5.8 bar (see Figure 5(b)), the pulse responses are very close. Note that it is vital to accurately predict the transient pressure response in the brake chamber since it is closely related to the brake force applied to the brake pad. From Figure 5(a),b, the simulated system response time during the brake chamber charge process is close to the experimental one in general. The simulated system response time during the discharge process is slower than that of the experiment ones. For example, under the reference pressure of 3 bar and supply pressure of 4.5 bar, the experimental response time is 220 ms in discharging process and simulated one is 240 ms. The simulated and experimental system performances (relative error and response time) are compared in  
Figure 5: Experiment setup. (**a**) Experiment schematic, (**b**) Test bench.  
Table 2, where the maximum relative error is defined as the ratio of the absolute difference between the maximum and reference pressure to the reference pressure during the charging and discharging processes; and maximum steady-state error is the ratio of the absolute difference between the maximum and reference pressure to the reference pressure during the holding process.