esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Prediction of Wind-Power probability Density Based on Quantile Regression LSTM  
### Quantile Regression Model  
The principle of quantile regression is to split the data according to the dependent variable, form multiple quantiles, and study the regression relationship under multiple quantiles. In general, quantile regression has the following functions:  
1. Analyze the influence trend of influencing factor \(X\) on variable \(Y\).
2. Stability analysis for the regression model.  
The assumed distribution function of the random variable is shown in Formula (1).  
\[F(y)=\Pr(Y\leq y) \tag{1}\]  
The \(\tau\) quantile of \(Y\) is defined as the minimum value \(\tau\), satisfying \(F(y)\geq\tau\), as shown in Formula (2).  
\[Q(\tau)=\inf\{y:F(y)\geq\tau\};0<\tau<1 \tag{2}\]  
Assuming that a group of random samples of \(Y\) is \(Y_{1},Y_{2},\ldots Y_{n}\) and that this is affected by \(k\) influencing factors \(X_{1},X_{1},\ldots X_{n}\), the quantile regression of samples essentially minimizes the sum of absolute values of weighting errors, and the calculation formula is shown in Formula (3).  
\[Q_{Y}(\tau|X)=\beta_{0}(\tau)+\beta_{1}(\tau)X_{1}+\ldots+\beta_{k}(\tau)X_{k} =\beta(\tau)X^{\prime} \tag{3}\]  
where \(Q_{Y}(\tau|X)\) represents the \(X=[X_{1},X_{2},\ldots,X_{k}]^{T}\) conditional quantile under the dependent variable \(y\) independent variable \(\tau\); \(\beta(\tau)\)\(=\)\((\beta_{1}(\tau),\beta_{2}(\tau),\ldots,\beta_{k}(\tau))^{T}\) represents the regression coefficient vector at the \(\tau\) quantile. Its expansion is shown in Formula (4).  
\[\underset{\beta}{\text{min}}\underset{i=1}{\overset{n}{\sum}}\rho_{\tau}(Y_{i }-X_{i}^{\prime}\beta(\tau)) \tag{4}\]  
where \(\rho_{\tau}(u)\) is the inspection function. The definition formula is shown in Formula (5).  
\[\rho_{\tau}(u)=(\tau-I(u<0))u \tag{5}\]  
where \(I(u)\) is an indicator function, \(u\) is a conditional relationship, and when \(u\) is true, \(I(u)=1\), and vice versa: \(I(u)\)\(=0\).  
For the conditional quantile function, the parameter estimation value can be obtained by solving Formula (4), as shown in Formula (6).  
\[\hat{\beta}=\text{argmin}_{\beta\in R}\left\{\underset{i=1}{\overset{n}{\sum} }\rho_{\tau}(Y_{i}-X_{i}^{\prime}\beta(\tau))\right\} \tag{6}\]  
In Formula (6), arg min represents the value of \(\beta\) when the function takes the minimum value.  
From the above analysis, for any \(\tau\in(0,1)\), the parameter \(\beta(\tau)\) is referred to as the \(\tau\) regression quantile coefficient. The essence of quantile regression is to estimate different conditional quantiles of dependent variables by taking different \(\tau\) values and using independent variables.
## 2 Architecture of CCSDS-like QC-LDPC Code for Overlap
### Architecture of the Proposed QC-LDPC Code
```
Input:\([m,a,b]\)
1\(l=m\);
2 for\(k1=1\) to \(m\)do
3\(i=a*k1+b\operatorname{mod}(m+1-k1)\);
4\(\pi_{A}(k1)=m-s_{i}\);
5 for\(k2=i\) to \(l-1\)do
6\(s_{k2}=s_{k2+1}\)
7 end for
8\(l=l-1\);
9
10 end for Output:\(\pi_{A}\)
```  
**Algorithm 1**Permutation Vector. Upon obtaining the permutation vectors, we proceed to uniformly arrange the data in the submatrices with a size of \(511\times 511\), according to the shift factors from the vectors. This is achieved by multiplying the permutation vectors by \(\left[511/56\right]=9\) to obtain the shift factors. Moreover, to enhance the BER performance, we employ a swapping scheme that compares adjacent factors originated by the permutation vector, and exchanges them with others if their distance is too small, thus maintaining a minimum interval of two diagonals. After these steps, the PCM we constructed is displayed in Figure 4.  
