esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Based on the principle of circular bioeconomy, where the subproducts and waste from industry or human consumption must be considered as raw material for other industrial processes, VFAs can be obtained from organic side streams via anaerobic digestion and can be used as a carbon source to produce Omega-3 PUFA and other bioactive compounds through the heterotrophic cultivation of microalgae [27; 28; 29; 30]. Several reports have been published in the last few years, indicating the capacity of certain species of microalgae to grow on VFAs [31; 32; 33]. In these studies, the authors focused on the culture conditions and DHA productivity, but the use of VFAs as a carbon source presents more advantages. The presence of odd chain VFAs (such as propionic or valeric acid) in the dark fermentation effluents and respective cultivation medium can modify the respective lipid profile in microalgae. The use of odd-chain short VFAs as a precursor for fatty acid synthesis leads to the formation of higher quantities of odd-chain fatty acids in the biomass. Aside from PUFA, odd-chain fatty acids have been receiving more attention in recent years. They consist of an odd chain of carbon, mainly pentadecanoic acid (C15:0) and heptadecanoic acid (C17:0) usually namely as OCFA. OCFA are presented in ruminants, bacteria and plants in small quantities. The interest in these compounds is based on pharmacology properties and their utility in industrial applications [34; 35; 36]. C15:0 was further studied by [PERSON] et al. [37], and recent studies suggest that they can have a positive effect on membrane fluidity as they have lower melting points than closely even chain fatty acids, with particular interest for diseases like multiple sclerosis [38; 39; 40]. Diverse studies have revealed that the level of OCFA in Alzheimer patients was lower than in other groups and, therefore, their application is suggested in the treatment of this type of disease [41]. Anticarcinogenic influence was also evaluated on carcinogenic cells by [PERSON] et al. [42]. Different large-scale epidemiological studies have now shown that the plasma OCFAs levels found are associated with reduced disease risks for CHD and T2D [36; 43]. The aim of the present study was to evaluate the capacity of _Schizochytrium limacinum_ to grow on waste-derived VFA from dark fermentation effluents as an alternative carbon source, and the effect produced by its use on fatty acid profiles, in particular OCFA and DHA for the health benefits associated with them.
## 3 System Architecture  
Without loss of generality, Figure 4 illustrates the block diagram of a system with one central site and two remote units. The same figure has also been included in [15]. The terms \"distributed unit (DU)\" and \"remote radio unit (RRU)\" are aligned with the 5G next-generation radio access network (NG-RAN) terminology. It should be noted that the proposed architecture can be deployed for other networks connecting one central site and multiple remote sites, e.g., fiber-to-the-room (FttR) or customized radio access networks for hot spots.  
The DU (Figure 5a) comprises a personal computer (PC) and a _Hitech Global_ HTG-930 board, which connects to the PC via the peripheral component interconnect express (PCIe) interface and has a _Xilinx Virtex UltraScale+_ FPGA (VU13P). (FPGA: field-programmable gate array.) The board is connected to a four-port QSFP FMC module. (QSFP: quad small form-factor pluggable; FMC: FPGA mezzanine card.) This PC is also used for performance monitoring. Each RRU (Figure 5b) consists of a _Xilinx Virtex Ultrascale_ FPGA (VCU108) and an active antenna unit (AAU), which is in-house developed and has four wireless transceivers. The components used in the setup can be found in Appendix A. Although the block diagram seems complicated due to the provided details, the hardware is actually straightforward and can easily be incorporated in an application-specific integrated circuit (ASIC).  
Figure 4: Network architecture. (PCIe: peripheral component interconnect express; FPGA: field-programmable gate array; SDM: sigma-delta modulator; QSFP: quad small form-factor pluggable; FMC: FPGA mezzanine card; E-O/O-E: electrical-to-optical/optical-to-electrical converter; Up-/Down-conv.: up-/down-conversion; PA: power amplifier; A: low-noise amplifier; ADC: analog-to-digital converter.)  
The DU is connected to the RRUs with BISDoF links, in which baseband signals are sigma-delta modulated and time-interleaved before being transmitted over fiber, as explained in Section 2. Considering the targeted use cases and the implementation cost, this setup uses multi-mode fibers (MMFs) and commercial QSFP-100G-SR4 modules. Each QSFP has four 850 nm vertical-cavity surface-emitting lasers (VCSELs) and built-in clock-and-data recovery modules (CDRs) for transmitting and receiving. To serve all four transceivers on one RRU, two MMFs are used for the DL transmission and two others for the UL between the DU and each RRU.  
