esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The increase in the number of mobile and Internet of Things (IoT) devices, along with the demands of new applications and services, represents an important challenge in terms of spectral coexistence. As a result, these devices are now expected to make an efficient and dynamic use of the spectrum, and to provide processed information instead of simple raw sensor measurements. These communication and processing requirements have direct implications on the architecture of the systems. In this work, we present MIGOU, a wireless experimental platform that has been designed to address these challenges from the perspective of resource-constrained devices, such as wireless sensor nodes or IoT end-devices. At the radio level, the platform can operate both as a software-defined radio and as a traditional highly integrated radio transceiver, which demands less node resources. For the processing tasks, it relies on a system-on-a-chip that integrates an ARM Cortex-M3 processor, and a flash-based FPGA fabric, where high-speed processing tasks can be offloaded. The power consumption of the platform has been measured in the different modes of operation. In addition, these hardware features and power measurements have been compared with those of other representative platforms. The results obtained confirm that a state-of-the-art tradeoff between hardware flexibility and energy efficiency has been achieved. These characteristics will allow for the development of appropriate solutions to current end-devices' challenges and to test them in real scenarios.  
platform; IoT; end-device; cognitive radio; edge computing; software-defined radio +
Footnote †: journal: Sensors
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Summary of Applicable Sensing Techniques for Detecting LPBF Defects
#### 4.2.1 Recommendations for Optical-Based Sensing Technologies  
Optical electron source-based technologies are designed to detect all types of defects. SEM technologies detect all defects, including balling, roughness, and vaporization. Oxidized surfaces are monitored by electron source-based technologies, except for EBSD, including surface oxidation behavior images (SEM, TEM, and EPS) and chemical composition (XPS and EPMA). LOF, gas, and keyhole properties are detected using different technologies, including SEM and TEM. EPMA provides chemical data that detect the formation of keyhole porosity. SEM, EBSD, and EDS are used to observe and detect different cracking types, while delamination is predicted using SEM. The chemical compositions are collected using EDS (EDX).  
Optical X-ray-based technologies are also applicable to detect most all LBPF part defects. Surface quality, liquidation cracking, and microstructural defects are monitored using XCT. XRD can initially detect surface oxidation, gas porosities, delamination, and surface roughness defects. Optical visible light-based technologies are reported to detect balling defects (OM and CLSM), surface oxidation (OM), and roughness and denudation (OM, CLSM, and DSLR). OM and CLSM can detect LOF, gas, and keyhole porosities. Other defects, including cracking, are predicted using OM and DIC, while delamination is visualized with CLSM and DSLR. Table 7 summarizes the optical-based sensing technologies that detect different LBPF part defects.  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Defect Type** & **Defect** & \multicolumn{3}{c|}{**Sensing Technologies**} \\ \hline \multirow{4}{*}{Surface} & Balling [9, 121, 122] & SEM & OM & CLSM & - & - & - & - \\  & Surface oxidation [123, 124, 125, 126, 127, 128, 129, 130] & SEM & TEM & EDS & XPS & EPMA & XRD & OM & OES & APT \\  & Roughness [131, 132, 133, 134, 135, 136] & SEM & XRD & OM & CLSM & DSLR & APT & - & - & - \\  & Denudation [121, 12, 122] & SEM & OM & CLSM & DSLR & - & - & - & - \\  & Vaporization [137, 138, 11, 11] & EDS & XCT & OM & - & - & - & - & - \\ \hline \multirow{4}{*}{Microstructure} & LOF porosities [129, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 15, 15, 16, 16, 17, 18, 19, 15, 140, 141] & SEM & TEM & XCT & OM & CLSM & - & - & - & - \\  & Gas porosities [125, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 4 Discussion  
The satellite signal enables RTK differential positioning without barriers, achieving a GNSS/INS navigation accuracy within 1.5 cm. This accuracy serves as the baseline for the robotic vehicle's path trajectory during autonomous navigation. Field tests show that with satellite signal blockage, GNSS/INS positioning records an average error of about 1.24 m and a maximum error of roughly 2.23 m, alongside a standard deviation of 0.69 and an RMSE of 2.02 m. Conversely, the laser odometer's average error remains below 0.14 m, with the maximum deviation not exceeding 0.63 m, a standard deviation of 0.16, and an RMSE of 0.03 m. By the fusion of the laser positioning information, the problem of intermittent loss of satellite positioning signals in the complex environment of the orchard is effectively overcome. Although the positioning error fluctuates greatly, it can be used as the basis for the robotic vehicle's navigation and positioning when the satellite signal is lost or blocked. Its positioning accuracy is acceptable for orchard spraying, weeding,  
Figure 16: Positioning bias of robotic vehicle in orchard environment: (**a**) positioning error of laser odometer; (**b**) positioning error of GNSS/INS in blocked section of satellite signal.  
