esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Temperature and Salinity Measurement Using Frequency Comb
## 4 Results and Discussion  
### Measurement of Water Geometry Distance  
In this section, we describe the measurement of the water geometry distance. The environment parameters are: temperature 23.2 \({}^{\circ}\)C, pressure 1007.06 hPa, and humidity 16.67%. The group refractive index of air \(n_{a}\) can be calculated as 1.00026325 by Ciddor formula.  
Firstly, half part of the water tank is full of water. As is shown in Figure 3a, by scanning the moving stage at the speed of 3.5 mm/s, a pair of cross-correlation patterns can be obtained, which is consistent with the position of M2 and M4. Due to the strong dispersion of water, the cross-correlationpattern of MA1 compared with the MA2 is distinctively broadened to about 330 \(\upmu\)m (1.1 ps). The water temperature is 22.76 \({}^{\circ}\)C and the salinity is 0, so the water group refractive index \(n_{w}\) can be calculated as 1.35829166 by Equation (10). We do a Hilbert transform to the patterns in Figure 3a, and the results are shown in Figure 3b. Peak positions are marked in the picture, which are used to acquire the corresponding quantities of fringes in the Cw laser interferometer. Hence, the _OPD_ can be measured as 2.424 mm.  
Secondly, the water tank is empty. By scanning the moving stage at the speed of 30 mm/s, a new pair of cross-correlation patterns is shown in Figure 4a, where the inset is the detailed observation of the patterns. The corresponding Hilbert transform is performed, and the result is indicated in Figure 4b, where the inset is the expansion of the curve. Peak positions are used to acquire the corresponding quantities of fringes. Consequently, the _OPD_ can be measured as 76.716 mm.  
Hence, the geometry of the distance \(D\) can be calculated as:  
\[(76.716+2.424)/(1.35829166-1.00026325)=221.044\ \text{mm} \tag{19}\]
## 3 Results and Discussion
### Load Capacity and Moment Redistribution
\begin{array}{ll}\text{ACI}-15&\alpha_{1}=0.85\\ &\beta_{1}=0.85-\frac{0.05}{6.9}\cdot(f_{c}-27.6)\quad 27.6\ MPa\leq f_{c}\leq 55.2\ MPa\\ &\\ \text{CSA}-12&\alpha_{1}=0.85-0.0015f_{c}\geq 0.67\\ &\beta_{1}=0.97-0.0025f_{c}\geq 0.67\\ &\\ \text{EC2}-13&\alpha_{1}=1.0&f_{c}\leq 50\ MPa\\ &\alpha_{1}=1.0-\frac{f_{c}-50}{200}\quad 50\ MPa\leq f_{c}\leq 90\ MPa\\ &\\ \beta_{1}=0.8&f_{c}\leq 50\ MPa\\ &\\ \beta_{1}=0.8-\frac{f_{c}-50}{400}\quad 50\ MPa\leq f_{c}\leq 90\ MPa\\ \end{array}  \tag{3}\]  
Figure 8: Experimental and calculated failure loads for tested beams: (**a**) series 1; (**b**) series 2. In Figure 8 it can be seen that load capacities in accordance with code predictions are the smallest for the beams that are designed to achieve the highest moment redistribution (G1-25 and G2-25), and the largest for the beams for which moment redistribution is not designed (G1-0 and G2-0). This is because the moment redistribution assumes a decrease of reinforcement at the support, which leads to capacity being reached for the section in the middle support area before the section in the beam midspan, as measured by assessing internal forces along beams obtained by elastic analysis. Consequently, for the moment redistribution, lower failure load values are obtained. This certainly does not coincide with the obtained experimental results, since the internal force redistribution in continuous beams occurs in relation to elastic analysis, as can be seen in Figure 8. It can be concluded that internal forces along the continuous beam depend on the arrangement of the reinforcement along the beam, which is a consequence of the elastic redistribution present in the continuous beams with GFRP reinforcement.
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 5 Results and Discussion
### Simulation Results
#### 5.1.2 Message-Dropped Assaults  
Figure 9 depicts E2 ED in the presence of MITM, which caused a message drop of 2 s. It is clear that adding harmful nodes that drop valid communications within the network causes the E2 ED to rise. However, MITM attackers with message-dropping capabilities prevent the legal nodes from receiving all messages. Ideally, the legal nodes should receive all messages without dropping them. Additionally, Figure 9 shows that E2 ED grows as attackers are dispersed around the network.  
