esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Volatile Compounds
#### 3.3.1 Acids  
Carboxylic acids were the most abundant volatile compounds found (Table 2). The discovery of this wide range of carboxylic acids might be due to the lipolytic LAB activity [23]. Within these compounds, acetic, hexanoic and 3-methyl-butanoic acids were found  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Batches**} & \multirow{2}{*}{**Microorganism**} & \multicolumn{4}{c}{**Days of Ripening**} \\ \cline{3-6}  & & **0** & **45** & **60** & **90** \\ \hline \multirow{4}{*}{C} & TAM & 8.08 \(\pm\) 0.034 \({}^{\mathrm{\text{GB}}}\) & 8.29 \(\pm\) 0  
in higher levels in all batches (Table 2). In the LAB-inoculated batches Lc and Lg, 2-methyl-propanoic acid was also found to be the most abundant one at the end of the ripening period (Table 2). Acetic and hexanoic acids have been reported as the acids found in the highest amounts in ripened \"Torta del Casar\" cheese [22]. In general, most acids showed significant (\(p\leq 0.05\)) increases throughout the ripening process in both the uninoculated control and inoculated batches, especially the methyl-branched acids that were detected at low levels or non-detected at day 0 of ripening (Table 2). The formation of acids in \"Torta del Casar\" cheese is caused by enzymes, mainly from vegetal rennet, and microbial activity [31; 32]. Regarding linear n-acids evolution, only scarce differences were found between batches throughout the ripening process. Thus, only acetic acid at day 0, butanoic acid at 45 and 60 and octanoic and propanoic acids at day 30 showed significantly higher amounts in one of the inoculated batches than in the control one (Table 2). In addition, hexanoic acid showed a lower level in batch Lg than in the control batch at 45 days of ripening. However, none of these differences between batches in n-acids abundance was found at the end of ripening, except for propanoic acid that showed a significantly higher amount in batch Lg than in the control batch (Table 2). Acetic and propanoic acids could have a microbial origin as a result of lactose fermentation; mainly some LAB [33]. Short-chain fatty acids have low perception thresholds and provide typical aroma notes to cheees such as Cheddar, Roncal, Emmental, Camembert and Grana Padano [31].
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 8 Conclusions and Future Work  
The HTTP-GET message is vital for any WS to work correctly. However, it is vulnerable to DoS and DDoS attacks, and HTTP GET-based attacks are among the most dangerous and devastating. Many proposed detection systems have detected DDoS attacks based on HTTP-GET. But, more often than not, the evaluation and comparison of these systems rely on self-generated datasets, and most existing datasets represent traditional environments, which are ineffective for CCEs. Furthermore, nonqualified features from non-CCEs are used to describe these datasets, resulting in misclassification problems for classifiers.  
Meanwhile, CCE-based datasets with nonsecurity objectives usually have limited attack scenarios, are unavailable for others to use, or contain unlabeled requests. As a result, they cannot be used to create, train, or/and test detection systems in CCEs for HTTP GET-based DDoS attack scenarios.  
The proposed dataset fulfilled all the requirements for an adequate dataset since it was created from real-world requests on CCEs, which is crucial since the nature and environ ment of the CCE are different from that of non-CCEs. It also includes all possible scenarios of HTTP-GET DDoS attacks and properly and wholly labeled requests, represented by using representative features, and a balanced ratio of normal and attack requests. Furthermore, the employed representation and features were evaluated by using several classifiers (with their default settings) to achieve an acceptable and reliable detection accuracy and False Positive rates. Furthermore, the proposed datasets outperformed the existing datasets qualitatively (according to the criteria) in several metrics.  
Although the proposed dataset is currently limited to IPv4-based HTTP-GET flood DDoS attacks on CCEs, future work will include other HTTP-based attacks, including HTTP-POST-based ones and IPv6-based attack requests. In addition, future work on the proposed dataset should keep updating and expanding it by using various tools to increase the number of attacks engaging in abnormal activity and the ratio of malicious requests. In addition, more clients (normal requests) will perform routine requests to avoid using balancing techniques in the future, such as SMOTE. These activities add more realistic dataset requests and are necessary for labeling and validating them.  
Writing--original draft preparation, Z.R.A. and M.A.; writing--review and editing, Z.R.A., M.A., S.D.A.R., B.A.A. and K.A.; project administration, Z.R.A.; resources, Z.R.A. and M.A.; supervision, Z.R.A. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Research Funding program, grant code (NU/RG/SERC/12/3).  
Not applicable.  
The data is accessible at [77].  
The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the General Research Funding program.  
The authors declare that they have no conflict of interest to report regarding the present study.
