esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology
### Intelligent Prediction of Stuck Pipe Probability Based on LSTM
#### 3.2.3 Training, Testing and Optimization of Intelligent Prediction Model for Stuck Pipe  
Generally, the optimal hyper-parameter combination is obtained through trial and error. We optimize the following parameters: time-stepping of the input data, the learning rate, the number of LSTM network layers, the number of LSTM neurons, and the activation function. In order to reduce the number of tests, the orthogonal test method (L18.3.5) is used to set the test plan [28].  
The FAR (false alarm rate), MAR (missed alarm rate) and F1 are used to evaluate and optimize the model. The evaluation indexes of classification problems are shown in Table 2, The relevant calculation formulas are as follows.  
\[\text{Precision}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}} \tag{14}\]  
\[\text{FAR}=1-\text{Precision} \tag{15}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{16}\]  
\[\text{MAR}=1-\text{Recall} \tag{17}\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{18}\]  
We combine FAR, MAR, F1 and model complexity to optimize the model. The evaluation indicators of each model are shown in Figure 7.  
F1 has been widely employed in information retrieval which can well evaluate the advantages and disadvantages of the classification model [29]. Therefore, our criterion for model selection is to select a model with low complexity when F1 are not significantly different. The larger the F1, the better the model. As shown in Figure 7a, the first, second, fourth, seventh, ninth, tenth, thirteenth, fifteenth and seventeenth models have higher F1, and the values are close to each other. Considering there are not many training samples, in order to prevent over fitting and improve the generalization ability of the model, as shown in Figure 7b, the first and the seventeenth model with lower complexity are selected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Real Category**} & \multicolumn{2}{c}{**Forecast Category**} \\ \cline{2-3}  & **Positive (Stuck)** & **Negative (Non Stuck)** \\ \hline Positive (Stuck) & True Positive & False Negative \\ Negative (Non stuck) & False Positive & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation indexes of classification problem.  
In the process of drilling, we pay more attention to the MAR, that is, we can accept a certain FAR, but cannot accept the MAR (all stuck can be predicted, but some non-stuck may be predicted as stuck). Therefore, when using the FAR and the MAR to optimize the model, the model with the lower MAR should be selected. As shown in Figure 7d, the first model with smaller MAR is selected as the best model.  
The prediction results of the best model are shown in Section 3.3. The prediction result is given by stuck pipe probability, and when the probability is greater than 0.4, it is considered as stuck pipe, otherwise it is not.
# Observations on the Computation of Eigenvalue and Eigenvector Jacobians +
## 4 Compact Expressions for Eigenvalue and Eigenvector Jacobians
### Eigenvector Jacobian  
The objective is now to solve Equation (39) for \(\partial\mathbf{v}/\partial\mathbf{A}_{\text{vec}}\). Unlike the result from [39], there is no need to incorporate a Null Space Matrix (or to use a pseudoinverse) since the term \(\mathbf{A}-\lambda\mathbf{I}-\mathbf{v}\mathbf{v}_{0}^{H}\mathbf{A}/\alpha\) is already full rank and invertible as long as the eigenvalue under consideration is non-zero. This fact is straightforward to show by considering the column space of the term \(\mathbf{A}-\lambda\mathbf{I}\), which will generally be rank \(n-1\) (assuming \(\lambda\) is simple and \(\mathbf{A}\) is full rank). Specifically, \(\mathbf{A}-\lambda\mathbf{I}\) spans \(\mathbb{C}^{n-1}\) with a null space in the direction of \(\mathbf{v}\). Now, consider the column space of the term \(\  
which is a function of only \(\mathbf{A}\), \(\lambda\), \(\mathbf{v}\), \(\mathbf{v}_{0}\), and \(\alpha\). Additionally, manipulating the Kronecker products allows for a final form of  