Figure 3: Distribution of 1s in Submatrices. (**a**) CCSDS (8176, 7154) LDPC code; (**b**) proposed (8176, 7154) LDPC code for overlap. Utilizing the matrix we have proposed, it becomes remarkably convenient to design decoders with higher rates and lower resource consumption using the overlap scheme. When the submatrix parallelism level is set to 57, this approach can achieve convenient and regular deep overlap scheduling. The specific scheduling methodology will be discussed in subsequent sections.
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 5 Simulation and Experimental Results Analysis  
To validate the proposed algorithm, MATLAB simulations were conducted to compare the bit error rate (BER) performance of the DSDT under different amplitude, frequency, and phase error estimation methods. Additionally, high sampling rate satellite multi-relay transmission was simulated using NI-USRP, and the constellation diagram after sub-spectrum error compensation, as well as the measured signal BER curve, were provided.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
###### Abstract  
One of the key elements of future 5G and beyond mobile technology is millimeter-wave (mmWave) communications, which is targeted to extreme high-data rate services. Furthermore, combining the possibility of a wideband signal transmission with the capability of pencil-beamforming, mmWave technology is key for accurate cellular-based positioning. However, it is also well-known that at the mmWave frequency band the radio channel is very sensitive to line-of-sight blockages giving rise to unstable connectivity and inefficient communication. In this paper, we tackle the blockage problem and propose a solution to increase the communication reliability by means of a coordinated multi-point reception. We also investigate the advantage of this solution in terms of positioning quality. More specifically, we describe a robust hybrid analog-digital receive beamforming strategy to combat the unavailability of dominant links. Numerical examples are provided to validate the efficiency of our proposed method.  
5G positioning; coordinated multi-point; mmWave MIMO; beamforming; blockage +
Footnote †: _[[https://www.mdpi.com/journal/sensors_](https://www.mdpi.com/journal/sensors_)]([https://www.mdpi.com/journal/sensors_](https://www.mdpi.com/journal/sensors_))
## 3 Emotional Regulation, Dysregulation, and Twin Studies
### Emotional Regulation and Emotional Dysregulation: Definitions and Comorbidities
[8] demonstrated that ED levels significantly predicted internalizing (e.g., depression and anxiety) and externalizing (e.g., aggression and rule-breaking) symptomatology in both neurotypical and neurodivergent adolescents. These effects were independent of diagnostic labels, suggesting that ED reflects a general liability toward emotional and behavioral dysregulation. As a result, ED has been included in the Research Domain Criteria (RDoC) framework as a core dimension in understanding psychopathology [38]. The negative impact of ED extends into long-term developmental outcomes. Children and adolescents with high ED are more likely to exhibit academic underachievement, school dropout, and social withdrawal. In adulthood, ED is associated with lower socioeconomic status, reduced occupational functioning, poor physical health, and increased rates of suicidal ideation and self-harm [36]. From a mechanistic perspective, ED may mediate the relationship between environmental stressors--such as trauma, neglect, or peer victimization--and later psychiatric outcomes. Several studies suggest that early adversity disrupts normative emotion regulation development, increasing vulnerability to depression, anxiety, and behavioral problems [34; 35]. The chronic activation of stress-response systems in the absence of effective regulation may result in maladaptive patterns that persist over time. Given its central role across psychopathologies and developmental trajectories, ED has become a key target for clinical interventions. Treatments aimed at improving emotional regulation--such as Dialetical Behavior Therapy (DBT), emotion-focused therapy, and mindfulness-based cognitive therapy--have shown efficacy across a range of conditions in both youth and adults. These approaches typically focus on increasing emotional awareness, reducing reactivity, and enhancing flexible use of regulation strategies. Nevertheless, despite its clinical importance, the conceptualization and measurement of ED remain inconsistent across studies. Researchers often focus on isolated components of ED (e.g., emotional impulsivity or mood instability) rather than adopting an integrative framework. This heterogeneity hampers the ability to generalize findings and underscores the need for more refined theoretical models and psychometric tools. In summary, ED represents a complex, multidimensional construct with far-reaching implications for mental health and functioning. It not only interferes with emotion-related processes but also compromises cognitive development, behavioral regulation, and social adaptation. As such, it should be considered a central focus in both developmental research and intervention design.
