esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive pXRF on Prehistoric Obsidian Artifacts from the Central Mediterranean
## 5 Applications of pXRF on Obsidian Artifacts from Central Mediterranean Sites
### The Small Island of Ustica, Northwest of Sicily  
Ustica is a small island, about 8 km\({}^{2}\), located \(>\) 50 km north of Capo Gallo (coast north of Palermo in western Sicily). Despite its isolated location, there is archaeological evidence of its occupation also beginning in the Neolithic time period, indicating the rather long-distance open-water travel capabilities at that time. The use of obsidian artifacts, coming from both Pantelleria and Lipari, was first shown with analyses of artifacts from excavations of a Bronze Age site [62], and more recently at several different sites with more than 1100 obsidian artifacts found and analyzed [63; 64; 65; 51] (Figure 13). In comparison to travel of much more modest distances from northern Corsica through the Tuscan archipelago to mainland Italy, and across the central Adriatic to Croatia, Ustica was not a stop along the way to other places but instead the only reason for travel in its direction.  
As noted already, there is no direct or indirect evidence of the boats, rafts, or other open-water vessels that were used in the 6 th millennium BCE to reach islands in the Mediterranean (or in the following 4000 years), but it strongly appears that such travel was regular and able to transport not only a few people but also domesticated animals and other items of their material culture. Obsidian from Lipari, in the Aeolian Islands, may have been transported over 25 km to northeastern Sicily, then along the shorelines to Palermo and from there to Ustica, but the proportions of Lipari to Pantelleria obsidian (average of 90% Lipari, 10% Pantelleria), when compared to what was found at the site of Grotta dell'Uzzo in northwest Sicily, suggest more direct open-water travel of \(>\)100 km from the westernmost Aeolian island of Alicudi to Ustica [65]. This is supported by the open-water distance from Pantelleria to southwest Sicily (\(-\)100 km), followed by travel along the western coast and then \(>\) 50 from Capo Gallo to Ustica. The small but seemingly consistent percentage of  
Figure 12: Map with directions to archaeological sites with Palmarola obsidian artifact attributions, including across the Adriatic.  
obsidian from Pantelleria reflects not only the much greater distance of travel, but also its visual and physical properties and their demand when compared with Lipari obsidian.
## 4 Experimental Results
### Ablation Study
#### 4.5.2 Analysis of Pruning and Fine-Tuning  
We analyze the pruning and fine-tuning process in detail for the ResNet-18 model using the ImageNet dataset. Figure 2 shows the validation accuracy across 0 to 100 training epochs. Once the baseline training is completed, as shown in Figure 3, iterative pruning is carried out to achieve desired FLOPs reduction rate. In order to maintain the validation accuracy, fine-tuning is performed at least for one training epoch after every 3% FLOPs reduction rate, which is obtained empirically. The starting learning rate for fine-tuning is set to 10% of the original learning rate. From Figure 3 we can see that after the first 3% FLOPs reduction, and after fine-tuning, the validation accuracy drops from 70.65% to 63.26%. This  
Figure 1: VGGNet validation accuracy across various values of \(w_{mag}\) on the CIFAR-10 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & \multirow{2}{*}{**Method**} & **Baseline** & **Pruned** & **Top-1 Acc.** & **Baseline** & **Pruned** & **Top-5 Acc.** & **FLOPs** \\  & & **Top-1 Acc.** & **Top-1 Acc.** & **(\(\downarrow\))** & **Top-5 Acc.** & **Top-5 Acc.** & (\(\downarrow\)) & (\(\downarrow\)) \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{8}{*}{50} & ThiNet [26] & 72.88 & 72.04 & 0.88 & 91.14 & 90.67 & 0.47 & 36.7 \\  & SFP [13] & 76.15 & 74.61 & 1.54 & 92.87 & 92.06 & 0.81 & 41.8 \\  & FPCM [15] & 76.15 & 75.50 & 0.65 & 92.87 & 92.32 & 0.21 & 42.2 \\  & MetaPruning [51] & 76.60 & 75.40 & 1.20 & - & - & - & 51.2 \\  & LFC [52] & 75.30 & 73.40 & 1.90 & 92.20 & 91.40 & 0.80 & 50.0 \\  & MFP [16] & 76.15 & 75.67 & 0.48 & 92.87 & 92.81 & **0.06** & 42.2 \\  & MFP [16] & 76.15 & 74.86 & 1.29 & 92.87 & 92.43 & 0.44 & 53.5 \\  & GFI-AP [25] & 75.95 & 74.07 & 1.88 & - & - & - & 51.9 \\  & MSVFP(ours) & **76.64** & **76.49** & **0.15** & **93.15** & **92.90** & 0.25 & 42.4 \\  & MSVFP(ours) & **76.64** & 75.53 & 1.11 & **93.15** & 92.60 & 0.55 & **53.5** \\ \hline \hline \end{tabular} Note that bold numbers in the table indicate the best result.  
