esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Natural disasters and catastrophes not only cost the loss of human lives, but adversely affect the progress toward sustainable development of the country. As soon as disaster strikes, the first and foremost challenge for the concerned authorities is to make an expeditious response. Consequently, they need to be highly-organized, properly-trained, and sufficiently-equipped to effectively respond and limit the destructive effects of a disaster. In such circumstances, communication plays a vital role, whereby the consequences of tasks assigned to the workers for rescue and relief services may be streamlined by relaying necessary information among themselves. Moreover, most of the infrastructure is either severely damaged or completely destroyed in post-disaster scenarios; therefore, a Vehicular Ad Hoc Network (VANET) is used to carry out the rescue operation, as it does not require any pre-existing infrastructure. In this context, the current work proposes and validates an effective way to relay the crucial information through the development of an application and the deployment of an experimental TestBed in a vehicular environment. The TestBed may able to provide a way to design and validate the algorithms. It provides a number of vehicles with onboard units embedded with a credit-card-size microcomputer called Raspberry Pi and a Global Positioning System (GPS) module. Additionally, it dispatches one of the pre-defined codes of emergency messages based on the level of urgency through multiple hops to a central control room. Depending on the message code received from a client, the server takes appropriate action. Furthermore, the solution also provides a graphical interface that is easy to interpret and to understand at the control room to visualize the rescue operation on the fly.  
IEEE 802.11p; VANET; Ad hoc networks; Raspberry Pi; safety applications; disaster management; emergency response; TCP/IP architecture; message dissemination; information exchange +
Footnote †: journal: Sensors
## 3 Test Result and Analysis  
### Failure Patterns and Crack Development  
Figure 11 shows the failure patterns of the selected SCGC specimens (taking SCGC2, SCGC5, and SCGC6 for examples). Figure 12 shows the crack propagation in the SCGCs (taking SCGC2, SCGC5, and SCGC6 for examples). While the failure pattern of SCGCs under combined compression and bending is similar to the eccentric compression patterns observed in ORC columns by Li [22], the SCGCs exhibit greater lateral deformation.  
Figure 11: The failure patterns of typical SCGC specimens.  
Figure 12: Crack development diagram. Note: Numbers 1–22 in the figure show the chronological order of cracks.  
When the external load reached 10% to 16% of the ultimate load capacity (\(N_{\rm u}\)), the concrete in the tension zone of the eccentrically loaded columns began to crack, initiating the first horizontal crack. When the load reached 18% to 25% of \(N_{\rm u}\), a second horizontal crack appeared in the tension zone, and in some cases, a third crack was observed. At 30% to 70% of \(N_{\rm u}\), the cracks formed in the earlier stages continued to propagate toward the compression zone, while additional fine cracks emerged in the tension zone and progressively widened. When the load reached 70% to 82% of \(N_{\rm u}\), the number of cracks stabilized, and longitudinal cracks began to form at the supports and in the compression zone. When the ultimate load was reached, the concrete in the compression zone suddenly crushed, signaling the failure of the specimen. The total number of cracks ranged between 16 and 22.  
Figures 13 and 14 present the experimental results of the cracking load (\(N_{\rm cr}\)) and ultimate load (\(N_{\rm u}\)) of the SGC. As the eccentricity increased, both \(N_{\rm cr}\) and \(N_{\rm u}\) of the SGC decreased; with the increase in the reinforcement ratio, \(N_{\rm u}\) increased, but \(N_{\rm cr}\) showed no significant change. For the SGC with the same \(\rho_{\rm s}\), when \(e_{0}\) was increased to 2, 3, and 4 times its initial value of 70 mm, \(N_{\rm cr}\) decreased by 25%, 67%, and 75%, respectively, while \(N_{\rm u}\) decreased by 40%, 71%, and 80%. For the SGC with the same \(e_{0}\), when \(\rho_{\rm s}\) was increased by 1.77 times and 2.77 times from the initial 0.302%, \(N_{\rm u}\) increased by 12.6% and 27.2%, respectively. However, before the SGC cracked, the strain in the SSR was minimal, resulting in negligible tensile stress. Therefore, the tensile resistance of the SGC was primarily provided by the concrete, making the influence of \(\rho_{\rm s}\) on \(N_{\rm cr}\) insignificant.  
