esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 5 Federated Training of Edge Autoencoders
### IID--Partitioned Dataset
#### 5.2.2 Robustness to Anomalous Training Data  
To assess the robustness of FL in the presence of anomalous data, we extend the previous IID scenario by introducing corrupted training samples to a subset of devices. Specifically, while most ESP32-CAM devices train on standard MNIST subsets, one or more selected devices are assigned fully corrupted datasets from MNIST-C, which con tains anomalous versions of digits with distortions such as blur, noise, fog, and other environmental effects.  
This setup simulates realistic deployment conditions, where certain edge devices may collect low-quality or distorted data due to environmental factors. For example, a device operating outdoors might gather anomalous data during sudden weather changes like fog or rain. If such a device trains solely on these corrupted data, its local model may lead to poor global performance.  
In contrast to approaches that attempt to detect or exclude such anomalous clients, our study evaluates how much natural resilience standard FL provides without relying on additional defense mechanisms. Most existing FL robustness research focuses on identifying and mitigating the influence of malicious clients [53], often through techniques such as gradient clipping, client selection, or outlier detection, which aim to reduce their impact on the global model or exclude them from the FL process (e.g., [54; 55; 56]). But there is less emphasis on understanding how much anomaly exposure a federated model can naturally tolerate without intervention. Our work fills this gap by empirically evaluating how standard FL setups handle scenarios where certain clients are trained entirely on anomalous data. This allows us to assess whether clean models from unaffected devices can outweigh the influence of corrupted ones during aggregation, and whether corrupted clients can improve through federated aggregation.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### The Preprocessing of the Iterative Positioning Algorithm
#### 3.2.1 RSSI Ranging Algorithm  
The concept of the RSSI ranging algorithm is to calculate the distance between the transmitting signal node and the receiving signal node by measuring the received signal strength, since there is a varying degree of losses in the propagating process of wireless signals. Therefore, it is imperative to build an appropriate RSSI ranging model. At present, the model used commonly is the log-distance distribution model [40; 41; 42; 43].  
_Pd_ is used to indicate the power measurement corresponding to the distance between two nodes, denoted \(d\). _Pd_\({}_{0}\) is used to indicate the power measurement corresponding to the distance between two nodes, denoted _d_\({}_{0}\). The relationship between _Pd_ and \(d\) can be expressed as  
\[Pd=\frac{Pd_{0}}{\left(d_{0}/d_{0}\right)^{n}}, \tag{1}\]  
where \(n\) is a signal propagation factor, which is usually obtained by empirical value or actual calibration.  
The logarithmic processing is performed on both sides of Equation (1), and after arranging, Equation (2) can be obtained as  
\[\lg Pd=\lg Pd_{0}-n\lg\left(\frac{d}{d_{0}}\right), \tag{2}\]  
The relationship between RSSI value and power can be expressed as  
\[RSSI= 10\lg p, \tag{3}\]  
Thus, a mathematical model of RSSI ranging can be obtained as  
\[P(d)= P(d_{0})-10n\lg\left(\frac{d}{d_{0}}\right), \tag{4}\]  
where _P_(_d_) is the RSSI value when the distance between two nodes is \(d\) and _P_(_d_\({}_{0}\)) is the RSSI value when the distance between two nodes is _d_\({}_{0}\).
## 6 Simulation Results and Discussion
### Network Performance Parameters: Discussions
#### 6.2.1 Stability Period and Network Lifetime  
Figure 6 shows the stability period and network lifetime of the compared protocols. It is observed from the figure that the existing cooperative scheme, Co-CESat, has less of a stability period than InCo-CESat and ElInCo-CESat. In Co-CESat, cooperative nodes always forward the data irrespective of the channel conditions. In this strategy, nodes consume more energy in extra transmissions of the same data. Each cooperative node forwards the data of two other nodes along with its own data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 8 \\ \hline Number of sink & 1 \\ \hline Initial energy &
\begin{tabular}{c} Cooperative node: 0.3 J \\ Normal node: 0.15 \\ \end{tabular} \\ \hline Offered load & 10,000 bits/node \\ \hline Average wait time [33] & 4 s/packet \\ \hline BER threshold & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters for WBAN protocols.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node No.** & **X-Axis (m)** & **Y-Axis (m)** \\ \hline
1 & 0.45 & 1.6 \\
2 & 0.2 & 1.5 \\
3 & 0.7 & 1.5 \\
4 & 0.1 & 0.85 \\
5 & 0.8 & 0.85 \\
6 & 0.2 & 0.5 \\
7 & 0.7 & 0.5 \\
8 & 0.7 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The coordinates of nodes deployed on the human body.  
