esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Segmentation and Classification
Several artificial intelligence methods that are widely used in image processing are Artificial Neural Networks or ANNs. The method that has recently begun to be applied in studies of mapping flood potential and vulnerability is to use machine learning. Some of the methods that were implemented include Adaptive Neuro-Fuzzy [34], Support Vector Machine (SVM) [35; 36], Convolutional Neural Network (CNN) [36; 38], and Swarm Intelligence [39; 40; 49]. [PERSON] et al. used Gamma Maximum A-Posteriori (GMAP) to filter out speckle noise from SAR images, then performed surface texture analysis using the Gray Level Co-Occurrence Matrix (GLCM) [34]. Although being the most common basic method on flood mapping, NDFI/NDWI as the most straightforward method tends to amplify noise greatly. Otsu thresholding suffers from high computational requirements since it is an early optimization method. The SMUF, SVM, GRNN [34; 35], and most recently CNN [50] still perform the classification process in a 2D plane and then perform the 1D multi-temporal process. Due to the complexity of the factors that influence the occurrence of floods in urban areas, the most effective and efficient classification method is needed. As a classification technology developed based on feature matching, the ML method produces a more accurate recognition than feature matching. However, it has limited extraction features that can cause errors in the computation process. This study proposes three-dimensional CNN in mapping flood areas to filter and weigh neurons and map the potential flood areas in urban areas with fewer images compared to the previous study [36; 51]. CNN features unsupervised feature extraction compared to Artificial Neural Network (ANN), in which the process is achieved through the training phase to recognize flood areas. In ANN, all neurons of a layer are fully connected to every neuron from other layers, whereas in CNN, only the last layer of neurons is fully connected due to the parameter-sharing nature of the CNN, therefore the computational load of CNN is less than ANN.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.4 Training Objective  
DDPM is trained using an MLE objective [50]. The goal is to maximize the likelihood of observing the true clean image \(X_{t}\) given the noisy counterpart \(X_{0}\) after the diffusion process. The training objective is formulated as:  
\[\theta^{*}=\text{argmax}_{\theta}\sum_{t=1}^{T}\log p(X_{t}|X_{0}) \tag{4}\]
## 3 Use Case: UMTS Networks
### Normalization  
Normalization is required prior to any data aggregation, as the indicators in a data set often have different measurement units. While taking into account that the network can include thousands of cells, a method decreasing the computation time should be used. With this aim, each KPI is divided here by the median of its distribution. Note that the normalization method needs to be consistent with the aggregation step when building the composite indicator. In our case, the geometric method is used, which weights each category so the sum adds to 100.  
Applying the geometric method requires positive numbers. Because all of the variables that were considered in each category are either negative or positive, we guarantee that, by dividing them by the median, they are all positive. Moreover, all the variables are centered in 1. Thus, a value that is equal to 1 denotes that it is a KPI above the median, while values below the median will be between 0 and 1, and values above the median will be above 1. As a drawback, the magnitude information of the value is lost with the normalization.  
The normalization step allows for establishing rankings in order to compare clusters. For example, in our scenario, clusters of cells could be ranked without valuing the magnitude of KPIs themselves, in a way that a 'good' cluster and a 'bad' cluster can be identified, even if all of the clusters show actually a good performance.
## 5 Discussion
### RQ1: What Are the Elements for Providing Robot Behaviors in the Long-Term?  
We have identified the \"Executor\" component as the crucial element for defining the complete architecture and being able to recover under canceled or blocked plans.  
Besides, it is necessary to have in mind the good definition of the PDDL approach for the deliberative layer and the precise decomposition of the task into subtasks.  
The experiment with 120 Pols demonstrated that, even with this level of assistance, we have an architecture ready for working more than a punctual case. The results present a robot working for more almost one hour, and prove that MERLIN reduces the time in almost 15 min.  
RQ2: How We Can Integrate State-of-the-Art Components, or in This Case, Off-the-Shelf Software Components, for Creating a Cognitive Architecture?  
It is not a straightforward process that requires reading source code, their documentation and extract as much information as possible from source code. For instance, we obtained an initial version of packages dependencies using Doxygen software [32].  
The authors believe that the architecture section presents an overview that clarifies the main components involved during the development of new robot behavior. Furthermore, it is included an example with programming details that provides an overview and a lesson learned for the scientific community.  
