esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overall System
### Specific Features of s-Guard
#### 3.2.3 Sensors  
Core embedded sensors related to temperature, six-axis, and SpO\({}_{2}\) are presented in Table 2. On the board, SpO\({}_{2}\) includes algorithms, quick evaluation and data collection, stream data to mobile apps, schematics, or Gerbers to download, with components of MAX30101 (Optical Module) and MAX32664A (Algorithm MCU). The sampling rate per second (SPS) for the temperature sensor, six-axis sensor, and SpO\({}_{2}\) sensors were 2 SPS, 10 SPS, and 10 SPS, respectively.
### High Power Oscillator  
A block diagram of the high power oscillator is shown in Figure 5. Port 1, 2, and 3 are represented as an input of the power amplifier, an output of the oscillator, and an output of the feedback circuit, respectively. S\({}_{31}\) and S\({}_{21}\) are simulated and plotted in Figure 6. The simulated magnitude and phase of S\({}_{31}\) are +2.4 dB and +1.7 degrees at 2.445 GHz, which meets the Equations (1) and (2).  
A precise schematic diagram of the high power oscillator connecting Ports 1 and 3 is shown in Figure 7. The simulated output spectrum of the high power oscillator is shown in Figure 8. An output power of 54.2 dBm (263 W) and a drain efficiency of 61.2% has been obtained at 2.453 GHz. In comparison with Figure 3 of the power amplifier, the output power becomes smaller by 0.2 dB and efficiency of 1.1%, which greatly attributes to the insertion loss of around 0.15 dB of the feedback circuit as well as the variations of the matching condition of the power amplifier.  
Figure 5: Block diagram of the high power oscillator.
# Retinitis Pigmentosa Classification with Deep Learning and Integrated Gradients Analysis
## 2 Materials and Methods
### Raw Data  
Two public hospitals in Portugal provided a collection of 2798 autofluorescence retinal images. These images were labeled by clinical experts, who categorized them into two primary groups: non-syndromic retinitis pigmentosa (RP) and syndromic RP. In addition to the clinical diagnosis, each image was also annotated with the specific mutant gene, resulting in a dataset encompassing 66 genes linked to syndromic and non-syndromic RP.  
The dataset featured images from each patient's eyes, captured using multiple imaging modalities to provide a more diverse and comprehensive view of the retina. Some images were taken with a 30-degree field of view using standard fundus photography, while others had a wider 55-degree field of view. Additionally, some images were captured using an Optos Ultra-Wideview camera (Optos plc, Dunfermline, UK), offering a 200-degree field of view. This variety in imaging techniques added depth to the dataset. It ensured that different areas of the retina were represented, potentially enhancing the model's ability to identify and classify features associated with different genes.  
Although the dataset includes images with different fields of view (30, 55, and 200 degrees), no distinction was made based on this factor. All images were treated equally during model training, without any specific consideration for field of view.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 2 Related Work
### Step-Length Estimation
Recent state-of-the-art approaches address this issue through the use of classifiers and detectors to identify the placement of the navigation device, and motion mode of the user to enhance the navigation solution. The problem with using the classifier approach is the lack of uniform standards for the classifier categories and classes, while also requiring a vast database for training and the need for user dependant parameters in some cases. Furthermore, the addition of the classification stage imposes a new challenge for when the classification fails and/or misclassification takes place. While there are many attitude and heading estimation approaches, the correction of the tilt angles without imposing motion restriction is still an issue. This issue propagates to the azimuth correction aided by magnetometers due to the requirement of leveling magnetic measurements with accurate tilt angle estimates. Furthermore, errors and drift in the attitude angles result in poor step heading estimation. All these factors combined result in fast and high drift from the true trajectory, hence the need for frequent aiding by other technologies and techniques such as MM, Wi-Fi fingerprinting, pseudo indoor GNSS systems, and more. The problem with these aiding techniques is that they might rely on phenomena that are changing over time--such as magnetic mapping--and hence would require regular surveying and system maintenance and calibration. Although PDR require aiding by other technologies, it is appealing to improve the solution of PDR as a standalone system in the case of limited and infrequent corrections to make them reliable for an extended runtime. Enhancing PDR performance results in less complex and costly aiding systems requirements.