The burden on cooperative nodes reduces as the normal source node dies, thereby making the network stable from \(2\times 10^{4}\) s to \(2.9\times 10^{4}\) s in InCo-CESStat. It is obvious from Figure 6 that incremental relay-based approaches prove to be more stable with greater network lifetime. The reason is that incremental relaying adapts to the channel conditions, and relays forward the data only when they is needed. EInCo-CESStat has 24% less of a stability period than InCoCESStat. This is due to the extra energy consumption by the third node, which acts as a relay when two stages of cooperative relaying are unsuccessful. The third relay node, which is invoked for forwarding the data in the fourth phase, is always a normal source node having less residual energy than cooperative nodes. Therefore, the first node in EInCo-CESStat dies earlier than in InCo-CESStat, which has only two advanced nodes as cooperative nodes. In InCo-CESStat and Co-CESStat, some nodes remain alive for a greater duration of time due to the extra energy assigned to them at the start of the network. After the death of normal source nodes, the burden on cooperative/advanced nodes reduces, which makes the network stable for a certain period.
## 4 D-PMU Placement Order Determination Based on Spatial Electrical Distance
### Determining the Order of D-PMUs  
According to the spatial electrical distance between the buses, the order of placement of D-PMUs can be determined to maximize the state estimation accuracy during each D-PMU placement stage. The specific steps of the determination method are as follows:  
1. All buses in the optimal solution that need to be installed with D-PMU are combined into a candidate set;2. For each bus in the candidate set, all newly added observable buses should be found after the given bus has been assigned a D-PMU;
3. By summing the associated weights \(w_{ij}\) of the bus with those of the newly added observable buses, the integrated weight \(C_{i}\) is obtained;
4. The bus with the largest value of \(C_{i}\) is chosen as the next location for installing the D-PMU. Then, this bus is removed from the candidate set;
5. Is the candidate set empty? If the answer is yes, end. If the answer is no, go to step 2. The flow of the proposed OPP method is shown in Figure 3.
# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 3 Algorithm Design
### USC Descriptor  
Commonly, feature point description methods include Spin Image descriptor [22], PFH descriptor, FPFH descriptor [23] and 3 DSC descriptor. Reference [24] proposes PFH feature descriptor and FPFH feature descriptor, and uses FPFH feature descriptor and SAC-IA (SAmple Consensus Initial Alignment) for registration. Reference [25] proposes a new 3D descriptor LP-PPF, which can identify repetitive structures correctly and achieve accurate registration between adjacent point clouds pairs.  
Figure 3: The original point clouds, the point clouds after voxel filter and the point clouds after improved voxel filter of the Bunny model.  
3D Shape Context [26] (3 SDC) is a 3D local feature point descriptor extended from 2D shape context. Reference [27] uses the 3 DSC descriptor to describe the extracted feature points, then RANSAC algorithm is used for coarse registration and ICP algorithm is used for fine registration. Compared with the ICP algorithm, the SAC-IA + ICP algorithm and the 3 DHoPD + ICP algorithm, the 3 DSC + RANSAC + ICP has a faster registration speed. Figure 3a shows the meshing diagram of 3 DSC. It takes the normal vector \(n\) of a point as the local reference coordinate axis, and its disadvantage is that it lacks a repeatable local reference coordinate system and needs to calculate descriptors in multiple rotations in different directions. Therefore, the 3 DSC descriptor can be improved to use the unique shape context (USC) descriptor. It only needs to calculate one descriptor at each model feature, which not only reduces the memory consumption, but also improves the calculation efficiency. The main steps for USC to describe feature points are as follows:  
(1) Given a feature point \(k\) and a spherical neighborhood with \(k\) as the center and \(R\) as the radius, the weighted covariance matrix \(M\) of the points in the neighborhood is shown in Formula (4):  
\[\left\{\begin{array}{l}M=\frac{1}{Z}\sum\limits_{s:d_{s}\leq R}(R-d_{s})(k_{ s}-k)(k_{s}-k)^{T}\\ d_{s}=\|k_{s}-k\|\\ Z=\sum\limits_{s:d_{s}\leq R}(R-d_{s})\end{array}\right. \tag{4}\]  
where \(k_{s}\) is any point in the spherical neighborhood, and \(s\) is the number that satisfies the condition of \(\|k_{s}-k\|\leq R\).  
