esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurements
#### 2.3.4 Electromyographic (EMG) Analysis  
In a third measurement session, separated by 72 h from the previous session, the EMG of the quadriceps femoris (rectus femoris and vastus lateralis), biceps femoris (long head), tibialis anterior, and gastrocnemius (medial head) muscle groups was measured when performing 5 repetitions on the SLP and BIOFIT-LP machines at 60 and 75% of 1 RM. These intensities were chosen as they are commonly used in different training programs, as well as in different EMG studies with guided machinery [30]. The OFE-SLP EMG was analyzed with a single intensity of the exercise allowed (self-weight).  
For electrode placement, the different skin areas where the electrodes would be placed were prepared by shaving the hair and cleaning the skin with 96% alcohol and sterile gauze to reduce skin impedance [11; 18]. Gelled disposable surface electrodes were used, placed with a 2 cm center-to-center distance, aligned with the longitudinal axis of the muscle fibers, following the methodology from previous studies [31]. The electrodes were placed according to the recommendations of the Surface Electromyography for the Non-invasive Assessment of Muscles (SENIAM) [31] for each of the muscles measured: quadriceps femoris (rectus femoris and vastus lateralis), biceps femoris (long head), tibialis anterior, and gastrocnemius (medial head) (Figure 2). Consistent with similar research, bilateral symmetry was assumed during exercise performance, so electrode placement on the right side of the body was standardized [32]. EMG activity during the exercises was recorded using a surface electromyography system at a sampling rate of 1500 Hz (MuscleLab Ergotest Innovation AS, Stathelle, Norway). The electromyographic signal during the calculation of the 1 RM was not taken, since the variability that the EMG signal would present due to fatigue because of the procedure used following the indications of the National Strength and Conditioning Association could lead to bias. In addition, as the sessions in which 1 RM was calculated and the sessions in which EMG data were collected were different, variables such as skin impedance or hydration could include more intra-subject variability [11; 18]. Therefore, it was decided to measure EMG activation only on the third day, which was the day on which the performance on the different machines was compared.  
Prior to the study, intra-instrument reliability was assessed with a group of 10 training young men by means of test-retest. The tests described above were performed two times in the same session on the same volunteers [33]. It was determined that the electromyographic analysis used in the present study was highly reproducible with a coefficient of variation between 7 and 14% for all muscles on all machines, confirming that the measurement used was sufficiently reproducible.
## 4 Methodology
### Architecture and Training
#### 4.3.5 MSE Exploration  
Given its significant role in the network's operation, it is imperative to explore the mean square error (MSE) of the network's reconstruction output. In the era of AI, clarifying how decisions are made in the network is crucial. This involves examining both the reconstructions (the actual outputs of the network) and the errors associated with these reconstructions.  
Figure 10A illustrates the MSE of 101 segments (specifically segments 1360 to 1460). It is essential to note that each point in this graph represents the MSE of an entire output segment (a reconstruction of 2048 samples) versus the original input (the original 2048 samples corresponding to the output).  
In the normal class, a burst of Bluetooth behaviour occurs every five seconds, leading to an expected rise in the MSE. However, this error increase should not surpass the baseline significantly and should be markedly lower than any error spike caused by an anomaly.  
Upon closer inspection of Figure 10, some concerns arise. Despite the network's relatively good metric-based performance, the MSE output for the anomalous window (e.g., window 1410, highlighted in red) appears too close to the normal Bluetooth segments (e.g., windows 1400 and 1423, highlighted in green). A detailed examination of these windows reveals that the network excels in reconstructing normal Bluetooth behaviour (Figure 10B,C) but, unexpectedly, reconstructs the anomaly at window 1410 quite well (Figure 10D). This can be attributed to the unique overfitting nature of autoencoders, wherein, without sufficient compression, they can reconstruct most inputs.  
While the network undeniably functions, the results on anomalies, such as example 1410, which have much lower power than the brute force segments, exhibit errors too close to normal behaviour for the network to be deemed robust. For improved robustness, the network must encounter more challenges in reconstructing anomalous behaviour, thus establishing a more substantial gap between the MSE of normal behaviour and anomalies.  