RQ3: What Are the Mechanisms for Successfully Managing Expected or Unexpected Requests Associated with a Given Robot Duty?  
We already defined the Executor and the Goal dispatcher as the main components for recovering after canceling the mission or a non-recovery state in the scenario. Thus, the testing scenarios present a fully usable architecture, and in addition, our proposal, MERLIN, reduces almost 40% of the time spent in the Naive approach, and around the 45% of enhancement when facing the covered distance. This is illustrated in Figures 10 and 11. These values allow us to say that MERLIN is a better approach for reducing the noise associated with a robot moving at home, such as physical noise (motors of the robot) or physiological disturbances (why the robot is doing that if it should be doing something else).  
Figure 11: Traversed distances for fulfilling the missions using Naive and MERLIN approaches.  
Besides, it is interesting to reduce the consumption when working in scenarios where requests could be easily canceled (this research proposes 50%) and it is necessary to manage a lot of unsuccessful tasks.
## 3 System Model and Symbol Definitions
### mMTC Access Delay  
In a contention-based scenario, the minimum latency for the RACH procedure is at least 15 ms, excluding step 1, waiting time [40]. In this section, we further explore the latency details to analyze the impact of collisions on the overall latency.  
As mentioned above, the possibility of collision exists in a contention-based RACH. When it happens, it impacts the overall latency of the device's transmission operation. The overall latency of a device is mathematically represented as follows:  
\[D_{MTC_{j(init)}}=D_{nach-1}((1+R_{max})\times x_{j})+R_{max}(T_{Back}\times x_{ j}) \tag{11}\]  
\[D_{MTC_{j(final)}}=D_{nach-2}+D_{trx} \tag{12}\]  
\[D_{MTC_{j}}=D_{MTC_{j(init)}}+D_{MTC_{j(final)}} \tag{13}\]  
\[x_{j}=\left\{\begin{array}{ll}1&\text{if device\,j collided}\\ 0&\text{otherwise}\end{array}\right.. \tag{14}\]Thus, the operation delay of a device accessing the network is relative to its initial access attempts. At the same time, its re-transmission is conditioned on its failure to successfully process MSg3, and the back-off time facing it is conditioned to wait before the next attempt for the maximum defined re-transmission attempts. Thus, \(D_{MTC_{(init)}}\) and \(D_{MTC_{(final)}}\) define the initial and the final RACH attempts, respectively. \(T_{Back}\) is the back-off time. The average access delay defines the average time an mMTC device takes to access the network. This is mathematically described as the ratio of the total delay of each device over the total number of devices.  
\[D_{MTC_{\text{err}}}=\frac{\sum D_{MTC_{j}}}{N} \tag{15}\]
## 4 BER Computational Strategies for Decision Unit--Background Tutorial
### Estimating BER Based on the Log-Likelihood Ratio  
A receiver can implement soft-output decoding to reduce the signal stream's BER (e.g., a posteriori probability (APP) decoder). The APP decoder may output probabilities or Log-Likelihood Ratio (LLR) values. Let \((a_{k})1\leq k\leq K\in\{+1,-1\}\) be the bit stream and let \(X_{k}\); \(k=1,2,\ldots,K\) represent the received values. Hence, the definition of LLR can be expressed just as follows:  
\[\text{LLR}_{k}=\text{LLR}(\text{a}_{k}|X_{k}=\text{x}_{k})=\log\frac{\text{P}( \text{a}_{k}=+1|X_{k}=\text{x}_{k})}{\text{P}(\text{a}_{k}=-1|X_{k}=\text{x}_ {k})} \tag{11a}\]Hence, when using [PERSON]'s theorem, we obtain the following:  
\[\text{LLR}_{k}=\log\frac{\text{P}(\text{a}_{k}=+1)}{\text{P}(\text{a}_{k}=-1)}+ \log\frac{\text{P}(\text{a}_{k}=+1|X_{k}=\text{x}_{k})}{\text{P}(\text{a}_{k}=- 1|X_{k}=\text{x}_{k})} \tag{11b}\]  
In Equation (11b), the first term on the RHS represents a priori information, and the second represents channel information. The hard decision expression is implemented by computing the LLR sign as follows:  