## 3 Survey of Techniques for Updating
### Update Fault Detection and Recovery
#### 3.4.3 cSimplex  
cSimplex [80] supports the reliable updating of multicast communication software in Wireless Sensor Networks. Fault detection is performed by monitoring the new communications software for entry into inadmissible states, using statistical checks and a stability definition. Recovery takes place by reverting to a well-tested, reliable, communications _safety module_. When a new (or _experimental_) communications software module is uploaded, its behavior is compared to that of a baseline (high assurance) module. Three basic critical requirements are introduced: _agreement_ (all valid nodes receive the same set of messages), _validity_ (all messages sent by valid nodes are received, and _integrity_ (no invalid messages are received). From this, eight basic invariants are defined for a _stable_ protocol--these can be augmented by application-specific requirements (e.g., timeouts, performance,application-specific features such as causal ordering). Received and transmitted messages are checked, and cSimplex state is added to every message (in a special header); cSimplex _Session Messages_ are also exchanged between nodes. Stability checking is distributed over the network, using statistical methods, evaluating: the _Long Term Error Rate_, _Per-Period Error Rate_, _Per-Period Error Rate with History_, and a _Multiple-Period Method_. Following fault detection, a publish-subscribe scheme is used to allow communication software to be dynamically replaced without shutting down the software.  
The cSimplex system also defines a complementary application/communication fault detection and recovery mechanism that could run in parallel to UFDR.
## 7 Supervised Classification  
Facing Coverage and Resolution Inconsistencies: 100 kHz SSS Mosaic Segmentation for Object Based Classification  
Object Based Image Analysis (OBIA), has become a standard practice for gaining detailed delineations of habitats through swath sonar images. It involves similar pixels being grouped into connected segments (objects), classified using some intrinsic information. It has mostly been applied to MBES data [12; 15; 16; 18; 56; 67; 68], where the objects are extracted through segmentation of MBES backscatter mosaics and the available information (bathymetric, backscatter and angular backscatter responses or acoustic modeling parameters) is averaged within each segment. Segmentation practices are also used in SSS analyses using backscatter information for habitat mapping [38; 47], and more recently for SBP data [47]. In this study, object-oriented analysis offers the means to successfully combine MBES and dual-frequency SSS data into an integrated classification procedure, although the dataset has systematic gaps throughout the surveyed area and there are different resolutions between one system or another. The 100-kHz SSS system was the only one with full coverage of the survey-area; its mosaic thus formed the basis for object extraction through image segmentation techniques. The average value of each derivative within each segment constitute a new feature vector, composed of all 49 features considered in this study, fully covering the survey-area. It is assumed that at least some data derivative points from each available system are located within each segment, to assure that the size of the segments should be bigger than the data gaps that may exist between any pair of consecutive survey-lines. Thus, any segmentation technique used should allow for scale adjustment.  
To meet the goal of image segmentation with selectable scales, the Segmentation by Weighted Aggregation, as implemented in the SWA tool [69] was used. This method constructs a pyramid of graphs, which represents aggregates of pixels of similar properties. At fine levels these aggregates represent regions of homogenous intensities while at coarser levels coherent regions of more complex third order image texture measures. The 100-kHz SSS greyscale mosaic was down-sampled to 1000 \(\times\) 700 pixels in order to make the segmentation algorithm run efficiently. The intensity \"fine\" and \"average\" coefficients were set to 8 and 5 respectively, the intensity variance minimum scale was set to 3 with a coefficient equal to 0.5 and the 6 th scale was chosen as the most suitable for the needs of this work. Figure 8 shows the result of the segmentation process overlaid on and compared side-by-side to the 100-kHz SSS mosaic. Examples showing the average response of selected texture, terrain and angular response analysis features within each segment are shown in Figure 3.  
Figure 8: Segmentation (**right**) of the 100 kHz SSS mosaic (**left**) using the Segmentation by Weighted Aggregation (SWA) method.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 4 Damage Identification Experiment of a Submerged Cantilever Steel Beam
### Damage Identification
#### 4.4.2 Determination of the Additional Mass  
To improve the accuracy of damage identification, the additional mass that leads to higher sensitivity frequencies of the cantilever beam is selected, according to Section 2. Under different water depth conditions, each substructure is added with mass ranging from 0 kg to 2 kg. The relative sensitivity of the first four frequencies of the 10 substructures is calculated. When the immersion depth is 0.4 m, the frequency relative sensitivity curves of substructure 7 are shown in Figure 10. The \"frequency sensitivity\" in Figure 10 is the relative sensitivity of the natural frequency \(R_{j,l}(\mu,m)\), which is calculated by Equation (1).  
Figure 10 shows that the relative sensitivity of the first frequency decreases with the increase of the mass value; the relative sensitivity of the second and third frequencies increase as the mass value increases; the relative sensitivity of the fourth frequency of substructure 7 remains stable when the additional mass value varies. Furthermore, when the additional mass is 0.3 kg, the relative sensitivity of the first frequency decreases 2%, while the relative sensitivity of second and third frequencies increases by 10%. Overall, 0.3 kg additional mass improves the sensitivity of frequencies and avoids the structural damage caused by excessive mass. Based on these relative sensitivity curves of the 10 substructures, the additional mass is determined to be 0.3 kg when the immersion depth is 0.4 m.  
Figure 10: Relative sensitivity of the first four frequencies with different mass for substructure 7.  