Figure 15 shows the relationship between \(N/f_{\rm c}bh\) and the crack width. It can be observed that an increase in \(e_{0}\) or a decrease in \(\rho_{\rm s}\) accelerates the rate of crack width development, and the crack width development for each SGC shows an approximately linear trend.  
Figure 14: Ultimate load.  
Figure 13: Cracking load.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 4 Globally-Referenced Electro-Optical SLAM (GEOSLAM)
### Multi-Session Mapping  
Refinement of the visual feature map over multiple sessions with time-separated GNSS measurements is central to the idea of approaching the accuracy limit of mapping with standard GNSS. Consider a vehicle revisiting an area mapped previously in one or more sessions. When GEOSLAM matches greater than \(T\) features in the current keyframe to the features already present in the prior map, the keyframes from the previous sessions in that section of the map are included in the covisibility window of the current keyframe. After such a merge is detected and verified, a BA may be performed on the covisible keyframes from multiple sessions to average time-separated standard GNSS errors. It is important to note that multi-session mapping can only be realized when sufficient feature matches are found between multiple sessions. This is not a straightforward task, as evidenced by recent efforts on lifelong feature mapping efforts [63]. This issue is further discussed in Section 5. In the current section, multi-session map database management and map merging are discussed.
## 3 PV Generation Forecaster Models
### Artificial Intelligence Models  
#### 3.2.1 Feed Forward Neural Network  
An FFNN is the simplest model of an ANN. Therefore, it also represents the definition of the ANN, and its main characteristic is the neurons. Moreover, as can be seen in Figure 4, an FFNN is composed of three layers, an input layer, a hidden layer, and an output layer. These denominated layers can have a different number of neurons. The number of neurons in the input and output layers are the same as the input and output data, respectively. On the contrary, the number of neurons in the hidden layer has to be adjusted depending on the complexity of the problem to be solved in order to achieve the most accurate prediction possible.  
These neurons are connected by adjustable weights. The initial weights, together with the biases, are modified during the learning process to minimize the cost function of the network. The mathematical expression of a cell is as follows [21]:  
\[u_{k}=\sum_{j=1}^{n}w_{kj}\cdot x_{j}+b_{k}, \tag{1}\]  
\[y_{k}=\varphi(u_{k}) \tag{2}\]  
where, \(x_{j}\) is the input vector of the cell, \(w_{kj}\) is the weights matrix, \(b_{k}\) is the bias vector, \(u_{k}\) is the output before the activation function, \(\varphi\) is the activation function and \(y_{k}\) is the output of the cell. \(k\) is the number of the cell of the hidden layer, and \(n\) is the number of inputs. More information about FFNNs can be found in [22].
## 3 Experimental Results and Analysis  
To evaluate the performance of the proposed method, three pairs of SAR and optical images are experimented. The test data consists of different characteristics including different resolutions, incidence angles, seasons etc. Experimental results are shown in Figures 2-4. To quantitatively evaluate the registration performances, we adopt the root-mean-square error (RMSE) [12] between the corresponding matching keypoints, and it can be expressed as:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(x_{i}-x_{i}{{}^{\prime}})^{2}}+ {(y_{i}-y_{i}{{}^{\prime}})^{2}}} \tag{1}\]  
where (\(x_{i}\),\(y_{i}\)) and (\(x_{i}{{}^{\prime}}\),\(y_{i}{{}^{\prime}}\)) are the coordinates of the \(i\)th matching keypoint pair; \(n\) means the total number of matching points. In addition, the correct matching ratio (\(CMR\)) is another effective measure, which is defined as:  
\[CMR=\frac{correctMatches}{correspondences} \tag{2}\]  
\"_correspondences_\" is the number of matches after using PROSAC, \"_correctMatches_\" is the number of correct matches after removing false ones. The results of quantitative evaluation for each method are listed in Table 1.  
Figure 2: \(Cont.\)  
Figure 3: _Cont_.  
Figure 2: (**a**) Optical image; (**b**) SAR image; matches found in pair 1 using (**c**) SIFT, (**d**) SAR-SIFT, and (**e**) the proposed method. The reference image is shown on the left and the sensed image on the right.  