Figure 15: Localization trajectories of the robotic vehicle moving along the tree lines with intermittent GNSS dropout.  
transportation, and other uses of mechanical mobile platforms. The results are expected to improve the navigation capability of autonomous operating equipment in orchards.
## 6 Future Applications
### NOMA Based Heterogeneous Network Model  
The authors in Reference [33] have proposed energy efficient resource allocation in a NOMA based heterogeneous network. In their work, they describe an overlay system architecture in which a macro-cell with a macro base station (macro-BS) is overlaid with multiple small cells, each small cell incorporating the power domain multiplexing-based NOMA technique. In such a system, multiple small cell-UEs (SUEs), operating within each small cell, are multiplexed on the same sub-band. It is also possible to co-multiplex Macro-cell UEs (MUEs) with the SUEs on the same sub-band. The energy efficiency in such a system is defined to be the ratio of total system sum-rate to the total power consumption. The work focusses on energy efficiency maximization by formulating a probabilistic mixed non-convex optimization problem, under the constraints of outage probability, maximum transmission power and total number of multiplexed users on each sub-band. In this work also, the authors apply conventional SIC for NOMA decoding. However, under low SNR conditions, a conventional SIC based receiver can impair system performance. A receiver architecture, which can operate under very low SNR conditions, can relax the constraints on maximum number of users multiplexed within a sub-band. The proposed FRESH-SIC structure can be applicable in such a scenario for further analysis.
## 2 Materials and Methods
### Artificial Neural Network Model  
#### 2.2.1 ANN Structure  
We constructed a multilayer ANN model consisting of input, hidden, and output layers. The ANN connects the input layer via the hidden layer to the output layer. In this way, the model operates on a feed-forward structure without a direct connection between the input and output layers or between each neuron. The input layer receives external data and relays it to the hidden layer, which stores the required data based on each feature being analyzed through learning. The output layer produces the final outcome of the learned data. The function applied in transferring data between each layer was a sigmoid non-linear activation function producing values between 0 and 1. As sigmoid functions are based on  
Figure 1: Field data collection sites in the Youngsan River estuary (South Korea). The aerial images were obtained from DAUM (www.daum.net) (accessed on 18 June 2021).  
curves rather than straight lines, differentiation is possible by using a back-propagation algorithm that mediates learning towards reducing the difference between output and predicted values by controlling the weight upon disagreements (the generalized delta rule) and terminating learning upon agreement [22; 29]. In the present study, the ANN consisted of 14 input groups, 22 hidden groups, and 3 output groups (Figure 2). MATLAB R2021a was used for model implementation.
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 3 System Design
### Overlay Node Placement
The MOP algorithm evaluates \(F\) at each interval, and the derivative is approximated using the finite difference:  
Figure 3:0  
When \(\beta=\epsilon\), the corresponding value \(x\) is selected as the optimal number of overlay nodes. If two candidate values of \(x\) satisfy the condition, the larger (right-hand side) value is chosen to ensure sufficient coverage of performance gains.