The anomaly featured in Figure 10 is particularly challenging to detect due to its low amplitude. Overall, it is evident that the network fits exceptionally well with this type of anomaly, as evidenced by the remarkably low MSE error (around 7e-6) and the amplitude of the anomalous error compared to the surrounding Bluetooth regular data. Applying a straightforward classification threshold to the MSE (as depicted in Figure 10A at around 6e-6) highlights the closeness of anomalous classification to normal Bluetooth information.  
Figure 9: Dragon_Slice_1024 DragonBoard and Pi reconstructions.  
Therefore, enhancements to the network architecture are necessary architecture to make it more difficult to reconstruct abnormal, unseen information, ultimately creating a more resilient network. Even if the performance improvements are marginal, a network that struggles significantly with abnormal data would be far more trustworthy and robust.
## 3 Computational Methodology
### CORE-Clustering Algorithms
#### 3.2.3 A Greedy Alternative for CORE-Clusters Detection  
We propose an alternative strategy to the CORE-clusters detection algorithm: The edge treatment queue may be ordered by following decreasing edge weights instead of increasing edge weights. The nearest edges are then first gathered, making coherent COREclusters as in Algorithm 2, although one CORE-cluster may contain several representative variables. To avoid gathering noisy information, the for loop on the edges (row 6 of Algorithm 2) should also stop before meaningless edges are treated. This strategy has a key interest: It can strongly reduce the computational time dedicated to Algorithms 1 and 2. By doing so, Algorithm 1 and modified Algorithm 2 are purely equivalent to Algorithm 3.  
```
0: Graph \(G(N,E)\) with nodes \(N_{i}\), \(i\in 1,\cdots,p\) and edges \(E_{k}\), \(k\in 1,\cdots,K_{E}\).
0: Weight of edge \(E_{k}\) is \(W(E_{k})\).
0: Granularity coefficient \(\tau\) and threshold \(\xi\).
1: Sort the edges by decreasing weights.
2: Define the number of edges \(\gamma\) having a weight higher than \(\xi\).
3: Assign label \(L(N_{i})=i\) to each node \(N(i)\).
4: Set \(CORElabel=-1\).
5:for\(k=1:\gamma\)do
6: We denote \(N_{i}\) and \(N_{j}\) the nodes linked by edge \(E_{k}\).
7:if\(L(N_{i})!=L(N_{j})\)then
8: Propagate the label \(L(N_{i})\) to the nodes that have label \(L(N_{j})\).
9:if number of nodes with label \(L(N_{i})\in\{\tau_{*},\cdots,2\tau-1\}\)then
10: Label \(CORElabel\) is given to the nodes with label \(L(N_{i})\)
11:\(CORElabel=CORElabel-1\)
12:endif
13:endif
14:endfor
15:for\(n=1:N\)do
16:if\(L(N_{n})>0\)then\(L(N_{n})=0\) else\(L(N_{n})=-L(N_{n})\)
17:endfor
18:return Labels \(L\).
```  
**Algorithm 3** Greedy CORE-clustering algorithm  
Again, the structure of this algorithm is very similar to the structure of the maximum spanning tree strategy in Section 3.1. The algorithmic cost of the sort procedure (row 1) is \(\mathcal{O}(K_{E}\log{(K_{E})})\). Then, the loop rows 5 to 14 scans \(\gamma\) edges, where \(\gamma\) is the number of edges having a weight higher than \(\xi\). In most cases, \(\gamma\) should be much lower than \(K_{E}\), which strongly limits the computational impact of this loop. Labels propagation in this loop (rows 8 and 10) are also limited to \(2\tau-1\) nodes. The average performance of the for loop is therefore \(\mathcal{O}(\gamma\log{(\tau)})\).
## 3 Simulation Analysis Using SSA for GREATEM Signal De-Noising
### Parameter Selection
#### 3.1.2 Reconstruction Order  
Many scholars use dichotomy, the mean truncation method, etc., to reconstruct the signal in selecting the reconstruction order of the SSA algorithm [26]. To choose the  
Figure 4: PSO optimization algorithm fitness curve. (**a**) Window length; (**b**) root mean square error.  