# Dynamic Routings in Satellite Networks: An Overview
## 5 Potential Technologies and Future Directions
### Potential Technologies
Hence, for the giant multi-layer satellite networks, more attention should be paid to the cooperation of mobile edge computing. **Digital Twin:** Digital twins are physical models created digitally to analyze and optimize physical objects [124; 125; 126]. The technology is supposed to have the characteristics of scalability, interoperability, scalability, and fidelity [127; 128]. Based on the digital twin satellite network model and simulation technology, a dynamic topology model of multi-layer satellite networks is constructed in the information space to predict and simulate dynamic routing changes. On the one hand, the prediction results of the simulation are applied to select routings and forward decisions. On the other hand, the large amount of data generated by monitoring the solid satellite platform is used to improve the accuracy and predictability of information space models., significantly reducing the computational complexity of the dynamic routing strategies in multi-layer satellite networks. DT can reproduce the operational status of satellites using available data to assist decision making, prevent anomalies and problems from worsening, and improve satellite failure detection. In addition, during content delivery, virtual routing in digital twin networks (DTNs) can be used to calculate and verify optimal routing paths [129; 130].
# Efficient Algorithms for the Maximum Sum Problems
## 2 Parallel Algorithms for the MSA Problem
### Parallel Algorithm 3
## 3 Review of Sequential Algorithm for the MCS Problem

We start from describing a sequential algorithm for \(W\) based on column sums, given by [PERSON] et al. [PERSON] et al. (2016). We call the rightmost column of a \(W\)-shape the anchor column of \(W\). The array portion of general array \(b\), \(b[i,...,k][j,...,l]\), is the rectangular portion whose top left corner is \((i,j)\), and the bottom right corner is \((k,l)\). The column \(b[i,...,k][j,...,j]\) is abbreviated as \(b[i,...,k][j]\). In Algorithm 5, \(W[i][k][j]\) is a \(W\)-shape based on the anchor column of \(a[k,...,i][j]\). The sum of this anchor column is given by \(column[i][k][j]\). The computation proceeds with the strip of the array from row \(k\) to row \(i\) (Figure 6) based on dynamic programming. Within this strip from row \(k\) to row \(i\), variable \(j\) sweeps to compute \(W[i][k][j]\) by adding \(column[i][k][j]\) to \(W[i][k][j-1]\) (Case 1 of Figure 7) or extending a \(W\)-shape downward or upward by one (Cases 2 and 3 of Figure 7, respectively). Note that the width of the strip is given by \(t+1\). That is, we go through from thinner to thicker strips, so that the results for thinner ones are available when needed for Cases 2 and 3. Obviously, Algorithm 5 takes \(O(n^{3})\) time. Figure 5: Illustration for Algorithm 4. ```
Initialization
```
1:for all\(0\leq i,j,k\leq n\)do
2:\(W[i][k][j]\gets 0\)\(\triangleright\)\(W[i][k][j]\) stores maximum \(W\) for anchor column \(column[k,\ldots,i][j]\)
3:endfor ```
Main
4:for\(j\gets 1\) to \(n\)do
5:\(sum[0][j]\gets 0\)
6:for\(i\gets 1\) to \(n\)do
7:\(sum[i][j]\gets sum[i-1][j]+a[i][j]\)
8:endfor
9:endfor
10:for\(t\gets 1\) to \(n-1\)do
11:for\(k\gets 1\) to \(n-t\)do
12:\(i\gets k+t\)
13:for\(j\gets 1\) to \(n\)do
14:\(column[i][k][j]\gets sum[i][j]-sum[k-1][j]\)\(\triangleright\) sum of \(a[k,\ldots,i][j]\)
15:\(cand_{1}\gets W[i][k][j-1]+column[i][k][j]\)
16:\(cand_{2}\gets W[i-1][k][j]+a[i][j]\)
17:\(cand_{3}\gets W[i][k+1][j]+a[k][j]\)
18:\(W[i][k][j]\leftarrow\mathbb{MAX}\{cand_{1},cand_{2},cand_{3}\}\)
19:endfor
20:endfor
21:endfor
```  
