esa-sceva/satcom-chunk-collection huggingface dataset:
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
## 5 Conclusions  
The present study is the first one that attempts to examine the performance of the PlanetScope CubeSats in calculating Satellite-derived Bathymetry and explores whether the results fit into the requirements of IHO for nautical maps of navigation. The availability of almost daily satellite images in the archive allows the selection of the most suitable data based on cloud coverage, water surface conditions, and intense visible sunlight--an asset of the high spatial and temporal satellite constellation. The results from the applied empirical method with the intermediate preprocessing steps are promising and show that it has great potential for coastal bathymetry estimation, especially in the shallow waters. However, given that one site has been tested, more work is needed to understand the nature of PlanetScope CubeSats in estimating SDB by including several sites distributed in different water bodies that cover both case I and II waters and systematic collection of in-situ soundings that correspond to each month. Also, the analysis of monthly and annual image composites, as these are provided by Planet for commercial use, will support the elimination of issues related to the absence of cloud mask information, and the low signal-to-noise ratios of the PlanetScope imagery. All in all, given that the technology of CubeSats is improved (i.e., higher signal-to-noise ratios and more spectral bands in the visible wavelengths), we expect, in the next decade, a boom of fleets that can be eventually exploited to carry out scientific and operational mapping and monitoring of the coastal aquatic environment at a fraction of the cost of \"traditional\" satellite platforms.  
[PERSON] conceived the idea, performed the analysis, and prepared the manuscript; [PERSON] supported the analysis and contributed to the manuscript. [PERSON] and P.R. contributed to the manuscript.  
[PERSON] and [PERSON] are supported by the European H2020 Project 641762 ECOPOTENTIAL: Improving future ecosystem benefits through Earth Observations. [PERSON] is supported by a DLR-DAAD Research Fellowship (No. 57186656).  
This work contributed to and was partially supported by the European H2020 Project 641762 ECOPOTENTIAL: Improving future ecosystem benefits through Earth Observations. [PERSON] is supported by a DLR-DAAD Research Fellowship (No. 57186656). We would like to thank the two anonymous reviewers for their constructive comments that improve our work. We also would like to thank Planet's Ambassadors program as Planet satellite images (Planet Team, 2017) are provided through this program.  
The authors declare no conflict of interest.
## 2 Data Collection and Analysis
### Data Analysis
#### 2.2.3 Lane-Change Behavior  
The hyperbolic tangent function lane-change trajectory model is closely linked to driver behavior and provides a better fit for the actual lane-change trajectory [29]. Nevertheless, the model's expressive range does not fully encompass the function, leading to a discrepancy between the obtained lane-change width through fitting and the actual width. To address this discrepancy, the function model is adjusted by incorporating width boundary conditions, as shown in Equation (7):  
\[\begin{array}{l}x(t)=\frac{\beta}{\tan(\tau/2)}\text{tanh}\Big{(}\frac{\tau} {LV_{\text{d}}}\Big{(}t-\frac{L}{2V_{\text{d}}}\Big{)}\Big{)}+\frac{x_{0}+x_{ T}}{2},t\in\Big{[}0,\,\frac{L}{V_{\text{d}}}\Big{]}\\ \beta=\left\{\begin{array}{ll}W/2&(\text{left lane change})\\ -W/2&(\text{right lane change})\end{array}\right.\end{array} \tag{7}\]  
where \(\beta\) denotes the transverse distance of the lane change (m), \(x(t)\) represents the transverse position of the vehicle at the moment \(t\) (m), \(x_{0}\) and \(x_{T}\) signifies the transverse position of the vehicle at the initiation and completion of the lane change (m), \(L\) corresponds to the longitudinal displacement during lane change, which is the length of the lane change (m), \(V_{\text{d}}\) is the longitudinal average vehicle speed in the process of lane change (m/s), \(W\) stands for the total width of transverse displacement during the lane change (m), \(\tau\) is the emergency coefficient of the lane change, while \(t\) denotes the duration of the lane change (s).  
Figure 6 displays the temporal aspects, length, width characteristics, and distribution of the urgency factor derived from 64 instances of lane change to the right side (LCR) observed at Interchange X.  