If a longer transmission distance is required, single-mode fibers can be used instead. As NRZ signals are transmitted over fiber, the impact of fiber nonlinearities can be viewed as optical bit errors. Section 4.1 will further discuss the high bit error tolerance of sigma-delta modulated signals. Consequently, we expect similar performance.  
A user (Figure 5c) has a _Xilinx Kintex 7_ FPGA (KC705), an analog front end evaluation kit (_Analog Device_ FMCOMMS1-EBZ), and a printed circuit board (PCB), which contains a low-noise amplifier (LNA) for reception, a power amplifier (PA) for transmission, and a switch.
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 5 Implementation
### Submitting a Document  
Figure 5 illustrates the details of the operations conducted by the SDV API for document submission. During Setup, a master key is generated using a pseudorandom number generator (PRNG) and a random seed value. This master key is used to derive the server keys for the cloud storage servers. Server keys are derived using HMAC-SHA256 and the master key, with the server identifiers as inputs. When a document is sent in via the SDV App, the API divides them into blocks. The block size is a user configurable parameter, e.g., a 100 MB file is divided into 100 blocks if the block size configured is 1 MB per block.  
As mentioned previously, for our prototype only the filename acts as the keyword token. This token is generated through HMAC-SHA256 using the master key and the filename as input. Once the document is divided, every block is randomly assigned to one of the available storage servers. The assignment information is recorded as part of the index entry. The assigned block is then encrypted with the corresponding server key. Finally, the encrypted blocks and the index entry are sent to the SDV controller, which then proceed to process and upload the encrypted blocks to the designated cloud storage servers. All encrypted blocks will then be hashed with SHA256 to produce the block identifier, which will be used in the index to locate the block during retrieval. As an example, Figures 6 and 7 present the execution steps of submitting a 5 MB file to Google Drive and a local storage server. The file is listed (Figure 7a) once it is successfully uploaded (Figure 6b). The submissions to Google drive and a local server through the SDV controller are shown in Figure 7b.  
Figure 4: SDV dashboard: Document submission and retrieval.  
Figure 5: SDV API: How document submission is performed (e.g., given four cloud storage servers). AES-CTR: Advanced Encryption Standard in counter mode.  
Figure 6: Submitting a document. (**a**) Uploading file; (**b**) Upload successful.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### General Architecture
#### 3.1.4 Step 4: JND Modulation and Embedding with QIM  
The core of the watermarking process is the actual embedding of data into selected video blocks. This step implements a perceptually adaptive embedding scheme based on QIM. While QIM provides robustness, it does not inherently account for the sensitivity of the human visual system. For that reason, we modulate QIM's quantization step \(\Delta\) using a JND model \(J(i,j)\), ensuring that watermark energy remains invisible to the human eye, particularly in smooth or uniform regions [40].  
Let \(C(u^{*},v^{*})\) be a mid-frequency DCT coefficient of block \(B_{ij,j}\). The embedded coefficient is as follows:  
\[C^{\prime}(u^{*},v^{*})=\Delta\cdot\left|\frac{C(u^{*},v^{*})}{\Delta}\right| \ +0.25+0.5w\, \tag{6}\]  
where \(w\in\{0,1\}\) is the watermark bit, and \(\Delta\) is the JND-modulated quantization step:  
\[\Delta\ =Q\cdot J(i,j), \tag{7}\]  
where \(Q\) is a global scaling factor (experimentally set) and \(J(i,j)\) is the perceptual threshold for block \(B_{ij,j}\). The JND threshold \(J(i,j)\) combines three perceptual effects:  
\[J(i,j)=L(i,j)+M_{s}(i,j)+C_{m}(i,j), \tag{8}\]  
\[L(i,j)=\frac{1}{64}\sum_{(m,n)\in B_{ij}}I(m,n)\,\]  
\[M_{s}(i,j)=\text{Var}\bigl{(}B_{ij,i}\bigr{)}=\frac{1}{64}\sum_{(m,n)\in B_{ ij}}\Bigl{(}I(m,n)-\mu_{B_{ij}}\Bigr{)}^{2},\]  
\[C_{m}(i,j)=\sqrt{\left(\partial_{x}I\right)^{2}+\left(\partial_{y}I\right)^{2 }}\,\]  
where \(L(i,j)\) is the luminance masking to locate perception changes based on local brightness, \(M_{s}(i,j)\) is the spatial masking, enabling the detection of areas with increased variance where greater distortion can be concealed, and \(C_{m}(i,j)\) is the contrast masking based on local gradient magnitude. This comprehensive JND model is consistent with perceptual theories and empirical models described in [40] and provides a content-adaptive embedding strength that preserves image quality.  