**Algorithm 5** Sequential algorithm for \(W\). After all \(W\) are computed, we compute the \(N\)-shape in array \(N\) for all anchor columns by a mirror image of the algorithm for \(W\). Then, the finalization of the \(WN\)-shape is given by Algorithm 6. Figure 6: Sequential computation with position indices \(i\), \(j\), \(k\). Figure 7: Three cases for candidates. ```
1:\(S\leftarrow-\infty\)
2:for\(j\gets 1\) to \(n\)do
3:for\(k\gets 1\) to \(n\)do
4:for\(i\gets k\) to \(n\)do
5:\(S\leftarrow\texttt{MAX}\{S,W[i][k][j]+N[i][k][j]-column[i][k][j]\}\)
6:endfor
7:endfor
8:endfor
```  
**Algorithm 6** Combining \(W\) and \(N\). Note that the \(W\)-shape and the \(N\)-shape share the same anchor column, meaning we need to subtract one \(column[i][k][j]\). This computation is done for all possible \(i\), \(j\) and \(k\), taking \(O(n^{3})\) time, resulting in \(O(n^{3})\) time for the maximum convex sum.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Two-Stage Framework
#### 2.2.2 Stage 2: Damage Classification  
In Stage 2, the same backbone network as in Stage 1 is used, as shown in Figure 1b. In this stage, the weights from Stage 1 are used here for weight initialization. The pre-disaster and post-disaster images are sent into the two-branch network separately. The feature maps generated by the two branches are concatenated and put into a convolution layer of 1 \(\times\) 1 so that the location information of the pre-disaster images can guide the damage classification of post-disaster images. The generated output is then used for the final classification of damage levels.
## 4 Cluster-Based Multipath Routing in WMSN
### Route Optimization and Local Repair  
In order to optimize the found routes in route discovery phase, path loops and path cycles should be prevented. For _path loops_, each CH that receives CH-Msg from other nodes checks first the IDs of the nodes joining the path to know whether it already joined this path before or not as shown in Figure 4(a). If a CH receives CH-Msg belongs to one of the paths already found before, it checks the conditions and the status of the given path in order to update its routing information about this path and reflects these changes (if any) on its decision of selecting the proper path for each type of data. Moreover, for path optimization with minimum number of hops, a CH checks for every given path whether it is a child for any participating node in this path (except of course its direct parent in this path). In this case, it is better for the CH to communicate directly to that parent instead of making a path cycle as shown in an example in Figure 4(b). Thus, if a _path cycle_ is found, the cluster head deletes this path from its routing information (as it is just a longer version of already found path) and keeps the shorter path.  
Figure 3: Pseudo-code of the Main Part of the Routing Protocol.  
In order to detect any node or link failure, an acknowledgment system has been used in the network. Each source node or intermediate CH keeps track of sent data packets toward the sink. After receiving a certain number of data packets, a CH sends an acknowledgment message (Ack-Msg) to the sender and in the same manner waits an Ack-Msg from its parent confirming receiving the data packets. If a node did not receive an Ack-Msg from its parent (or its CH), it will assume that there is a node-failure or link-failure and it will select another parent (path) -depending on its routing information tables - suitable with the current type of data. Notice that in this process there is no need to initialize the entire network to overcome the existing failure; it just affects the nodes along the failed path and because of that it is called _local repair_. Then the source node starts sending its data again to the base station through the new parent. Notice that in this case, the affected CH will reflect the changes on its next CH-Msgs in order for all participating nodes in the old path to update their routing information according to the new state of that path.