\end{table}
Table 5: _Cont._drop is obvious because also in Figure 2 we see that at the same learning rate, the original network accuracy is also around 64%. During pruning, the accuracy maintains above 61% achieving a 54% FLOPs reduction rate, and finally reaches 68.43% after completion of the final fine-tuning. Figure 3 also shows how abruptly accuracy drops if fine-tuning is not performed during pruning. That means in Algorithm 1, if we skip Lines 22 \(\sim\) 25, accuracy drops to almost 1% as soon as we reach FLOPs reduction rate to 40%. As shown in Figure 2, we start final fine-tuning from 25 th epoch, because during pruning we also run fine-tuning at least 15 times. Therefore it is not necessary to start fine-tuning from 0 th epoch. Also, it is seen that pruned model converges in the early epoch and fine-tuning is not required until the 100 th epoch. Therefore, a much less number of training epochs are required for fine-tuning the slimmed model compared to the baseline training.
## 4 Discussion  
This study utilizes metagenomic analysis to decipher the genetic complexities of carotenoid biosynthesis in the Antarctic Ocean near King Sejong Station, aiming to enrich the understanding of carotenogenic microbial diversity and the array of biological functions they mediate. Metagenomic analysis reveals a diverse array of carotenoid genes at 16 and 25 m in the Antarctic Ocean, with \(\beta\)-carotene ketolase predominating at both depths. Differential abundance analysis uncovers significant variations in the presence of specific carotenoid genes between the two depths, indicating depth-dependent variations in carotenoid biosynthesis pathways. The profiling unveils a rich tapestry of gene abundance that sustains carotenoid production under the unique physicochemical conditions found in Antarctic waters, reflective of a highly adaptive microbial population.  
Astaxanthin, known for providing survival advantages under high UV radiation and low temperatures, exhibits depth-specific adaptations in its biosynthesis [28; 29]. Genes  
Figure 7: The biosynthetic pathways of C30 carotenoids (**A**) and C50 carotenoids (**B**) depicted based on metagenomic data. Each enzymatic step is annotated with its corresponding KEGG identifier. Color coding shows carotenogenic genes with elevated abundance at specific depths. The absence of genes encoding enzymes is marked by ’NA’, denoting undetectable genes at those depths. Numerical values within boxes represent transcripts per million (TPM), offering a quantitative measure of gene abundance of carotenogenic enzymes.  
encoding CrtZ and CrtW show increased abundance at 25 m, indicating environmental adaptation mechanisms of astaxanthin-producing microbes to the depth of the Antarctic Ocean.  
Spirilloxanthin, crucial in bacterial photosynthetic processes and membrane stabilization, exhibits depth-specific adaptations [33]. Genes encoding CrtD and CrtF show higher abundance at 16 m, suggesting a targeted response to varying light conditions and oxidative stress levels with increased depth. This niche-specific adaptation enhances survival in low-light conditions, where sprilloxanthin plays a pivotal role in photosynthetic efficiency.  
Bacteriourberin, known for protecting microorganisms against oxidative and temperature stress, shows varied distribution patterns of genes involved in its synthesis [39; 40]. Genes encoding CrtD and CrtF exhibit higher abundance at 25 m depth, suggesting a depth-dependent adaptation to environmental stress. The absence of the gene encoding Lye) in the deep-water samples aligns with existing research on the capabilities of halobacteria and related marine microorganisms in producing C50 carotenoids [42; 43].