## 3 Membrane Performance Measurements  
### Test Methods for Water Vapor Permeability  
As the most popular method to measure permeability of membrane, the cup method measures the change in cup weight in an environment where a water-or desiccant-filled cup is stored at a constant temperature and humidity [12]. When a water-filled cup is used, the relative humidity inside the cup is assumed to be 100% and the amount of evaporated water is calculated. If a desiccant-filled cup is used, the water vapor mass is calculated through the vapor absorbed at 0% relative humidity. Although the cup method is commonly used, it has the disadvantage of creating a boundary layer according to the concentration of the target gas before and after the membrane. Accordingly, the measurement varies greatly  
Figure 1: Water vapor permeation through (a) porous membrane (b) dense membrane.  
depending on the distance from the water and desicant to the membrane; hence, error analysis is essential. Further limitations include being unable to include experimental variables such as pressure changes.  
To overcome these disadvantages, devices have been suggested to combine simple gravimetric methods with the simulation of various experimental conditions. A typical example is a constant-volume variable-pressure method that permeates water vapor from the feed side to the permeate side [13]. The pressure on the permeate side is dependent on the amount of water vapor permeated through the device, which is used to calculate the permeability. By default, calculations are made using time-lag techniques [14]. However, in the case of gases mixed with other gases and water vapor, only the permeation of water vapor can be measured. In contrast, the constant-pressure variable-volume method measures a constant pressure, primarily the pressure on the permeate side at 1 atm. If the gas used in the experiment is a gas mixture, a hydrodynamic analysis is required. In addition, the temperature conditions are limited to 100 \({}^{\circ}\)C or higher because they are greatly affected by the saturation point. The disadvantage of this method is that special experimental devices are required to maintain the pressure, and the calculation process is complex.  
In addition, the method of measuring the mixed gas fed into the test cell is frequently used, while the permeability is often calculated using equipment such as a gas chromatograph or dew point mirror (mainly for water vapor). It is important to develop an optimal method depending on the experimental conditions because each experimental device has its advantages and disadvantages, and there are many factors to consider, such as the price and environment.
## 1 Introduction
### Modules  
The research of this work is aimed at the gross error detection and replacement (GEDR) and quality module (QM). Nevertheless, the QM is conceptually considered.  
Figure 1: Overview of modules relevant to gross error detection and replacement (GEDR) and quality module (QM).  
* **Process and Instrumentation**: The process values can be influenced by sensor aging or incorrect calibration. Biases model effects them, and they are supposed to be smaller in comparison with Gross Errors (GEs).
* **DCS/RTDB** (Distributed control system/Real-time database systems): The data logged by DCS/RTDB includes the values from the process and instrumentation block such as noise, outliers and the interference from the measurement; possible effects to the anti-aliasing, analogous digital (AD) conversion, and quantization noise; and possibly LP filtering.