Figure 4: _Cont._  
Figure 3: **(a)** Optical image; **(b)** SAR image; matches found in pair 2 using **(c)** SIFT, **(d)** SAR-SIFT, and **(e)** the proposed method. The reference image is shown on the left and the sensed image on the right.  
It can be observed from Table 1 that the correct matching rate obtained by the SIFT and SAR-SIFT algorithms is relatively low. The proposed algorithm can obtain more correct matching point pairs, which shows that this algorithm is better in suppressing the radiation difference between optical and SAR images. Compared with the other two algorithms, the CMR of this algorithm is improved by 88.21%, 81.89% and 89.73%, respectively in the three groups of images, and the RMSE is reduced by 0.5128, 0.8127 and 0.4982, respectively.
## 5 Discussion
### Interpretation of Key Findings  
Logistic regression emerged as the best-performing model, achieving an accuracy of 0.962 and an AUC of 0.987. This result is consistent with the observed strong linear relationships between key features (e.g., G1, G2, absences, and failures) and the academic outcome. The significant coefficients identified in the logistic regression model further confirm the appropriateness of linear assumptions in this dataset. Visual inspection of the scatter plots between G1/G2 and final grades (see Figures 11 and 12) also indicate pronounced linear trends.  
The underperformance of Random Forest and XGBoost, despite hyperparameter tuning, suggests that model complexity may not necessarily enhance predictive performance, particularly on small or moderately sized datasets with well-behaved features. The observed patterns imply that simpler linear classifiers may generalize better under such conditions.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
The complete medical and dental history of each participant was collected. Participants older than 18 years of age, requiring oral rehabilitation of the upper jaw were included [24]. Excluded criteria were: insufficient bone for implant therapy; a visible lack of primary stability at the time of implant insertion (low primary stability was considered when <30 Ncm or ISQ value < 60 for immediate loading [26; 27]); decompensated metabolic diseases; motor difficulties that impeded or hampered hygiene; pregnancy; periodontal disease without previous treatment; and a history of smoking, radiotherapy, or the use of bisphosphonates.  
Bone quality was assessed in the preoperative cone-beam computed tomographic (CBCT) images. A standard CBCT (5i-CAT(r) Cone Beam 3-D Imaging System, Imaging Sciences International, Hatfield, PA, USA) was used with standard exposure parameters set and operated by the same technician to minimize variation in the intensity of the voxel values between the patients [28; 29]. The DICOM images were loaded into the Blue Sky Plan software (version 4.2.5; Blue Sky Bio, Grayslake, IL, USA), and a virtual implant was positioned in each implantation site. Next, using the \"density value measure (Hu)\" tool [30; 31; 32; 33], the bone density (BD) at the implantation site was determined by the mean of the voxels' grey values for trabecular bone in the cervical, body, and apical regions (Figure 1). Following [PERSON]'s classification to determine bone quality [34], only implantation sites presenting BD values of 150-350 and 350-850, corresponding to D4 and D3 bone qualities, respectively, were included in the present study [35; 36]. The CBCT evaluations of each implantation site were performed by an independent expert (CFABM) who had no contact with the patients.
## 2 HGF Model
### Data and Preprocessing  
The 30 m resolution hyperspectral images used in this study were obtained by the GF-5 and ZY1-02D satellites, with 330 and 166 spectral bands, respectively. The multispectral images were obtained by the Gaofen-1 (GF-1) and Sentinel-2B satellites. The spatial resolution of multispectral bands for the former is 8 m and the latter is 10 m. The basic information of the image is shown in Table 1. Considering that multisensor images may have some differences in sensor attitude, angle, atmospheric conditions, acquisition time, and other aspects, it is necessary to conduct geometric and radiation normalization for multisensor remote sensing images [2]. Images from different sensors were processed with radiometric correction, orthorectification, and geometric correction.