## 3 Derivation of 2-D Point Target Frequency Spectrum
\begin{split}\psi_{0}(f_{\rm a})&=-2\pi\frac{f_{ \rm c}}{c}(R_{\rm t}+R_{\rm r0})\\ &\quad+\pi\frac{f_{\rm c}}{c}\Big{(}A_{1}k_{1}^{2}-\frac{2}{3}A_{ 2}k_{1}^{3}+\frac{1}{2}A_{3}k_{1}^{4}\Big{)}\\ &\quad+\pi\frac{f_{\rm c}}{f_{\rm c}^{2}}\big{(}A_{1}-2A_{2}k_{1}- 3A_{3}k_{1}^{2}\big{)}f_{\rm a}^{2}\\ &\quad+\pi\frac{c^{2}}{f_{\rm c}^{2}}\big{(}{-}\frac{2}{3}A_{2}+ 2A_{3}k_{1}\big{)}f_{\rm a}^{3}+\pi\frac{c^{3}}{f_{\rm c}^{2}}\frac{1}{2}A_{3} f_{\rm a}^{4}\end{split}  \tag{15}\]  
\[ \begin{split}\psi_{1}(f_{\rm a})&=\pi\frac{c}{f_{ \rm c}^{2}}\big{(}A_{1}-2A_{2}k_{1}+3A_{3}k_{1}^{2}\big{)}f_{\rm a}^{2}\\ &\quad+\pi\frac{c^{2}}{f_{\rm c}^{2}}\Big{(}\frac{4}{3}A_{2}-4A_{ 3}k_{1}\big{)}f_{\rm a}^{3}-\pi\frac{c^{3}}{f_{\rm c}^{2}}\big{(}\frac{3}{2}A_{ 3}\big{)}f_{\rm a}^{4}\end{split}  \tag{16}\]  
\[ \begin{split}\psi_{2}(f_{\rm a})&=-\pi\frac{1}{\gamma}+ \pi\frac{c}{f_{\rm c}^{3}}\big{(}A_{1}-2A_{2}k_{1}+3A_{3}k_{1}^{2}\big{)}f_{\rm a }^{2}\\ &\quad+\pi\frac{c^{2}}{f_{\rm c}^{2}}(-2A_{2}+6A_{3}k_{1})f_{\rm a }^{3}+\pi\frac{c}{f_{\rm c}^{2}}3A_{3}f_{\rm a}^{4}\end{split}  \tag{17}\]  
\[ \begin{split}\psi_{3}(f_{\rm a})&=\pi\frac{c}{f_{ \rm c}^{4}}\big{(}-A_{1}+2A_{2}k_{1}-3A_{3}k_{1}^{2}\big{)}f_{\rm a}^{2}\\ &\quad+\pi\frac{c^{2}}{f_{\rm c}^{2}}\big{(}{\frac{8}{3}}A_{2}-8A_ {3}k_{1}\big{)}f_{\rm a}^{3}-\pi\frac{c^{3}}{f_{\rm c}^{2}}5A_{3}f_{\rm a}^{4} \end{split}  \tag{18}\]  
\[ \begin{split}\psi_{4}(f_{\rm a})&=\pi\frac{c}{f_{ \rm c}}\big{(}A_{1}-2A_{2}k_{1}+3A_{3}k_{1}^{2}\big{)}f_{\rm a}^{2}\\ &\quad+\pi\frac{c^{2}}{f_{\rm c}^{2}}\big{(}{-}\frac{10}{3}A_{2}+ 10A_{3}k_{1}\big{)}f_{\rm a}^{3}+\pi\frac{c^{3}}{f_{\rm c}^{2}}\frac{15}{2}A_{ 3}f_{\rm a}^{4}\end{split}  \tag{19}\]  
\[\psi_{\rm a}(f_{\rm a})=2\pi\Big{(}A_{1}k_{1}-A_{2}k_{1}^{2}+A_{3}k_{1}^{3} \Big{)}f_{\rm a} \tag{20}\]  
\[\psi_{\rm r}(f_{\rm r})=-\frac{\pi}{c}\bigg{(}2(R_{\rm t}+R_{\rm r0})+A_{1}k_{1 }^{2}-\frac{2}{3}A_{2}k_{1}^{3}+\frac{1}{2}A_{3}k_{1}^{4}\bigg{)}f_{\rm r} \tag{21}\]  
The azimuth compression term \(\psi_{0}(f_{\rm a})\) only depends on \(f_{\rm a}\); thus, it can be compensated in the range time azimuth frequency domain. \(\psi_{1}(f_{\rm a})\) represents the residual RCM after LRWC. The first term in \(\psi_{2}(f_{\rm a})\) denotes the range modulation while the others are the SRC terms. \(\psi_{3}(f_{\rm a})\) and \(\psi_{4}(f_{\rm a})\) are high-order range-azimuth coupling terms. \(\psi_{0}(f_{\rm a})\) and \(\psi_{\rm r}(f_{\rm r})\) represent the range and azimuth positions of target \(P\) after LRWC, respectively.