Next, Figure 10 illustrates the number of messages dropped by harmful nodes and suggests that a high number of messages are discarded when the proportion of harmful nodes in the network increases. For instance, with five attackers in the network, the simulation dropped 1721 messages.  
Figure 8: Packet Loss Ratio from message-delayed assaults.  
Figure 7: Delayed messages caused by message-delayed assaults.  
Figure 11 shows PLR in the presence of harmful nodes. It demonstrates that PLR rises as the number of harmful nodes in the network rises. For example, when the network is flooded with five harmful nodes, only about three packets are received by the destination in the presence of attackers, as shown in Figure 12.  
Figure 11: Packet Loss Ratio from message-dropped assaults.  
Figure 10: Dropped messages caused by message-dropped assaults.  
Figure 9: End-to-End Delay caused by message-dropped assaults.
## 3 Materials, Methods, and Findings
### Model Implementation
Early stopping ensures that training is stopped when the validation loss does not improve for a certain period during training. In this study, early stopping is used to prevent overfitting and to optimize the training process. The verification loss was calculated at the end of each epoch and the training was terminated when no improvement was observed by a certain patience value. The patience value was determined as 3 epochs. The performance of the trained models was evaluated with metrics such as accuracy, precision, recall, F1 score, and the confusion matrix. In addition, the learning curve of the model was analyzed by plotting the validation and training losses for each model according to the epochs. In this way, it was determined when the model started to overlearn or underlearn. The hyperparameter settings and values used for training all the algorithms are shown in Figure 20:0 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Hyperparameter** & **Epochs** & **Batch Size** \\ \hline Naive Bayes & alpha = 1.0 & 1 & 16 \\ \hline SVM & kernel = ’rbf’, C = 1.0 & 1 & 16 \\ \hline K-NN & n\_neighbors = 5 & 1 & 16 \\ \hline Decision Tree & criterion = ’gini’, max\_depth = None & 1 & 16 \\ \hline Random Forest & n\_estimators = 100 & 1 & 16 \\ \hline LSTM & units = 100, dropout = 0.2, recurrent\_dropout = 0.2 & 30 & 64 \\ \hline DNN & units = 128, activation = ’relu’ & 30 & 64 \\ \hline BERT & num\_labels = 2, max\_len = 128, warmup\_steps = 500 & 30 & 16 \\ \hline DistilBERT & num\_labels = 2, max\_len = 128, weight\_decay = 0.01 & 11 & 8 \\ \hline T5 Encoder & max\_len = 128, hidden\_dim = 768 & 100 & 32 \\ \hline RAG & max\_len = 128, hidden\_state & 10 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameter values of the algorithms. The main reason for using different epoch numbers is to ensure the optimal learning process for training each model and to minimize problems such as overfitting or underfitting. Simple models such as Naive Bayes or SVM usually do not require training on the data more than once. Therefore, the number of epochs is not applied (N/A), since these algorithms can achieve an optimum result with one training iteration. Deep learning models usually require a longer training period of 30 epochs to learn more complex relationships. When we analyze the table, we see that the models are trained in different epoch numbers and some models (e.g., T5 Encoder, LSTM) require a long training time, while other models (DistilBERT, RAG) complete the training process in a shorter time. This is due to the use of the early stopping technique.