\[\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}}=\mathbf{v}^{T} \otimes\left[\left(\mathbf{A}-\lambda\mathbf{I}-\frac{\mathbf{v}\mathbf{v}_{0 }^{H}\mathbf{A}}{\alpha}\right)^{-1}\left(\frac{\mathbf{v}\mathbf{v}_{0}^{H} }{\alpha}-\mathbf{I}\right)\right]. \tag{41}\]  
In the case where it is necessary to find the eigenvector Jacobians when the eigenvalue is zero, the Null Space Matrix can be used as discussed in [39] to make the left-hand side invertible, resulting in  
\[\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}}=\mathbf{v}^{T} \otimes\left[\left(\mathbf{A}-\lambda\mathbf{I}-\frac{\mathbf{v}\mathbf{v}_{0 }^{H}\mathbf{A}}{\alpha}+\mathbf{N}\right)^{-1}\left(\frac{\mathbf{v} \mathbf{v}_{0}^{H}}{\alpha}-\mathbf{I}\right)\right]. \tag{42}\]
## Nomenclature  
\begin{tabular}{l l} K\({}_{ij}\) & Element of \(i\)th row, \(j\)th column of the stiffness matrix \\ \(x,y\) & Displacement of the wheel in the \(x\) and \(y\) directions \\ \(x_{a},y_{a}\) & Displacement of the bearing at the left end in the \(x\) and \(y\) directions \\ \(x_{b},y_{b}\) & Displacement of the bearing at the right end in the \(x\) and \(y\) directions \\ \(\mathbf{K}_{x},\mathbf{K}_{y}\) & Stiffness matrix of the \(o^{\prime}xz\) plane and \(o^{\prime}yz\) plane of the rotor system \\ \(\mathbf{K}_{c}\) & Stiffness matrix considering the axial bending deformation without elastic support \\ \(\theta_{x},\theta_{y}\) & Yaw angle of the wheel around the \(x\) axis and the \(y\) axis \\ \(T_{x},T_{y}\) & Torsional moment in the \(x\) and \(y\) directions \\ \(\omega\) & Rotational angular velocity of the rotor \\ \(\omega_{d}\) & Angular velocity of the air-excited turbine disk \\ \(p_{x},p_{y}\) & Rub-impact force in the \(x\) and \(y\) directions \\ \(m_{d},m_{a},m_{b}\) & Mass of disk, left and right support \\ \(c_{c},c_{d},c_{b}\) & Damping of disk, left and right support \\ \(k_{a},k_{b}\) & Support stiffness at left and right end \\ \(l,a,b\) & Shaft length and length between left, right ends and wheel \\ \(\ddot{x},\ddot{y}\) & Acceleration of the wheel in the \(x\) and \(y\) directions \\ \(\ddot{x},\dot{y}\) & Velocity of the wheel in the \(x\) and \(y\) directions \\ \(\ddot{x}_{a},\ddot{y}_{a}\) & Acceleration of the bearing at the left end in the \(x\) and \(y\) directions \\ \(\dot{x}_{a},\dot{y}_{a}\) & Velocity of the bearing at the right end in the \(x\) and \(y\) directions \\ \(\ddot{x}_{b},\ddot{y}_{b}\) & Acceleration of the bearing at the left end in the \(x\) and \(y\) directions \\ \(\ddot{x}_{b},\dot{y}_{b}\) & Velocity of the bearing at the right end in the \(x\) and \(y\) directions \\ \(\ddot{\theta}_{x},\ddot{\theta}_{y}\) & Yaw angles acceleration of the wheel around the \(x\) axis and the \(y\) axis \\ \(\dot{\theta}_{x},\dot{\theta}_{y}\) & Yaw angles velocity of the wheel around the \(x\) axis and the \(y\) axis \\ \end{tabular}
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### Taxonomy of Energy Consumption Solutions
#### 3.7.1 Routing Protocols
The EERH scheme is dynamically established the routing paths according to the transmission directions of event packets and the residual energy of the underlying sensors and their neighbors. According to [134] presented energy and collision aware WSN routing method for IoT networks. The proposed algorithm is based on AODV protocol; however, it replaces the hop count metric with the link quality and collision count. The protocol improves performance in terms of path stability, energy efficiency, network lifetime and delay. Study by [135] proposed a gateway clustering energy-efficient centroid (GCEEC)-based routing protocol. In this protocol, the sensing filed is divided into clusters and each cluster has a gateway node and the CH node in this cluster is chosen from the centroid position. The gateway reduces the data traffic from the CH node and dispatches the data to the final destination and thus extend the network lifetime.