The mean goodness of fit achieved by the Python 3.7 computation for the modified hyperbolic tangent function permutation model applied to the right lane permutation trajectory stands at 97.18%. This result signifies a high degree of feasibility and compatibility of the model with the LCR trajectory. As depicted in Figure 6, \(\tau\) values for the LCR predominantly range from 2.5 to 3.5. To ensure smooth and safe lane changes for the majority of vehicles navigating the diversion area, it is imperative to set the maximum value of the emergency coefficient of lane change \(\tau_{\text{max}}\) at 3.5.  
Figure 5: 2-stage Erlang distribution fitting time headway at Interchange X.
## 4 Results and Discussion
### Waveband Processing

Table 2: Band selection table. Figure 10: (a) F-norm\({}^{2}\) operations and (b) variance operations. tions, the PCA dimensionality reduction method was used to select the first 11 principal components (the cumulative contribution rate of principal components was 99.10%) and the full-waveband image for training, respectively, and then they were compared with the results of the first category of runs, which showed that the results of the first category of runs had the best performance. In addition, an overall analysis of the results in Table 2 shows that the model using the full-band image operation had the longest training time and the lowest accuracy, and the model performance was poor, which also verifies the necessity of band selection for hyperspectral images. Therefore, 126-136 bands were selected as the model input bands.
## 3 Our Solution: CSS
### Resource Management
#### 3.3.2 CTBRA (Container Type-Based Resource Allocator)  
Algorithm 2 shows the CTBRA algorithm that calculates the range of resources to be allocated for each container type and performs resource allocation. It takes as input \(R\), \(C^{io}\), \(C^{cpu}\), which is the result of the SBCC algorithm collected by the system call monitor, and \(\upmu\), which determines the size of the minimum resource allocated to \(C^{io}\). The result of the \(allocResource(c_{i},start,end)\) function is transmitted to the container controller, leading to the operation of updating the _cgroups_ policy.  
```
0:\(R\), \(C^{io}\), \(C^{cpu}\), \(\mu\)
0:\(null\)
01:for\(c_{i}\) in \(C^{io}\)
02:if\(|c^{cpu}|\) = 0 then
03:\(allocResource(c_{i},1,\mu)\)
04:else
05:\(Equal\ Allocation(R,c_{i})\)
06:endif
07:endfor
08:for\(c_{i}\) in \(C^{cpu}\)
09:\(Equal\ Allocation(R,c_{i})\)
10:endfor
11:returnnull
```  
**Algorithm 2**CTBRA  
From line 2 to line 4, the algorithm allocates the minimum resource for all containers of \(C^{io}\). If there is no CPU-intensive container, equal resource distribution is performed. However, if the CPU-intensive container and the IO-intensive container are executed at the same time, the operation of allocating the minimum resource to the IO-intensive container is performed. The algorithm between line 5 and line 9 evenly divides and allocates all CPU resources of the host machine to all containers of \(C^{cpu}\); the equal allocation algorithm is shown in Algorithm 3. The amount of CPU resources allocated to each container is determined by the number of CPUs that the host machine has and the number of currently running containers in \(C^{cpu}\).  
```
0:\(c_{i}\), \(M\), \(N\)
0:\(null\)
01:if\(N>M\)then
02:\(part\)\(|N/M|\)
03:\(start\)\(\leftarrow\)\(i^{*}part\)
04:\(end\)\(\leftarrow\)\(start+part\)
05:else
06:\(start\)\(\leftarrow\)\(i\)\(mod\)\(N\)
07:\(end\)\(\leftarrow\)\(start\)
08:endif
09:\(allocResource(c_{i},start,end)\)
10:returnnull
```  
**Algorithm 3** Equal Allocation  
This algorithm takes as inputs \(c_{i}\), the \(i\)th container; \(M\), the number of containers; and \(N\), the number of CPU cores in the host system. Lines 1 to 4 of Algorithm 3 indicate the situation in which the number of CPU resources is greater than the number of containers, and lines 5 to 7 indicate the situation in which countless containers are simultaneously executed. \(i\) in line 3 is the index of the \(c_{i}\).