## 6 Conclusions and Future Perspectives
### Directions for Future Research
#### 6.3.2 Enhancing Validation Across Space and Time  
To ensure the generalization capability of DL models in oceanographic applications, future research could consider enhancing model validation across different space and time periods. For example, rather than using random splits, DL models can be trained on data from one region and tested on a geographically distinct region or trained on data from historical years and validated on subsequent years. Such validations in spatiotemporal context enable a more realistic assessment of a model's ability to generalize under varying environmental conditions. In addition, stratified performance evaluation, i.e., not only reporting overall performance but also breaking down results by spatial subregions or seasonal periods, should be implemented. For example, in wind speed estimation tasks using NDBC buoy data, model performance can be reported separately for different spatial regions, such as the Pacific Ocean, the U.S. West Coast, and the Gulf of Mexico, or across different seasonal intervals, such as summer (June to August) and winter (December to February). These stratified performance validations are beneficial in identifying whether the DL model is overfitting to certain regions or seasons and revealing other specific performances in model predictions. Moreover, domain adaptation techniques may be explored to improve model transferability across regions or time periods with different data distributions. Approaches such as transfer learning and domain-invariant representation learning could help bridge the gap between training and deployment environments.
## 5 Collaborative Management Framework and Results Discussion
### Results Analysis and Discussion
The proposed collaborative management framework, integrating the six main global resources management dimensions through the underlying management paradigms, consists of original input, as the co-authors did not come across any more or less closely related work mentioning the combined use of this whole set of management paradigms either in academia or industry, as previously shown through the compiled information in Table 2. Therefore, regarding the whole and diversified set of benefits expected through its use, further developments regarding the combination of the underlying six management paradigms is highly recommended, as each one enables us to tackle specific main issues in the context of GRM, being considered to be of highest relevance in the current Industry 4.0 era. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Management Paradigm** & **Main Characteristics** & **References** \\ \hline Dynamic & Adapts to changing manufacturing conditions & [11; 14; 18; 20; 29; 51; 52; 61; 64; 65; 66; 76; 77; 78; 80; 83; 86; 86; 89; 94; 97; 99; 100] \\ \hline Distributed & Decomposes complex management problems & [29; 47; 48; 55; 50; 61; 65; 67; 69; 70; 72; 74; 79; 80; 83; 85; 88; 90; 96; 98] \\ \hline Intelligent/predictive & Processes big data in complex and highly demanding and uncertain manufacturing environments & [18; 50; 51; 52; 55; 64; 66; 68; 69; 71; 73; 76; 77; 80; 82; 8
# Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts
## 3 Clustered kNN Shipping-Cost Estimation
### The kNN Cost Estimation  
Once the pre-processing was completed and substituted with the clustering information, the data could be transformed into a tree-like structure, which facilitated the kNN estimation. At first, any new contract for the specific carrier, which is subject to estimation, was assigned to the connection between existing clusters. Next, the classification was conducted according to the main classifiers, which were (starting from the root):  
Figure 4: Cluster centers: DBSCAN and manual ones.  