## 4 Performance Analysis
Figure 9: ISR = 20 dB, BER comparison of the proposed acquisition algorithm over AWGN channel. In Figure 10, the relationship between ISR and BER is shown. As ISR increases, the BER also increases across the entire SNR range. However, the single-stage PIC algorithm effectively reduces the BER loss caused by MAI. When ISR is at 30 dB, the degradation caused by MAI can reach a maximum of 6.5 dB. After applying the single-stage PIC optimization, this degradation is reduced by 4 dB. Furthermore, the performance of the other curves is optimized by approximately 1 dB. The result shows that the PIC algorithm can enhance the BER of receivers. When ISR is less than 20 dB, the performance of the algorithm after single-stage PIC optimization approaches the theoretical limit as seen in Figure 8.  
In conclusion, the proposed algorithm outperforms the traditional correlation-based acquisition algorithm in multi-user systems. When the ISR is below 20 dB, \(P_{cd}\) and BER are nearly equivalent to a no-interference environment. Additionally, the improved PIC algorithm reduces the BER of receivers by up to 4 dB compared to the correlation-based acquisition algorithm.
## 5 Experiment
### Experimental Setup  
#### 5.1.1 Dataset  
We used four real-world datasets. Table 1 shows summary of the datasets used in this study.  
1. **MNIST** ([[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) (accessed on 25 July 2022)) contains 70K images of hand-written digits and labels corresponding to each digit. We randomly selected 20K images and colored embeddings along the corresponding labels. MNIST contains a cluster structure of each digit, and the embedding should preserve the cluster structure.
2. **COIL-20**[54] contains 1440 grayscale images of rotated objects. We used the first ten objects and colored embeddings along each object. Images of COIL-20 have a rotating structure, and the embedding should preserve it.
3. **DBPedia** ([[https://wiki.dbpedia.org/](https://wiki.dbpedia.org/)]([https://wiki.dbpedia.org/](https://wiki.dbpedia.org/)) (accessed on 29 July 2022)) contains 530K Wikipedia articles classified into 14 categories. We used 20K random articles and extracted feature vectors by FastText following [37]. We colored the embedding following the category information, and the low-dimensional representation should preserve the cluster structure.
4. **Fashion MNIST (FMNIST)**[55] contains 70K images of 10 kinds of fashion items and labels corresponding to each item. We randomly selected 20K images and colored embeddings to match the corresponding labels. Although FMNIST has a cluster structure similar to MNIST, the categories of FMNIST are more correlated than those of MNIST, and separating clusters in a low-dimensional representation is more difficult.
## 1 Introduction
This application of high-resolution optical satellite images to sea ice deformation measurement provided smaller scale deformation than the mid-resolution radar remote sensing with spatial resolutions from several tens of meters to around a hundred meters [10; 11; 12; 13; 14]. Measuring sea ice motion from the low-resolution satellite datasets have been validated using buoy location records [23]. However, it is difficult to obtain the reference data for the validation because location records from buoy or ice drift station are sparse. To overcome deficiency of reference dataset, alternative validation approaches have been devised, e.g., assessing suitability of image blocks for matching [24]. Nowadays, high-resolution optical imaging satellites have become more available. Moreover, earth observing near-polar orbit satellites have an advantage on more frequent visiting in high-latitude regions as trajectories converge around polar regions [25]. Although optical satellite remote sensing is limited to cloud free conditions, combinations of sequential high-resolution optical images acquired from operational multiple sensors in high latitude regions can contribute to measuring long-term trends in ice motion, and can also be a reliable tool for monitoring sea ice in shipping lanes or in the vicinity of oil and gas platforms. This study aimed to evaluate the feasibility of multi-sensor high-resolution optical satellite remote sensing for precise sea ice motion measuring using an MCC approach. To validate the measured sea ice motion, a comparison of the high- and resampled mid-resolution satellite image-derived sea ice motion with buoy trajectory was demonstrated, and causes of measurement errors (considering the effects from degraded spatial resolution) were investigated. Additionally, from the sea ice motion data, the sea ice deformation was estimated, and the applicability of the multi-sensor high-resolution optical satellite images for measuring kinematic characteristics of sea ice discussed.