## 2 Experimental Methods
### Measurement of 24 h Integrated PM\({}_{2.5}\) and Aerosol Light Absorptions
Real-time aerosol light absorption coefficients were observed only at the CNU site every 1 min using a dual-spot seven-wavelength (370, 470, 520, 590, 660, 880, and 950 nm) aethalometer (AE33, Aerosol d.o.o., Slovenia) equipped with a PM\({}_{2.5}\) inlet impactor operated at a flow rate of 5 L/min. The wavelength-dependent mass absorption cross-sections used to calculate the BC concentration were 18.47, 14.54, 13.14, 11.58, 10.35, 7.77, and 7.19 m\({}^{2}\) g\({}^{-1}\) for 370, 470, 520, 590, 660, 880, and 950 nm, respectively. Detailed descriptions of the aerosol light absorption measurement methods were published previously [40; 47]. In this study, the wavelength-dependent BrC absorption (Abs\({}_{\rm{BrC},\lambda}\)) was calculated by subtracting the BC absorption (Abs\({}_{\rm{BC},\lambda}\)) from the total aerosol absorption (Abs\({}_{\rm{abs},\lambda}\)) [48]. Additionally, the wavelength-dependent BC absorption at wavelengths of \(<\)880 nm was estimated by assuming that most of the total aerosol absorption at 880 and 950 nm is associated with BC [48; 49]. Figure 3 shows the diurnal cycles of aerosol absorption coefficients at 370 and 880 nm during summer and winter at the CNU site. The aerosol absorption coefficients at 370 and 880 nm during both summer and winter at the CNU site were calculated by subtracting the BC absorption (Abs\({}_{\rm{BC},\lambda}\)) from the total aerosol absorption (Abs\({}_{\rm{abs},\lambda}\)) [48]. Additionally, the wavelength-dependent BC absorption at wavelengths of \(<\)880 nm was estimated by assuming that most of the total aerosol absorption at 880 and 950 nm is associated with BC  
and winter exhibited clear diurnal patterns; there were two peaks, corresponding to the morning and evening rush hours, of which the morning peak was stronger, which is fairly consistent with the trends in hourly traffic volume. The higher absorption coefficient at 370 nm was due to an increased contribution of light absorbing organic aerosols at lower wavelengths. Figure 2: Hourly total traffic density of (**a**) all vehicles at the BKO and CNU sites, (**b**) passenger vehicles at BKO, and (**c**) diesel vehicles at BKO.
# YOLOv4-MN3 for PCB Surface Defect Detection
## 4 Experiment
### Impacts of Different Backbone Networks  
The CSPDarknet53 backbone network in the original YOLOv4 suffers from a large number of parameters and a huge computational burden. We tried replacing it with lightweight networks; however, not without a loss of detection performance. Six backbone networks--VGG16, Resnet50, Darknet53 (backbone network of YOLOv3), CSPDarknet53 (backbone network of YOLOv4), MobileNetV2 and MobileNetV3--were selected and comparison experiments were conducted to verify their impact on YOLOv4. VGG16 and Resnet50 are commonly used backbone networks, while Darknet53 and CSPDarknet53 are the SOTA backbone networks for one-stage detectors. The low-power and low-latency of parameterized MobileNet is commonly used for small models [33]. The comparison results are given in Table 3 according to the evaluation metrics given in Section 4.1.  
It can be seen that MobileNetV3 obtained the highest _mAP_ among all backbone networks. Compared with VGG16, Resnet50, Darknet53, CSPDarknet53, and MobileNetV2, the value of _mAP_ obtained by MobileNetV3 improves by 1.26%, 2.16%, 1.65%, 0.77% and 2.31% respectively. Meanwhile, MobileNetV3 achieved the highest \(F_{1}\) score among the six backbone networks with 2.66%, 5.16%, 2.83%, 0.66% and 4.50% greater improvement over VGG16, Resnet50, Darknet53, CSPDarknet53, and MobileNetV2 respectively. The results of the _mAP_ and \(F_{1}\) score presented in Table 3 indicate that MobileNetV3 outperforms other compared backbone networks in prediction accuracy. Comparing the results from  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone Network** & _mAP_ **(\%)** & \(F_{1}\) **(\%)** & _Params_ **(M)** & _Madds_ **(G)** & FPS \\ \hline VGG16 & 96.00 & 93.17 & 51.80 & 206.03 & 55.58 \\ Resnet50 & 95.10 & 90.67 & 61.54 & 53.98 & 56.74 \\ Darknet53 & 95.61 & 93.00 & 77.93 & 74.24 & 54.93 \\ CSPDarknet53 & 96.49 & 95.17 & 63.96 & 59.75 & 51.64 \\ MobileNetV2 & 94.95 & 91.33 & 38.66 & 26.71 & 56.99 \\
**MobileNetV3** & **97.26** & **95.83** & **39.59** & **26.15** & **57.12** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different backbone network.  