\[a_{k}=\left\{\begin{array}{c}+1\;if\;LLR_{k}(a_{k}|\;x_{k})>0\\ -1\;otherwise\end{array}\right. \tag{11c}\]  
In [98], some basic properties of LLR values are extracted, and new BER estimators are proposed based on the statistical moments of the LLR distribution. If we are examining the succeeding criterion:  
\[\text{P}(X=+1\;|\;Y=y)+\text{P}(X=-1\;|\;Y=y)=1\]  
Solving Equation (11b) utilising the criterion mentioned above permits us to derive a posteriori probabilities \(\text{P}(a_{k}=+1\,|\,x_{k})\) and \(\text{P}(a_{k}=-1\,|\,x_{k})\); then, we can write the following:  
\[\text{P}(a_{k}=+1\,|\,x_{k})=\text{e}^{(LLR)}/1+\text{e}^{(LLR)}\text{ and }\text{P}(a_{k}=-1\,|\,x_{k})=1/1+\text{e}^{(LLR)} \tag{11d}\]  
If \(LLR_{k}\) = A, then we could infer the probability that the hard decision of bit kth is wrong and  
\[p_{k}=1/1+\text{e}^{-\text{A}} \tag{11e}\]  
Now, the BER estimate can be expressed as follows:  
\[p_{e,1}^{*}=\frac{1}{K}\sum_{k=1}^{K}p_{k} \tag{12a}\]  
\[p_{e,2}^{*}=\int_{y}g_{A}(y)\;\frac{1}{1+e^{y}}dy \tag{12b}\]  
The constraints of the LLR method are as follows:  
1. The first estimate of BER given by Equation (12a) may not be as efficient as the second BER estimate given by Equation (12b) since \(g_{A}(y)\) is usually Gaussian and smooth.
2. The second estimator is extra complicated to execute because an estimate of \(g_{A}(y)\) ought to be computed (for instance, utilising a histogram) prior to the integral.
3. Both methods are sensitive to channel noise variance as the LLR distribution vigorously relies upon the accuracy of the SNR estimate. We should note that the earlier estimators implicitly presume that the SNR is well-known to the decoder.
# Electromagnetic Switching Multiple Grasping Modes Robot Hand
## 2 Design Concept of the ESMGM Hand
### Partial Effective Transmission Mechanism  
After using the switchable CPS mechanism to realize the grasping mode fusion, it needs to drive the whole robot hand reasonably. Since the whole grasping action can be divided into two stages, the first of which is parallel grasping or coupled grasping, it then enters the self-adaptive grasping stage through a partial effective transmission mechanism, by which the movement process can be decomposed into two phases.  
As shown in Figure 3, the components of the partial effective transmission mechanism mainly include the phalange shell, a torsion spring, a driving gear, a joint axis, a bump, and a partial effective driven disc with an idle-stroke sliding groove. The whole motion period of the partial effective transmission mechanism includes two stages: the idle-stroke stage and the effective-stroke stage.  
At first, the motor drives the driving gear rotation, and the mechanism will enter the idle-stroke stage from the initial position. The proximal phalange is attached to the driving gear with a torsion spring; thus, it will rotate under the torque of the torsion spring without external force interference. There is a bump on the driving gear, and during the idle-stroke stage, it will slide along the idle-stroke sliding groove, and the idle-stroke driven disc will maintain the resting stage. The idle-stroke stage ends when the entire idle stroke of the sliding groove is finished by the bump, the bump contacts with the edge of the groove, and the mechanism enters the effective-stroke stage. In this stage, the idle-stroke driven disc will rotate around the proximal joint axis because it is pushed forward by the bump.  
By adopting the partial effective transmission mechanism, the ESMGM robot hand can integrate the two process stages expediently. Specifically, the ESMGM robot hand executes the parallel grasping mode or coupled grasping mode during the idle-stroke stage and executes the self-adaptive grasping mode after it moves into the effective-stroke stage.