# A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
## 2 SRDCNN-Based Intelligent Fault Diagnosis Method
### Residual Dilated Convolutional Layer  
#### 2.2.1 Dilated Convolution  
The dilated convolution (also known as convolution with holes) was first proposed by [PERSON] and [PERSON] [28] to widely reference to multi-scale contextual information without losing resolution. However, it is difficult to design dilated convolution structure for specific application objects. In recent years, dilated convolution has been applied to solve problems such as semantic segmentation [29], speech synthesis [30], and machine translation [31], etc. [PERSON] et al. [32] proposed a conditional time series forecasting method based on the dilated convolution, the prediction model can be expressed as: \(\mathbb{P}(y|x_{1},x_{2},x_{3},\ldots,x_{n})\). They held that time series data follows a certain probability distribution that could be learned by training. The \(y\) predicted was the next value \(x_{n+1}\) of the time series. From another point of view, the probability that the data belongs to a certain class can also be determined according to the distribution law of the time series data. Since the bearing vibration signal is a typical time series data, it is a promising method to determine the state of the bearing by using a dilated convolution, and to obtain the probability distribution of the bearing vibration signal.  
As shown in Figure 2, the dilated convolution filter skips input values with a certain step. A dilated convolution is equivalent to a convolution with a larger filter, obtained by filling the original filter with zeros. However, the former is significantly more efficient, and the latter may consume unnecessary costs of training time and storage space. A dilated convolution increases the receptive field without losing information, and the output of each convolution layer contains a wide range of information from input data, which has a great effect on avoiding local false features. For the vibration signal, if the length of a sample is short, the accuracy of the diagnosis model will be greatly affected by the local false features. An effective way is to adopt dilated convolution to accept a longer sample with a large amount of redundant information.
## 2 Adaptability of FDP to PV Station Access
### Influence of CT Saturation on Action Criteria in PV Station  
CT saturation makes the measured value of fault current smaller than the actual value [26], which has a more significant impact on external faults and a smaller impact on internal faults. When an external fault occurs, the CT is likely to enter a nonlinear operating state (saturation state) within a few milliseconds. In this case, the high DC component current on the primary side will lead to the enhancement of the conductive ability of the magnetized branch of CT, thus leading to a large secondary current, which may be misjudged as an internal fault, causing the protection system to operate incorrectly [27]. Due to the controllability and current limiting characteristics of grid-connected inverters of PV stations, the amplitude of grid-connected fault current \(I_{p}\) is small. Especially in strong systems, external or internal faults on the system side have little impact on the action criterion, and CT saturation rarely occurs. However, with external faults on the PV side, the differential current to braking current ratio increases significantly, potentially causing CT saturation and leading to protection failure or system misoperation [28].
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### Channel Selection and Route Request Initiation
On the other hand, the achievable data rate, \(R_{s,i}(\varpi)\), in the case where, due to spectrum sensing error, the PU is not detected, although it is active, is:  
\[b_{f,i}(\varpi)=\beta\text{log}_{2}\left(1+\frac{S_{rec}^{su}}{n(\varpi)+Sp( \varpi)+I_{su,i}}\right)\]  
\[R_{f,i}(\varpi)=\left(P_{on,i}-P_{d,i}\right)\frac{T_{su,i}-\overline{T_{s,i}} -\overline{T_{r}}}{T_{su,i}}b_{f,i}(\varpi) \tag{24}\]  
Thus, the attainable spatio-temporal data rate, \(R_{i}\), on a channel is:  
\[R_{i}\left(\varpi\right)=e^{-\theta\frac{T_{su,i}}{\tau_{un,i}\varpi_{i}^{2}}R_ {s,i}(\varpi)}+\left(1-e^{-\theta\frac{T_{su,i}}{\tau_{un,i}\varpi_{i}^{2}}} \right)R_{f,i}\left(\varpi\right) \tag{25}\]  
where \(e^{-\theta_{1}\frac{T_{su,i}\mathbb{C}}{\tau_{un,i}\varpi_{i}}}\) is the probability that the PU keeps transmitting on the channel throughout the entire packet transmission time \(T_{su,i}\). Depending on the kind of sensor activated on the source node, the application layer encapsulates the minimum required data rate into the route request payload. Alongside, it encapsulates its distance from the sink (POS) and its chosen data channel. It is important to note that, in the case of the source node, both the node's receiving channel field, Cr, and sending channel field, Cs, will be the same. This is illustrated in Figure 3. Contrarily, as will be discussed in the next session, the Cr and Cs fields of all other nodes may vary. Finally, the source sets the channel switch count and hop count to zero. Afterwards, it will broadcast the packet via CCC. Figure 3: Route request (RREQ) operation with payload.
