esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Setup of Controller Parameters  
Considering that the full-load power of VSC converter stations is 1.1 times the rated operating power. From (20) and (21), the initial droop coefficients of VSC2 and VSC3 are calculated to be \(K_{\text{H2}}=25\), \(K_{\text{L2}}=2.5\); \(K_{\text{H3}}=7.5\), \(K_{\text{L3}}=1.5\). The constant m in both cases is set to 0.007.  
We run the simulation model in PSCAD/EMTDC. After the system reaches steady-state operation, we apply a 2% step disturbance to the set current controller of the LCC station at 2 s. We use the step disturbance to the set value as input and the speed difference between the selected generators G1 and G2 as output. We employ the VFF-RLS algorithm based system identification method to identify the open-loop system, obtaining the following:  
\[G(s)=\frac{-0.0003015s^{6}-0.005026s^{5}-0.1082s^{4}-0.3252s^{3}-3.302s^{2}-0. 3488s}{1.004s^{6}+3.4986s^{5}+87.83s^{4}+207.1s^{3}+1834s^{2}+2814.7s+214.8}. \tag{23}\]  
This study plots the root locus diagram of the system as derived from Equation (23), as shown in Figure 5. The root locus diagram of the system shown in Figure 5 indicates that all the poles of the system are located to the left of the imaginary axis, signifying stability. However, the dynamic characteristics of the open-loop system are determined by the pole \(d_{0}=-0.137\pm 5.88\)i nearest to the imaginary axis, which is isolated from other poles or zeros, resulting in weak damping with a damping ratio of 2.3%. To achieve better system dynamic characteristics, the desired damping ratio of the dominant closed-loop poles is \(\zeta=0.5\), and the undamped natural oscillation frequency is \(\omega_{n}=2\text{ rad}/\text{s}\). The location of the dominant closed-loop poles is determined to be \(s_{d}=-1\pm 1.7321\)i. Given that gain adjustment alone is insufficient to relocate the system's dominant pole to the desired position, the introduction of a correction section is necessitated.  
Substituting the dominant poles into Equation (10), it can be determined that \(T_{1}=3.4606\), \(T_{2}=1.0125\), and by substituting the already-determined parameters and calculating the amplitude condition, \(K_{\text{c}}=280.2219\). Thus, the final determined controller transfer function is as follows:  
\[G_{c}(s)=\frac{750s+216.725}{s+0.9876}. \tag{24}\]  
Figure 5: Root Locus diagram of the system.  
Building upon the parameter tuning of the AFC controller, the optimization and solution for the PI controller parameters of AGC using the DQN algorithm yield the optimal values: \(K_{\text{P}}\) = 0.8, \(K_{\text{I}}\) = 0.4.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### CoAP  
The _Constrained Application Protocol (CoAP)_, defined in the _Internet Engineering Task Force (IETF)_ RFC 7252 [14], is a service layer protocol used by resource constrained, low-power sensors and devices connected via lossy networks, especially when there is a high number of sensors and devices within the network. CoAP is one of the most popular IoT communication protocols, in particular in the context of advanced metering and distributed intelligence applications, given that it extends the reach of HTTP to constrained devices. CoAP is being used, for instance, with IEEE802.15.4 low-power radios.  
Based on a client/server model, CoAP relies on REST thus increasing its interoperability. Its design has been carefully worked to fit constrained devices in terms of battery, memory, storage. Running over UDP, CoAP clients communicate to servers via 4 messages, GET, PUT, POST and DELETE. Its lightweight design makes it a promising protocol for embedded devices.  
In addition to its wide availability, and due to running over UDP, CoAP integrates DTLS (and not SSL/TLS), usually supporting RSA and AES, or ECC and AES. As a service layer protocol, CoAP can also support other application layer protection solutions, such as the Object Security for Constrained RESTful Environments (OSCORE) [15]. Another advantageous design feature of CoAP is resource discovery. CoAP servers provide a list of current resources, with metadata, via a specific file (/.well-known/core, defined in application/link-format media type). Clients can discover which resources are provided and the respective media types. While widely available and highly interoperable, also providing inbuilt support for content negotiation and discovery, CoAP remains a one-to-one protocol based on a client/server model (while in contrast, counterparts such as MQTT provide support to many-to-many communication).
