esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Previous Work on UAV Images Georeferencing without GCPs  
Several studies have considered using image georeferencing without GCPs in the production of UAV-photogrammetric-derived products. In the following paragraphs, we present a short overview of the results provided only by UAV imagery georeferencing, using BBA without GCPs (BBA_PPK). A complete overview of the reviewed studies is available in Appendix A.  
Summarizing the reviewed studies, the study area, in most of cases was a flat agricultural area [37; 38; 39; 40; 41], or an industrial/residential area [25; 27; 32; 33; 42; 43; 44; 45; 46; 47]. Fewer studies have focused on areas, such as quarries [36; 41; 48], coastal areas [49], forests [28], and cultural and archeological sites [31; 50]. These studies have largely used two types of UAV--senseFly eBee RTK [25; 27; 28; 32; 36; 43; 47] and DJI Phantom 4 RTK [31; 44; 45; 46; 47; 49; 50]--while others have used different UAV options [33; 37; 38; 39; 40; 41; 42]. Some studies have combined different UAV RTK drones and cameras [27; 37; 40]. BBA approaches without GCPs points methods mostly have used receiver stations located within the area of interest, while in some cases positional data were applied explicitly from the CORS stations. The configuration of the UAV flights was mostly parallel to the surface and cross-grid with fixed flying height. Some of the studies also tested different configurations, with regard to the flying heights and patterns, including different angles for acquiring images. The front overlap of images was mostly above 70%, and side overlap was about 60%.  
The accuracy of photogrammetric-derived products can be assessed by comparing the photogrammetric models against a reference, or by using verification points (VPs) surveyed with traditional topographical methods, and identified within the BBA and orthomosaics/DSM [51]. In this review, we only included the results for the latter case, as we used the same approach, and focused on horizontal and vertical accuracies that were either compared for BBA, orthomosaics, and DSM. Observing the results for BBA, studies have reported a horizontal accuracy between 0.020 and 0.060 m, and vertical from 0.040 up to 0.300 m. Some studies have also reported lower accuracies: the specifics of these UAV flights were higher flying altitudes of the UAV, flying in only one direction, surveying vegetated areas and areas that are more diverse with extreme elevation changes, and with the CORS station at larger distances (especially affecting the vertical accuracy) [28; 31; 39; 41; 48; 49]. Horizontal accuracies of orthomosaics have revealed similar results, with values of 0.020-0.050 m, except for [PERSON] et al. (2016) [38] and [PERSON] et al. (2019) [28], where the accuracies resulting from different flight setups and vegetation conditions varied between 0.044 and 0.087 m. Vertical accuracy estimated from the DSM is, in the best case 0.030 m, reaching up to 0.200 m, with some exceptions exceeding these values [37; 38].
# Secure Cooperative Routing in Wireless Sensor Networks
## 5 Conclusions  
In this work, a lightweight non-cryptographic intrusion detection routing protocol is proposed which is named Multipath Intrusion Detection Protocol (MIDS). This MIDS mechanism combats the two main routing attacks of the network layer, which are sinkhole attack and wormhole attack, resourcefully and with less communication overhead. In this work, various existing studies are examined which attempt to deal with sinkhole and wormhole attacks in WSNs.  
The comparative analysis of the cooperative MIDS algorithm is done with an existing IDS mechanism and MS-LEACH in relation to overall network energy consumption, lifetime, and throughput. The simulation results show that the overall performance of an IDS mechanism increases when combined with a multipath routing algorithm.  
MIDS provides comprehensive security coverage, ensuring robust protection against diverse threats. MIDS is designed to operate within LEACH-based networks, but its flexibility allows for adaptation to various WSN architectures and protocols, enhancing its applicability across different deployment scenarios. It has real-time detection capabilities that enable prompt response to malicious nodes, enhancing network integrity and reliability. Potential application scenarios for MIDS can be environmental monitoring, Industrial IoT (IIoT), smart agriculture, healthcare monitoring, and smart cities.  
