esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation  
### Communications Architecture  
The proposed communications architecture, which is based on a modular structure with mobile nodes and readers interconnected through Bridges, uses a combination of transport (Bluetooth 5 [33]) and a network protocol (libp2p) to handle communications. Among the potential available communications technologies, Bluetooth 5 is preferred over other technologies like Wi-Fi, LoRa [34], or Zigbee [35] because, as it can be observed in Table 1, it provides a better balance among power consumption, data speed, range, and mesh networking capabilities. This is especially true for mobile-centric applications where seamless connectivity with smartphones and wearables and energy efficiency are critical. Figure 4: Proposed opportunistic protocol stack. Figure 5 shows the protocol stack with the protocols selected for the implementation. In addition, libp2p has been chosen over other P2P protocols like IPFS or ZeroMQ [36] because of its flexibility, modularity, and integrated support for peer discovery, routing, and security. It can easily incorporate several transport protocols, including Bluetooth, TCP, WebRTC, and others, which enables it to adjust to the changing conditions and limited resources that opportunistic communication networks frequently require. The OEC Network Layer plays an important role in managing P2P communication, combining discovery, routing, and security strategies:  
* FloodSub: used for opportunistic routing between nodes, allowing messages to be relayed on the network without the need for a predefined hierarchical structure. The protocol is explained in more detail in Section 4.5.3. * BLE advertising: uses Bluetooth 5 advertising capabilities to allow nodes to advertise their presence on the network without requiring active connections. This allows nodes to discover new opportunistic peers before establishing persistent connections. * Yamux [37]: a multiplexing protocol that allows multiple data streams over a single underlying connection. It is employed within libp2p. It facilitates efficient management of multiple peer-to-peer communication sessions without opening new connections. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Technology** & **Standard** & **Frequency Band** & **Maximum Range** & **Data Rate** & **Technology** & **Entry Line** & **Power** & **Scalability** & **Latency** & **Cost** \\ \hline \multirow{3}{*}{RVD} & \multirow{3}{*}{-} & 197 -3.0 Mb/4k & \multirow{3}{*}{from cm. (1.7) to} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} \\  & & (33.0 Mb/4k) & 1.7 & \multirow{3}{*}{from cm. (1.7) to} & & & & & & & & & \\  & & (33.0 Mb/4k) & 1.6 & \multirow{3}{*}{from cm.
# Mdpj _sciences_
## 6 Discussion
### Practical Implications  
The findings from these studies have significant implications for urban planning and the design of green infrastructure:  
1. The high efficiency of certain plant species in reducing CO\({}_{2}\) and PM suggests that urban planners should incorporate these species into city landscapes and building designs to improve air quality.
2. Policy-makers can use these insights to develop regulations and incentives for the adoption of green systems in urban areas. For instance, mandating the installation of green walls in new buildings could be a policy measure to enhance urban air quality.
3. The detailed performance metrics provided by the studies can guide the design and optimization of green systems. Designers can select plant species and system configurations based on the specific pollutants targeted and the environmental conditions of the installation site.  
The reviewed studies underscore the potential of green systems in mitigating environmental pollution. However, to fully harness this potential, future research should address the identified gaps and standardize methodologies. Practical implementation of these findings requires collaboration between scientists, urban planners, and policy-makers to create healthier and more sustainable urban environments.  
Future research should focus on long-term studies to assess the durability and effectiveness of green systems over time and include economic feasibility analyses to understand the cost-benefit ratio of large-scale implementations. Additionally, more real-world validation of simulation models is needed to ensure their reliability and applicability in practical scenarios.
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.5 Ablation Experiment Analysis  
To systematically validate the contributions of Orga-Date's improvement modules, a series of ablation experiments was conducted using the lung organoid detection dataset. Enhancement modules were incrementally introduced to the baseline model (YOLOv11n), forming the following experimental configurations: A: Baseline YOLOv11n; B: Replaces FPN + PAN with BiFPN; C: Adds MPCA after C2 PSA; D: Upgrades the classification loss to EMASlideLoss. The results are summarized in Tables 10 and 11.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Model** & **[EMAIL_ADDRESS]** & **Params (M)** & **GFLOPs (s)** \\ \hline YOLOv11n & 77.9 \(\pm\) 0.1\% & 2.58 & 6.3 \\ YOLOv11n + BiFPN & 79.9 \(\pm\) 0.1\% & 1.92 & 6.3 \\ YOLOv11n + BiMAFPN & 78.8 \(\pm\) 0.2\% & 2.12 & 6.6 \\ YOLOv11n + EMBSFPN & 79.1 \(\pm\) 0.1\% & 2.60 & 6.8 \\ \hline \end{tabular}
\end{table}
Table 7: Comparative experiments on feature pyramid architectures.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Model** & **[EMAIL_ADDRESS]** & **Params (M)** & **GFLOPs (s)** \\ \hline YOLOv11n + BiFPN & 79.9 \(\pm\) 0.1\% & 1.92 & 6.3 \\ YOLOv11n + BiFPN + CA & 80.1 \(\pm\) 0.1\% & 1.93 & 6.3 \\ YOLOv11n + BiFPN + CBAM & 78.3 \(\pm\) 0.3\% & 1.99 & 6.3 \\ YOLOv11n + BiFPN + SimAM & 77.1 \(\pm\) 0.2\% & 1.92 & 6.3 \\ YOLOv11n + BiFPN + MPCA & 80.7 \(\pm\) 0.1\% & 2.25 & 6.3 \\ \hline \end{tabular}
\end{table}
Table 8: Comparative experiments on different attention mechanisms.  