# Relationship Between Ball Speed and Spin in Elite Youth Table Tennis Players Using Optical Sensors
## 3 Results
### Effect of Stroke Type and Incoming Spin on Spin/Speed Ratio  
To observe the effect of the stroke type (backhand or forehand) and the incoming type of spin (backspin or topspin) on spin/speed ratio, an ANOVA test was conducted. Results from the ANOVA revealed no significant main effect of stroke type and incoming spin on ball spin/speed ratio with respectively (\(p\) = 0.17 and \(p\) = 0.123).
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
## 4 Discussion
Second, variation in coating thickness--though they are a defect--may enhance acoustic sensitivity due to the reduction in the sample's outer diameter. Consistent with theoretical estimates, \"Fiber 4\" should rank just below \"Fiber 5\" in sensitivity, slightly outperforming acrylate-coated fibers. Experimental data support this, with \"Fiber 1\" and \"Fiber 6\" displaying comparable curves (Figure 8b), likely due to their similar coating type, thickness and diameter. The slightly lower \(S_{i}\) of \"Fiber 6\" relative to \"Fiber 1\" suggests that the MCVD production method's higher inherent losses compared to those of OVD do not significantly enhance Rayleigh backscattering. The reduced sensitivity of \"Fiber 2\" aligns with the purpose of its design: it was intended to minimize sensitivity to external disturbances, which it achieves for acoustic frequencies above 2500 Hz. It demonstrates notable sensitivity below 1500 Hz, exceeding that of \"Fiber 1\" within this range. \"Fiber 2\" may be advantageous in DAS applications focused on low-frequency acoustic detection, such as early pest detection [55], pipeline monitoring [56], sound design [57] and voice recognition [58]. Figure 8: Acoustic sensitivity test results comparing selected fiber samples. (**a**) Sensitivity results for “Fiber 3” and “Fiber 7”, highlighting similarities in frequency response. (**b**) Sensitivity results for “Fiber 1” and “Fiber 6”, showing comparable performance across the tested frequency range. In summary, the obtained results largely align with theoretical predictions of the FUTs' acoustic sensitivity, confirming that a lower \(kL\) parameter corresponds to higher sensitivity.
## 3 Research Method  
We conducted a systematic mapping study (SMS). SMSs are mostly implemented as a beginning step to analyzeprimary studies or systematic reviews. The guidelines proposed in [16; 24] were adopted here. An SMS provides an overview of a research field and its classification [25; 26]. The aim of performing an SMS is to point out research tendencies by separating a research area into several significant areas of evidence related to the study's target. Furthermore, one aims to categorize the quantity and type of investigation and the available results. Likewise, it is important to map the occurrences of publications over time to understand trends and to categorize the conferences and journals whereresearch has been published. The planning activity made it possible to recognize the necessity for this review, specifying the research questions, the search string, and the inclusion/exclusion criteria.  
An alternative to an SMS is a systematic literature review (SLR) [24], in which the work review phase is much more rigorous. This makes it possible to establish the state of evidence through the exhaustive extraction of quantitative data and meta-analysis studies and respond to much more specific research questions. These two types of studies are related, and their goal is to identify research opportunities. An SMS is widely considered to be a fundamental early step in determining in which explicit topics of a field it may be interesting to undertake a more detailed SLR. According to [25], an SMS is designed to structure a research area, collect and synthesize evidence, and present research findings. Furthermore, the application of an SMS allows for the detection of gaps in the current literature.  
The present study was conducted based on the following steps:  
* Construction of research questions;
* Collection of the data: structuring a search plan for searching for articles (identification process), then selecting the relevant articles (selection process);
* Extraction of the selected data;
* Mapping of the data by classifying and analyzing the information extracted (mapping process).  
As far as we know, the literature lacks studies such as SMSs that point out the research trends of RE in the area of the IoT. Therefore, the results of this review provide research topics with regard to RE for IoT software systems to boost the level of investigation in this field and to bridge research gaps.