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results
### Results on SST
#### 5.2.4 Spatial Distribution Analysis  
Figure 10 shows the output result and error distribution of January 2018 that reflects the order of the SP-CRN results in Table 4. Figure 10a shows the prediction output  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Avg. RMSE (K)** & **SD of RMSE (K)** \\ \hline CNN & 3.32 & 0.05 \\ ConvGRU & 3.03 & 0.06 \\ ConvLSTM & 3.02 & 0.06 \\ SP-CRN (GRU) & 0.77 & 0.13 \\ SP-CRN (LSTM & 0.88 & 0.09 \\ \hline SP-CRN-1 (1,1,0) & 0.79 & 0.10 \\ SP-CRN-2 (0,6,0) & 0.61 & 0.05 \\ SP-CRN-3 (0,0,2) & 0.55 & 0.04 \\ \hline SP-CRN-1+2 (1,6,0) & 0.74 & 0.08 \\ SP-CRN-1+3 (1,0,2) & 0.56 & 0.05 \\ SP-CRN-2+3 (0,6,2) & 0.65 & 0.05 \\ SP-CRN-1+2+3 (1,6,2) & 0.61 & 0.05 \\ SP-CRN-1+3-Meta (1,0,2) & 0.43 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of different settings on the SST prediction for SP-CRN and comparison methods.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Avg. RMSE (K)** & **SD of RMSE (K)** \\ \hline SP-CRN-1 (0,1,0) & 0.88 & 0.09 \\ SP-CRN-1 (1,1,0) & 0.79 & 0.10 \\ SP-CRN-1 (2,1,0) & 0.82 & 0.08 \\ SP-CRN-1 (3,1,0) & 0.84 & 0.12 \\ \hline SP-CRN-2 (0,2,0) & 1.29 & 0.11 \\ SP-CRN-2 (0,3,0) & 0.96 & 0.05 \\ SP-CRN-2 (0,4,0) & 0.63 & 0.06 \\ SP-CRN-2 (0,5,0) & 0.63 & 0.05 \\ SP-CRN-2 (0,6,0) & 0.61 & 0.05 \\ \hline SP-CRN-3 (0,0,2) & 0.55 & 0.04 \\ SP-CRN-3 (0,0,3) & 0.56 & 0.06 \\ SP-CRN-3 (0,0,4) & 0.57 & 0.05 \\ SP-CRN-3 (0,0,5) & 0.57 & 0.05 \\ SP-CRN-3 (0,0,6) & 0.56 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of different settings on SST predictions for SP-CRN with three proposals. The highlighted result in blue shows the best prediction accuracy for each proposal, while the highlight in gray is the SP-CRN baseline result.  
in which the methods that use periodicity components can capture changes in high and low temperature. Compared to the CNN, the ConvGRU, and ConvLSTM rarely captured the Pacific Ocean's high temperature. Only the smooth change pattern was presented in the prediction output.  
Figure 10b shows the error distribution of comparison methods. The CNN, ConvGRU, and ConvLSTM showed that the extremely high temperature (Pacific Ocean) and low  
Figure 10: The prediction and error distribution of SST on January 2018. (**a**) Ground truth and prediction results. (**b**) Error distribution (absolute error).  
temperature (North and South Pole) have high error distribution, but for the model, considering the periodicity, it was not much different. SP-CRN (LSTM) showed a higher error distribution in more areas than the SP-CRN (GRU) and SP-CRN variant methods.
## 4 Trust-Driven Incentive and Aggregation Mechanism
### Trust-Aware Incentive Allocation Modeling  
Incentive mechanism design is critical for motivating clients and ensuring behavioral quality in federated learning systems. Based on trust modeling, we use the trust score \(\tau_{i}^{t}\) and the client's contribution \(v_{i}^{t}\) as the main factors in reward allocation, avoiding the manipulation that arises from using static metrics such as data volume or training epochs.  
Specifically, the raw contribution \(v_{i}^{t}\) is defined as the normalized \(L_{2}\) norm of the uploaded update as follows:  
\[v_{i}^{t}=\frac{\|\Delta w_{i}^{t}\|_{2}}{\sum_{j\in\mathcal{S}_{i}}\|\Delta w _{j}^{t}\|_{2}}, \tag{5}\]  
The incentive reward function is then defined as follows:  
\[r_{i}^{t}=\frac{R_{t}\cdot\tau_{i}^{t}\cdot v_{i}^{t}}{\sum_{j\in\mathcal{S}_{ i}}\tau_{j}^{t}\cdot v_{j}^{t}}, \tag{6}\]  
This function satisfies the incentive compatibility and prioritizes high-trust, high-contribution clients under the budget constraint \(R_{t}\).  