_Params_ and _Madds_ shows that MobileNetV3 greatly simplified the backbone network, requiring only 76.43%, 64.33%, 50.80% and 61.90% of the model parameters used by VGG16, Resnet50, Darknet53 and CSPDarknet53, respectively, and it achieved the lowest _Madds_ among these networks as well. The results also show that MobileNetV3 exhibited no significant difference between either MobileNetV2 or MobileNetV3 with respect to _Params_ and _Madds_. The _FPS_ comparison indicated that MobileNetV3 achieved the highest detection speed, with 57.12 _FPS_. Notably, the _Madds_ of MobileNetV3 reduced by 33.60 G, whereas its _FPS_ increased by 5.48 as compared with CSPDarknet53 in the original YOLOv4. In summary, MobileNetV3 achieved the highest detection accuracy, low model parameters, lowest _Madds_ and fastest detection speed, making it distinctly advantageous for detection.
# An Overview of CubeSat Missions and Applications
## 3 CubeSats as Earth Observation and Earth Science Platforms
### The Role of CubeSats in Earth Observation Missions
SATURN aims to deliver an initial demonstrative swarm of three 16U CubeSats equipped with a miniaturized SAR instrument which is developed by ARESYS S.r.l. (Vimodrone-Milan, Italy) and Airbus Italia S.p.A. (Rome, Italy). The SAR arrays of the SATURN CubeSats will operate in Multiple-Input-Multiple-Output (MIMO) mode (where all satellites will both transmit and receive signals) and are expected to deliver imagery with a 5 m resolution and a swath of 30 km. If the initial three-satellite swarm proves successful, a larger constellation comprising of 16 swarms of three satellites, distributed in four different orbits will follow. Furthermore, the SAR payloads developed for this demonstration mission may become the first commercially available SAR solutions for CubeSat applications. SATURN completed its preliminary design review in April 2024 with developmental work on the mission still continuing as of May 2025 [107; 108; 109]. Another relevant example can be traced on the other side of the Atlantic where SRI International, with the CubeSat Imaging Radar for Earth Science (CIRES) program, aims to deliver a 16U CubeSat equipped with a miniaturized deployable SAR payload in the form of a 5 m\({}^{2}\) deployable membrane. This SAR payload is also expected to provide a spatial resolution of 5 m and is designed for Interferometric Synthetic Aperture Radar (InSAR) operations in orbits with 500 km altitude [110]. One more example is the Zhixing-3 A Spacecraft, a 6U CubeSat that was developed by Beijing Smart Satellite Space Technology Co., Ltd. (Beijing, China). It was launched on 5 September 2022 and was deployed into orbit 188 days later from the Tianzhou-4 spacecraft. Its mission was to test and validate a series of technologies vital for the company's upcoming SAR imaging microsatellite constellation such as platform design, energy balance, operations, communication, attitude control, something that it successfully performed before eventually deorbiting [111; 112].
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
###### Abstract  
The use of Light Fidelity (LiFi) can enable the reduction of satellite mass by reducing the wiring harness while avoiding electromagnetic interference. In this paper, a LiFi-transceiver suitable for Telemetry and Telecommand (TMTC) intra-satellite communication is developed and evaluated. The focus of the implementation is on miniaturization and energy-efficiency. First test results with a simple Transimpedance-Amplifier and an investigation of the achievable eff. data rate depending on different distances and Error-Correcting-Codes and the energy-consumption of the developed transceiver are presented. The results show that the LiFi-transceiver achieves a payload data rate of 77.6 kbit/s with Error-Correcting-Code protection and thus can be used for a reliable TMTC communication within the satellite bus.  