\begin{table}
\begin{tabular}{l l l l} \hline
**IoU** & **Lumen** & **No Lumen** & **[EMAIL_ADDRESS]** \\ \hline CIoU & 75.7 \(\pm\) 0.3\% & 84.5 \(\pm\) 0.2\% & 80.1 \(\pm\) 0.2\% \\ EIoU & 72.6 \(\pm\) 0.2\% & 83.4 \(\pm\) 0.0\% & 78.0 \(\pm\) 0.1\% \\ GIoU & 75.1 \(\pm\) 0.2\% & 81.9 \(\pm\) 0.2\% & 78.5 \(\pm\) 0.2\% \\ Focal loss & 76.8 \(\pm\) 0.1\% & 85.4 \(\pm\) 0.3\% & 81.1 \(\pm\) 0.2\% \\ EMASlideLoss & 76.2 \(\pm\) 0.2\% & 86.5 \(\pm\) 0.1\% & 81.4 \(\pm\) 0.1\% \\ \hline \end{tabular}
\end{table}
Table 9: Comparative experiments on different loss functions.  
Replacing the original FPN + PAN with BiFPN (Experiment B) yields substantial performance gains: AP for small and medium targets increases by 3.7% and 2.6%, respectively, without incurring significant computational overhead. Moreover, parameter count and model size are reduced by 25.6% and 23.1%, respectively, confirming that BiFPN effectively suppresses background noise and enhances target discrimination through its bidirectional, cross-layer fusion and learnable weighting.  
Adding the MPCA module after C2 PSA (Experiment C) further improves performance, increasing AP for small targets by 4.0% and medium targets by 1.1%. While this enhancement leads to a 12.1% increase in parameters (from 2.58 M to 2.89 M), the frame rate remains high (300 FPS), maintaining real-time viability.  
Finally, upgrading the classification loss to EMASlideLoss (Experiment D) introduces no additional parameters or computational cost, yet significantly mitigates the sample imbalance problem, resulting in a measurable accuracy boost.
## 3 Remaining Useful Life Prediction Algorithm Based on Double Bidirectional Long Short-Term Memory
### RUL Prediction Model  
#### 3.4.1 RUL Prediction Network Model Process  
The RUL prediction mainly involves two steps: training and testing. Training set \(\{X_{t},Y_{t}\}_{t=1}^{T}\) (where \(X_{t}\in\Re^{N_{1}\times 1}\) contains \(N_{1}\) features at time \(t\) and \(Y_{t}\) is the true label related to bearing degradation at time \(t\)) is considered as the network input. Through Double BiLSTM layers, a deep structure is designed to simultaneously process past and future temporal information. In addition, the bearing degradation information propagates in forward and backward directions through the Double BiLSTM layers.  
The network structure is shown in Figure 6. It combines DBiLSTM with attention mechanism. Two BiLSTM layers are double: BiLSTM1 and BiLSTM2. The former serves as input to the latter BiLSTM2. The final hidden state is multiplied by attention weights (\(a_{t,1}\), \(a_{t,2}\), \(a_{t,3}\), \(\ldots\), \(a_{t,T}\)), and the results are added to generate the final output of the network (\(y_{att}\)). Attention weights are assigned to input data vectors, selecting highly relevant information from long-distance input sequences. RUL values are obtained through the regression layer. The model in the regression layer [37]:  
\[L=\frac{1}{T}\sum_{t=1}^{T}\left\|Y_{t}-\hat{Y}_{t}\right\|_{2}^{2} \tag{35}\]  
where \(Y_{t}\) is the true label of input data, \(\hat{Y}_{t}\) is the value predicted by the DBiLSTM network, and \(T\) denotes the total time points of the samples. During the testing phase, selected prognostic-sensitive features are directly input into the trained DBiLSTM network, which yields RUL values.  