## 3 Results and Discussion
### Grain Size  
Since columnar grains are highly anisotropic and characterized by lengths that can reach up to the height of the sample itself [35], measuring grain size directly, as in conventionally produced materials, can lead to unreliable results. It is then common practice in AM materials to measure only the grain width, approximately perpendicular to the sample development section [39; 51]. Finding a proper way for measuring grain size is necessary, because it greatly affects the final component mechanical properties and moreover it is directly influenced by the thermal history of the material. Previous studies [37; 52] demonstrated that during deposition a layer is subjected to a complex thermal cycle, characterized by subsequent heating and cooling phases, caused by the laser action. Nevertheless, the thermal path of the reference layer is also a function of its distance from the base (Z). In fact, heat dissipation is much more effective when the layer is very close to the baseplate, which effectively acts as a heatsink [10; 53]. It was already discussed how variations on the cooling path might relevantly affect the final properties of the component. Being prior-\(\beta\) grains size directly dependent on the cooling rate from the \(\beta\) region, when cooling from T \(>\) T\({}_{\beta}\)[54], the correlation between the cooling rate and the Z position can be investigated by measuring the evolution of grain size along the direction Z. Figure 6 reports the results of this analysis, in which grain width was evaluated using the intercept method along the whole sample at different distances from the baseplate. As visible from the graph, there is a clear increasing trend that correlates grain width and vertical position for all the samples, coherently with the considerations made on the cooling rate variations. ArS samples are characterized by a steeper slope with respect to SE-CB and SE-HB, which leads to more consistent grain width variations along Z. This difference might be caused by the variations in composition due to the different processing environment. In fact, interstitials elements lead to a different solidification behaviour, due to the changes in the alloy typical temperatures (e.g., Ms). Comparing SE-CB and SE-HB samples instead, the grain widths are consistent with the assumptions made. In fact, from a thermal point of view, the samples built in the SE-HB condition undergo a lower cooling rate than the SE-CB condition, due to the difference in temperature between the melt pool and the baseplate itself. In fact, as confirmed by Figure 6, SE-HB samples show larger grains than SE-CB samples at all the Z values considered. Furthermore, these two trends are parallel. This correlation between processing environment and grain width is consistent with the platform preheating.  
Figure 6: Prior-\(\beta\) grain average width and relative linear interpolation for all conditions plotted as a function of Z.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 4 Data Categorization
### Data Categorization by Character  
The character of the data is defined by the level of detail of the temporal and spatial area it represents. According to this character, we divide the data into operational, tactical and strategic [13].  
Operational data are provided in real time and focuses on a short period of time. These relate only to the area in which the vehicle is located and its immediate surroundings. The information contained in these data relates to actual traffic accidents, impassable road sections or other problems that need immediate decisions to be made [13].  
Tactical data have a medium level of detail, providing information about traffic situations affecting the entire location. Unlike operational data, tactical data may not be provided in real time but at larger time intervals. However, it is necessary that the data must be up-to-date and provide the recipient with the information to make tactical decisions, such as choosing to bypass a traffic jam [13].  
Strategic data have the smallest level of detail. In terms of time, it is a period of at least one month. In terms of space, the minimum size of the district. However, their analysis can provide information that deals with traffic density in individual sections and, thus, helps in making strategic decisions, such as building infrastructure [13].
## 4 System Model
### An Energy Consumption Model  
The energy consumption model in [20], is used to calculate the total energy consumption of ICN network. Total energy consumption \(E_{tot}\) of the network is a sum of energy consumed while transmitting \(E_{t}\) and caching the contents \(E_{c}\) as shown in Equation (3).  
\[E_{tot}=E_{t}+E_{c} \tag{3}\]  
Since, here, we only deal with the data packets instead of chunks thus, for any node \(n_{i}\), it is assumed that \(s_{k}=1\). The request rate for the content \(\epsilon_{k}\) at node \(n_{i}\) is \(q_{i}^{k}\) within time interval \(t\). Each node consumes \(\omega_{c}\) caching power density in its buffer with \(\omega_{l}\) energy density of the link. According to energy model in [20], the caching energy \(E_{c}\) at each individual node \(n_{i}\), is consumed to cache the content at time \(t\) as shown in Equation (4).  