# Review of Hygroscopic Coating on Aluminum Fin Surface of Air Conditioning Heat Exchanger
## 3 Hydrophobic/Hydrophilic Coated Heat Exchangers
### Applications in Air Conditioning Systems  
Under the operating conditions of air conditioners, part of the water molecules in the moist air will condense to the fin surface, due to the fin surface temperature being lower than the air dew point temperature [85]. Different condensation modes will affect the heat and mass transfer performance of heat exchangers and other performance parameters. Many researchers have done a lot of research on the effects of treated fins on air conditioning heat exchangers. [PERSON] et al. [86] studied the effects of hydrophilic coating on air-side performance and found that the flow state of condensed water directly affects the influence of the hydrophilic coating on the heat transfer performance of the surface. When a large amount of condensed water flows, the hydrophilic coating can improve the heat transfer performance, and when a small number of condensed water forms on the surface of fins, the hydrophilic coating will reduce the heat transfer performance.  
[PERSON] et al. [87] studied the condensation properties of hydrophilic, superhydrophilic, hydrophobic, and hydrophobic-hydrophilic hybrid pattern surfaces, respectively. They found that the heat transfer coefficient of the hydrophilic surface was lower than that of the hydrophobic surface, and the superhydrophilic surface was the worst because of the filmwise condensation of water molecules on the surface. [PERSON] et al. [88] proposed that under completely dry surface conditions, the influence of the hydrophilic coating on the thermal-hydraulic performance was negligible, and the pressure drops were approximately 15-40% lower than those without coating. [PERSON] et al. [89] examined the influence of environmental parameters on the performance of both hydrophilic coated and uncoated surfaces of the fin-tube heat exchangers in wet conditions, finding the effect of inlet relative humidity on the heat transfer performance was small. They also found that because of the condensate flow pattern along the fin surface, the effect of inlet relative humidity has a pronounced effect on pressure drops for un-coated surfaces. [PERSON] et al. [90] applied hydrophobic, hydrophilic, and hydrophobic-hydrophilic hybrid pattern coatings to the fins of the heat exchanger, respectively. Through experimental testing, they found that the hydrophilic unit had a lower air-side pressure drop than the others.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 2 Related Work
### SDN Mechanisms against DDoS Attacks  
In [16], the authors have utilized Self Organizing Maps (SOM), an unsupervised artificial neural network trained with features of the traffic flow, to classify the network traffic flows as either normal or abnormal. They extended NOX controller and monitor registered switches during predetermined time intervals to retrieved information from the flow of interest. This sample information is then passed to the SOM module that classifies the traffic as normal or attack.  
Reference [17] proposed a network reconfiguration scheme using SDN against an attack mounted by botnets. They maintain a pool of public IP addresses and in case of a possible DDoS attack, the server redirects the protected service to a new set of IPs by leveraging the central and dynamic network management offered by the SDN paradigm. A similar redirection approach is employed in [18]. However, instead of redirecting services to new IP addresses, they identify the attack traffic and re-route it away from the victim to alternate routes or sinkholes.  
In [19], the authors introduced a use case of SDN-based DDoS attack mitigation system to provide an autonomous and prompt configuration for suspicious network traffic. This work in its current form is very basic without any proof of concept and evaluations.  
[PERSON] [20] proposes a flexible DDoS defense system that leverages NFV capabilities to elastically vary the required scale (attack volume) and type (e.g., SYN proxy vs. DNS reflector defense) of DDoS defense. [PERSON] steers the suspicious traffic through the defense strategy graphs which are based on the packet counts and predefined suspicious behaviors.  
In [21], the authors used a combination of SDN and machine learning techniques to detect and block amplification reflection attacks (DrDoS). The OpenFlow switch copies the traffic to the detection agent, which applies the support vector machine (SVM) method to classify the packets as malicious or non-malicious. On detection of malicious behavior, the detection agent notifies the controller to block the malicious packets.  
In [22], the authors used sFlow with security-centric SDN to effectively detect and mitigate DNS amplification attack. If the incoming traffic contains a matching request Query ID in the flow records, it is classified as a normal DNS response, otherwise, it is marked suspicious, since no query was initiated in the first place. Marked flows are then forwarded to the SDN controller for mitigation purpose.  
Floodlight based guard system (FL-GUARD) [23] proposed a three step approach against a DDoS attack. First, they apply dynamic IP address binding to solve the problem of IP spoofing, next they use SVM algorithm to classify attack traffic, and finally they take advantage of the centralized controller to block attacks at the source port.