To prevent manipulation through pseudo-contributions (e.g., uploading high-norm but low-benefit updates), we introduce a validation-based actual gain function \(g_{i}^{t}\) as follows:  
\[g_{i}^{t}=\mathcal{L}(w_{t})-\mathcal{L}(w_{t}+\Delta w_{i}^{t}), \tag{7}\]  
The corrected contribution is computed as \(\vartheta_{i}^{t}=v_{i}^{t}\cdot\frac{g_{i}^{t}}{\mathcal{L}(w_{i})}\) and is used in both incentive and aggregation processes.
## 1 Introduction
### Cadmium-Responsive Genes in Podarcis Siculus Embryos
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **Gene Symbol** & **Biological Pathway** & **Cd-Effect** \\ \hline _Basic transcription factor 3_ & BIT3 & Transcription regulation & Inhibition \\ \hline _Three-hydroxy-3-methylglutaryl coenzyme A_ & HMGR & Cholesterol biosynthesis & inhibition \\ _reductase_ & & & \\ \hline _Fizzy and cell division cycle 20 related 1_ & Fzr1 & Control of cell proliferation & inhibition \\ \hline _Rearranged L-myc fusion_ & RLF & Zinc finger transcription factor & Induction \\ \hline _Topoisomerase 1-binding protein with a_ & & & \\ _broad-complex, Tranttrack and bric a bras/Pox_ & Topol-1 bp, BTB/FOZ domain & Transcription regulation—protein–protein interactions & Induction \\ _virus and Zinc finger_ & & & \\ \hline _Ortho-dendatic protein homolog 2_ & Otx2 & Transcription factor—Brain and eye development & Induction \\ \hline _Paired Box 6_ & Pax6 & Transcription factor—Eye development & Induction \\ \hline _Development and differentiation-enhancing_ & & & \\ _factor 1_ & DDEF1 & ADP-ribosylation factor—Vesicular trafficking & Induction \\ \hline _Voltage-gated sodium channel_ & VGSC & Sensory transduction and synaptic plasticity & Induction \\ \hline \(\gamma\)_-aminobutyric acid type B receptor_ & GABA\({}_{\text{R}}\) & G protein-coupled inhibitory receptors & Induction \\ \hline _Lymphocyte function associated antigen 3_ & CD58 & Production of inflammatory cytokine & Induction \\ \hline _Metallothione_ & MT & Heavy metals homeostasis and detoxification & Induction \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cadmium-responsive genes identified in 20-days-old _P.iculus_ embryos. Figure 1: Representative identification of cadmium-responsive genes by using the mRNA differential display reverse transcriptase Polymerase chain reaction analysis (DDRT-PCR) performed on total RNAs extracted from control- (Ctr) and Cd-treated (+Cd) embryos incubated for 20 days after laying in Cd-contaminated soil. The result of such analysis was validated by subsequent reverse dot blot hybridization [8].
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 4 Results and Discussion
### Proposed Prototype  
A Printed Circuit Board (PCB) was designed and realized to include all the required components for the module to work as a stand-alone system. The module is provided with a power supply unit, which regulates the DC input supply voltage, ranging in [8 V, 16 V], to a lower value (3.3 V) to power all the components. No data cables are required since both configuration and data transfer are outsourced to the wireless link. A microcontroller, with a on-board BLE transceiver, was exploited to minimize the number of components. The overall dimensions of the module, i.e., 47.25 mm \(\times\) 32.35 mm, allow it for being located in a confined space.
## 4 Results and Discussion
#### 4.1.3 Time and Computational Efficiency  
Random Forest with Extra Tree Regressor and Linear Regression models are computationally efficient and relatively faster to train than ANN and LSTM, which require extensive computational resources due to iterative processes. LSTM, being a deep learning model, is the most computationally demanding, requiring significant resources for training and inference. The Random Forest with Extra Trees Regressor achieved the fastest runtime, with a training time of 7.34 s and a prediction time of 0.05 s. Other models, such as the ANN and LSTM, required longer training, consistent with the increased computational demands of neural networks and time-series architectures.
