esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Additive Manufacturing Technology for Chair Parts Connections
## 2 Materials and Methods
### 3D Printing  
The software used to design the 3D model is SolidWorks 3D CAD, version 2016 developed by Dassault Systemes, France. The file _STL type was imported in the software of SPRO 60 SD printer. The installation assists the manufacturing of products made of ceramic, metallic and polymeric or composite powders, regardless of their geometrical complexity. The sintering furnace attached to the SLS installation sinters the ceramic and metallic powders, and according to the desired specifications, their infiltration with different metals. The maximum dimensions for the specimen can be 315 \(\times\) 315 \(\times\) 415 mm.  
The SLS technology used for 3D printing the connectors uses a high-power laser beam, which, by melting (sintering) powder in alternating layers, leads to obtaining the desired 3D model. The 3D model is initially converted into cross-sections and then sent to the printer. Based on the information received, the moving laser beam selectively melts (sinters) the powder layer on the build platform, according to each cross-section. The following parameters were used for 3D printing the experimental connectors: fill laser power of 16 W, outline laser power of 12 W, 1 for outline scan count, slicer fill scan spacing of 0.15 mm, and 1 for sorted fill enabled. It was applied to the raster alternating in the X and Y direction in order to obtain a high density, as recommended in the literature [7]. The position of printing the connector is shown in Figure 2.  
The deposition of the successive powder layers measures between 20 \(\upmu\)m and 150 \(\upmu\)m [5]. The microscopic images taken with 200\(\times\) magnification show the sizes of the particles of the new powder (Figure 3), measuring up to 100 \(\upmu\)m.  
In the present research, new powder has been used, and according to the image in Figure 3, the sizes of the particles varied from about 20 \(\upmu\)m to 100 \(\upmu\)m, and the height of each sintered layer by the laser beam is 100 \(\upmu\)m (Figure 4). In case of the used powder, the non-fused powder is removed after the specimen is 3D printed. In Figure 4, there is a schematic representation of the sintering process by laser beam. The red area is the scanned area by the laser beam. A part of the particle, as seen in the detail (left), was sintered, whilst the rest remained unfus  
Figure 2: Position of printing the connector (yellow circled one).
## 5 Conclusions  
This study proposes an MC-TIRP that detects malicious nodes in MC-WSNs and transmits mission-critical data through paths that guarantee trustworthiness and reliability. MC-WSN must ensure the transmission of mission-critical data. Therefore, reliability attributes as well as methods to ensure sufficient energy, trustworthiness, and reliability are essential. The technique proposed in this paper supports quick and safe data transmission while fulfilling these requirements. The proposed technique for the survivability of sensor devices focuses on maximizing performance and minimizing calculations for trustworthiness, energy, and delay compared to other technologies. It was found that MC-TIRP demonstrates superior performance in terms of E2E delay, PDR, throughput, and energy when compared to competing mechanisms. In the future, research should be conducted to ensure the trustworthiness of mission-critical data through improved learning and trust evaluation techniques.  
Methodology, D.K. & Y.-B.K.; Software, D.K.; writing--original draft, D.K.; writing--review and editing, Y.-B.K. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Throughput plotted against the proportion of malicious nodes.  
Figure 9: Delay plotted against the proportion of malicious nodes.  
**Funding:** This work has been supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development (UD190033 ED).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Backgrounds
### Performance Evaluation Metrics
#### 2.4.2 Mean Squared Error (MSE)  
MSE is a general performance measure used in RUL prediction problems. It is defined as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}RUL_{i}-R\hat{U}L_{i}\Bigr{)}^{2}, \tag{7}\]  
where \(RUL_{i}\) and \(R\hat{U}L_{i}\) are the observed and the predicted RUL values of the \(i\)-th sample among a total of \(N\) samples, respectively.