Figure 3: The influence of different window lengths for signal separation.  
reconstruction order for GREATEM data de-noising, we use noisy simulation and measured data to study. Figure 5 shows three groups of different GREATEM signals and their singular spectra. Figure 5 is a calculated GREATEM signal with white noise; Figure 5 is a calculated GREATEM signal with power frequency noise and a little white noise; Figure 5 is a set of GREATEM attenuation curves measured in front of the Geological Palace in Changchun. We can conclude that the singular values of GREATEM signals are the first two. Therefore, the SSA algorithm is used for GREATEM data de-noising, and the reconstruction order is 2.
## 2 Materials and Methods
### Data Analysis  
Stem diameter growth and cork-ring width data series were de-trended by fitting polynomial functions [18; 19] to maximize the inter-annual fluctuations due to climate. Using this weighted polynomial forecasting technique (SPSS software, version 21.0.0 was used), individual stem diameter growth indices (\(\rm ID_{bh}\)) and cork growth (or cork-ring width) indices (\(\rm IC_{rw}\)) were computed as residuals (\(\rm\varepsilon_{j}\), zero mean random error).  
To address and compare the sensitivity to climate, \(\rm ID_{bh}\) and \(\rm IC_{rw}\) were plotted against mean and monthly precipitation and temperature, and the bootstrapped correlation coefficients were computed. Heatmaps for the level of significance of Pearson's correlation coefficients were built.  
Selected climate parameters (with the highest bootstrapped Pearson's correlation coefficients) were then predictors (fixed effects) of \(\rm ID_{bh}\) and \(\rm IC_{rw}\) in linear mixed-effects models, which explicitly integrated among-tree variations (considered as random effects) related to the initial tree stem diameter \(\rm D_{bh}\) immediately after cork harvesting (in 2000). Four tree's stem diameter classes were established based on those defined by [20] to account for the trees' growth dynamics with aging: \(\rm D_{bh,1}\) (\(\rm D_{bh}\leq 35\) cm); \(\rm D_{bh,2}\) (\(35\) cm \(<\rm D_{bh}\leq 55\) cm); \(\rm D_{bh,3}\) (\(55\) cm \(<\rm D_{bh}<70\) cm); and \(\rm D_{bh,4}\) (\(\rm D_{bh}\geq 70\) cm).  
Model fitting was initiated by adding random effects of tree, first through random intercept alone and then by random intercept and slope. The generalized linear mixed-effect models were formulated according to the equation:  
\[\rm Y_{ji}=\alpha_{0}+(\alpha_{1}+\beta_{Treei})\times X_{ji}+\mu_{Treei}+ \epsilon_{ji} \tag{4}\]  
where \(\rm Y_{ji}\) denotes each response variable, related with tree growth patterns \(\rm ID_{bh}\) and \(\rm IC_{rw}\) of year \(j\)th in tree \(i\)th; \(\rm X_{ji}\) is the selected climatic parameter t in the stem of tree \(i\)th, in year \(j\)th; \(\alpha_{0}\) and \(\alpha_{1}\) are the parameters of the fixed part of the model; \(\rm\beta_{Treei}\) and \(\rm\mu_{Treei}\) are the random parameters associated with the between-trees variations in stem diameter; and \(\rm\varepsilon_{ji}\) is the residual error, with mean zero and variance \(\rm\sigma_{\varepsilon}\) and independent from random effects. To adjust the linear mixed-effects models, the maximum likelihood algorithm (ML)of the _lmer_ function, available in the _lme4_ library of the R software (version 4.0.3), was used. The best model produced the lowest values of Akaike's information criterion (AIC). For the selected linear mixed models, the estimates of covariance parameters are presented (please see Tables S1 and S2 in the Supplementary Materials).
## 2 Preliminaries
### Structure and Properties of BCCC  
BCCC is a structure that recursively connects servers layer by layer. In BCCC(n,k), \(n\) denotes the n-port server and \(k\) denotes the dimension. First, we let \(n\) servers connect to an n-port switch as an element, denoted by BCCC(n,0). BCCC(n,k) is composed of \(n\) BCCC(n,k-1)s connecting \(n^{k}\) elements. Two types of switches are used in BCCC, which are type A switch and type B switch. A type A switch has n ports for forming an element, and a type B switch has \((k+1)\) ports for connecting different elements. Overall, to build BCCC(n,k), we need \((k+1)n^{k+1}\) dual-port servers, \((k+1)n^{k}\) type \(A\) switches and \(n^{k+1}\) type \(B\) switches.  