# Study of Mobility Enhancements for RPL in Convergecast Scenarios
## 4 The RSSI, Rank, and Dynamic+ (RRD+) Mechanism  
In this paper, we consider only convergecast data collection in mobile scenarios. In these scenarios all or part of the nodes are free to move except the sink node. Every node sends periodic data at a constant rate to the sink node, which is the only destination. Before sending packets, each node will select a best next-hop from its neighbours based on the rank mechanism and metric values. According to RPL, nodes can only select the nodes with lower ranks as the next-hop. However, in mobility, topology changes frequently and positions of nodes will not stay the same. If rank values cannot be updated timely according to the movement of nodes, loops may appear. In [13], an enhancement mechanism, called RSSI and level (RL), is proposed. RL is based on a combination of received signal strength indicator (RSSI) monitoring and level updating. This mechanism makes faster decisions for updating next-hop neighbours but suffers from high overhead. In [14], an enhancement mechanism for RPL, called RSSI, rank, and dynamic (RRD), is proposed. RRD is based on a combination of received signal strength indicator (RSSI) monitoring, rank updating, and dynamic control message management. Compared to RL, RRD not only makes faster decisions for updating next-hop neighbours, but also reduces the network overhead. When applied to RPL, RRD enhances the ability of RPL to cope with mobility scenarios, and thus, enhances the overall performance of the network. Rank updating is an important mechanism in our proposal. However, in reality, according to multipath propagation effects (known as fading effects), the received power at a certain distance follows a random behaviour [15]. Nodes that are close to the edge of transmission range will suffer from frequent rank updating, which may cause parent node loss in the parents set. Therefore, in this paper we proposed RRD+, which is an improvement over RRD that takes into account hysteresis of the coverage zone of the transmission range of nodes. In what follows we will describe in details how RRD+ operates.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Results and Analysis
#### 4.2.2 Experimental Results
Figure 18: Classification confusion matrix of our method on WHU-RS19 dataset. This research attempted to correct the four images misjudged by the model in Figure 19a with the hopes of improving the model classification performance. First, with regard to the reason for the wrong judgment of the \"residential\" image, we believe that the other images of the residential category in the dataset contained various bright colors as a whole, while the roofs of the buildings in industrial areas tended to be mostly white. The house colors tended to be white, which may have led to the classification errors. Therefore, the color of the \"residential\" image was increased in saturation to make it more similar to the other \"residential\" images in the dataset. From the super-pixel area of the \"forest\", the cut block contained a part of the bare land, which was different from the other images in this category, which mostly only contained forests. The colors and the details were also blurred. Therefore, the color contrast was enhanced, the overall sharpness of the image was increased, the shadows between the trees were intensified, and the image was prevented from being judged as a \"park\" again. The image of the \"farmland\" depicts that the light of the horizontal road in the image is quite obvious, and a green straight line is included in the captured image features. Therefore, we reduced the brightness of the strong part of the image. In the \"farmland\" parts, we performed a small sharpening to try to remove the noise in the image and strengthen the details of the interval between farmlands. In the last \"bridge\" image, the feature did not contain the \"bridge\" feature at all. Therefore, we increased the brightness of the \"bridge\" itself and the color saturation and sharpness of the image. We also tried to increase the chance of a \"bridge\" edge being captured as a feature. Figure 20a,b present the four corrected images and their corresponding super-pixel feature regions, respectively. Finally, the four corrected images were replaced with the original images, and the category prediction of the entire dataset was again performed. Consequently, the result reached an overall accuracy of 100%. Figure 21 displays the corrected classification matrix. Figure 20: After correction of misclassified images on WHU-RS19 dataset: (**a**) corrected image; (**b**) super-pixel explanation by LIME analysis.
## 3 Results and Discussions
### Coating Corrosion Resistance
Therefore, although noise may suggest repassivation, it is crucial to consider experimental limitations when interpreting results [54; 55]. Figure 8 shows scanning electron microscopy images of the AA2024 aluminum alloy: an untreated sample (a, b) and a sample treated with the PEO process in experiment 3 (c, d). Figure 8: SEM of AA2024 aluminum alloy, untreated (**a,b**) with magnification in 1000\(\times\) and 5000\(\times\) and treated (**c,d**) with magnification in 1000\(\times\) and 5000\(\times\). The treated sample (c, d) exhibits a surface with a compact morphology and few pores, featuring a thin coating with a thickness of 1.7 \(\pm\) 0.5 \(\upmu\)m. This is attributed to both the applied electrical regime, with a final voltage of around 320 V and a constant current in 60 mA, and the electrolyte used (Na\({}_{2}\)B\({}_{4}\)O\({}_{7}\)-10H\({}_{2}\)O), which allowed the development of a compact and thin layer. Other authors have observed that morphology strongly depends on the applied electrical potential; higher potentials result in thicker oxide coatings due to the dielectric layer breakdown [2; 11; 56]. The PEO coating showed a low number of pores, which can be explained by the electron avalanche that occurs during the process. This phenomenon generates rapid ionization and an intense electrical discharge, leading to the uniform melting and solidification of the material, minimizing the formation of pores on the coating surface. Thus, the high energy density provided by the electron avalanche contributes to a more compact and homogeneous structure. This phenomenon causes localized temperatures to briefly reach between 10,000 K and 25,000 K, followed by rapid cooling with the aqueous solution [8; 57]. It is observed that the untreated sample in Figure 8a,b exhibits pores with distinct morphologies across the surface, indicated by the yellow arrows. Within these pores, microcracks are visible, which could potentially allow the substrate to be exposed to a chloride-rich environment, thereby increasing the corrosion rate, as illustrated in Figure 9.  