Embedding is made in mid-frequency DCT coefficients, specifically \((u^{*},v^{*})\) = (2, 1), which offer the best compromise between robustness and imperceptibility [25].
## 5 Performance Analysis and Optimization  
In this section, by applying the previous statistical characteristics of the output SNR for SSC, SEC, and SECps schemes, various performance measures of wireless communication, such as the OP, the average SNR, the AoF, the ABEP/ASEP, and the average capacity, are evaluated.
## 5 Visualization of Experimental Data and Testing Conditions  
All described sensors were connected to a computational unit with the UNIX system--Ubuntu 20.04 LTS supporting the ROS 1 environment. During the configuration of the perception system, ROS nodes software packages were used.  
In the ROS environment, each sensor is typically controlled by a dedicated program called an ROS node. The node is responsible for handling low-level communication with the sensor hardware and reading data based on its protocol. The node then publishes these data as an ROS topic using standardized message formats. Other nodes in the ROS system can subscribe to these topics to receive the sensor data and use it for their own purposes. A very useful tool built into the ROS environment is ROSBAG Record, which enables recording of data published by ROS nodes on specified topics, which can then be saved in a compressed format. ROSBAG files store large amounts of data with timestamps that can be synchronized for later analysis based on the system clock. This makes it easy to compare data from different sensors, which is important in environmental perception applications in which multiple sources are used to create situational awareness.  
We recorded data using ROSBAG for different driving scenarios that a UGV may encounter in an off-road environment. One field test drive consisted of 12 special stages capturing files with a total size of approx. 90 GB. It should be underlined that ROSBAG data can be analyzed offline to evaluate the performance of the perception system and its sensors. Foxglove Studio software (Figure 8) was used to visualize the collected data and to perform further analysis of the results [28].  
During the performance tests, additional objects were placed in the off-road environment to be recognized against the background. For this purpose, barrels and a suitcase were used to evaluate the ability of the system to distinguish objects from surrounding vegetation and identify them under various lighting and environmental conditions.  
Additionally, we verified recognition of people in rough terrain to simulate soldiers or other combatants in a battlefield scenario. Vegetation can make it difficult to detect and recognize a person, as the human body can be partially or fully occluded by leaves, branches, or other objects in the environment.  
Figure 8: Main view of the Foxglove Studio application showing the following sensor data: (**a**) point cloud data (Ouster/Velodyne); (**b**) daylight camera view; (**c**) thermal camera view; (**d,e**) ZED2i camera RAW data from the left and right, respectively.
## 2 Materials and Methods
### Reference Data  
We identified 150 samples for each burned and unburned class from the high-resolution CNES/Airbus satellite imagery available from Google Earth Pro. The imagery was acquired on 12 December 2018--few days after the fire incident in the studied area. Burned patches were clearly distinct from unburned vegetation, and a historical imagery function was used to verify vegetation conditions. The samples were randomly distributed throughout the fire zone. Sample layers were converted from the keyhole markup language to shapefile format in the Environmental Systems Research Institute (ESRI) ArcMap environment and later imported to GEE for classification, as shown in Figure 2.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Severity Level** & **Color Code** & **MNBR Values** \\ \hline Unburned & \(<\)–0.1 \\ Low severity & 0.1–0.26 \\ Moderate-low & 0.27–0.43 \\ Moderate-high & 0.44–0.65 \\ High severity & \(>\)0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Severity levels and the differenced normalized burn ratio (dNBR) range.
## 5 Conclusions and Outlook  
This study discusses the estimation of natural frequencies from GBR displacement measurements by a least squares approach. The approach fits the model of a damped sinusoid to the vibration after a vehicle crossing. With the additional test for goodness of fit, we are able to reliably estimate the linear relationship between the first natural frequency and temperature. Compared to strain sensors, the GBR results have a lower standard deviation and fewer outliers, which benefits the detection of frequency changes caused by damage to the bridge. The least squares approach is also validated with the Frequency Domain Decomposition. While both methods obtain very similar results, the linear regression for determining the relationship between temperature and frequency is more stable in the case of the least squares approach. The approach can better tolerate a simulated reduction of measurement time than the FDD, especially if entire temperature ranges are omitted. It is also possible to separate the vehicle crossings into two different weight classes. Heavy vehicles lead to a lower frequency mean even though the analysed vibration occurs after a vehicle has already left the bridge. Frequency changes beyond the temperature influence can be detected with a hypothesis test, enabling a reliable damage detection approach.  