* **Quality Module (QM)**: DC includes some functions of the QM. In the case of measuring the same physical property by three independent sensors, the value with a \"bad\" stamp is automatically discarded, and the values of other two sensors will be applied. Other scenarios, including soft sensors, are possible and they depend on the DCS system and configurations. In our case, the QM can bring more performance by means of information from GEDR. Therefore, on input, QM incorporates two streams of information. One of the streams is derived from the DCS/RTDB and the other stream is derived from the GEDR. Properties being referred to the data points combined and gave additional information to other devices: DR, PE, and Data Verification. Series of properties developed by DCS/RTDB can be good/bad, while series of properties developed by GEDR can be replaced/not replaced.
* **GEDR**: The data based on the DCS/RTDB block must be elaborated by Gross Error Detection and replacement. The data will be cleared by means of GEDR before proceeding with data (DR) reconciliation (DR) and parameter estimation (PE). The GEs, after being found and deleted, are to be put under analysis made after the DR and PE. It should be done to estimate if they are really GEs and consecutively to configure the filter parameters newly. GE is substituted by GEDR, whose value corresponds to the local behavior of the discovered variable. For this purpose, the average of the last couple of valid data points can be applied. This module is an important part of our research.
## 5 Simulations and Results
### Cost Consumption
#### 5.2.1 **Smart Contracts Cost**  
The transaction and execution costs of smart contracts such as: ACC, RC, JC are illustrated in Figure 4. As it is shown in the aforementioned figure, gas units consumed by ACC are higher than other smart contacts, i.e., RC and JC. It is obvious that ACC is the main smart contract that manages the overall access control and transactions among two IoT devices and it performs more computational tasks. Tasks performed by the ACC are access control management, granting service permission to users, and transactions management. Therefore, more gas units are consumed by the ACC. After that, RC consumes more gas units to perform user registration task and also creates user registration table. All users' records are maintained in the user registration table. RC only manages registration; that is why it has less cost consumption than ACC. Furthermore, JC has less cost consumption rate. As thepurpose of JC is to check the behavior of users and to assign the penalty to users who misbehave. The misbehavior is recorded in the form of table. Moreover, the gas units are converted into ether, as it is the cryptocurrency of ethereum blockchain. The gas to eth conversion is done by the criteria given above in Section 5.2.  
Transaction cost: The transaction cost of smart contracts: ACC, RC, and JC is shown in the above figure. It is clear from the figure that the transaction cost of ACC is much more than RC and JC. It manages the overall access control of the system and it contains more functions than other smart contracts. Therefore, more cost is used according to the size of smart contract. The transaction cost of ACC is 4,000,000 gas units, which is equal to 0.08 eth. The transaction cost of RC is 1,600,000 gas units, which is equal to 0.032 eth. The cost of RC is not much high, because it only performs registration operations of users and creates a user registration table. Additionally, the transaction cost of JC is 1,300,000 gas units, which is equal to 0.026 eth. JC consumed less transaction cost as it implements the judging methods for users.  
Execution cost: The execution cost of smart contracts is also shown in the figure. Likewise the transaction cost, the execution cost of ACC is also higher than other smart contracts. The cost is 3,000,000 gas units, which is equal to 0.06 eth. ACC contains more computational operations, function calls and global variables to control access of the system. After ACC, the execution cost of RC is high, i.e., 1,200,000 gas units that is equal to 0.024 eth. JC consumes less cost as compare to other smart contracts, which is 1,000,000 gas units and is equal to 0.02 eth.
## 4 Construction of Attack-Defense Game Model
### Quantification of Attack and Defense Benefits  
The goal of the attacker and defender is to find the strategy that maximizes the discounted payoff, and we quantify the payoff function of the attacker and defender in this subsection.  