As already reported in the literature, Cl\({}^{-}\) ions interact with AlOH functional groups, reacting to form soluble aluminum chloride, as shown in Equation (5). This process leads to an increase in surface acidity due to the hydrolysis of Al\({}^{3+}\) ions [58; 59; 60]. \[Al\big{(}OH\big{)}_{3}+3 Cl^{-}\to AlCl_{3}+3 OH^{-} \tag{5}\]  
The passive layer of the alloy allows Cl\({}^{-}\) ions to penetrate easily, reaching the base material and promoting localized corrosion. However, the PEO treatment facilitated the growth of a thicker oxide layer with lower porosity, effectively mitigating the interaction of the alloy with Cl\({}^{-}\) ions. This phenomenon has also been observed in other studies [61; 62].
## 2 Materials and Methods  
### Reagents and Standards  
Sodium chloride and the internal standards (gas chromatographic grade) used for the quantification of volatile compounds including ethyl acetate (CAS No. 141-78-6, purity \(\geq\) 99.8%), isoamyl acetate (CAS No. 123-92-2, purity \(\geq\) 99.8%), 2-methylbutyl acetate (CAS No. 624-41-9, purity \(\geq\) 99.8%), ethyl lactate (CAS No. 687-47-8, purity \(\geq\) 99.8%), ethyl hex-anoate (CAS No. 123-66-0, purity \(\geq\) 99.5%), ethyl octanoate (CAS No. 106-32-1, purity \(\geq\) 99.8%), diethyl succinate (CAS No. 123-25-1, purity \(\geq\) 99.6%), phenethyl acetate (CAS No. 103-45-7, purity \(\geq\) 99.6%), ethyl decanoate (CAS No. 110-38-3, purity \(\geq\) 99.8%), ethyl nona-noate (CAS No. 123-29-5, purity \(\geq\) 99.8%), ethyl dodecanoate (CAS No. 106-33-2, purity \(\geq\) 99.7%), ethyl hexadecanoate (CAS No. 628-97-7, purity \(\geq\) 99.8%), isobutanol (CAS No. 78-83-1, purity \(\geq\) 99.8%), cis-3-hexen-1-ol (CAS No. 928-96-1, purity \(\geq\) 99.6%), 1-hexanol (CAS No. 111-27-3, purity \(\geq\) 99.7%), 1-heptanol (CAS No. 111-70-6, purity \(\geq\) 99.7%), 1-octanol (CAS No. 111-87-5, purity \(\geq\) 99.8%), phenethyl alcohol (CAS No. 60-12-8, purity \(\geq\) 99.6%), dodecanol (CAS No. 112-53-8, purity \(\geq\) 99.5%), butanoic acid (CAS No. 107-92-6, purity \(\geq\) 99.8%), hexanoic acid (CAS No. 142-62-1, purity \(\geq\) 99.5%), octanoic acid (CAS No. 124-07-2, purity \(\geq\) 99.6%), linalool (CAS No. 78-70-6, purity \(\geq\) 99.5%), geraniol (CAS No. 106-24-1, purity\(\geq\) 99.8%), nerol (CAS No. 106-25-2, purity \(\geq\) 99.6%), citronellol (CAS No. 106-22-9, purity \(\geq\) 99.8%), \(\beta\)-damascenone (CAS No. 23696-85-7, purity \(\geq\) 99.8%), nerolidol (CAS No. 7212-44-4, purity), 2-octanol (CAS No. 123-96-6, purity), and benzaldehyde (CAS No. 100-52-7, purity \(\geq\) 99.6%)were purchased from Sigma-Aldrich (Shanghai, China). Deionized water (<18 MW resistance) was prepared using a Milli-Q purification system (Molecular, Chongqing, China). All reagents were of analytical purity.
# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 2 Previous Research
### Acquisition Image Preprocessing and Feature Point Extraction  
The images acquired from a fixed mount in outdoor environments, where lighting is insufficient and focusing is difficult, are vulnerable to problems with illumination, contrast, and noise. Therefore, an image filter algorithm was applied to improve the quality of the damage analysis performance based on an image of the exterior of the railway bridge. Among several filter algorithms, bilateral filter, which can learn and analyze the data for deep learning with optimal quality, was used to remove noise from the image data. This filter is nonlinear, in that it removes noise while preserving edges, and the intensity value at each pixel is replaced by a weighted average of the neighboring pixels, which makes the boundaries more distinct and the rest of the image smoother, improving accuracy.
## 2 Materials and Methods
### Cell Culture, Sample Preparation, and SERS Analysis  
Peripheral blood samples were collected from healthy donors upon informed consent according to the principles of the Declaration of Helsinki. About 9 mL of blood was obtained from each donor using EDTA-coated collection tubes, and PBMCs were isolated by Ficoll-Histopaque-10771 gradient centrifugation, according to the manufacturer's instructions. Anticoagulated blood was diluted with an equal volume of phosphate-buffered saline, pH 7.4 (PBS; Gibco), containing 2% fetal bovine serum (FBS; Gibco). Diluted blood was layered over the Histopaque samples that were centrifuged at 400\(\times\)\(g\) for 30 min at room temperature in a swing-out rotor with no brakes. The PBMC interface was carefully removed by pipetting and washed with PBS-2% FBS by centrifugation at 300\(\times\)\(g\) for 10 min. The PBMC pellet was re-suspended in PBS-2% FBS followed by the cell number assessment by standard hemocytometer count.  
SW480 human colon cancer cells (ECACC, CCL-228) were cultured in Dulbecco's modified Eagle's medium (DMEM; Gibco) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (Corning) and incubated at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\).  
For the SERS analysis, 1 mL of each cell suspension containing 1 \(\times\) 10\({}^{6}\) cells was centrifuged, re-suspended in 300 \(\mu\)L of ethanol absolute, and ultrasonicated to trigger cell rupture and mechanical lysis. The resulting 300 \(\mu\)L of the sample was deposited in the GNSs paper substrate after removal of the PDMS reservoir, and allowed to dry before analysis. The resulting substrates were analyzed using the 785 nm laser excitation line. Five spectra for each of the samples were acquired for 10 s.
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 7 Architecture and Prototypical Implementation  
The proposed SOLIOT approach serves as gateway between the production networks and the open Internet. Similar to SOLID, the related entity is described by its profile and presented as an LDP Resource. However, the extension of the supported protocols to CoAP and MQTT added IoT-specific terms and concepts and designed tailored interaction patterns. Widely deployed architectures rely on a distinct integration layer or specialized gateways connecting the data-providing Things with the data-consuming applications [6; 33; 49; 50]. Figure 7a shows a simplified representation of this concept, where the Digital Twins appear at the Edge Layer (also called integration layer, IoT platform, gateway depending on the context). The Thing from the example is deployed in the Manufacturer's shop floor network, and its data output hosted on an edge gateway. The operator of this gateway is responsible to grant or deny access, lift information and forward requests. The basic architecture of the SOLIOT is closer to the approach of [PERSON] et al. [2] or [PERSON] and [PERSON] [18] as an equal node on the same network level as the IoT device and the (external) consumers (see Figure 7b).  
SOLIOT does not make any differences between the connected components. Theoretically, also the Data Analytics Platform of the Analyst could represent itself as a Digital Twin on a SOLIOT server. The distinction between data producers and consumers is therefore not specified by their location in a network but their applied permissions. The permissions themselves are also accessible resources (ACL files in Figure 7b). This follows the vision of reducing the network barriers, merging shop floor network with office floor application and even the public Internet. While both the Data Analyst and the Business Partner have encoded ACL policies, this is not the fact for the Competitor. Consequently, his access requests will be denied. Nevertheless, the dissolution of network barriers also exposes previously shielded areas and devices, requiring proper security and protection mechanisms. However, following the 'zero trust' argumentation, the increasing degree of interconnections, required gateways and introduced foreign applications makes a network-based security approach more and more challenging.  
SOLIOT has been prototypically implemented as an open-source project [51] under an Apache 2.0 license. The NodeJS reference implementation of SOLID serves as the core server, extended with a CoAP server [52] and MQTT [53] capabilities. The SOLIOT reference server provides RESTful, LDP-conform interactions for CoAP clients, and resolves event-based interactions using its MQTTadapter. Currently, an external Mosquito MQTT message broker [54] collects and distributes MQTT messages.