esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring ISAC: Information-Theoretic Insights
## 3 Mono-Static ISAC with Sensing Distortion
### Finite Blocklength Results  
A similar model as introduced in [56] is considered in [59]. Definition 1 needs to be adapted for the nonasymptotic regime as follows:  
**Definition 2**.: _Given a blocklength \(n\), the rate-distortion-error triple \((R,D,\epsilon)\) is said to be achievable if there exist encoding, decoding, and estimation functions \(\{f^{(n)},g^{(n)},h^{(n)}\}\) satisfying_  
\[\frac{1}{n}\log_{2}(W) \geq R, \tag{18}\] \[\epsilon^{(n)} \leq\epsilon,\] (19) \[\Delta^{(n)} \leq\mathsf{D}. \tag{20}\]  
The main results of [59] include the derivation of achievability and converse bounds on the rate-distortion-error tradeoff in the finite blocklength regime.  
**Theorem 3**.: _Given a blocklength \(n\), the rate-distortion-error tradeoff \((R,D,\epsilon)\) is achievable if there exists a \(P_{X}\) and a constant \(K>0\) such that the following conditions are satisfied:_  
\[R \leq I(X;Y)-\sqrt{\frac{V}{n}}Q^{-1}(\epsilon-\beta_{u})-\frac{K \log(n)}{n}, \tag{21}\] \[\mathsf{D} \geq\sum_{x\in X}\sum_{s\in S}\sum_{z\in\mathcal{Z}}d(s,s^{*}(x,z))P_{X}(x)P_{S}(s)P_{Z|XS}(z|x,s), \tag{22}\]  
_where_  
\[\beta_{u}:=\frac{1}{nK}+\frac{0.7975T}{\sqrt{nV^{3}}},\]  
_and the mutual information \(I(X;Y)\) and the central moments \(V\) and \(T\) are defined based on the joint pmf \(P_{XY}(x,y)=P_{X}(x)P_{Y|X}(y|x)\). Conversely, any rate-distortion-error triple \((R,D,\epsilon)\) is not achievable if for all \(\delta>0\) and pmfs \(P_{X}\) satisfying the distortion condition, the following lower bound holds:_  
\[R\geq I(X;Y)-\sqrt{\frac{V}{n}}Q^{-1}(\epsilon+\beta_{l})+\frac{\log(n)}{2n}- \frac{\log\delta}{n},\]  
_where_  
\[\beta_{l}:=\frac{0.7975T}{\sqrt{nV^{3}}}+\frac{\delta}{\sqrt{n}}.\]  
Note that here, distortion is measured as an expected distortion over all messages. For a small number of messages, i.e., small blocklengths \(n\), the encoder and decoder might need additional randomness to construct the desired distribution. A different approach was taken in [85], where sensing performance is measured with an excess distortion criteria  
**Example 1**.: _Consider a binary channel with a multiplicative Bernoulli state:_  
\[Y=SX, \tag{23}\]  
_where all alphabets are binary \(\mathcal{X}=\mathcal{S}=\mathcal{Y}\in\{0,1\}\), the state is Bernoulli-\(q\) with \(q\in(0,1)\) and the feedback is perfect, i.e., \(Z=Y\). We consider the Hamming distortion measure \(d(s,\hat{s})=s\oplus\hat{s}\)._  
_Figure 4 illustrates the achievability and converse bounds in the above theorem for \(\epsilon=10^{-3}\), \(q=0.4\), \(\mathsf{K}=0.5\). As can be seen from this figure, the bounds are tight for large values of \(n\)
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 4 Workspace Analysis
### Obtaining the Useful WS
#### 4.2.6 Intersection of the WS Subsets  
Finally, to obtain the WS\({}_{\text{useful}}\), the intersection of the previous WS subsets is carried out. For a specific initial position or assembly mode, the set of discrete poses within the WS that fulfills the conditions of all the WS subsets will be part of the WS\({}_{\text{useful}}\), as shown in Equation (33).  
\[\text{WS}_{\text{useful}}=\text{WS}_{\text{DKP}}\cap\text{WS}_{\text{KCs}} \cap\text{WS}_{\text{platform}}\cap\text{WS}_{\text{points}} \tag{33}\]  
Figure 21 represents an example of the rotational and translational WS\({}_{\text{useful}}\) of the PM in the \((+-+-)\) working mode configuration and an initial position at \(x=0\) m, \(z=0.3\) m, \(\varphi=0^{\circ}\) and \(\theta=0^{\circ}\). These results were obtained using the geometrical parameters gathered in Table 1: links that are cylindrical bars of 0.01 m thickness, a mobile platform composed of only two crossed-links, and the same kinematic joint range as that specified in Table 3.  