For the attacker, when he moves laterally, he needs to detect the surrounding node information and adopt the attack strategy, which causes a certain attack cost, which is represented by \(AC\). After a successful lateral movement, the attacker will get valuable information on the invaded node and obtain a certain payoff, where \(V_{i}\) denotes the value of node \(i\). If the attacker is found by the defender, the attack fails, the attacker cannot launch any more attacks, and the defender will obtain all the attack information, at which time the attacker will pay a huge cost, denoted by \(AF\). It can be concluded that the attacker's payoff at step \(t\) is as follows.  
\[R_{a}^{t}=\left\{\begin{aligned} & V_{i}-AC&\text{if move succeeded}\\ &-AF&\text{if been captured}\end{aligned}\right. \tag{1}\]  
For the defender, he needs to start from the defense node and investigate the internal network nodes one by one, and he needs to pay a certain tool cost, denoted by \(DC\). When the defender inspects a new node, it will obtain certain security information of the node, and thus it can obtain a certain profit, denoted by \(DG\). When the defender finds the attacker in the intranet node, the defense goal is achieved, and he will obtain a large security gain at this time, denoted by \(SG\). However, when the attacker finds the target node, the defender fails to find the attacker, and when the defense fails, the attacker will obtain a large amount of high-value information, which brings a huge security loss to the defender, denoted by \(DF\). Then it can be concluded that the defender's payoff at step \(t\) is as follows.  
\[R_{d}^{t}=\left\{\begin{aligned} & DG-DC&\text{if move succeeded}\\ & SG&\text{if capture}\\ &-DF&\text{if defend failed}\end{aligned}\right. \tag{2}\]  
If the node values do not sufficiently compensate for the attack costs, the attacker must decrease the number of lateral movements. Additionally, it is imperative for the attacker to elude detection by the defender and ensure that the defender does not acquire any attack-related information, resulting in the generation of \(AF\).  
Similarly, the rational defender will try to keep moving and obtain enough security information to offset the tool cost \(DC\). If it is not enough, the defender will reduce the number of inspection nodes, find out the attacker as soon as possible, and prevent him from successfully reaching the target node, so as to obtain the security benefit \(SG\) and avoid the generation of \(DF\).
## 1 Introduction
In [23], the system grabs a color image as input and extract the 2D locations of anatomical keypoints, and then applies an architecture for jointly learning parts detection and parts association. The pros of [23] is 2D pose estimation of multiple people in images, that can be applied in social context identification assisting system for Autism or security monitoring system for home or public spaces, but may not be sufficiently accurate for medical assessment. In our study, we extract both color and depth information from RGB-D sensors, and rebuild a new upper extremity skeleton and skeleton points. To provide an efficient rehabilitation system that can assist the patients to trainspecific joints and muscles, we need to obtain not only the pose changes, but also to find out these changes coming from which joints in real human body skeletons. Although there are many pose estimation systems and somatosensory games have been developed and presented, most of those existent systems are performed based on extracting skeletons or skeleton points from a depth sensor and mainly focus on providing human pose estimation, rather than determining the joint movements (include directions and angles). In this study, we desire to obtain not only the pose changed, but also try to find out these changes come from which joint in real human body skeleton. Most of those systems and game sets may not focus on training some specific actions for the purpose of medical rehabilitation, have insufficient evidence base on medical applications, and might be unsuitable for someone who are unable to stand or stand for long, lack of standing balance, or only need to rehabilitate their upper extremity, such as patients with spinal cord injuries, hemiplegia, or advanced age. To overcome the aforementioned challenges, this study proposes an action identification system for home upper extremity rehabilitation, which is a movement training program for specific joints movement and muscle groups, and have advantages such as cost-economic, suitable for sitting position rehabilitations, and easy for operating at home. The major contributions of the study are: (1) the proposed system is not only a physical exercise game, but also a movement training program for specific muscle groups; (2) The hardware of upper extremity Rehabilitation System included a personal computer with personal computer and a depth camera. These are economic equipment, so that patients who need this system can set up one set at home; (3) patients can perform rehabilitation actions in sitting position to prevent him/her from falling down during training; (4) the accuracy rate of identifying rehabilitation action is as high as 98%, which is sufficient for distinguishing between correct and wrong action when performing specific action trainings; (5) The proposed upper extremity rehabilitation system is real-time, efficient to vision-based action identification, and low-cost hardware and software, which is affordable for most families.