(2) According to the eigenvector decomposition of \(M\), the local reference coordinate system of the feature point is determined, and the spherical neighborhood around the feature point is uniquely divided into grids along the three coordinate axes of the local reference coordinate system. The weighted value of the points in each grid is the USC descriptor, as shown in Formula (5):  
\[w(k_{j})=\frac{1}{\rho_{j}\sqrt[3]{V(x,y,z)}} \tag{5}\]  
where \(V(x,y,z)\) represents the volume of \(x\) direction, \(y\) direction and \(z\) direction, and \(\rho_{j}\) is the density of points in the corresponding volume.  
Figure 4 shows the meshing diagram of 3 DSC and USC.  
Figure 4: The meshing diagram of 3 DSC and USC. (**a**) The meshing diagram of 3 DSC. (**b**) The meshing diagram of USC.
## SEIR Epidemic Model with Eventual Vaccination of Newborns
### Equilibrium Points  
There are two equilibrium points, that is, the disease-free one, which is obtained from (1) by equalizing to zero all the time-derivatives of Equation (1) and equalizing the infectious to zero. This leads to  
\[EQ^{0}(q)=\left(S^{0}(q),E^{0},\ I^{0},R^{0}(q)\right)=\left((1-q)A/\mu,0,\ 0,\ qA/\mu\right) \tag{4}\]  
On the other hand, the endemic equilibrium point is obtained from Equation (1) by equalizing again to zero all the time-derivatives while keeping a non-zero infectious subpopulation which yields:  
\[EQ^{*}(q)=\left(S^{*}(q),E^{*}(q),\ I^{*}(q),R^{*}(q)\right)\]  
where:  
\[S^{*}(q)=\frac{(1-q)A}{\beta I^{*}+\mu}\]  
\[E^{*}(q)=\frac{\mu+\gamma+\alpha}{\sigma}I^{*} \tag{5}\]  
\[I^{*}(q)=\frac{(1-q)A\sigma}{(\mu+\sigma)(\mu+\gamma+\alpha)}-\frac{\mu}{\beta}\]  
\[R^{*}(q)=\frac{\gamma I^{*}+qA}{\mu}\]  
Recombining the above equations yields:  
\[S^{*}(q)=\frac{(\mu+\sigma)(\mu+\gamma+\alpha)}{\beta\sigma}\]  
\[E^{*}(q)=\frac{(1-q)A}{\mu+\sigma}-\frac{\mu(\mu+\gamma+\alpha)}{\beta\sigma} \tag{6}\]  
\[I^{*}(q)=\frac{(1-q)\sigma A}{(\mu+\sigma)(\mu+\gamma+\alpha)}-\frac{\mu}{\beta}\]  
\[R^{*}(q)=\frac{A}{\mu}\bigg{(}\frac{(1-q)\gamma\sigma}{(\mu+\sigma)(\mu+\gamma +\alpha)}+q\bigg{)}-\frac{\gamma^{\prime}}{\beta}\]  
From Equation (5) into Equation (3) equalized to zero yields the total endemic equilibrium population:  
\[N^{*}=\frac{A-\alpha I^{*}}{\mu}=\frac{A}{\mu}\bigg{(}1-\frac{(1-q)\alpha\ \sigma}{(\mu+\sigma)(\mu+\gamma+\alpha)}\bigg{)}+\frac{\alpha}{\beta} \tag{7}\]  
Note that all the subpopulations of Equation (5), see also Equation (6), have to be non-negative for the endemic equilibrium point to exist in the first orthant of the state-space so that it be reachable. Otherwise, Theorem 1 would be violated. Thus, we have the subsequent result:  
**Proposition 1**.: _The endemic equilibrium point is reachable if and only if the transmission rate is large enough to be non less than a critical value according to:_  
\[\beta\geq\beta_{c}(q)=\frac{\mu(\mu+\sigma)(\mu+\gamma+\alpha)}{A\sigma(1-q)}\]  
_for any \(q\in[0\,\ 1)\). If the transmission rate equalizes its critical value, then the endemic equilibrium point is coincident with the disease-free one._
## 1 Introduction
The real-time monitoring capabilities of the OSWS play a crucial role in providing accurate data that aids decision-making processes in diverse domains such as agriculture, transportation, and air quality monitoring applications [18]. The primary objectives of this research revolve around developing and implementing an OSWS that offers a cost-effective solution for environmental monitoring, especially catering to small users. This paper aims to harness the open-source nature of the OSWS to enhance public data sharing and ensure seamless communication between the sensing platform and end users, ensuring that valuable data is readily available for making informed decisions and addressing environmental challenges stemming from climate change. Furthermore, the design, development, and evaluation of this weather station serve as a model for future low-cost and efficient monitoring systems. Its affordability, accessibility, and real-time monitoring capabilities can significantly contribute to advancing sustainability efforts and mitigating the adverse effects of climate change. The WS's interface with end users is crucial in facilitating effective data communication and