In the intersection of WS subsets, the choice of the initial position is critical, as it influences the size of the regions within each WS subset, modifying the WS\({}_{\text{useful}}\). In Figures 22 and 23, the effect of the initial position choice on each WS subset can be observed. The selected initial position (black rectangle) is such that the intersection leads to the WS\({}_{\text{useful}}\) in Figure 20.  
This type of representation provides information about the most restrictive factor (WS subset), and thus, the factor that must be optimized by changing the geometrical parameters and the selection of the initial position. In this example, the most limiting factor during the intersection of WS subsets is the kinematic joint range limitation for both the translational and rotational WS.  
Figure 20: Translational and rotational WS\({}_{\text{points}}\) for different working modes. Magenta-colored poses can be reached without surpassing the kinematic joints motion range.  
Conducting this analysis for each working mode leads to an exhaustive understanding of the RRU-3 RSS PM's kinematic behavior for a given set of geometric parameters. The flexibility of the code enables the systematic calculation of the same results with different parameters. Hence, this methodology can be used as an iterative design tool to find the geometric parameters that will allow the PM to meet the specific requirements of its final application.  
Figure 21: Translational and rotational \(\text{WS}_{\text{useful}}\) when \(x=0\) m, \(z=0.3\) m, \(\varphi=0^{\circ}\), \(\theta=0^{\circ}\) is selected as the initial position. Valid poses are represented in purple.  
Figure 22: Influence of the initial position (black rectangle) on translational WS subsets (\(x\) and \(z\) range is \(\pm 0.7\) m).
Sensors_**2015**, _15_, 9481-9518; doi:10.3390/s150409481  
**DESIGN ACCESS**  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring**  
**[PERSON]**  
Politecnico di Torino, Dipartimento di Elettronica e Telecomunicazioni, Corso Duca degli Abruzzi 24, I-10129 Torino (TO), Italy; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-011-090-4111  
Academic Editor: [PERSON]  
_Received: 2 December 2014 / Accepted: 15 April 2015 / Published: 22 April 2015_  
**Abstract:** Long-term wildfire monitoring using distributed _in situ_ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.  
**Keywords:** WSN; IoT; pervasive environmental monitoring; long-term operation; low maintenance; low-cost WSN nodes; low-cost WSN deployment; protocol design; low power
## 3 New Concept for Assessing the Technical Condition of HV Switching Bay Devices
The product of \(N\) terms represents the combined effect of the multiplicative grades of \(N\) multiplicative criteria. \(GCR_{i}\) denotes the selected grade of the \(i\)th multiplicative criterion \(CR_{i}\). These multiplicative criteria are entirely dependent on the presence of detected DIDs. The lowest grade among them is used if multiple DIDs are identified during the maintenance process. These multiplicative criteria reflect the current state of the device associated with the detected DIDs. On the other hand, the additive criteria grading \(GCR_{1}\) through \(GCR_{M}\) represent the device's operational history. \(GCR_{j}\) represents the selected grade of the \(j\)th additive criterion \(CR_{j}\), where \(j=1\), \(2\),..., \(M\), and \(M\) is the total number of selected additive criteria. Each selected additive grade \(GCR_{j}\) is multiplied by the corresponding weight \(w_{j}\) for criterion \(CR_{j}\). This structure corresponds to the algorithm in Equation (2). The grades of all the additive criteria are normalized by the sum of the maximum possible grades for those criteria, each multiplied by their associated weight \(w_{j}\). The multiplicative criterion estimates (represented by the first term in parentheses in (2)) contribute 50% to the overall condition estimate, while the additive criteria (the second term in parentheses in (2)) contribute the remaining 50%. The result of evaluating the technical condition of the selected HV SB is a scalar value \(c\), with the highest value of 1 representing the best possible condition and the lowest value of 0 representing the worst condition. A uniform estimate of \(GCR_{i}\) and \(GCR_{j}\) is selected for each selected criterion \(CR_{i}\) and \(CR_{j}\). The new concept for the condition assessment of the HV SB is designed to function as a fully automated process, updating after each maintenance task to reflect the current condition. This