## 7 Experimental Results and Analysis
### Avalanche Effect Test
#### 7.2.2 Keep the Plaintext Unchanged and Change One Bit of the Key  
We then let the plaintext be \"planTextabc\". When encrypting, we kept the plaintext unchanged and changed one bit of the key. The plaintext was encrypted using the conventional DES and chunking disorder algorithms. The chunking disorder algorithm simulated the process by changing the chunk size. In order to avoid experimental chance, the same plaintext \"planTextabc\" was encrypted with 8 test items that differed by only one bit each, and the chunking disorder algorithm also used a strong cryptographic random number generator (RNG) to avoid the influence of pseudo-random numbers on the experimental results.  
The experimental results show that by keeping the plaintext unchanged and changing one bit of the key, the messages are encrypted with the traditional DES algorithm and the chunked disorder algorithm. The traditional DES and chunked disorder algo  
Figure 14: “Avalanche effect” of changing the plaintext test results.  
Figure 15: “Avalanche effect” compared to AES and RC4 algorithm when changing the plaintext test results.  
rithms have an excellent \"avalanche effect\". By keeping the plaintext unchanged and changing one bit of the key, the ciphertext is changed by more than half of the original ciphertext. The \"avalanche effect\" of the chunking algorithm is somewhat similar to the \"avalanche effect\" of the traditional DES algorithm. Figure 16 illustrates results of \"avalanche effect\" test with different encryption factors.  
Furthermore, we conducted a comparison with the AES and RC4 symmetric encryption algorithms. The data were encrypted and compared in the same manner as described above, with a one-bit key change. The experimental results show that the chunking algorithm has the same \"avalanche effect\" as AES and RC4 when changing one key. That is, when changing one key, the number of bits of the ciphertext is altered by more than half of the original number of bits. The change ratio remains essentially between 0.7 and 0.8, indicating a good avalanche effect. Figure 17 illustrates avalanche effect\" compared to AES and RC4 algorithm regarding the secret key test results.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 6 Some Future Research Challenges  
This section describes some of the limitations of the current in-body antenna designs and future challenges, which need to be resolved to improve performance.  
1. Generally, the coupling of in-body antennas with lossy tissue causes the absorption of the EM wave in the near reactive and far field, which is commonly not considered in the design phase. This results in a significant reduction in the radiation efficiency and peak gain, causing inefficient antenna operation. This is inevitable in the far field. However, it is possible to reduce the absorption of the EM wave by covering the in-body antenna with biocompatible material in the near field. Therefore, designing in-body antennas with biocompatibility covering the near field will be a conceivable future research challenge.
2. The human body is formed with inhomogeneous biological tissues and organs. Furthermore, the characteristics and dimensions of biological tissues vary every so often, including by gender. Therefore, the detuning effect of the in-body antenna inside the human body is considered as one of the primary research and design IBBD applications. To date, in-body antenna design and experiments are mostly restricted only to a single tissue environment, which will be a noteworthy shortcoming for diverse biological tissue environments. Therefore, the upcoming in-body antenna research focus must be the investigation of diverse biological environments for efficient in-body antenna operation.
3. The implantation of a device operating at radio frequency inside the biological tissue may lead to a severe long-term health problem due to radiative power absorption. Therefore, an effective and optimized in-body antenna design with a SAR value limit as standard and an appropriate selection of biocompatible materials will be the key future research investigation.
4. Traditional antenna miniaturization techniques tend toward narrow operational bandwidths. Such narrowband operation can cause a detuning effect of the in-body antenna inside the biological environment. Biocompatible encapsulation of the in-body antenna can be utilized to increase the radiation efficiency and gain. However, this inflates the overall IBBD thickness. Therefore, an antenna design technique with acceptable operational bandwidth, radiation efficiency and gain is still a challenging matter in IBBDs. Increasing the operation frequency band can increase the miniaturization scale. However, this increases the loss and tissue absorption, which introduces additional design challenge.
5. Lastly, battery-powered IBBDs have a limited lifetime and bulky dimension, which can cause insufficiency in an in-body antenna power system. Furthermore, the replacement of the battery through a surgical procedure is complex and costly. Therefore, designing power-efficient IBBDs for in-body antennas is a crucial design challenge for the future.