In the future, the algorithm can be improved to enhance security at a multilayer level by mitigating attacks at other layers. Another scope of research is the optimization of memory size used at each level, i.e., sensor nodes, cluster heads, and base stations, in an IDS mechanism.  
Conceptualization, R.B. and N.B.; methodology, R.B. and N.B.; software, R.B.; validation, R.B., N.B. and S.A.; formal analysis, R.B., N.B., S.A. and N.M.; investigation, R.B., N.B., S.A. and N.M.; resources, R.B.; data curation, R.B.; writing--original draft preparation, R.B., N.B., S.A. and N.M.; writing--review and editing, R.B., N.B., S.A. and N.M.; visualization, R.B., N.B., S.A. and N.M.; supervision, N.B. and S.A.; project administration, N.B. and S.A.; funding acquisition, N.B. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Sr \#** & **Technique** &
\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & **Lifetime** & **Throughput** \\ \hline
1 & MIDS (Proposed) & 43\% less & 62\% High & 65\% High \\ \hline
2 & LEACH (Reference) & Medium & Medium & Medium \\ \hline
3 & MS LEACH & High & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of results.  
Figure 12: Lifetime of the network.
# Multi-Modal Entity Alignment Method Based on Feature Enhancement
## 3 Experiment and Discussion
### Ablation Study  
To verify the effectiveness of our proposed correction schemes, we designed additional ablation experiments on FB15K-DB15K/YAGO15K, and the experimental results are shown in Table 6. Where MEAFE (none), MEAFE (only MD), and MEAFE (only OCR), respectively, refer to MEAFE models that do not add any information, only add modal distribution information, and only add OCR information.  
Compared with models without any additional optimization, adding only OCR information or only MD information encouraged a certain improvement in the entity alignment effect of the model, and both were weaker than models that added both information simultaneously. Due to the richness of OCR information, the improvement is significant, while MEAFE with only MD information was lower in some indicators than MEAFE without  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Set Ratio**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**FB15K-DB15K**} & \multicolumn{3}{c}{**FB15K-YAGO15K**} \\ \cline{3-8}  & & **H@1** & **H@10** & **MRR** & **H@1** & **H@10** & **MRR** \\ \hline \multirow{4}{*}{20\%} & MEAFE (none) & 0.456 & 0.720 & 0.547 & 0.430 & 0.667 & 0.511 \\  & MEAFE (only MD) & 0.464 & 0.731 & 0.543 & 0.397 & 0.640 & 0.480 \\  & MEAFE (only OCR) & 0.592 & 0.802 & 0.665 & 0.566 & 0.752 & **0.630** \\  & MEAFE & **0.617** & **0.817** & **0.686** & **0.567** & 0.745 & 0.628 \\ \hline \multirow{4}{*}{50\%} & MEAFE (none) & 0.603 & 0.825 & 0.680 & 0.578 & 0.778 & 0.650 \\  & MEAFE (only MD) & 0.612 & 0.827 & 0.689 & 0.572 & 0.772 & 0.642 \\  & MEAFE (only OCR) & 0.695 & 0.869 & 0.713 & 0.648 & 0.812 & 0.705 \\  & MEAFE & **0.712** & **0.880** & **0.771** & **0.678** & **0.837** & **0.734** \\ \hline \multirow{4}{*}{80\%} & MEAFE (none) & 0.730 & 0.893 & 0.788 & 0.659 & 0.845 & 0.724 \\  & MEAFE (only MD) & 0.724 & 0.902 & 0.787 & 0.669 & 0.844 & 0.730 \\ \cline{1-1}  & MEAFE (only OCR) & 0.788 & 0.919 & 0.835 & 0.734 & **0.888** & 0.788 \\ \cline{1-1}  & MEAFE & **0.804** & **0.934** & **0.851** & **0.740** & 0.884 & **0.791** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiment result. The best results are marked in bold and the second best results are underlined.  
Figure 4: Modality attention weight on two cross kg datasets.  
any modifications. The possible reason for this situation is that the original features of the entity modal distribution information have a lower dimension and contain less information, making it difficult to significantly affect the entity alignment effect of the local model alone. However, when used as an additional supplement to OCR information, it still improved the model's understanding and modeling ability for multi-modal information to a certain extent.