enables the creation of a condition database for each device, supporting visualizations such as time-based condition trends or sequence diagrams (\(c\)-curve). It is evident that the maximum possible value of (2) is 1 and the minimum is 0. A new faultless device can have a calculated value of 1, while a value of 0 indicates that the device's condition is in such a state that its operation is no longer possible, necessitating replacement. The multiplicative factor enhances the linear concept, which was previously solely determined using additive criteria. The following sections introduce the concept of classifying the DIDs of HV SB components (CBs, Ds, and ITs). In the new concept, the detected DIDs are directly used to calculate the estimates of the multiplicative criteria. In contrast, the existing concept described using (1) incorporates the results of maintenance activities indirectly within certain additive criteria.
###### Abstract  
When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, it is becoming increasingly complicated but essential to provide interpretations for outlier detection results in high-dimensional space as a consequence of the large number of features. To alleviate these issues, we propose a new model based on a Variational AutoEncoder and Genetic Algorithm (VAEGA) for detecting outliers in subspaces of high-dimensional data. The proposed model employs a neural network to create a probabilistic dimensionality reduction variational autoencoder (VAE) that applies its low-dimensional hidden space to characterize the high-dimensional inputs. Then, the hidden vector is sampled randomly from the hidden space to reconstruct the data so that it closely matches the input data. The reconstruction error is then computed to determine an outlier score, and samples exceeding the threshold are tentatively identified as outliers. In the second step, a genetic algorithm (GA) is used as a basis for examining and analyzing the abnormal subspace of the outlier set obtained by the VAE layer. After encoding the outlier dataset's subspaces, the degree of anomaly for the detected subspaces is calculated using the redefined fitness function. Finally, the abnormal subspace is calculated for the detected point by selecting the subspace with the highest degree of anomaly. The clustering of abnormal subspaces helps filter outliers that are mislabeled (false positives), and the VAE layer adjusts the network weights based on the false positives. When compared to other methods using five public datasets, the VAEGA outlier detection model results are highly interpretable and outperform or have competitive performance compared to current contemporary methods.  
outlier detection; variational autoencoder; genetic algorithm; abnormal subspace
# SPIN: A Blockchain-Based Framework for Sharing COVID-19 Pandemic Information across Nations
## 2 Design  
We utilized the blockchain technology [20; 21; 22] to design the SPIN framework that allows one to share COVID-19 information between countries, specifically vaccination and test-taking information. At the heart of SPIN is a permissioned blockchain network of peers, representing participating countries. Peers are the blockchain nodes and accept requests from clients, which represents entities that are involved in issuing vaccine and test certificates, such as hospitals and clinics, and verifying them, such as border security departments. Clients issue transactions, such as generating a vaccine certificate, to their corresponding peers, which execute the transactions on the blockchain network.  
The framework facilitates the exchanging of public and private data between peers (see Section 2.2 for details). Public data may include aggregate data, such as the number of vaccinated people and the number of daily cases. Private data include travel requests for individuals containing their COVID-19 test and vaccination certificates.  
Moreover, our framework employs a fingerprint mechanism based on template protection [23] to authenticate private information about individuals, such as their COVID-19 vaccination certificates (see Section 2.3 for details). Fingerprint authentication is commonly used to verify people across borders [24] because it has several desired features; for example, it is nontransferability and difficult to spoof. For instance, a person may easily pass her travel documents, or have them stolen by a fraudster pretending to be her. Committing such a crime becomes more difficult with fingerprint and biometric authentication in general.  