## 4 Research Case
### Data Sources and Data Processing
#### 4.2.1 Dependent Variable Data Sources and Processing  
The research data primarily come from the Automatic Fare Collection (AFC) system of Shenzhen's rail transit, including swipe records with information such as entry time, entry line, entry station name, exit time, exit line, and exit station name, as shown in Table 5. The available AFC data cover a continuous week from 15 June to 21 June 2019. Given that peak ridership times are concentrated between 08:00 and 09:00 on weekdays [7], data from 17 June to 21 June during this time frame were selected for analysis as the dependent variable. This ensures the focus on high traffic periods for accurate assessment.  
This study uses Python version 3.10 for AFC data cleaning, with the following specific methods:  
Field Selection: Retain core fields such as entry and exit times, lines, and station names while deleting irrelevant fields.  
Time Logic Verification: Remove records where entry time is later than exit time (e.g., entry at 09:00 and exit at 08:30).  
Operational Hours Filtering: Exclude records outside operational periods (before 06:00 or after midnight).  
Cross-Day Record Removal: Delete trips where entry and exit dates differ (e.g., entry on 15 June at 23:50 and exit on 16 June at 12:10).  
Excessively Long Trip Filtering: Remove records of trips lasting more than 3.5 h (Shenzhen Metro regulations require an additional fee of CNY 14 for stays over 3.5 h).  
Same Station Entry-Exit Removal: Delete invalid trips where entry and exit stations are the same (e.g., both entry and exit at \"Futian Station\").  
Missing Value Handling: Remove records with zeros or empty values in critical fields such as station names and times.
# The Specialist's Paradox: Generalist AI May Better Organize Medical Knowledge
## 2 Materials and Methods
### Evaluation Metrics  
Clustering performance was evaluated using both internal and external validation metrics. For internal evaluation, we calculated the Silhouette coefficient (ranging from \(-1\) to \(1\)) for each model's clustering, which measures how well-separated the clusters are by comparing the average distance of each point to points in its own cluster versus points in other clusters [38].  
Given an observation \(i\), let \(a(i)\) be the mean distance from \(i\) to all other points in its own cluster and \(b(i)\) the minimum mean distance to points in any other cluster. The per-point Silhouette is  
\[s(i)=\frac{b(i)-a(i)}{\max\{a(i),\ b(i)\}},\]  
A higher Silhouette value indicates more distinct, well-separated clusters.  
We also computed the Davies-Bouldin Index (DBI), which is an internal metric where lower values signify better clustering (it captures the average similarity between each cluster and its most similar cluster) [39]. For each cluster \(C\_k\) with centroid \(c\_k\) and mean intra-cluster scatter \(S\_k\), we compute the similarity \(R_{kj}=\left(S_{k}+S_{j}\right)/\parallel c_{k}-c_{j}\parallel\) to every other cluster and take  
\[\text{DBI}=\frac{1}{K}\sum_{k=1}^{K}\max_{j\
eq k}R_{kj},\]  
so, a lower DBI signals tighter, more distinct groupings.  
For external evaluation, we leveraged the fact that each title's true specialty label is known. We computed the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) between the K-Means cluster assignments and the actual specialty [40; 41]. ARI measures the agreement between the clustering and the ground truth labels, adjusted for chance alignment, with \(0\) indicating random agreement and \(1\) indicating perfect recovery of the true classes.  
\[\text{ARI}=\frac{\text{Index}-\text{Expected}}{\text{Max}-\text{Expected}} \in[-1,1].\]  
NMI measures the amount of mutual information shared between the cluster labels \(C\) and the true labels \(S\), normalized to range from \(0\) (no mutual information) to \(1\) (perfect correlation):  
\[\text{NMI}(C,S)=\frac{2\ I(C;S)}{H(C)+H(S)},\]  
These external metrics provide insight into how well the unsupervised clusters align with the expected specialty groupings.  