Figure 5: Attention mechanism network structure.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Wi-Fi RSSI Characteristics
#### 3.1.2 Sparsity  
The other important characteristic is the sparsity of the raw Wi-Fi RSSI measurements. In a typical building, many Wi-Fi APs are installed, such as commercial APs, private internet-connected devices, and WLAN printers. For example, 193 APs were scanned in our experimental floors, and 531 APs are used in the the UJIIndoorLoc open dataset [46].  
Not all the APs can be scanned at one point position because a single AP does not cover the entire positioning area. Thus, the RSSIs of APs located far from a user are recorded empty, as illustrated in Figure 3. The typical method to deal with the empty elements is to replace them by the possibly minimum RSSI value, e.g., \(-100\) dBm. However, the sparsity still exists because the minimum RSSI values replace most of the elements, which may deteriorate localization performance. Therefore, we need to extract a feature. Further, substantially reduced dimensionality is helpful to reduce computational load.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Ablation of Binary Classifier  
In this research, random forest, the binary classifier, is employed for adversarial attack detection on CT segmentation. To validate this choice and ensure robustness, an extensive ablation study encompassing a range of classifiers is conducted. These include the Gaussian process classifier, Gaussian naive Bayes, K-nearest neighbor classifier, multi-layer perceptron classifier, support vector machine, decision tree Classifier, and XGBoost. All these classifiers were implemented using their respective libraries: scikit-learn [25] and XGBoost [26].  
Figure 13 displays box plots comparing the performance of various classifiers when subjected to adversarial attacks. Each plot aggregates results derived from 64 filters and varies according to different epsilon values and iteration counts. From the visualization, it is evident that the random forest classifier exhibits a consistent and robust performance across different attack types, particularly when compared to other classifiers. This empirical evidence from the box plots solidifies the selection of random forest as the primary binary classifier for this work, given its resilience and reliable performance against diverse adversarial challenges.  
Figure 12: Visualization under BIM attack, with EPS set to 0.01 and NITER at 15, of a gastrointestinal polyp image: Comparison of features from the first layer between genuine and adversarial samples for the most sensitive filters 62 and least sensitive filters 3.  
Figure 13: Box plots illustrating the performance of various classifiers across different attack configurations, including parameters like the number of iterations and perturbation levels (EPS) for CT segmentation model.
## Materials and Methods
### Protein Extraction and Western Blot Analysis  
The homogenate from the small intestine tissues was collected, and the lysate was used to lyse the hepatocytes for 30 min on ice. The BCA (bicinchoninic acid) method was used to determine the protein concentration. The sample was added to 5\(\times\) loading buffer by volume and heated in a water bath at 100 \({}^{\circ}\)C for 5 min. The hepatocytes were sonicated by SJIALAB (Ningbo Yinzhou Sjialab Equipment Co., Ltd., Ningbo, Zhejiang, China) for 2 min (10% ultrasound intensity) and centrifuged for 10 min to obtain the supernatant. The extracted protein was subjected to polyacrylamide gel electrophoresis (80-120 V), transferred to a PVDF membrane, sealed and incubated overnight at 4 \({}^{\circ}\)C with the addition of primary antibody of c-Kit (cat no. ab62154, Abcam, Cambridge, MA, USA), SCF (cat no. ab83866; Abcam), TRPV1 (cat no. ACC-030, [PERSON]; Beijing, China), GDNF (cat no. ab18956; Abcam), NOS (cat no. sc-49058; Santa Cruz Biotechnology, Inc., Dallas, TX, USA) and \(\beta\)-actin (cat no. ab8226; Abcam).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gene Name** & **Sequence** \\ \hline c-Kit & \begin{tabular}{c} Forward: 5\({}^{\prime}\)-CATAGCCCAGTAAAGCCAAAT-3\({}^{\prime}\) \\ Reverse: 5\({}^{\prime}\)-GAACATCCAGAATCGTCAACTC-3\({}^{\prime}\) \\ \end{tabular} \\ \hline \multirow{2}{*}{SCF} & \begin{tabular}{c} Forward: 5\({}^{\prime}\)-TCAGGGACATACGCCTGCAAG-3\({}^{\prime}\) \\ Reverse: 5\({}^{\prime}\)-AAGAGCTGGCAGACCGAACGA-3\({}^{\prime}\) \\ \end{tabular} \\ \hline TRPV1 & \begin{tabular}{c} Forward: 5\({}^{\prime}\)-CCGCTTTTTTGGAAGGGT-3\({}^{\prime}\) \\ Reverse: 5\({}^{\prime}\)-GAGACAGGTAGGTCCATCCAC-3\({}^{\prime}\) \\ \end{tabular} \\ \hline GDNF & \begin{tabular}{c} Forward: 5\({}^{\prime}\)-GGGGTTATGGAGAAGTTGCTG-3\({}^{\prime}\) \\ Reverse: 5\({}^{\prime}\)-CTATGGAATGCTGCCAAAA-3\({}^{\prime}\) \\ \end{tabular} \\ \hline NOS & \
\begin{tabular}{c} Forward: 5\({}^{\prime}\)-CAGCGAAACGGAGCGCAAGCA-3\({}^{\prime}\) \\ Reverse: 5\({}^{\prime}\)-TGACACGCACGGCAGGAT-3\({}^{\prime}\) \\ \end{tabular} \\ \hline GAPDH &
\begin{tabular}{c} Forward: 5\({}^{\prime}\)-TGACCACCACAACTGCTTAG-3\({}^{\prime}\) \\ Reverse: 5\({}^{\prime}\)-GATGCAGGGATGTTG-3\({}^{\prime}\) \\ \end{tabular} \\ \hline \end{tabular}  
* \end{table}
Table 1: Sequences of reverse transcription-polymerase chain reaction primers were used in this study.  