## 2 Materials and Methods
### Sample Entropy Calculation  
SampEn is defined as the negative natural logarithm of the conditional probability that a sequence of data points, each separated by a specified distance \(m\), will repeat within a distance of \(m+1\), excluding self-matches:  
\[\text{SampEn}(m,r,N)=-\text{ln}\bigg{(}\frac{A^{m}(r)}{B^{m}(r)}\bigg{)}\]  
where \(B\) represents the total number of matches of length \(m\). \(A\) represents the subset of \(B\) that also matches for \(m+1\). \(N\) is the total number of data points in the time series, \(m\) signifies the length of the vectors compared during data waveforms, and \(r\) denotes the sensitivity threshold in which comparable vectors are considered similar.  
Sample entropy was calculated for six perturbation possibilities (Table 1) on CoM-vert, CoM-AP, and CoM-ML signals using a code obtained from the Physionet tool [32]. The values chosen for \(m\) were 2, 4, 6, 8, and 10, and \(r\) was set to 0.2 times the mean standard  
Figure 2: An example of raw data for treadmill acceleration during the Pre-Swing phase: (**A**) vertical ground reaction force trajectories (GRF–vert), (**B**) center of mass (CoM) trajectories for medial–lateral (ML), vertical (vert), and anterior–posterior (AP) displacements.  
deviation of the time series (\(r=0.2\)\(\times\) SD) [18; 20; 21]. The selection of \(r=0.2\) for the time series was based on the method proposed by Lake et al. [33].  
In the case of human movement, a steady or periodic gait pattern has a low SampEn value, while a more complex gait pattern (a time series with large distances between data points) should have a higher SampEn value.
# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 3 Results  
### Spectral Signature of Cultivars  
The result of the spectral curves extracted from the corrected Landsat-8/OLI TOA image for the 100 repetitions of each cultivar is represented in Figure 2. Visually, there is a slight difference between the spectral signatures for the five cultivars, considering the eight spectral bands analyzed. The variations occur more intensely when isolating each variety cultivar's maximum and minimum values (Figure 2a-f).  
Figure 2: Average spectral behavior of the five soybean cultivars (**a**) and their isolated minimum and maximum reflectances (P98y12 RR—cv1 (**b**), Desafio RR—cv2 (**c**), M6410 IPRO—cv3 (**d**), M7110 IPRO—cv4 (**e**), and NA5909 RR—cv5 (**f**)).  
The physiological appearance of the analyzed soybean cultivars (cv1... cv5) as a function of the mean spectral curves (Figure 3a) is shown to be healthy, and can be noticed mainly by the high reflectance for B5 (\(\sim\)0.865 \(\upmu\)m) and absorptions by B4 (\(\sim\)0.655 \(\upmu\)m), B6 (\(\sim\)1.61 \(\upmu\)m), and B7 (\(\sim\)2.2 \(\upmu\)m).  
The OLI sensor's reflectance values for all cultivars were consistent compared to the target healthy green vegetation behavior. It is considered as collection data of the curves the day of the scene passage in January, which refers to the vegetative vigor of the soybean crop in the site studied, clearly in the phen  
Figure 3: Vegetation indices calculated for the five cultivars (cv) evaluated using monotemporal OLI/Landsat-8 images.
# A Novel Optimization Framework to Replicate the Vibro-Acoustics Response of an Aircraft Fuselage
## 4 Optimization Process
### Efficient Global Optimization (EGO)  
Instead of a standard algorithm, a tailored one was created and exploited for this problem; the optimization strategy was an Efficient Global Optimization (EGO) [15] derived methodology, simplified (without the stochastic process model) and customized to better drive the optimization algorithm to find a solution in such a complex optimization problem.  
Due to the computational cost of the simulations, the methodology had to be able to re-use all the previous analyses; additionally, surrogate models had been used to approximate the model and identify candidate points without the need for extensive simulation campaigns.  