* /ANY \(\rightarrow\) any existing contract for the current route
* /INTERMODAL \(\rightarrow\) intermodal shipping
* /ROAD \(\rightarrow\) road truck only shipping
* /LEASED \(\rightarrow\) leased carriers
* /SPECIFIC \(\rightarrow\) specific leased carrier
* /OTHER \(\rightarrow\) other leased carrier
* /EXTERNAL \(\rightarrow\) external carriers
* /SPECIFIC \(\rightarrow\) specific external carrier
* /OTHER \(\rightarrow\) other external carrier  
The results were returned for all possible executions for further selection. In case of a validation (during algorithm design and testing) we selected the most similar one to the tested example. Once there were no contracts in the current leaf, i.e., for instance, the estimated carrier had no history over the route, we took into account the same route but operated by other carriers. Finally, the following features were selected (from the ones mentioned in Section 3.1) for the final kNN operation at the actual leaf of the tree for the order being estimated. Features are listed in importance order--reflected by the weight \(w_{i}\):  
* weight \(w_{i}=10\)\(\rightarrow\)**TimeTillNow**;
* weight \(w_{i}=5\)\(\rightarrow\)**isCooler**;
* weight \(w_{i}=3\) \(\rightarrow\)**FuelPrice**;
* RouteLength**;
* weight \(w_{i}=1\) \(\rightarrow\)**PaymentTerm**;
* MinShippingTime**;
* MaxShippingTime;
* LoadNo;
* UnloadNo;
* CargoWeight;
* PaymentTerm;
* EmptyDist;
* TonnesKM;
* TimeDiff.  
The above \(w_{i}\) weights were optimized out of their initial values \(w_{i}=1\), for all \(i\), using the Bayesian optimization algorithm implemented in Python library Optuna [34].  
The final price estimation, **RouteEstimCost**, was evaluated as the weighted mean out of \(k=5\) Nearest Neighbors. Moreover, all the found neighbors' costs were re-scaled to the current process using the inflation rate coefficient Inflate. Apart from the estimated cost, the algorithm evaluated the estimated minimum and maximum cost, which was calculated as the cost of the cheapest and the most expensive Nearest Neighbor. Additionally, the travel times were returned in form of the trimmed (trimming factor equal to 1) route time out of the selected \(k=5\) Nearest Neighbors.
# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 3 Radio Access Network Algorithms
### Network Optimization
For the handover optimization algorithm, the objective function is given by (adapted from [24]):  
\[f_{H}(\Gamma,\Psi)=e^{-\text{RSRP}(\Gamma,\Psi)+\text{CH}(\Gamma,\Psi)+\text{ HO}(\Gamma,\Psi)} \tag{3}\]  
where \(\Gamma\) represents the set of handover parameters of a cell, and \(\Psi\) represents the set of handover parameters specific for cell neighborhood relationships. \(\text{RSRP}(\Gamma,\Psi)\) evaluates the coverage level, \(\text{CH}(\Gamma,\Psi)\) penalizes configurations very distinct from the initial configuration, and \(\text{HO}(\Gamma,\Psi)\) evaluates the number of handovers, which is minimized by the proposed objective function. More details on the antenna tilt and the handover algorithms are given in [22; 24]. Regarding the applicable network technologies, both the crossed sector/feeder algorithm and the antenna tilt optimization algorithm can be applied from 2 nd Generation (2G) to 5G networks, while the handover parameter optimization is still specific for 4G.
# Source Rock Prediction Using Well Log and Seismic Data:
## 4 Results
### Maturity of Organic Matter in Source Rocks  
The Lower-Cretaceous-Albian-stage source rocks in the Cote d'Ivoire Basin have high abundance and goog types of organic matter, but the organic matter can only be transformed into oil and gas through thermal evolution [30]. The maturity of the organic matter is an indicator reflecting the degree of thermal evolution and is an important basis for evaluating the type and production of oil and gas [9]. The vitrinite reflectance ranges from 0.3% to 0.9%, with an average of about 0.5%. The R\({}_{\text{O}}\) of 64 samples was more than 0.5%, indicating that it has entered the low-mature to mature stage; 5.6% of the samples have an R\({}_{\text{O}}\) value greater than 0.7%, constituting the mature stage, and all of the samples are in the low-mature to mature stage. As shown in Figure 4, when the burial depth is about 2600 m, the vitrinite reflectance reaches 0.5%, and the organic matter reaches the threshold of hydrocarbon generation and begins to generate oil. When the burial depth is  
Figure 2: S\({}_{1}\) + S\({}_{2}\) relationship with TOC of Lower-Cretaceous-Albian-stage source rocks in the Côte d’Ivoire Basin.  
about 3500 m, the vitrinite reflectance reaches 0.7%, enters the mature stage, and begins to generate a large amount of oil (Figure 4).