We explain the main components of SPIN, as shown in Figure 1, in the following subsections. Figure 1 shows four different countries that are represented by their peers. A client is able to communicate with its representative peer in the given country. Each peer may execute a transaction to the blockchain containing public data for other peers to read. Any peer can privately send data to exchange COVID-19 certificates with another peer residing in a different country, while its hashed data are written on the blockchain.  
To simplify the discussion and without a loss of generality, we use the term \"COVID-19 certificate\" to refer to both COVID-19 vaccine and test result certificates. The certificates contain all necessary information, such as the administration date, validity period, and result.
# Construction Progress and Aviation Flight Test of BDSBAS
## 3 BDSBAS Service Performance Evaluation
### Integrity  
An integrity event is defined as an event where the PE is greater than or equal to the corresponding service PL (as shown in Figure 7).  
Figure 6: Using a 15 s sliding window to assess continuity risk. For a specific epoch, if the system is available at the beginning, and becomes unavailable for at least one second in the next 15 s (for example, the signal is lost for more than four seconds or the protection level exceeds the alarm threshold), then a continuity event occurs. If a service is unavailable within a second, this will result in 15 continuity events.  
Generally, the safety index can be used to describe the degree of coverage of the PL to the PE [35]:  
\[\text{Safety index}=\text{PL}/\text{PE} \tag{26}\]  
which indicates that if the PL can 100% cover the PE during the evaluation period, the safety index of each epoch will be greater than 1. At this point, this means that the system can correctly determine the availability of the service, no alarms will be missed, and no integrity events will occur. In addition to the safety index, the Stanford triangle diagram is often used internationally to represent the integrity of the service.
# Underwater Sea Cucumber Identification Based on Improved YOLOv5
###### Abstract  
In order to develop an underwater sea cucumber collecting robot, it is necessary to use the machine vision method to realize sea cucumber recognition and location. An identification and location method of underwater sea cucumber based on improved You Only Look Once version 5 (YOLOv5) is proposed. Due to the low contrast between sea cucumbers and the underwater environment, the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm was introduced to process the images to enhance the contrast. In order to improve the recognition precision and efficiency, the Convolutional Block Attention Module (CBAM) is added. In order to make small target recognition more precise, the Detect layer was added to the Head network of YOLOv5s. The improved YOLOv5s model and YOLOv5s, YOLOv4, and Faster-RCNN identified the same image set; the experimental results show improved YOLOv5 recognition precision level and confidence level, especially for small target recognition, which is excellent and better than other models. Compared to the other three models, the improved YOLOv5s has higher precision and detection time. Compared with the YOLOv5s, the precision and recall rate of the improved YOLOv5s model are improved by 9% and 11.5%, respectively.  
YOLOv5; sea cucumber identification; object detection; deep learning; computer vision; models
## 4 Conclusions  
An amperometric-type sensor consisting of two electrochemical cells based on YSZ electrolyte and two Pt electrodes for the measurement of CO and CO\({}_{2}\) concentrations was fabricated and tested in the temperature range of 600-750 \({}^{\circ}\)C in the presence of up to 10% CO and CO\({}_{2}\) in nitrogen. One of the cells was designed for sensing CO, while the other one was for measuring the concentration of CO\({}_{2}\). It was found that the limiting current in the cells linearly increased with the rise in the relevant carbon oxide concentrations, and there was no perceptible cross-sensitivity effect toward the other carbon oxide. However, a significant cross-sensing effect toward water vapor was detected, which caused the need to dry the analyzed gas prior to testing. The sensor showed a good reproducibility degree, with a scatter below 1%, and a satisfactory transient time not exceeding 6 min. The developed sensor allows simultaneous measurements of two gas components, CO and CO\({}_{2}\), which is more convenient and cost effective than using separate devices for sensing CO and CO\({}_{2}\). The capability of simultaneous measurement of CO and CO\({}_{2}\) concentrations in inert atmospheres is of high importance, as the carbon oxides may coexist in many practical applications. The sensor can be used for atmosphere control in food packaging, preservation and storage systems, in the pharmaceutical and chemical industry. Our future research aims to investigate the role of such impurities as hydrogen sulfide, hydrocarbons, oxygen, which may exist in an analyzed gas, in the sensing properties.  