## 2 Materials and Methods
### Description of the Proposed Control Strategies
#### 2.1.1 Non-Linear Model Predictive Controllers  
The MPC is an advanced controller that provides the optimal action for set-point tracking and control movements by solving an optimization problem, usually with constraints. In the MPC algorithm, the vector of manipulated inputs \(\mathbf{u}\) is computed periodically and applied to the process each certain time, named sampling time (\(\tau_{S}\)), from the optimization of a control performance index considering constraints on inputs, outputs and states. The typical performance index (cost function) in the MPC is a quadratic penalization of the target (set point) deviations and the magnitudes of the manipulated inputs adjustments that represent the rate of variation of actuators. The set point can be a fixed value, or a computed value provided by an upper level optimization. A model of the process to be controlled is used to predict the future behavior, in a given time horizon \(\tau_{N}\)[23].  
Different types of models can be used for predictions in the MPC algorithm, e.g., linear or non-linear models expressed in discrete-time or in continuous-time [24]. In this work, a non-linear model of the system is selected, which is stated as a continuous time state-space model:  
\[\overset{\bullet}{\mathbf{x}}(t)=\mathbf{f}(\mathbf{x}(t),\mathbf{u}(t), \mathbf{v}(t))\mathbf{y}=\mathbf{g}(\mathbf{x}(t)) \tag{1}\]  
where \(\mathbf{x}\in X\subseteq\mathbb{R}^{nx}\) is the state vector that contains the process variables that are relevant in the control problem, and \(X\) is the set of admissible states; \(\mathbf{u}\in U\subseteq\mathbb{R}^{nn}\) is the vector of manipulated inputs, and \(U\) represents the physical limitations of the manipulated variables; \(\mathbf{v}\in V\subseteq\mathbb{R}^{nn}\) is the vector of measured disturbances, and \(V\) is the set of measurable disturbances, and \(\overset{\bullet}{\mathbf{x}}\) denotes the time derivative of the state. Note that only measurable disturbances are considered here.
# Situation-Aware Survivable Network Design for Tactical Environments
## 2 Related Work
The scheme is designed based on the optimized link-state routing (OLSR) protocol and uses past transmission history to split the traffic into multiple paths. Simulation results show that the strategy provides a long-lasting network, while maintaining performance comparable to that of the OLSR protocol. In Ref. [24], The authors propose an energy efficient and reliable routing algorithm based on Dempster-Shafer (DS) evidence theory (DS-EERA) to achieve higher QoS. It uses DS evidence fusion rules to fuse three attribute indexes of nodes such as residual energy, traffic, and the closeness of its path to the shortest path. Using the fusion results, it selects the best next hop. Finally, each node transmits traffic through this routing strategy. However, none of the works consider link failures and environments where network topology changes. In Ref. [25], the authors propose ad hoc on-demand multipath distance vector with the fitness function (FF-AOMDV) protocol. The FF-AOMDV uses two metrics such as the residual energy and the distance from the transmitting and receiving node to find the optimal path to reduce the energy consumption. The results demonstrate that the proposed FF-AOMDV outperforms AOMDV and ad hoc on demand multipath routing with life maximization (AOMR-LM) in throughput, packet delivery ratio, and end-to-end delay. The authors in Ref. [26] propose a topological change adaptive ad hoc on-demand multipathdistance vector (TA-AOMDV) for providing QoS in topological changing environments with high-speed nodes. In this scheme, it finds several alternative paths with better QoS performance by the alternative path selection algorithm and selects the stable path by primary path selection algorithm among the alternative path. The primary path is selected based on the Link Break Probability (LBP) and Path Stability Probability (PSP) values. Simulation results show that it can guarantee the QoS by obtaining the stable path under the dynamic topology changing networks. In Ref. [27], The authors propose a novel routing scheme named Mobility, Residual Energy, and Link Quality Aware Multipath (MRLAM). This algorithm uses a Q-learning algorithm for maintaining stability, reliability, and lifetime of the network. Through the simulation results, it performs high performance in respect to energy cost, end-to-end delay, and packet loss ratio. However, studies on providing resilient connectivity and robust end-to-end path in WLAN are insufficient. Therefore, new techniques are required to ensure the stability of network during link failure.