The implemented MDO optimization procedure is composed of an initial DOE (either performed as part of the optimization or previously run) and a sequence of iterations with a stopping criterion. Among the possible DOEs, the Latin Hypercube Design (LHD) [13] was the first choice in force of its extensive design space exploration and orthogonality of the experiments, however, other methods or combination of methods can be used. Traditional DOEs are not iterative in nature, therefore all the experiments are decided before the first analysis is performed. This can limit the effectiveness in identifying the problem characteristics.  
For each configuration of the sources, a single, systematic LHD has been performed, accompanied by prior secondary DOEs, mainly used to validate the integration and analysis procedure, and therefore verify their reliability for a wide range of scenarios.  
The decision to aim for a single DOE has been driven by the computational effort of the simulations; in order to provide a quantitative representation of the problem (required by its planned applications, namely the database and the metamodels generation, and the non-linear behavior of the outputs), the DOE required a significant number of experiments. In force of the LHD nature, it would have not been possible to perform smaller, independent and differently initialized DOEs and later combine them in a single database while preserving the experiments decoupling. Random initialization has been used.  
The main DOE has been merged with the lesser ones (different by both seed and number of experiments) and compared with the unmerged one in terms of correlation (Spearman and Pearson), showing no significant variations.  
The general pattern of almost null linear and monotone correlation between phase and outputs and average (around 0.5) between amplitude and outputs has been observed in all the source configurations.  
The dataset generated by the optimization procedure can be used to replace (or integrate) the original DOE, due to its compatibility in terms of inputs and outputs.
## 5 Materials and Methods
### Methodological Steps  
5.5.1. Requirement Analysis  
An initial examination focused on existing data acquisition processes, revealing heterogeneous industrial environments with multiple proprietary protocols and controller types.  
5.5.2. Concept Development  
Building on the identified requirements, a modular architecture was designed to accommodate new data sources or protocols with minimal effort. The work emphasizes that such an extensible strategy is essential for high-dynamic production settings, preventing semantic or format conflicts.  
5.5.3. Prototyping and Implementation  
A stepwise approach was used to achieve the following:  
* Integrate OPC UA, Modbus TCP, and native protocols for data retrieval from both legacy and modern controllers.
* Configure ibaHD-Server and InfluxDB for long- and short-term data storage, respectively.
* Provide an interactive interface (ibaQPanel) for real-time data display and initial analysis.  
5.5.4. Validation and User Testing  
Subsequent test runs evaluated performance metrics, latency, and system stability. Additionally, user feedback was gathered to refine interface requirements, showing that easy extensibility and clear data visualization are key to practical acceptance [79].  
5.6. Data Security and Privacy  
Data security was ensured by role-based access control, network protection (VPN, firewall), and encrypted connections. Anonymization and pseudonymization methods were applied if personal data appeared in the process data streams. The findings indicate that while technical safeguards (e.g., TLS encryption) are integrated into iba and InfluxDB environments, continuous monitoring and maintenance are indispensable for preventing unauthorized access.  
5.7. Summary  
The methodological framework presented in the work combines comprehensive requirement analysis, conceptual system design, prototyping, and validation. The selected tools and technologies (ibaPDA, OPC UA, Modbus TCP, ibaHD-Server, and InfluxDB) form a robust basis for dynamic data acquisition and analysis within industrial environments. The user studies highlight that a modular, user-friendly solution is critical to effectively leveraging process data for knowledge modeling while covering both real-time and long-term analysis needs.  
6. Validation  
The validation of the developed prototype is a crucial step to ensure its stability, reliability, and alignment with industrial requirements [80]. This process involves defining precise evaluation criteria to assess the system's functionality, efficiency, and usability.  
Through a structured validation approach, the developed solution is tested under real industrial conditions to verify its technical capabilities and to identify areas for improvement before deployment [80,81].