Conceptualization, A.V.; methodology, A.K. and A.V.; validation, A.V. and L.D.; formal analysis, L.D.; investigation, A.K. and A.V.; data curation, A.V. and L.D.; writing--original draft preparation, A.V.; writing--review and editing, L.D.; visualization, L.D.; supervision, A.V.; project administration, A.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure
## 2 Dynamic PMSM Model with Presence of Rotor Fissure  
The typical mathematical model of the synchronous machine can be described in the rotor reference frame (\(dq\)) coordinates as [1,2]:  
\[\frac{di_{d}}{dt}=\frac{V_{d}}{L_{d}}-\frac{r_{s}}{L_{d}}i_{d}+\frac{L_{q}}{L_ {d}}\omega_{r}i_{q} \tag{1}\]  
\[\frac{di_{q}}{dt}=\frac{V_{q}}{L_{q}}-\frac{r_{s}}{L_{q}}i_{q}+\frac{L_{d}}{L_ {q}}\omega_{r}i_{q}-\frac{\lambda_{m}}{L_{q}}\omega_{r} \tag{2}\]  
where \(L_{d}\) is the \(d\)-axes stator inductance and \(L_{q}\) represent the \(q\)-axes stator inductance, \(i_{d}\) is the stator current in \(d\)-axes, \(i_{q}\) represent the stator current in \(q\)-axes, \(V_{d}\) is the stator voltage in \(d\)-axes and \(V_{q}\) represent the stator voltage in \(q\)-axes, \(r_{s}\) represent the stator resistance per phase, \(\omega_{r}\) is the rotor mechanical angular speed and \(\lambda_{m}\) is the rotor flux. The differential equations that define the dynamics of the PMSM rotor with respect to angular velocity and the angular position (\(\theta\)) of the rotor are determined as follows [1, 2, 3, 9, 10]:  
\[\frac{d\theta}{dt}=\omega_{r} \tag{3}\]  
\[\frac{d\omega_{r}}{dt}=\frac{1}{f_{t}}\Big{[}T_{e}-T_{L}-F_{f}(\omega)-\beta \omega_{r}\Big{]} \tag{4}\]  
where \(P\) is the number of pole pairs, \(I_{t}\) is the moment of inertia taking into account the degradation of the rotor shaft, \(F_{f}(\omega)\) are other disturbances caused by coupling the load torque, \(\beta\) is the viscous friction coefficient and \(T_{L}\) is the load torque. The term \(T_{L}\) can deteriorate the control performance if it is not appropriately estimated. The friction model has been used in [22,23] to describe the compensation of the unknown frictions. In the control law, it should be considered that the mathematical models of friction can be defined as intermittent or continuous events in some instants of time during the operation of the motor, causing the control scheme to calculate non-smooth control signals. Therefore, the friction torque \(F_{f}(\omega)\) can be given by  
\[F_{f}(\omega)=a_{1}\tanh(c_{1}\omega)+a_{2}[\tanh(c_{2}\omega)-\tanh(c_{3} \omega)] \tag{5}\]where \(\omega\) represent the speed rotor; \(a_{1}\) and \(a_{2}\) are different friction levels; and \(c_{1}\), \(c_{2}\), and \(c_{3}\) represent coefficients to estimated different friction effects and are positive constants. In this work, the following assumptions are made: (a) \(\omega_{r}\), \(i_{d}\) and \(i_{q}\) are available; (b) \(T_{L}\) is unknown, \(\dot{T}_{L}\) is continuously differentiable; (c) \(F_{f}(\omega)\) is continuously differentiable and (d) the desired speed \(\omega_{rd}\) is constant and \(\dot{\omega}_{rd}\), \(\dot{\omega}_{rd}\) are continuously differentiable. The input of dynamic Equation (4) is the motor torque vector \(T_{e}\) which is calculated by  
\[T_{e}=P\left[\frac{3}{2}\big{(}L_{d}-L_{q}\big{)}i_{d}i_{q}+\frac{3}{2}\lambda _{m}i_{q}\right] \tag{6}\]