# An Improved Product Code-Based Data Hiding Scheme
## Product Code-Based Data Hiding
### Trivial Solution
#### 3.1.1 Embedding  
_Step 1_: Calculate the 2-D syndrome of \(\mathbf{X}\) denoted as S(\(\mathbf{X}\)) which equals P(\(\mathbf{X}_{\text{IB}}\)) \(\oplus\)\(\mathbf{X}_{\text{P}}\), where \(\oplus\) denotes the element-wise EXCLUSIVE OR (XOR) operation between P(\(\mathbf{X}_{\text{IB}}\)) and \(\mathbf{X}_{\text{P}}\).  
_Step 2_: Calculate the 2-D toggle syndrome \(\mathbf{TS}\), which equals S(\(\mathbf{X}\)) \(\oplus\)\(\mathbf{M}\), as shown Figure 4a.  
_Step 3_: Attach the zero block of size \(4\times 4\) into the lower right corner of the toggle syndrome \(\mathbf{TS}\), as shown Figure 4b, to obtain a \(7\times 7\) toggle array \(\mathbf{T}\). That is, the syndrome of \(\mathbf{T}\) denoted as S(\(\mathbf{T}\)) equals \(\mathbf{TS}\).  
_Step 4_: Perform the element-wise XOR operation between \(\mathbf{X}\) and \(\mathbf{T}\), i.e., \(\mathbf{X}\)\(\oplus\)\(\mathbf{T}\) = \(\mathbf{Y}\), to finish the embedding, as shown in Figure 4c.
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Smart Infrastructure
#### 6.2.2 Video Surveillance Systems  
These systems are able to show high-resolution images or videos within the train (onboard, during the operation of the train), along the tracks (for the advanced monitoring of assets) or in the station [93].  
Intelligent CCTV cameras not only provide a record of events in case of an incident, but actively provide real-time alarms on the occurrence of potential problems, allowing for obtaining timely intervention responses and potentially reducing service outages. Moreover, if video recording are required by a law enforcement agency, there is no need to send personnel on-board to obtain the hard drive manually. The images collected can be either stored in a local server or transmitted in real-time to TCCs.  
In the past, operators detected the lack of a surveillance on-board solution, particularly because of the absence of broadband wireless communication systems between trains and the control center. Moreover, there are just a few examples in the literature that study video surveillance in railways. An example is the Security Management System proposed by [PERSON] et al. [94], who integrated devices for access control and surveillance. Their work also includes the development of a security platform that uses a middleware to embed heterogeneous sensors and that is able to adapt QoS requirements to the priority of the data transmitted.  
A video surveillance platform deployed in subway of Beijing (China) is presented in [95]. The development includes different modules that allow the platform to manage different elements of the system, setup alarms, detect failures automatically or visualize all the data on a GIS map.  
Apart from the subway lines, there are few CCTV systems for conventional trains and high-speed trains due to the lack of effective wireless train-to-ground communications. An exception of such systems is the one proposed by [PERSON] et al. [96], which is aimed at correlating data from different sources like environmental sensors, intrusion detection sensors, positioning systems, identification systems, video-surveillance devices and even from CBRNE (Chemical Bacteriological Radiological and Nuclear) sensors. Regarding this last type of radioactivity sensors, they are deployed for preventing attacks based on the so-called dirty bombs. To detect explosives, there exist specific devices usually installed near the turnstiles that can detect evidence of gun powder on the hands and clothes. Furthermore, to detect weapons and explosives on passengers, it is currently under research the use of terahertz cameras.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 4 Results and Discussion
### Practical Implementation Potential  
By calculating and examining the parameter _SPC_ and the proposed _SPI_ parameter, the method for quantifying the state of speech privacy in different types of premises is demonstrated. In the future, this should prompt practical adjustments of standard measurement procedures in buildings, with the aim of providing users with a clearer indication of the achieved conditions. A potential first step might include expanding the form for displaying sound insulation measurement results to include additional data on the  
Figure 7: Relation between the AI and the SPI (calculated with Equations (10) and (11)).  