###### Abstract  
A novel method of investigating the temperature dependent variation of aspects of the complex refractive index \(n^{\ast}\) in samples in the THz range using continuous, non-polarised, synchrotron radiation is presented. The method relies on the use of ATR apparatus, and retains the advantage of minimal sample preparation, which is a feature of ATR techniques. The method demonstrates a \"proof of concept\" of monitoring temperature reflectance whilst continuously heating or cooling samples by using a temperature variable Thermal Sample Stage. The method remains useful when the refractive index of the sample precludes attenuated total reflection study. This is demonstrated with the water reflectance experiments. The temperature dependent ATR reflectance of tissue-representative fats (lard and Lurpak\({}^{\text{\textregistered}}\) butter) was investigated with the novel approach. Both are within the ATR range of the diamond crystal in a \"true\" ATR mode. Lard showed no clear temperature variation between \(-15\)\({}^{\circ}\)C and \(24\)\({}^{\circ}\)C at 0.7 to 1.15 THz or 1.70 to 2.25 THz. Lard can be regarded as having invariable, constant, dielectric properties within mixtures when biological substances are being assessed for temperature dependent dielectric variation within the stated THz ranges. Lurpak\({}^{\text{\textregistered}}\) butter (water content 14.7%) displayed temperature dependent reflected signal intensity features with a steady decline in reflectivity with increasing temperature. This is in line with the temperature-dependent behaviour of liquid water. There is no rapid change in reflected signal intensity even at \(-20\)\({}^{\circ}\)C, suggesting that emulsified water retains liquid-water-like THz properties at freezing temperatures.  
Keywords:ATR; THz; synchrotron radiation; biological tissues; temperature variation +
Footnote †: journal: Applied Sciences
## 2 Optimization Model
### Interpretation of the Mathematical Model  
The mixed-integer nonlinear optimization model described in (1)-(16) aims to optimize FACTS integration in electrical distribution grids. This model incorporates both binary and continuous variables. The placement of these devices is determined by the former. The latter is associated with several variables, including the active and reactive power of the generators, power demands, the power flowing through the transmission lines, complex nodal voltages, and the size of the FACTS.  
The following can be noted regarding this mixed-integer nonlinear optimization model. The objective function is defined by two terms. The first term, which implies an annual cost of \(f_{1}\), represents the energy losses of the electrical distribution system. In contrast, the second term calculates the investment costs of FACTS (\(f_{2}\)). Expressions (2) and (3) represent the active and reactive power balance, respectively, for each node and time period under analysis. Equations (4) and (5) represent the active power flow that is transmitted through each branch of the transmission lines at each studied time interval. Equations (6) and (7) represent the reactive power flowing through the same transmission lines. Inequalities (8) and (9) limit the maximum apparent power that can flow through each branch of the transmission lines in any given period. Equation (10) sets the voltage at the substation, while inequalities (11) and (12) constrain the nodal voltages to their respective minimum and maximum values at each time step. Inequality (13) restricts the maximum value that the FACTS may reach. Inequality (14) sets the limit for the maximum reactive power that the FACTS may deliver or absorb at each node. Finally, inequality (15) determines the maximum number of FACTS that can be installed.
## 3 Methodology
### BFRS Representation Based on the Weighted Clustering
#### 3.6.1 Refinement Using the Buffered Window  
Suppose the detected result is subtracted with a buffered window \(w_{buf}\) that slides from the start time to the end of the recording, which can be denoted as \(w_{buf}\{BFRS_{info}^{i},BFRS_{info^{\prime}}^{i+1},\cdots,BFRS_{info}^{i+n}\}\). Then, the refinement process is conducted for each result in \(w_{buf}\). The detected result in \(w_{buf}\) is judged as BFRS under the following three conditions:  
1. More than half of the detected result \(BFRS_{label}\) is not labeled as \"road\".
2. More than 60% percent of detected results in the buffered window \(w_{buf}\) is BFRS.
3. The probability \(BFRS_{prob}\) of a detected result is larger than the probability threshold \(Prob_{min}\).  
The point in the buffered window is relabeled as \"true\" and taken as the right detected BFRS, or the detected is taken as \"false\", and the operation can be depicted in (14).  
\[BFRS_{refined}=\text{Refine}\Big{(}BFRS_{info},w_{buf},Prob_{min}\Big{)} \tag{14}\]  
In (14), the length n of the buffered window \(w_{buf}\) is usually set as 10, or some other larger value that with the high confidence of the detection result, and the probability threshold \(Prob_{min}\) is set as 0.7, which is a little higher than the probability detected by the CNN model, as depicted in Figure 6a.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 4 Classifiers  
Classifiers are used to classify a new observation into a set of categories, on the basis of a training set of data containing the observations. From the literature review of multiple research papers on fault detection [1; 9; 10; 14], we selected some classifiers to implement in our scenario. In [1], SVM was used for fault detection in WSN, and CNN was used for binary classification and object detection in [9], whereas the authors in [10] used the RF method to detect and classify faults, as well as to rank the features in wind turbines. In [14], PNN was used to classify different kinds of nodes in the network. This helped us narrow down six classifiers that are used for fault detection by binary classification. The basic explanation of these classifiers is given in the subsections below.