Since the least squares approach ideally requires uninterrupted damped vibrations, it is only applicable to smaller bridges with a higher likelihood of singular vehicle crossings. Additionally, it has only been tested for the first natural frequency. In future work, we aim to determine the second natural frequency, which should be possible considering the accuracy of GBR. With additional targets, it is also possible to determine mode shapes, which can be used for damage detection and localisation. Considering the substantial influence of temperature change to natural frequencies, further study on the variability of temperatures throughout the structure would benefit the reliability of damage detection.  
All authors prepared the methodological concept of this study, the original draft, as well as the editing of the manuscript. [PERSON] designed the software, curated the data, and performed the investigation, formal analysis, and validation. All authors contributed to the visualization of the data and results. [PERSON] initialized the related research and provided didactic and methodological inputs. All authors have read and agreed to the published version of the manuscript.  
This work is part of the project ZEBBRA, funded by the German Ministry for Education and Research (BMBF).  
The data are not publicly available as they contain sensitive information about the investigated bridge structure.  
We thank [PERSON], [PERSON], and the Buro fur Strukturmechanik for supporting the measurements. We also thank [PERSON] for his support. We acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology.  
The authors declare no conflict of interest.
## 5 Influence of Major Factors on the Performance of Building Settlement Prediction
### Activation Function  
Different activation functions will have a certain impact on the prediction result when predicting sedimentation; this paper mainly compares the effects of three activation functions, relu (rectified linear unit), softplus, and tanh, on the prediction and evaluation indices of the LSTM-RA-ANN model. The relu function is a kind of linear and unsaturated activation function, which provides relatively wide activation boundaries, and can effectively solve the problem of gradient disappearance; the functional expression is Formula (6). The softplus function is similar to the relu function, but it is smoother than the corresponding curve of the relu function, and the functional expression is Formula (7). tanh is a saturated activation function, and the functional expression is Formula (8). Using different activation functions in the constructed LSTM-RA-ANN model, the calculation results of the model assessment indicators at monitoring point F1 are shown in Table 5.  
\[\text{relu}(x)\ =\ \text{max}(0,\ x) \tag{6}\]  
\[\text{softplus}(x)\ =\ \text{ln}(1+\text{e}^{x}) \tag{7}\]  
\[f(x)\ =\ \text{tanh}(x)\ =\ \frac{2}{1+e^{-2x}}-1 \tag{8}\]  
As can be seen from Table 5, when using the proposed LSTM-RA-ANN model, it is optimal when using the relu activation function: compared to using the softplus activation function, the RMSE is reduced by 36%, the MAE by 11%, and the \(R^{2}\) increases by 7%; and compared to using the tanh activation function, the RMSE is reduced by 45%, the MAE is reduced by 15%, and the \(R^{2}\) increases by 6%. Therefore, the activation function has a greater impact on the model performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monitoring Point** & **Distance from Pit Edge (m)** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline F1 & 60 & 0.14 & 0.15 & 0.96 \\ F2 & 55 & 0.18 & 0.17 & 0.95 \\ F3 & 50 & 0.19 & 0.18 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model evaluation indices considering influences of monitoring point locations on settlements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Activation Function** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline relu & 0.16 & 0.17 & 0.95 \\ softplus & 0.25 & 0.19 & 0.89 \\ tanh & 0.29 & 0.20 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model evaluation indices under various activation functions for monitoring point F1.