\[E_{c}=\omega_{c}t \tag{4}\]  
Let us suppose, a node \(n_{j}\) requested a content from another node \(n_{i}\). The distance between these nodes is \(h_{ij}\), which is calculated in terms of hops. The set of nodes along the path is \(h_{ij}+1\). The total energy consumed by the nodes and the links for handling and transmitting the request is \(q_{i}^{k}(h_{ij}+1)\) and \(q_{i}^{k}\omega_{l}h_{ij}\), respectively. The transmission energy \(E_{t}\) consumed by the nodes and the communication links while transmitting the content \(\epsilon_{k}\) from node \(n_{i}\) to node \(n_{j}\) is calculated using Equation (5).  
\[E_{t}=q_{i}^{k}[h_{ij}(\omega_{n}+\omega_{l})+\omega_{n}] \tag{5}\]  
As shown in Equation (6) the total energy consumed, is calculated by using the values of \(E_{t}\) and \(E_{c}\) from Equations (4) and (5), respectively.  
\[E_{tot}=\omega_{c}t+q_{i}^{k}[h_{ij}(\omega_{r}+\omega_{l})+\omega_{n}] \tag{6}\]  
The total server energy consumption \(E_{tot}^{{}^{\prime}}\) can be calculated using Equation (7).  
\[E_{tot}^{{}^{\prime}}=q_{i}^{k}[h_{ij}(\omega_{r}+\omega_{l})+\omega_{n}] \tag{7}\]
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks
## 4 DNN Training Results  
In this section, we summarize the quaternion prediction results of the data-based learning model generated in the first step. To verify the accuracy of the data-based learning model predictions, pointing maneuver profiles in the range of 2-68\({}^{\circ}\) were used as a reference for validation.  
Figure 6 shows the predicted quaternion results for a mission that performed continuous maneuvers at 27.7\({}^{\circ}\), 41.2\({}^{\circ}\), 33.0\({}^{\circ}\), 24.6\({}^{\circ}\), and 34.8\({}^{\circ}\). As shown in the figure, similar predictions are possible for different target-pointing maneuvers. However, significant prediction errors occurred for target-pointing periods or particular attitude changes, such as a final sun-pointing maneuver. This study aimed to minimize the errors between target-pointing maneuvers; thus, the changes in the target pointings and attitudes were excluded from the training process, resulting in the observed performance. The data from the mission duration and certain attitude changes exhibited characteristics of the time-series data that were difficult to generalize. Therefore, including this data in the training step would result in performance degradation between pointing maneuvers.  
Figure 5: Maneuver reference profile generation for LEO satellites.  
Figure 6: Quaternion prediction results.  
Because the maneuvers between target pointings were in the form of time-series data, it was difficult to judge the performance of the entire sequence based on the accuracy at a single point. In addition, when creating a maneuver profile, the attitude control performance was affected by the angular rate error at the final point of the maneuver. Furthermore, the conversion to the angular rate and time uploaded to the satellite was meaningful for the entire duration of the maneuver. Therefore, the root mean square (RMS) value of the maneuver duration was used to confirm the prediction performance.  
As mentioned in Section 3, we used a dataset to validate the performance of the quaternion prediction model used to represent the maneuver duration based on various maneuvering angle conditions and differences in the angular rate at the initial and final points of the maneuver. Figure 7 shows the RMS error (RMSE) of the quaternion prediction model for various angle maneuvers, including roll, pitch, and yaw. As seen in the figure, there are minimal differences in performance depending on the maneuvering angle, and the prediction is accurate to an error of less than 0.02.
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 4 Power Line Connection Scheme for Wireless Sensor Networks
### Estimation of Power Transmission Loss  
In a sensor network with N nodes, a total of \(\mathrm{N}-1\) power lines need to be connected. As long as the length of each power line and the power transmitted through it are calculated separately, the overall power transmission loss of the system can be calculated according to Formula (6).  