## 4 System Testing and Performance
### Result Discussion  
The results obtained prove that the technological solution put forward in this paper is something to consider when collecting information from a mobile network. From the experiments performed, their expected results and what was obtained can be regarded as successful (Table 7 depicts these experiments). There were no significant deviations from the theoretically formulated results and what was obtained during testing activities.  
The information collected from the UAV flight (Figure 13) shows that, though there are significant values that can be regarded to be of major interest for end users and operators, they are still subject to minor errors in terms of location accuracy and signal power, especially if the values are too low. Pre-processing and filtering of information are also mandatory stages, as there might be collected data that are of little interest to end users or system operators.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Performed** & **Expected Result** & **Obtained Result. Deviations** \\ \hline Open-air flight under & & Regular flight of the UAV \\ normal conditions & & Regular landing of the UAV \\ Landing under & & Regular landing of the UAV \\ normal conditions & & Regular landing of the UAV \\ Open-air flight during & & \\ unfavourable & Acceptable flight of the UAV & Acceptable flight of the UAV \\ weather conditions & & Acceptable landing of \\ Landing under unfavourable & & Acceptable landing of \\ weather conditions & & the UAV \\ \hline Data collection from UAV & Information collected from the & Files with information from \\ mobile network & & the mobile network \\ Combined UAV and & & \\ MDAS landing & & Regular landing of the UAV \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experiments carried out and comparison between expected and obtained results.  
Figure 13: Map representation with mobile network signal values.  
Lastly, it must also be considered that the UAV used for this solution was tailored to the needs of the proposal, due to the fact that: (a) a Galileo-friendly UAV with the suitable chipset was required, and (b) a separated MDAS had to be built for data collection. Consequently, despite being valid as a prototype used to validate the formulated hypothesis, its usability as a commercial system could be limited when compared to already existing UAV solutions sold in the market. Further integration of the UAV and the MDAS is therefore desirable in future work.
## 3 Results and Discussion
### Changes in Lipids in Cells Irradiated at 4.5 kV  
In our previous study, we observed a significant reduction in cell viability following microplasma irradiation at 4.5 kV, with an additional 1 h incubation period. Also, we did not find any differences in the drug absorption into cells between the control cells and those irradiated with microplasma at 4.5 kV [12]. In this current study, we investigated the changes in lipids at this specific irradiation condition. The cells were subjected to microplasma irradiation at 4.5 kV and then processed for lipid extraction within 10 min post-irradiation (referred to as B4.5K0H). Our analysis revealed no significant differences in both total cell membrane lipids and total non-membrane lipids between the B4.5K0H and control cell groups (Figure 5). Hence, these findings clearly indicate that drug absorption is directly associated with the changes in cell membrane lipids.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 5 Analysis of Hardware Implementation and Verification Results
### Simulation Results  
In order to verify the results, a MATLAB implementation was used to generate random input vectors written in text files for the architecture. These files were fed into a test bench that controlled the reading of the test vectors and the writing of the results.  
Figure 7 summarizes the testing process to obtain the results and compare them with the outputs of the MATLAB algorithm.  
Fixed point simulations were performed in order to determine the ideal _IP_ and _FP_ parameters depending on the BER performance of the algorithm. Figure 8 shows the comparison for different configurations of word length against the floating point model used (4 \(\times\) 4, QPSK). Taking as a reference Figure 8, the fixed point format _IP_ = 5 and _FP_ = 11 had a close performance to the floating point model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**2 \(\times\) 2, QPSK Detector Module** & **LCCombational** & **LC Registers** & **DSPElements** \\ \hline det1 & 308 & 152 & 16 \\ det2 & 408 & 140 & 12 \\ sort & 1279 & 0 & 0 \\ fd & 33 & 32 & 0 \\ ctrl & 69 & 37 & 0 \\ \hline
**2 \(\times\) 2, 16-QAM Detector Module** & **LC Combinational** & **LC Registers** & **DSP Elements** \\ \hline det1 & 386 & 320 & 16 \\ det2 & 835 & 288 & 20 \\ sort & 4696 & 0 & 0 \\ fd & 34 & 36 & 0 \\ ctrl & 84 & 42 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Resources used per module of the architecture.  