enabling informed decision-making. Overall, the affordability, accessibility, and real-time monitoring capabilities of the OSWS present a cost-effective solution for environmental monitoring and data sharing. The comprehensive approach in designing, developing, and evaluating this device establishes valuable information for developing similar low-cost and efficient monitoring systems, ultimately promoting sustainability and mitigating climate change impacts. To offer a clear outline of the paper, the structure is as follows: Beginning with the Introduction in Section 1, the background and significance of the study are explored. Section 2 presents a thorough literature review, highlighting essential research and relevant developments. In Section 3, materials and methods are detailed and subdivided into Section 3.1, focusing on the design and development of the weather station, and Section 3.2, discussing data validation methods. Section 4 presents the results, further segmented into Section 4.1, showcasing the design results; Section 4.2, assessing the ergonomics; and Section 4.3, comparing the data. Section 5 delves into the discussion, analyzing and interpreting our findings. The paper culminates with the conclusions in Section 6, summarizing our research outcomes and their implications.
## 8 Application and Discussion of the Results
### Application on SSI
#### 8.1.3 Consensus Layer  
Most publications within the consensus layer focus on PoW and PoS, either explicitly or implicitly. However, PoW and PoS are typically not chosen for SSI. For example, Hyperledger Indy uses a variant of redundant Byzantine fault tolerance (RBFT) called Plenum [144] as the consensus algorithm, whereas Hyperledger Fabric lets the administrators choose [145]. Following, the literature found for our survey and, hence, the threats, might not be applicable to SSI.
## 3 Fundamentals: Workpiece and Moving Head Kinematics
### Coordinate Systems and Transformations  
As with every light projection system, a relationship between the moving head projector and the illuminated object, or more specifically, between the moving head coordinate system (\({}^{MH}\)) and the object coordinate system, called the reference coordinate system (\({}^{Ref}\)), has to be defined. This relationship, indicated by the red arrow in Figure 4, is expressed by the \(4\times 4\) homogeneous coordinate transformation matrix \({}^{MH}_{Ref}\mathbf{T}\) in Equation (3), which generally consists of a scale, zero to three rotations around the coordinate axis, and a translation vector. The scale is 1 in this case, but can be adjusted for coordinate systems with different-length units. The rotation part is expressed by the \(3\times 3\) rotation matrix \({}^{MH}_{Ref}\mathbf{R}_{xx}\), where the right side subscript indicates the order of the rotations performed around the designated axis. The translation is expressed by the translation vector \([t_{x},t_{y},t_{z}]^{T}\).  
\[{}^{MH}_{Ref}\mathbf{T}=\begin{bmatrix}r_{11}&r_{12}&r_{13}&t_{x}\\ r_{21}&r_{22}&r_{23}&t_{y}\\ r_{31}&r_{32}&r_{33}&t_{z}\\ 0&0&0&1\end{bmatrix}=\begin{bmatrix}&\begin{matrix}&t_{x}\\ &^{MH}_{Ref}\mathbf{R}_{xx}&t_{y}\\ &^{MH}_{Ref}\mathbf{R}_{xx}&t_{z}\\ 0&0&0&1\end{bmatrix}\end{bmatrix} \tag{3}\]  
The left side sub- and superscripts of the matrix symbols in Equation (3) follow the tensor notation, where the subscript designates the source and the superscript the destination coordinate system.  
Calculating the transformation between the reference and the moving head coordinate system will be the core task of the calibration process explained in Section 4. Furthermore, it will be required for an assistance application in order to calculate the control inputs for the moving head from any given coordinate input in the reference coordinate system.
## Conceptual Scheme  
The set of applications supporting our monitoring and follow-up system for the elderly includes several modules, as shown in Figure 3, namely:  
* A data collection module for personal use, consisting of an ESP32-LoRa microcontroller with sensors, as mentioned above;
* A housing status data collection module, consisting of an ESP32/LoRa microcontroller with environmental sensors (temperature, humidity, carbon monoxide, gas and smoke).  