Figure 6 shows the power line connection structure generated by a network of several nodes. To calculate the energy of electric energy transmission, the power line between nodes B and D in the figure is taken as an example. It divides all nodes into two groups: group \(G_{1}=\{\mathrm{A},\mathrm{B},\mathrm{C},\mathrm{G},\mathrm{H},\mathrm{I}\}\) near  
Figure 5: Power line connection scheme.  
the tree root and group \(G_{2}=\{\mathrm{D},\mathrm{E},\mathrm{F}\}\) far from the tree root. The energy transmitted through the power cord is  
\[E_{\mathrm{BD}}=\left|\sum\left(E_{i}-\overline{E}\right)\right|,i\in G_{1},\]  
\(\overline{E}\) is the average energy of all acquisition nodes.  
For each node \(N\), there is a unique precursor node \(N_{\mathrm{pre}}\) in the minimum spanning tree. The length of the power line between the two nodes is \(l_{N}\). It divides all nodes into two groups, \(G_{1}\) and \(G_{2}\). \(G_{2}\) is a search tree with node \(N\) as its root, which is denoted as \(T(N)\). The transmission energy on this line is \(E_{N}\).  
\[E_{N}=\left|\sum\left(E_{j}-\overline{E}\right)\right|\,,j\in T(N)\,,\]  
According to Formula (6), the loss on this power line are as follows  
\[E_{\mathrm{loss}}=\frac{2 pi}{u}\times l_{N}\times\left|\sum\left(E_{j}- \overline{E}\right)\right|\,,j\in T(N), \tag{8}\]  
The overall system loss is  
\[E_{\mathrm{loss}}=\sum E_{\mathrm{loss}}(j) \tag{9}\]  
Formula (9) is used to estimate the loss caused by energy transmission over power lines.  
Battery-powered station energy is represented by voltage, while high voltage means more energy. In a system where energy can be transmitted to each other, the node whose voltage is higher than the average output energy and the node whose voltage is lower than the average receives energy. In the application of the scheme, the average voltage should be set to the acquisition node. If the local battery voltage of the node is higher than the average value, the node outputs the local battery energy to the power line, otherwise, the node prefers to work with the energy provided by the power line, and saves part of the energy to the local battery.
# E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios
## 4 Experiments
### Robustness  
We provide users with various editing options to test the algorithm's robustness. Robustness testing is conducted following the approach outlined in reference [18]. These options include highlighting, underlining, strikethrough, wavy lines, handwritten scribbles, and text overlay. Users can apply these edits to randomly selected locations within the watermarked OFD file.  
The visual appearance of the watermark information in the industry's OFD watermarking algorithm, which is based on an annotation structure, can be influenced by attacks like highlighting and underlining, but it does not compromise integrity.  
We conducted robustness testing on E-SAWM. Figure 11 illustrates an example of an attack, while Table 2 presents the results of watermark extraction. E-SAWM introduces the integration of semantically similar pseudo structures into structured files. Notably, any structural changes that may arise in the original file when incorporating features like highlighting or underlining have no impact on the pseudo structural content. Consequently, the results show that our OFD watermarking algorithm demonstrates effective resistance against the attacks listed in the table, achieving a 100% success rate in watermark extraction for OFD files under each attack type.  
Figure 10: Visual comparison of watermark effects. (**a**) OFD page before adding watermark. (**b**) OFD page after adding watermark.
# Usage of V2X Applications in Road Tunnels
## 3 Methodology
### Testing Scenarios
#### 3.2.1 Pre-Test  
The first part of the testing was performed under real traffic conditions. The vehicle was equipped with an OBU and went through the whole tunnel. The main assumption was that the unit moving in the tunnel would correctly estimate its position while GNSS was not available. This first case was necessary to prove for further testing and to verify that the measurements were valid for the second phase. During the test, the incoming communication over the LTE network was also analyzed (from the backend), which should  
Figure 3: Hybrid C-ITS OBU Smart GEAR used during the test.  
provide confirmation that the LTE is available in the entire Blanka tunnel, as defined in [42], or not. A schematic layout is shown in Figure 4.