Figure 7: Process of the generation of simulation results.  
Figure 9 shows the timing diagram of the detection process. When start = 1, _ytt_real_ and _ytt_imag_ were processed by **det1** and after five cycles in the case of a QPSK modulation, the flag _done_detection1_ was set to one (_END_DET1_ in Figure 9), indicating that **det2_p1** could start. At this point, Algorithm 1 was performed.  
The **det2_p1** module read the _yt1_real_ and _yt1_imag_ inputs and processed them according to Algorithm 2. Immediately after, **det2_p2** did the same with the _ylt_real_ and _yl_imag_ inputs and set _done_detection2_ to one (_END DET2_) when it finished, informing the **fd** module that the data were ready for it to make a decision. At this point, Algorithm 2 was performed.  
The **fd** module required one clock cycle to make the decision of accepting the current detected indexes or not. In the case of this simulation, said indexes were not accepted at first, so it sent the appropriate signals to start **det2** over.  
**det2** read its respective signals and was performed again. When it finished (represented by the rightmost _END DET2_ in Figure 9), the **fd** module took another clock cycle to decide. This time, the detected indexes were accepted, as the _finished_detection_ flag was raised, meaning that the detection process (Algorithm 3) was over and the detected data (_FINAL DATA_ in Figure 9) were valid.  
Figure 8: Fixed point analysis for the algorithm, 16-bit word length, 4 \(\times\) 4, QPSK.  
Figure 9: Simulation in Modelsim of the proposed detector.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 2 Preliminaries and Related Work
### LT Decoding Process  
In the decoding process at the receiver, the decoder uses the Gaussian elimination (GE) process to decode the received symbols and regenerate the source symbols. However, the GE decoder requires high computational complexity; specifically, for \(n\) received encoded symbols, the decoder requires \(\mathcal{O}(n^{3})\). As a result, [PERSON] proposed a new decoding process called belief propagation (BP), used in LT codes [28]. The BP decoder enables a low-complexity decoding process. Thus, it turns out that the complexity of BP is only \(\mathcal{O}(k\log k)\). The decoding process of the BP is shown in Algorithm 2[28].  
```
Input: Receive \(n\) encoded symbols, where \(n>k\): \(\{c_{1},c_{2},c_{3},\ldots,c_{n}\}\);  Identify received symbols of degree one and add them to a set called a ripple \(\kappa\); while\(\{\kappa\}\) is not emptydo  For each \(c_{i}\in\{\kappa\}\), recover its single (source symbol) neighbor \(u_{j}\);  Update all the neighbors of the recovered source symbol \(u_{j}\);  Add the new degree-one encoded symbols to the ripple \(\{\kappa\}\);  end while ifall the source symbols are recoveredthen  Decoding process is finished successfully; else  Decide decoding failure;  end while
```  
**Algorithm 2**BP Decoding Process of LT Codes  
Figure 3 illustrates a toy example of the BP decoding process. In the example, there are four source symbols and six encoded symbols. In the beginning, the decoder finds the degree-one encoded symbols \(c_{2}\), and then its source symbol neighbor \(u_{2}\) is recovered immediately (i.e., \(u_{2}=c_{2}\)), as shown in Figure 3a. Then, all the encoded symbol neighbors of the recovered source symbol will be updated: \(c_{1}=c_{1}\oplus u_{2}\) and \(c_{4}=c_{4}\oplus u_{2}\). Then, their connected edges to the recovered source symbols will be removed, as shown in Figure 3b. Repeatedly, the decoder searches for a degree-one encoded symbol, which is \(c_{1}\) in this case. Now, the encoded symbol \(u_{1}\) is recovered, \(u_{1}=c_{1}\). Then, the encoded symbol \(c_{3}\) is updated \(c_{3}=c_{3}\oplus u_{1}\), and its connected edge to \(u_{1}\) is removed. Consequently, \(u_{4}\) is recovered \(u_{4}=c_{3}\), as depicted in Figure 3c. Likewise, encoded symbols \(c_{4}\) and \(c_{5}\), which are neighbors of \(u_{4}\), are updated; \(c_{4}=c_{4}\oplus u_{4}\) and \(c_{5}=c_{5}\oplus u_{4}\), and their connected edges to \(u_{4}\), are removed. Finally, the source symbol \(u_{3}\) is recovered; \(u_{3}=c_{4}=c_{5}\), as shown in Figure 3d.  