## 2 Methods
### PPP-RTK/INS Tightly Coupled Integration Model  
In the error state of PPP-RTK and INS, \(\mathbf{x}^{e}_{GNSS}\) and \(\mathbf{x}^{e}_{INS}\) denote the position of the GNSS receiver antenna reference point (ARP) and IMU center, respectively. They do not represent the same position and their spatial relationship in the \(e\)-frame can be expressed as [42]  
\[\mathbf{x}^{e}_{GNSS}=\mathbf{x}^{e}_{INS}+\mathbf{C}^{e}_{b}\mathbf{l}^{b} \tag{11}\]  
in which \(\mathbf{l}^{b}\) means the lever-arm correction vector in the \(b\)-frame. As for the approximate coordinates \(\widetilde{\mathbf{x}}^{e}_{GNSS}\) and \(\widetilde{\mathbf{x}}^{e}_{INS}\), their relationship can be described as  
\[\widetilde{\mathbf{x}}^{e}_{GNSS}=\widetilde{\mathbf{x}}^{e}_{INS}+\widetilde{C}^{e}_ {b}\mathbf{l}^{b} \tag{12}\]  
where \(\widetilde{\mathbf{C}}^{e}_{b}\) is the approximation of \(\mathbf{C}^{e}_{b}\), and satisfies  
\[\widetilde{\mathbf{C}}^{e}_{b}=(\mathbf{I}-\mathbf{\phi}\times)\mathbf{C}^{e}_{b} \tag{13}\]  
Then, the following equation of \(\delta\mathbf{x}^{e}_{GNSS}\) and \(\delta\mathbf{x}^{e}_{INS}\) can be derived from Equations (11) to (13):  
\[\delta\mathbf{x}^{e}_{GNSS}=\delta\mathbf{x}^{e}_{INS}+\mathbf{C}^{e}_{b}\mathbf{l}^{b}\times \mathbf{\phi} \tag{14}\]  
The state error in the integrated navigation is defined as the observation minus the true value, whereas the state error in the GNSS is defined as the true value minus the observation. Thus, the signs of \(\delta\mathbf{x}^{e}_{GNSS}\) and \(\delta\mathbf{x}^{e}_{INS}\) are opposite. After adding a minus sign to Equation (14) and substituting it into Equation (2), the observation equation of the PPP-RTK/INS can be further expressed as  
\[\begin{array}{c}\Delta P^{s}_{r,f}=-\mathbf{h}^{s}_{r}\delta\mathbf{x}^{e}_{INS}-\bm {h}^{s}_{r}\mathbf{C}^{e}_{b}\mathbf{l}^{b}\times\mathbf{\phi}+\mathbf{r}_{r,sys}+\mathbf{\alpha}^ {s}_{r}\mathbf{S}\mathbf{T}_{w}+b_{r,f}+\varepsilon_{\mathbf{p}}\\ \quad\quad\quad\quad+\frac{\alpha_{0}}{\beta_{2}}\gamma^{s}_{r}(a_{0}+a_{1}dL+a _{2}dB+a_{3}dL^{2}+a_{4}dB^{2}+r^{s}_{r}+\varepsilon_{\widetilde{T}})\\ \Delta\Phi^{s}_{r,f}=-\mathbf{h}^{s}_{r}\delta\mathbf{x}^{e}_{INS}-\mathbf{h}^{s}_{r}\mathbf{C }^{e}_{b}\mathbf{l}^{b}\times\mathbf{\phi}+\mathbf{r}_{r,sys}+\alpha^{s}_{r}\mathbf{T}_{w}+ \lambda N^{s}_{r,f}+\varepsilon_{\Phi}\\ \quad\quad\quad\quad-\frac{\alpha_{0}}{\beta_{2}}\gamma^{s}_{r}(a_{0}+a_{1}dL+a _{2}dB+a_{3}dL^{2}+a_{4}dB^{2}+r^{s}_{r}+\varepsilon_{\widetilde{T}})\\ \widetilde{I}^{s}_{r}=a_{0}+a_{1}dL+a_{2}dB+a_{3}dL^{2}+a_{4}dB^{2}+r^{s}_{r}+ \varepsilon_{\widetilde{I}^{s}_{r}}\end{array} \tag{15}\]  
Combining the state vector \(\mathbf{x}_{PPP-RTK}\) in Equation (4) and \(\mathbf{x}_{INS}\) in Equation (10), the state vector of the PPP-RTK/INS can be described as  
\[\mathbf{x}=\left(\begin{array}{cccccccccccc}\delta\mathbf{x}^{e}_{INS}&\delta\mathbf{ v}^{e}_{INS}&\mathbf{\phi}&B&S&t_{r}&\delta T_{w}&b_{r}&\mathbf{N}_{r}&\mathbf{a}_{r}&\mathbf{r}_{r} \end{array}\right)^{T} \tag{16}\]
## 6 Results and Discussion
### Case Study Results
The results of cost and duration comparison analysis performed using two methods are shown in Table 6. When the DP algorithm was applied to the model, the total cost was calculated to be 62,991,065 W, and the total duration was 2431 d. Without applying the DP algorithm, the total cost was 62,631,441 W and the total duration was 3913 d. In  
Figure 4: Scheduling results (without the use of DP). Figure 5: Scheduling results (with the use of DP). the case where the DP algorithm was not applied to the model, the selection of activity alternatives would be directed toward the minimization of costs, regardless of the input target duration, thus resulting in a low cost but a significantly higher total duration. In contrast, when the DP algorithm was applied to the model, it derived the combination of activity alternatives that minimized the cost among the conditions that satisfied the input target duration. Therefore, when the DP algorithm was applied, the model obtained the values that minimized the total cost among the combinations that satisfied the initially input target duration of 2500 d. Thus, when the user sets the target duration of the construction period as input data during the earthwork planning stage, the model proposed in this study can obtain scheduling results that minimize construction costs among various conditions.