An example of BCCC(3,2) is shown in Figure 2. The study of the connectivity and diagnosability of the structure requires the equivalence of the network to a graph, which is calculated using knowledge of graph theory, where the vertices represent servers and the edges represent links. Switches are considered as transparent devices.  
Figure 1: An illustration for Lemma 1.  
**Definition 1**.: _For any integers \(k\geq 0\) and \(n\geq 2\), the \(k\)-dimensional transparent BCCC network with \(n\)-port switches is denoted by a simple graph \(BC_{n,k}=(V_{BC_{n,k}},E_{BC_{n,k}})\) with \(N\) vertices, where \(V_{BC_{n,k}}=a_{k+1}a_{k}a_{k-1}...a_{0}\), where \(a_{0}\in[0,k]\) and \(a_{i}\in[0,n-1]\), \(1\leq i\leq k+1\). Two servers \(a_{k+1}a_{k}a_{k-1}...a_{0}\) and \(a^{\prime}_{k+1}a^{\prime}_{k}a^{\prime}_{k-1}...a^{\prime}_{0}\) are neighbors, if and only if \(\exists i\), \(i=a_{0}+1\) or \(i=0\), such that \(a_{i}\
eq a^{\prime}_{i}\), and \(\forall j\), \(0\leq j\leq k+1\), \(i\
eq j\), such that \(a_{j}=a^{\prime}_{j}\). A \(BC_{n,k}\) contains \(n\)\(BC_{n,k-1}\) subgraphs._  
Figure 3 shows three examples of \(BC_{n,k}\)s.  
**Lemma 2** ([1]).: \(BC_{n,k}\) _has the following properties:_  
_(1)_ \(BC_{n,k}\) _is (n+k-1)-regular graph._ \(N=(k+1)n^{k+1}\)_._  
_(2)_ _The connectivity of_ \(BC_{n,k}\) _is_ \(\kappa(BC_{n,k})=n+k-1\)_._  
_(3)_ _There are_ \(n^{k}\) _vertex-disjoint paths connecting different_ \(BC_{n,k-1}\) _in_ \(BC_{n,k}\)_._  
**Lemma 3** ([25]).: _For any integers \(n\geq 3\), \(k\geq 2\) and \(0\leq g\leq n-1\), there exists a complete graph \(C\) of order \(g+1\) in \(BC_{n,k}\) such that \(|N_{BC_{n,k}}(V(C))|=(g+1)(k-1)+n\), and \(V(BC_{n,k})-N_{BC_{n,k}}V(C)\) has exactly two connected components: one is \(V(C)\) and the other is \(V(BC_{n,k})-N_{BC_{n,k}}[V(C)]\), where \(\delta(BCCC_{n,k}-N_{BC_{n,k}}[V(C)])\geq g\)._
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data  
This section describes the proposed method for practical implementation of DP in electronic healthcare data as demonstrated in Figure 3. In this model, global differential privacy is implemented on sensitive data to achieve privacy [45].  
In this part of the framework, users or data analysts connect with the database through a user interface. The user requests the desired data in the form of queries and obtains differentially private results. The protected system receives queries made by data analysts or the involved user; then, it pulls out the unprocessed information from the stored database.  
After this, it generates noise in the final outcome using DP in accordance with each query's global sensitivity. To achieve experimental results, Python was selected as the programming language on the basis of the need to process large datasets within the minimum time period and its ability to deal with computational tasks. Moreover, to handle large datasets, the PyDP [46], Pandas, Numpy, and matplotlib libraries are used. PyDP is a differential privacy project from Google in which all computation methods use Laplace noise only.
## 5 Discussion
### Comparison of LEACH, LEAC-GA, and LEACH-KH  
Compared to the CH selection mechanism in LEACH and LEACH-GA [105], and LEACH-KH, the latter proves to have a higher average PDR with the higher residual energy of the network, as shown in Figure 12.  
To summarize, it can be said that LEACH can equally disperse energy among the sensors, resulting in reduced transmission between nodes and BSs. This saves energy at each node and hence extends the network's lifespan. It also saves energy by applying data aggregation techniques to locally gathered data before sending it to a sink node or base station. The main problem with LEACH is that the CHs are chosen at random, without concern for the amount and distribution of CHs in a cluster. As a result, nodes with lower residual energy may be chosen as CH, resulting in the network's premature death.  