The protein was incubated overnight at 37 \({}^{\circ}\)C with the addition of a second antibody (A21241) for 2 h, and color detection and chemiluminescence imaging analysis system (Tanon 6200, Shanghai Tanon Technology Co., Ltd., Shanghai, China) were used to collect images [12].
## 4 Numerical Simulation Results and Discussions
### Laser Dynamic States under Double External Cavity
#### 4.2.2 Region of Long Double OFB (\(L_{\rm ex2}=10^{-1}\) m)
Figure 11 plots the temporal variation of \(S(t)\)/\(S_{av}\) and the corresponding chaos RIN spectral characteristics under double OFB of the long external cavity when \(K_{ex1}=0.029\) and \(K_{ex2}=0.018\), and \(K_{ex1}=0.04\) and \(K_{ex2}=0.015\), at optical phase \(\psi_{1}=\omega\tau_{1}\), \(\psi_{2}=0.0\) and \(\omega(\tau_{1}+\tau_{2})\). Figure 11(a1,a2) show an intensity-suppression and phase shift in the fluctuations of \(S(t)\)/\(S_{AV}\) on the increasing of the optical phase \(\psi_{2}\) from zero to \(\omega(\tau_{1}+\tau_{2})\), which is the same as the chaos state in single OFB and double OFB with short external  
cavities as shown in Figure 3(a3) and Figure 7a, respectively. The suppression and phase shift of the fluctuations of \(S(t)\)/\(S_{AV}\) due to increasing optical phase \(\psi_{2}\) from zero to \(\omega(\tau_{1}+\tau_{2})\) are translated in Figure 11(b1,b2) into suppression and frequency shift of the RIN spectrum. By comparison, the chaotic RIN spectra in Figure 3(b3) and Figure 7b with the spectra in Figure 11(b1,b2) are smoother, with two weaker and broader peaks. Figure 11(b2) shows that the frequency of the higher peak is 3.6 GHz and 3.8 GHz, as \(\psi_{2}\) = 0.0 and \(\omega(\tau_{1}+\tau_{2})\), which is higher than the stated peak in the single OFB chaos RIN in Figure 3(b3) by 400 MHz. The lower peak is found at 6.5 GHz and 6.3 GHz, as \(\psi_{2}\) = 0.0 and \(\omega(\tau_{1}+\tau_{2})\), and has no relation to the primary peak frequency, as shown in Figure 11(b2). Moreover, these two peak frequencies are not related to the resonance frequencies of the external cavities, \(f_{\rm{ex1}}\) = 5 GHz and \(f_{\rm{ex2}}\) = 4.286 GHz, of the laser. That means no information related to the optical system shape can be obtained from the RIN spectra. This characteristic indicates that the RIN power spectrum of the chaotic dynamic state with a double OFB in the long external cavity is unequal to that describing the single OFB. Therefore, the laser with double OFB and a long external cavity may be utilized to obtain smooth chaos signals used in a secure communication system.