Figure 3: A toy example of an LT decoding process, where there are 4 source symbols (circles) and 5 encoded symbols (squares). Figures from (**a**–**d**) show the decoding steps of the example.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 4 Integrating into VANET System Model for iSDVN  
As a result of this survey on iSDVNs, the following challenges have been identified.  
SDN provides a way to integrate large-scale wireless networks, such as vehicular networks and ITSs, among others [106]. The data plane and the control plane are separated in SDN. A decoupled forwarding plane provides efficiency, and flexibility makes forwarding devices available at forwarding plane nodes. The control plane provides flow rules and management information about packet forwarding to the data plane [11]. For SDN control plane and data plane communication, OpenFlow is the most commonly used protocol, as shown in Figure 5. In a VANET, planes can communicate with other vehicles, RSUs, and BSs via the OpenFlow protocol. An SDN controller can also facilitate communications between BSs and RSUs [41; 60].  
VANETs' network topologies change constantly due to their mobility, thus making it difficult for the control plane to maintain vehicle location, thereby increasing the overhead for SDN management. The overhead issue can be overcome by integrating SDN with VANET technologies. Figure 5 shows an illustration of an SDVIN. It improves the network performance and QoS [44]. In VANETs, SDN is utilized to calibrate packet- and network-level parameters [42]. The SDVN provides network resources for V2V communication through a logically centralized controller [37]. The SDN control plane also supports wireless data dissemination, further increasing the transmission range. In a dynamic network topology, maintaining network connectivity is extremely challenging. For network connectivity problems, an SDVN may provide the solution [107]. SDVN controllers face another challenge in queue management, especially during packet failure conditions [68]. As a result of excess packets flowing in the queue, the waiting time increases, impacting the overall performance of the SDVN. In an iSDVN, we can use queueing models to reduce controller response times in queues, improving its performance.
# Assessing the Stress Induced by Novel Packaging in GaN HEMT Devices via Raman Spectroscopy
## 3 Results and Discussion
This stress mitigation was attributed to the compression introduced by the packaging, which compensates for the intrinsic tensile stress present between the layers of the device. This result stems from a proper choice of material and packaging design, which must be optimized in terms of shape, dimensions, and thermal properties. Additionally, the incorporation of intermediate layers or buffer materials with more similar thermal properties contributes to reducing stress at interfaces between different materials and ensures effective stress mitigation across the mold. The mitigation of stress within the active layers of the device has a direct impact on its reliability and performance. Indeed, thermal-mechanical stresses and deformations are already well-known and extensively studied failure mechanisms. In general, lower stress leads to slower aging of the device. Furthermore, when current flows through a device, it undergoes thermal expansion proportional to the current itself due to the increase in temperature in the active region of the device [11]. Generally, this deformation is not uniform across the entire device but depends on the shape and arrangement of the semiconductor part through which the current flows. Particularly in power switching applications, current values can be very high, resulting in intense heat release and consequent mechanical deformation. Since the crystal can withstand defined stress values beyond which it breaks, it is evident that the presence of residual stress when the device is off can limit the maximum current that can flow through the device without destroying it. This aspect, along with more specifically thermal aspects, must be taken into account when defining the mission profile. Considering the above, residual stress mitigation is, among all other advantages, a potential way to improve the device's performance and increase its lifetime [32; 33]. Therefore, we can conclude that proper packaging design can effectively enhance the quality and overall reliability of the devices.