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 2 Related Work
[15] further introduced vector quantization technology into the variational autoencoder, which greatly reduced the storage and computational costs of the model through the discrete vector space, effectively alleviated problems such as gradient vanishing, and improved the generalization ability of the model. This is an effective means to reduce the resource consumption of the autoencoder and is very suitable for performance enhancement in communication modulation.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 2 Background
### Attacks in RPL
#### 2.2.1 Rank Attack  
The fundamental property of RPL is rank. It supports efficient and appropriate routing operations. Rank keeps track of control overhead, optimizes network topologies, and eliminates loop creation. An attack on the property of Rank has a major effect on RPL's overall function and routing topology. There is no function in RPL to control the node's behaviour, thus allowing chances for the occurrence of Rank attacks. The node picks a preferred parent depending on the objective function, and its rank value is computed as broadcasted by DIO messages in RPL routing. In the DODAG, the rank increases in the downward direction, that is, from the root towards the leaf node. A rank attack takes place when an attacker manipulates the rank values. Rank attacks can be divided into three categories. Worst-Parent, Increased Rank, and Decreased Rank attack. This research work focuses on defence against Decreased Rank attack. In a Decreased Rank Attack, the adversary is chosen as a preferred parent after lower rank is advertised by malicious node(s) to other nodes, as indicated in Figure 2. The effect of a Decreased Rank attack triggers substantial disturbance to network traffic and DODAG, which may result in increased energy consumption [38].  
Furthermore, A malicious node broadcasting a lower rank (decreased Rank attack), causes some DODAG's portion to link to the root through the attacking node and also enables it to eavesdrop on that network traffic-forwarding segment. RPL can be targeted in a number of ways by an internal adversary. However, some of these attacks have a slight  
Figure 2: Normal Topology (**left** side) and Decreased Rank Attack (**right** side) [38].  
effect on the network, while others have a substantial impact. An attack that leads to the reconstruction of DAG, the depletion of nodes' resources, or eavesdrops on network traffic has a significant impact. Modifying the DODAG node rank value is a type of internal adversary to fulfil such malicious targets. In this way, the adversary is able to advertise the fake rank value, thus causing a Rank attack in the network. It severely affects the network topology. Attacks on the topology can isolate a node or a subset of nodes in the RPL network, blocking them from communicating with other nodes. The effects of the Rank attack are packet delay, reduced packet delivery ratio, and generation of un-optimized paths and loops. Practically, the effects of Rank attack, particularly the delay of data delivery, in sensitive IoT applications, such as smart hospitals, healthcare monitoring, and smart vehicular infrastructure, is very critical and can lead to devastating consequences.
## 2 Low-Power Wireless Technologies
### Time-Slotted Channel Hopping
How the schedule is constructed, updated and maintained and by which component in the network is out of the scope of the IEEE 802.15.4e standard. Instead, these functions are addressed by the IETF (internet engineering task force) 6 TiSCH working group [16]. The IEEE 802.15.4e standard can be considered the latest generation of ultra-lower power and reliable mesh networking solutions for low-power lossy networks (LLNs). Commercial networking solutions are available today in which motes consume few micro-amps on average with end-to-end packet delivery ratios over 99.999% [17]. Today, the OpenWSN protocol stack, developed at the University of California Berkeley [18], is the only existing open-source implementation of this protocol. The IEEE 802.15.4e TSCH focuses on the MAC layer only enabling a clean layering fit under an IPv6-enabled protocol stack for LLNs, running 6 LoWPAN (IPv6 over low power wireless personal area networks) [7], RPL [19] and CoAP [8,20]. The IETF 6 TiSCH working group [21] is currently defining the latest component for an open standard-based protocol stack to put together IPv6 technologies with the operational technologies rooted to the TSCH technique and, especially, to IEEE 802.15.4e.