Light Fidelity (LiFi); intra-satellite communication; Telemetry and Telecommand (TMTC); Optical Wireless Communication (OWC); CubeSat; Transimpedance-Amplifier (TIA) +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 6 Conclusions and Outlook on Future Work  
In this paper, a novel ellipsoidal state estimation procedure was validated experimentally for real-life measurements from the area of marine applications. It was shown that a set-valued uncertainty modeling can be used to characterize the influence of external disturbances acting on a vessel. Moreover, an interval approach was shown that allows for characterizing the accuracy of GPS-based measurements, in combination with a fusion with a compass-based counterpart. Using all of these information sources, an ellipsoidal state estimation procedure was implemented that allows for estimating worst-case outer bounds of the reachable position of a ship and proving the safety of specific maneuvers with respect to collision avoidance.  
In future work, further quasi-linear system models will be investigated to improve observability and to combine numerous system formulations to tighten the result of the state prediction step. Moreover, the presented work represents the basis for the development of event-triggered state estimation procedures that only perform innovation stages as soon as certain indicator functions become active, such as the volumes of the predicted state ellipsoids exceeding certain thresholds or objects coming closer to an obstacle than a certain minimum safety distance. In addition, combinations of set-valued and stochastic uncertainty models will be investigated to establish mixed uncertainty representations. There, ellipsoidal state enclosures can serve as a representation for certain stochastically motivated confidence levels. Finally, it is desired to use the proposed estimation scheme for the implementation of set-based model-predictive control procedures for uncertain nonlinear dynamic systems.  
The ellipsoidal enclosure technique was developed by [PERSON] and [PERSON]. All experiments were performed by [PERSON], [PERSON] and [PERSON] under the supervision of [PERSON]. The estimation approach was reviewed by [PERSON], [PERSON] and [PERSON]. The paper was jointly written by all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 10: Interval bounds for the distance between the DDboats A and B.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
###### Abstract  
Complementing RSSI measurements at anchors with onboard smartphone accelerometer measurements is a popular research direction to improve the accuracy of indoor localization systems. This can be performed at different levels; for example, many studies have used pedestrian dead reckoning (PDR) and a filtering method at the algorithm level for sensor fusion. In this study, a novel conceptual framework was developed and applied at the data level that first utilizes accelerometer measurements to classify the smartphone's device pose and then combines this with RSSI measurements. The framework was explored using neural networks with room-scale experimental data obtained from a Bluetooth low-energy (BLE) setup. Consistent accuracy improvement was obtained for the output localization classes (zones), with an average overall accuracy improvement of 10.7 percentage points for the RSSI-and-device-pose framework over that of RSSI-only localization.  
indoor localization systems; RF fingerprinting; device pose; RSSI +
Footnote †: journal:  
therefore, have good measurement models (i.e., relationship between the measurement and the distance of the phone from the anchor). This makes UWB-based systems useful in calculating the point-estimate location of a phone. In contrast, WiFi and BLE signals that provide RSSI measurements are highly susceptible to these channel effects. Although some systems use indoor propagation models [2] or angle-of-arrival (AoA) measurements [3; 4] to improve the resolution of distance from the RSSI, often the measurement models are too poor in real-world deployments to resolve to point estimates. A common approach in RSSI-based localization literature [5] is the use of a pattern-recognition style methodology known as RF-fingerprinting to perform coarse-grained localization (or zoning). RF fingerprinting involves conducting an initial measurement campaign in different zones within the area of interest to collect and store the RSSI data of the zones (\"fingerprints\") in a database. Then, in an online phase, new RSSI measurements are compared with the fingerprints stored in the database, and the phone is localized to a zone, as depicted in Figure 2.
## 5 Result and Discussion  
Recalling the characteristics of an optimal IM described in Section 4, an optimal IM can be differentiated by higher values of slope (\(b\)) and correlation coefficient (\(R^{2}\)), whereas lower values of dispersion (\(\beta_{D|IM}\)) and modified dispersion (\(\zeta\)). Conventionally, much research illustrated that the demand of the structures follows a linear function of the IM in normal log space; therefore, PSDMs are generally determined by fitting a linear regression to the database in normal log space.
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 1 Introduction
It was established that different basic criteria were derived from common requirements of LLNs applications. Thus, if at least one of the criteria was not met, the routing protocol was considered as not sufficient. Based on the conclusions of the IETF study and with the necessity of solving the routing problems existent in LLNs, several extensions and new approaches have emerged. The next section presents the most relevant ones. This study has the objective of presenting a survey of routing solutions that may be employed in networks for IoT/LLN applications. Thus, only routing protocols for wireless networks with support for IPv6 are considered.