# Audio Feedback with the Use of a Smartphone in Sailing Training among Windsurfers
## 2 Methods
### Instrumentation and Software  
A smartphone equipped with a built-in GNSS receiver was used during the study; this smartphone uses signals from three navigation systems: GPS, Glonass, and Beidou. The sampling rate was 1 Hz. In the study, a Samsung Galaxy J5 (model SM-J500F; mobile operating system: Android version 6.0.1; RAM 1.5 GB; market launch 2015) was used.  
The SoniSailing application used in the current research is a custom-made program for mobile phone devices. Thanks to constant geometrical analysis of coordinates from a GNNS module, the SoniSailing application monitors current motion vector of the surfboard according to the race route. The reliability of angular change-of-direction measurement, estimated by the application, was tested [12]. The measurement is the basis for correct operation of major functions.  
Sound messages (vocal cues) were provided using bone conduction wireless headphones (AfterShokz, Bluez 2S AS500 model), connected with the smartphone with the use of Bluetooth 4.1 standard.
## 3 Problem Formulation  
The optimization goal of this paper is to maximize the total number of mMTC devices accessed under the QoS of each eMBB device. All of the mMTC devices access the network adhere to the following criteria:  
1. Each mMTC device is allowed to access no more than one subcarrier.
2. To ensure the transmission quality of the eMBB devices, the interference each subcarrier can suffer should be below a threshold.
3. Both eMBB devices and mMTC devices should satisfy their own transmission rate requirements. When the \(j\)th mMTC pair is allowed to access the subcarrier \(i\), the SINR of the receiver of \(M\)U\({}_{j}\) is  
\[\gamma_{j}=\frac{P_{j}^{d}\left|g_{j}\right|^{2}}{P_{i}^{c}\left|h_{i,j} \right|^{2}+\sum_{\begin{subarray}{c}j\to 1\\ j\
eq i\end{subarray}}^{M}\alpha_{ij^{\prime}}P_{j^{\prime}}^{d}\left|f_{f,j} \right|^{2}+\sigma_{0}^{2}}. \tag{8}\]  
Taking \(\gamma_{j}\) in (8) into (7), we can get the transmission rate \(R_{j}\) after mMTC pair \(M\)U\({}_{j}\) is allowed to access the subcarrier \(i\). In order to ensure the QoS of the mMTC pair \(M\)U\({}_{j}\), the achievable rate \(R_{j}\) of the mMTC pair \(M\)U\({}_{j}\) should not be less than the minimum required rate \(R_{j\_\_\_\_\_\min}\), i.e.,  
\[R_{j}\geq R_{j\_\_\_\min}. \tag{9}\]  
When the mMTC pair \(M\)U\({}_{j}\) is allowed to access the subcarrier \(i\), the minimum transmission rate of the eMBB device \(C\)U\({}_{i}\) is \(R_{C\_\_\_\min}^{i}\). Under the QoS constraint of the eMBB devices and the mMTC pairs,we maximize the number of mMTC pairs accessed in the network. Mathematically, the optimization problem is formulated as  
\[\max_{\alpha_{ij}}\sum_{i=1}^{N}\sum_{j=1}^{M}\alpha_{ij}\, \tag{10a}\] \[s.t R_{cu}^{i}\geq R_{cu\_min}^{i}\,\] (10b) \[R_{j}\geq R_{j\_min}\,\] (10c) \[\alpha_{ij}\in\{0,1\},\quad\forall i\in\mathcal{N}\quad j\in \mathcal{M}\,\] (10d) \[\sum_{i=1}^{N}\alpha_{ij}\leq 1. \tag{10e}\]  
The constraints described in (10b) and (10c) indicate that the respective minimum transmission rate requirements of the eMBB devices and the mMTC pairs should be satisfied. Equation (10e) reveals that each mMTC pair is allowed to occupy at most one subcarrier. The optimization problem is a binary optimization problem, and the traversal complexity of the problem is \(2^{MN}\). In the following section, we propose a lower complexity algorithm named interference-aware subcarrier allocation for mMTC communication.