To complement these standard indices, we computed _cluster purity_--the proportion of terms in each cluster that match that cluster's majority specialty--and its complement, the overall error rate. Purity is calculated as  
\[\text{Purity}=\frac{1}{N}\sum_{k=1}^{K}\max_{s}\mid C_{k}\cap S_{s}\mid,\]  
Purity provides an intuitive, one-number summary of how \"clean\" the partition is with respect to the ground-truth labels and is easily interpreted beside ARI and NMI. Because purity ignores minority labels within each cluster, we additionally logged the exact terms that disagreed with the majority vote; this term-level record forms the basis for the qualitative diagnostics described below.
## 3 Results
### Effect of Personalized Planning Parameters
Further investigation was conducted using images and ROIs of clinical pancreatic and lung cancer patients and the SBRT approach, adopting the PCa template and the \"SRS/SBRT mode\", which automatically creates a \"normal tissue\" ROIs closest to the target with a 3 mm margin (to avoid impacting PTV coverage). Of note, the \"standard fractionation mode\" automatically creates two ROIs to control normal tissue dose, i.e., D50 = patient external surface--(PTVs + 1 cm) and the D30 = patient external surface--(PTVs + 3 cm) and the optimizer uses a maximum dose goal for D50 of 50% and for D30 of 30% of the highest target dose with a priority of'very high'. Thus, also for SBRT plans, the PCa template represents an appropriate starting point using the \"SRS/SBRT mode\". \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**List of Parameters**} & \multicolumn{3}{c}{**Impact on**} \\ \cline{2-4}  & **PTVs** & **OARs** & **MUs** \\ \hline Conversion Cycles & + & ++ & + \\ Refinement Cycles & + & ++ & ++ \\ Initialize with Conformal Opening & +++ & +++ & ++ \\ Control MLC Modulation & +++ & +++ & +++ \\ Modulation Amount & ++ & +++ & +++ \\ MU Level & + & + & +++ \\ Normal Tissue Dose Control & + & + & + \\ Dose Fall-Off Priority & + & +++ & ++ \\ Weighting Balance & ++ & ++ & ++ \\ Target Transition Fall-Off Rate (\%/cm) & ++ & ++ & + \\ Match Target Coverage & +++ & +++ & +++ \\ Target Edge Weight & ++ & ++ & + \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grade of influence of each advanced parameter on PTV coverage, OAR sparing, and total MUs; +: low grade of influence; ++: medium grade of influence; +++: high grade of influence.
# RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
## 2 Materials and Methods
### Database Description  
Reliable data are crucial for achieving robust performance. This study utilized two separate ECG databases from well-known PhysioNet resources, including MIT-BIH [30] and BIDMC [31]. The MIT-BIH database contains recordings of normal sinus rhythms (NSR) and arrhythmia (ARR), while BIDMC focuses on congestive heart failure (CHF). Originally, the data had 162 recordings (96 recordings for the ARR class, 30 recordingsfor the CHF class, and 36 recordings for the NSR class). To address the imbalance in the number of recordings across the different classes, we used 30 recordings for each class (NSR, ARR, and CHF), resulting in a total of 90 recordings for analysis (refer to Figure 1). Additionally, we ensured that all recordings had a consistent sampling frequency \(w_{s}\) of 128 Hz.
# Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
###### Abstract  
With the rapid development of global satellite constellation clusters and the popularity of ground-based intelligent terminals, the interconnection between large-scale low Earth orbit satellites and ground base stations has become more frequent. The calculation of satellite position requires a large number of floating point square calculations and coordinate system conversions, with high computational complexity and long computation time. Based on the grid encoding method of GeoSOT, we propose a GeoSOT-based grid calculation for a satellite-ground position (GCSGP) model. We use binary bit operations instead of complex topological operations and floating point calculations, and use grid distance calculations instead of existing angle calculations. Thus, the relative positions of satellites to the ground can be quickly calculated and thus connected to more suitable satellites quickly. The results of the grid calculation were verified by simulation experiments. The calculation speed was improved by about 82% compared with the traditional method. The test accuracy error of the 10-level grid at a terminal 45\({}^{\circ}\) inclination was less than 1% at all heights, and the maximum error of the 10-level grid within a 10-45\({}^{\circ}\) inclination was 7.2% at 10,000 km altitude, which provides theoretical support for the subsequent grid space calculation.  
GeoSOT; satellite-ground communication link; spatial position calculation; covered LEO satellites; satellite handover +
Footnote †: journal: remote sensing  
Article