Although this technique ensures equitable energy dissipation across the network, cluster creation is unpredictable, resulting in uneven cluster distribution across the network. It also does not count the number of clusters in the area or their location inside each cluster.  
As a result, LEACH protocol restricts intra-cluster communication due to increased energy consumption. Because the CHs interact with the BS in a single hop, LEACH is  
Figure 11: Lifetime metrics (**a**) \(n\) = 100 (**b**) \(n\) = 400. Deployment area = 100 \(\times\) 100 m\({}^{2}\). BS location (50, 50). Initial energy = 0.5 J.  
Figure 12: Comparison graph of LEACH, LEACH-GA, and LEACH-KH. (**a**) Packet delivery ratio; (**b**) Residual Energy at Network half-life.  
not suitable for large-scale networks. Computational Intelligence paradigms, on the other hand, are well suited to be used and can adapt to the dynamic nature of WSN.
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 4 Conclusions  
In this article, a saliency-tuned YOLONet is proposed for off-shore ship detection from the optical satellite image. First, the density peak clustering is introduced to classify the different scene blocks by their histograms. The purpose of the scene classification is to reduce the intra-class variance of the dataset for targeted image enhancement processing. Second, since the ship can be regarded as a salient target, different saliency region extraction methods (e.g., SR, HC, and CASD) are utilized to extract prominent targets in different scene categories. Scene classification and targeted saliency detection can significantly reduce the interference of clouds and fog on the sea surface. Finally, a DNL-added YOLONet is proposed. By introducing an attention mechanism, the DNL module enables the feature maps in order to add more global information to the original local features. Thus, the neural network enhances its attention to salient targets and can capture more salient features. The advantages of our approach come from two aspects: On the one hand, the scene classification can effectively reduce differences within a class and pertinently process images in different scenes. On the other hand, the additions of the saliency map and the DNL module improve the saliency detection capabilities of the network model.  
The experimental results based on the South China Sea dataset returned by the Gaofen-1 satellite show that the detection accuracy of the proposed off-shore ship detection method outperforms other state-of-the-art methods. However, due to the noise of the saliency map, the recall rate is not greatly improved. This problem will be addressed in our future research.  
Conceptualization, Q.X. and J.G.; data curation, S.W. and Q.X.; methodology, Q.X. and S.W.; supervision, J.G. and Q.X.; validation, S.W.; writing--original draft, S.W.; writing--review and editing, S.W. and Q.X. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China under Grant 61972021.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.  
The authors declare no conflict of interest.
###### Abstract  
The aim of this work was the synthesis of (hyper)branched oligomers based on trialkoxysilane in various conditions and further application of them in order to modify the urethane prepolymers. Hydroxyl-terminated trialkoxysilane was used as a monomer for homo-condensation. It was obtained by reaction of 3-aminopropyl trialkoxysilane (APTES) with ethylene carbonate (EC). The reaction was based on the attack of amine at the carbonyl carbon atom followed by ring opening of the carbonate to give a urethane (carbamate) product. The next step was the condensation via substitution of ethoxy groups on silicon atom with the terminal hydroxylgly groups present in the primary product with the evolution of ethanol. Accordingly, the impact of temperature and type of catalyst on process efficiency was investigated. A quantitative analysis of reaction progress and products of the conversion of EC together with ethanol evolution was conducted by means of gas chromatography, which allowed us to determine the formation of monomeric product and, indirectly, of oligomeric products. It was found that at room temperature after 24 h, the majority of the monomeric product was isolated, whereas at elevated temperature in the presence of Ti-based catalyst, further condensation of the monomer into branched oligomers was preferred, and, moreover, the application of vacuum intensified that process. The obtained products were structurally characterized by \({}^{1}\)H and \({}^{29}\)Si NMR, MALDI-ToF and Gel Permeation Chromatography. Finally, two different alkoxysilane products, monomeric and oligomeric, were applied for modification of urethane prepolymer, forming silanized one (SPUR). The influence of the silanizing agent on the mechanical and thermal properties of the moisture-cured products was shown before and after additional conditioning in water.  
hyperbranched oligomers; silane; polyurethane; urethane prepolymers; silanized polyurethane; 3-aminopropyl trialkoxysilane; ethylene carbonate; hydroxyl-terminated trialkoxysilane; condensation Article