## 5 Discussion
In addition, we can infer that, as long as a GNSS signal is available, the method can be easily applied to other domains that are interested in terrestrial photogrammetry, including, but not limited to, urbanism, archaeology, and structural geology [33; 34]. Going forward, a possible improvement would be to replace the Topcon RTK GNSS antenna with a low cost GNSS positioning system to make the approach even more cost-effective and easily transposable to participatory science programs or citizen observatories. We have shown that using the Smartphone Geotag of the photographs is not sufficiently accurate. Furthermore, the accuracy of the Geotagging varies significantly (several meters) from one Smartphone model to another, from one day to another, and from a type of environment to another. For example, for this study, we obtaineda RMSE of 3.44 m with the Huawei Y5 Smartphone. A month later, with an iPhone 8 Smartphone, we obtained a RMSE of 5.11 m. In the literature, we can find Smartphone positioning RMSE ranging from 4.51 to 11.45 m under forest canopies [35] or from 7 to 13 m in an urban environment [36]. Using an open RTK GNSS network, for instance, such as that provided by _Centipede RTK_ ([[https://centipede.fr/](https://centipede.fr/)]([https://centipede.fr/](https://centipede.fr/))), which provides low cost systems for centimeter-scale accuracy positioning, could be a promising avenue for expanding the use of our RTK GNSS-assisted terrestrial SfM photogrammetry method.
# Ten Years of TerraSAR-X Operations
## Extension of the TerraSAR-X Ground Segment
### Receiving Capacities and Flexibilities  
Already in 2009, a TerraSAR-X data reception system was integrated at SvalSat, primarily to shorten near-real time (NRT) product latencies by using selected contact opportunities outside of the NSG visibility range. The data that were received at SvalSat are transferred in an encrypted raw data format to NSG, where they are processed in the same way as those that were received at the station itself. In answer to the increasing demand in TerraSAR-X products, the contacts that are used for the S-band support at SvalSat are also used for X-band data reception, thus enlarging the overall downlink capacity.  
In 2015, the receiving station pool concept was introduced. Before then, acquisitions had to be explicitly ordered for downlink at a given station. This allowed for using the GARS for background acquisitions, to place test orders for ISSF during station validation and to explicitly order NRT data takes either at SvalSat or NSG, depending on the nearest downlink opportunity. Grouping stations into one receiving pool and leaving the decision regarding which station to use for the downlink of a given data take up to mission planning, results in a better exploitation of limited resources like on-board memory and downlink capacity [12]. Mission planning uses the next free slot for a downlink. If necessary, pool reconfigurations can be done up to three days before the first downlink to a new station in the pool, in urgent cases, only six hours are needed for the event.  
Both the pool concept and the flexibility to use a given station either for TerraSAR-X or TanDEM-X or for both play an important role in exploiting downlink resources and to handle specific constraints that are given by specific mission campaigns.
## 4 Discussion
For calculation of GPP during a particular time frame, integration of the velocity of GPPcapacity is required. Our GPPcapacity estimation framework is the differential form. Moreover, our approach coincides with that of [PERSON] _et al._[41], who discussed that uncertainty in their crop GPP estimation model (GPP \(\propto\) VI \(\times\) PARpotential) arose because of its failure to detect variation in GPP related to short-term (minutes to hours) changes in controlling factors that do not immediately affect crop chlorophyll content. And [PERSON] _et al._[91] discussed that although annual mean LAI correlates well with annual GPP, seasonal LAI correlates poorly with seasonal GPP because it does not respond to short term stresses such as anomalous heat waves. Therefore, our approach using a VI to estimate GPP under the low-stress condition (GPPcapacity) is quite efficient. In the present study, we applied a VI to estimate canopy chlorophyll content of the effective leaf area exposed to light. The next step is to refine the effective leaf area by incorporating plant structural characteristics, such as leaf angle orientation and sunlit/shaded leaf and foliar clumping, which are important to GPP estimation because they affect light interception by leaves and light penetration into the canopy [16, 17, 92] and produce bidirectional reflectance effects in the canopy. In future studies, variation in the slope of the linear correlation between CIgreen and Pmax_capacity2000 for each plant functional type should be compared with results from a radiative transfer model that can predict the radiative transfer of solar energy or changes in leaf physiology as canopy profiles adapt to sunlight [75].