# A Sample Balance-Based Regression Module for Object Detection in Construction Sites
## 3 Materials and Methods
### EfocalL1-SEIOU Loss for BBR
#### 3.3.2. Efficient FocalL1 Loss  
The FocalL1 loss [33] emphasizes increasing the gradient of the easy sample regression and decreasing the gradient of the hard sample at the same time. However, compared with BalancedL1 loss [26], the effect of increasing the easy sample gradient is slightly less, but the hard sample gradient is reduced substantially. The outliers are harmful to the training,  
Figure 4: Differences between EIOU (green) and our method (both green and red).  
but the hard samples in the initial training are not necessarily outliers. It is mentioned in GHM [27] that better prediction of any example in the regression problem will directly improve the quality of the final bounding box. Therefore, it is crucial to increase the easy sample gradient and decrease the hard sample gradient appropriately. In this method, by increasing the gradient of easy samples and reducing the gradient of hard samples, the balance strategy of hard and easy samples is introduced into the loss FocalL1 loss.  
Similar to the definition of \(l_{n}\)-norm losses, \(x=1\) is used as the dividing point, the samples with regression loss less than 1 are divided into easy samples, and the samples with regression loss greater than 1 are divided into hard samples. According to the idea that the gradient value increases relatively in the area with small error and decreases relatively in the area with large error, we design a function curve of the BBR gradient magnitude. As \(\beta\) increases, the gradient of hard samples will be further suppressed, and the gradient of the easy samples will be further suppressed, as shown in Figure 5a, which is not what we expect. Therefore, the change in the gradient of the easy sample is controlled by the parameter \(\alpha\). As \(\alpha\) increases, the gradient of easy samples will further increase, as shown in Figure 5b. The final gradient loss function is written in the following form.  
\[f(x)\begin{cases}-\alpha xln\left(\beta x^{2}\right),&x<1\\ -\alpha xln(\beta),&otherwise\end{cases}, \tag{8}\]  
where \(x\) is the difference between the predicted box and the target box.  
By integrating the gradient formulation above, we can obtain the EfocalL1 loss for BBR,  
\[F(x)=\begin{cases}-\frac{\alpha}{2}x^{2}\ln\left(\beta x^{2}\right)+\frac{ \alpha}{2}x^{2},&x<1\\ C,&otherwise\end{cases}, \tag{9}\]  
where \(C\) is a constant value. To ensure that \(F(x)\) in Equation (9) is continuous at \(x=1\), we have \(C=-\alpha\ln(\beta)x-\alpha\ln(\beta)+\frac{\alpha}{2}[\ln(\beta)-1]\). As shown in Figure 6, the easy sample contribution can be increased substantially while the hard sample contribution can be decreased.  
Figure 5: Possible gradient curves: (**a**) use \(\beta\) to control curve shape; (**b**) use \(\alpha\) to control curve shape.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 7 Conclusions  
Many different contaminants may be present in beverages because of the stages involved in the production chain, agricultural process, or due to environmental conditions. For the analysis of these contaminants in beverages matrices, a sample preparation technique is usually a mandatory step prior to instrumental analysis, which permits analysts to isolate the target analytes from the matrix and concentrate them in order to get better results. The choice of an adequate sample preparation method principally depends on the analytes type, the quantity in which they are present, the matrix complexity and the analytical instruments available. Miniaturized sample preparation techniques usually offer several advantages over the classical techniques including less time and solvent consumption, decreasing handling errors, and are easier to be automated and on-line coupled.  
Sample preparation techniques coupled on-line with analytical techniques provide faster analysis and the possibility to analyze limited or small size samples. In addition, permits analysts to obtain lower detection and quantification limits, reduces the number of sample stages and is more environmentally friendly. In recent years, the number of new on-line approaches has notably increased, showing a better performance when compared to conventional analysis methods. Furthermore, on-line fully automated extraction-chromatography-tandem mass spectrometry is becoming a trend for high-throughput when it comes to multi-residue analysis and new emerging contaminants, that make it ideal for the analysis of complex samples in a single run. This approach has been demonstrated to be efficient, safe, quick, allowing the making of serial analyses from extraction to identification, reducing notably the analysis time, avoiding sample contamination and getting more reliable results. In addition, in the coming years we expect that fully automated extraction-chromatography-tandem MS systems will be extensively used in a wide range of applications. Newly developed methods validated usingthis approach, will be implemented and applied for regulatory purposes in the beverages arena. However, there are still some challenges as the development of new sorbent materials that improve the selectivity and the extraction capacity, is still under intense research. Additionally, the use of fully automated on-line systems is not yet widespread due to the lack of commercially available proper extraction devices, and the still elevated cost of MS detectors. Besides, more knowledge and a broader acceptance of these on-line methods is still forthcoming.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001. The authors are grateful to FAPESP (Grants 2017/02147-0, 2015/15462-5, and 2014/07347-9) and to CNPq (307293/2014-9) for the financial support provided for this research and to our laboratory.  
The authors declare no conflict of interest.