Figure 3: System conceptual scheme.  
The LoRa gateway is connected to the Internet, receiving the data that are periodically sent from the LoRa nodes. As for LoRa nodes, these are divided into two distinct types--personal LoRa nodes and residential LoRa nodes.
## 1 Introduction
[20] tested the potential of improving heavy-duty diesel engine performance using an ORC and a fixed geometry radial turbine. The results showed that the radial turbine presented a maximum power of 2.8 kW. [PERSON] et al. [21] investigated the possibility of capturing the wasted heat of engine coolants and exhaust gases using a supercritical ORC. An improvement of 8% in fuel efficiency was obtained. [PERSON] et al. [22] concluded from their experiment that the engine BSFC could be decreased by 7.5% when adapting an ORC to capture the wasted heat in the engine coolant. Recently, [PERSON] et al. [5] analyzed the performance of a preheated ORC as a waste heat recovery system in a ship two-stroke low-speed main engine. The authors utilized the wasted heat in both exhaust gases and coolants. The maximum cycle power and efficiency were 491.3 kW and 12.84%, respectively. It is obvious that studies that investigate the potential of variable geometry turbines in engine-ORC systems are extremely limited. In fact, although [16; 17; 19] evaluated the benefits of variable geometry turbines, they did not cover the design process of the turbine. Moreover, the authors in studies [16; 17; 18; 19] did not study the effects of the variable geometry turbines on the engines' power and BSFC. Therefore, the aim of this study is to shed more light on the design process of the turbine and explore its efficacy in improving heavy-duty diesel engines in terms of power and BSFC. It is worth mentioning that the authors have published several studies on the design and optimization of fixed geometry radial inflow turbines [23].
## 3 Frequency Characteristics of Radiation Noise under Various Rotational Speeds  
### Changing Patterns of Radiation Noise in a Wide Frequency Range  
By measuring the SPL characteristics of different monitoring points in a circumferential direction, the changing patterns of radiation noise in a wide frequency range were studied.  
P1 (270\({}^{\circ}\), in the direction against the outlet), P5 (0\({}^{\circ}\), the minimum noise point), P9 (90\({}^{\circ}\), in the direction facing the outlet) and P13 (180\({}^{\circ}\), the minimum noise point) [28] are selected. Figure 3 illustrates the frequency characteristics of the four monitoring points in a wide range of frequencies. It can be observed that at rated rotational speed (2900 rpm), the SPLs of different monitoring points show a fluctuating ascending trend in the low frequency range from 31.5 to 1000 Hz, due to the excitation results of periodic interference between the impeller and volute [29], according to Equations (6) and (7),  
\[f_{bpf}=\frac{nz}{60}1 \tag{6}\]  
\[f_{spf}=\frac{n}{60}l \tag{7}\]  
where \(f_{bpf}\) and \(f_{spf}\) represent BPF and shaft-passing frequency (SPF), \(z\) is the number of blades and \(l\) is harmonic sequence number (\(l\) = 1, 2, 3,... ), SPLs reach a peak value at each order of BPF and SPF.  
Unlike the frequency characteristics of flow noise in pump pipelines [11], the maximum value of radiation noise outside the pump appears in the range from 1000 to 2000 Hz, i.e., in the range of high order harmonics of BPF. The possible reason is that the native vibration frequencies of pump structure  
Figure 2: Arrangement of monitoring points in a circumferential direction.  
belong to this range [30]. After that, the SPLs of different monitoring points decrease gradually when the frequency is higher than 2000 Hz. Besides, the SPLs at most frequencies of P9 are generally higher than those of other monitoring points. This can be explained by that P9 is located in an outlet direction, the radiation noise at P9 is more affected by the internal flow noise in the outlet pipeline and the vibration of the outlet pipeline.  
In addition, the radiation noise frequency characteristics of P9 under various rotational speeds are compared in Figure 4. The radiation noise shows the same pattern of changes as those shown in Figure 3 under various rotational speeds. Moreover, with the increase in rotational speed, SPLs at various frequencies show an ascending trend and reach a maximum at 2900 rpm.  
Figure 4: Changes in SPL in a wide frequency range under various rotational speeds (P9).  
Figure 3: Changes in sound pressure level (SPL) at different monitoring points in a wide frequency range (2900 rpm).