measured value of the equivalent noise. This approach is particularly useful in cases where measurements are conducted in existing buildings due to tenant complaints. The analysis presented in this paper suggests that solutions can also be sought in the domain of ambient noise control within living spaces. Recently, the sound masking technique has been used to enhance privacy and reduce disturbance by creating background noise [55; 56]. This technique is based on the continuous emission of additional noise to increase ambient noise at the listener's location, thereby enhancing speech privacy. Specifically designed sounds are reproduced using a dedicated system, usually positioned in the ceiling. Various types of noise are used, traffic noise, fan noise, nature sounds, etc., with careful adjustment of reproduction levels [57]. One of the aims of future research is to conduct measurements in spaces with such systems and use the proposed SPI parameter to determine the effectiveness of the sound masking system.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 1 Introduction
### Novelty  
The novelty of this work lies in its integrated and lightweight approach to hybrid VLC-RF channel estimation within GFDM-based wireless sensor networks (WSNs). Unlike prior studies that either focused on individual RF or VLC domains or adopted computationally intensive models, our approach uniquely combines the following:  
1. We applied and benchmarked a tree decision-based regressor for hybrid VLC-RF channel estimation under GFDM modulation, representing a novel application of this method within this context to the best of our knowledge.
2. We bridged the performance-efficiency gap by achieving near-optimal regression accuracy comparable to complex approaches (e.g., random forest) while significantly reducing inference time, making the proposed work feasible for the latency-sensitive, low-power applications common in WSNs.
3. To support real-time use, the model is built for low-latency inference. After being trained offline, the tree-based model makes fast predictions since it does not require iterative processing. This allows it to meet real-time channel estimation demands, especially when it is further optimized using pre-computed lookup tables.
4. Moreover, the model is optimized for lightweight deployment, considering the limited computational capabilities of typical WSN nodes. The training process is conducted offline on more capable hardware, while inference is carried out on the sensor nodes.
5. We conducted a detailed performance evaluation using 18,000 samples across 36 SNR levels, and we compared our approach with traditional estimators (MMSE and LMMSE) and various machine learning models (SVM, RF, and linear regression).  
This combination of hybrid modeling, algorithmic efficiency, deployment practicality, and wide-range benchmarking offers a fresh and practical perspective on deploying ML-based estimators in next-generation WSNs.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 2 Traditional Methods of Modulation Recognition
### Identification Method Based on Likelihood Ratio  
The modulation identification method based on the likelihood ratio is regarded as a multiple-hypothesis problem, and the probability of signal misclassification is minimized by the maximum likelihood criterion to achieve the theoretical optimal identification. The flow of the method is shown in Figure 2:  
As can be seen from Figure 2, the key to the likelihood recognition method is to construct the likelihood function of the signal and set the appropriate threshold. Therefore, when using this method, it takes a lot of effort to construct the likelihood function and then select the appropriate threshold. The authors of [20] explored the performance of the likelihood-based algorithm in linear digital modulation classification, and the results showed that the computational complexity of the quasi-mixed natural ratio test method when using signal amplitude, phase, and noise power as unknown parameters was much smaller than the mixed likelihood ratio test method. The authors of [21] proposed a new modulation recognition algorithm by optimizing the combined log-likelihood function (LLF), which has a correct classification probability close to the theoretical limit. [PERSON] et al. proposed a likelihood estimation classifier based on an expectation maximization algorithm [22] to solve the modulation classification problem in a blind channel multipath environment. This classifier is able to find maximum likelihood estimates of the location parameters in a tractable manner. Experiments show that the performance of the algorithm in multipath channels is significantly improved under high signal-to-noise ratio environments.  
Figure 1: The structural layout of this paper.  
Figure 2: Flowchart of the likelihood modulation identification method.