Figure 9: Single damage diagram of cantilever beam.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
###### Abstract  
In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.  
localization of radiation sources; path planning; all-around view Compton camera; simple back projection; principal component analysis +
Footnote †: journal: Applied Sciences  
Article
## 3 Research Methodology
### CLP Predictive Modeling
#### 3.4.1 Artificial Neural Network (ANN)  
In the past few decades, ANN has become a popular and helpful technique for classification, clustering, pattern recognition, and prediction in many disciplines [58]. ANNs are able to deal with noisy or incomplete data and can be very effective, mostly in modeling problems where the relationships between inputs and outputs are not sufficiently known [59]. So, based on their abilities, ANN-based models can be ideal alternatives for modeling CLP. ANN consists of three types of layers: an input layer, hidden layers, and an output layer. In this study, a multilayer feedforward back-propagation network with one hidden layer was developed as an ANN model to predict CLP.
## 3 The Radiative Transfer Equation
### RTE Solution Considerations
#### 3.2.2 Turbulence Effects  
We should determine if we need to consider the effects of turbulence on light propagation in the underwater environment. Turbulence can cause fluctuations in the refractive index, leading to beam wandering, scintillation, and other effects that influence the performance of optical communication systems. Hence, we should incorporate turbulence models or empirical data into the RTE to account for these effects.
# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 6 Results and Discussions
### Phase 2:
One key difference between the proposed and existing approaches is that the proposed technique includes a fairness score. With the suggested method, the values for energy consumption, delay, and drop at speed 40 were 680 mJ, 395.189005 s, and 7.151254, respectively. In addition, greater throughput could be achieved using the new  
Figure 24: Plots of speed vs. fairness index in the suggested approach and existing approaches. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Parameters**} & \multirow{2}{*}{**Techniques**} & \multicolumn{4}{c}{**Speed**} \\ \cline{3-6}  & & **20** & **40** & **60** & **80** \\ \hline \multirow{3}{*}{**Delay (s)**} & **H-WDFOA-VANET** & 90.180295 & 395.189005 & 924.638246 & 1227.591591 \\ \cline{2-6}  & **RI-CS** & 138.843616 & 686.703072 & 1210.68834 & 1565.515813 \\ \cline{2-6}  & **EHA-CORP** & 100.98126 & 502.120993 & 1037.0019 & 1251.455006 \\ \hline \multirow{3}{*}{**Energy consumption (m)**} & **H-WDFOA-VANET** & 1320 & 680 & 900 & 1200 \\ \cline{2-6}  & **RI-CS** & 1980 & 2440 & 2640 & 3040 \\ \cline{2-6}  & **EHA-CORP** & 1980 & 2080 & 2040 & 2400 \\ \hline \multirow{3}{*}{**Drop**} & **H-WDFOA-VANET** & 13.334678 & 7.151254 & 9.450511 & 12.405151 \\ \cline{2-6}  & **RI-CS** & 19.80027 & 24.58287 & 26.7894 & 31.195048 \\ \cline{2-6}  & **EHA-CORP** & 19.858595 & 20.86455 & 20.441628 & 24.709388 \\ \hline \multirow{3}{*}{**Throughput (Bps)**} & **H-WDFOA-VANET** & 199 & 24 & 14 & 10 \\ \cline{2-6}  & **RI-CS** & 16 & 5 & 2 & 1 \\ \cline{2-6}  & **EHA-CORP** & 96 & 21 & 10 & 6 \\ \hline \multirow{3}{*}{**Fairness Index (FI)**} & **H-WDFOA-VANET** & 3 & 2 & 1 & 1 \\ \cline{2-6}  & **RI-CS** & 4.0746 & 2.819775 & 2.0302 & 1.672425 \\ \cline{1-1} \cline{2-6}  & **EHA-CORP** & 6 & 3 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative analysis of suggested approach with existing approaches at different speeds. method than existing methods. The proposed method reduces energy usage to 900 mJ, delay to 924.638246 s, and drop value to 9.450511 when traveling at 60 mph.
###### Abstract  
This study aimed to clinically and radiographically evaluate the results of guided bone regeneration (GBR) using three-dimensional preformed titanium mesh (3-D-PFTM) for non-contained horizontal defects in 100 consecutive cases. This study involved 100 patients (129 implants) with peri-implant non-contained horizontal defects. The patients were divided into three groups: 3-D-PFTM alone (Group 1), 3-D-PFTM plus cross-linked collagen membrane (Group 2), and 3-D-PFTM plus non-cross-linked collagen membrane (Group 3). Each implant was evaluated radiographically using CBCT at baseline and 6 months postoperatively. At the platform level, the mean horizontal hard tissue gain of all the sites was 3.1 \(\pm\) 1.3 mm at 6 months postoperatively. The mean rate of mesh exposure was 11.8% in Group 1, 4.2% in Group 2, and 5.0% in Group 3. The mean hard tissue gain rate was 71.0 \(\pm\) 23.0% in group 1, 84.2 \(\pm\) 21.5% in group 2, and 84.0 \(\pm\) 22.9% in group 3. Groups 2 and 3 showed significantly higher hard tissue gain rates than group 1. However, there was no significant difference between the rates in groups 2 and 3. Within the limitations of this study, 3-D-PFTM should be considered as a valuable option for GBR for peri-implant non-contained horizontal defects. The use of an additional resorbable collagen membrane provides additional advantages.  
be obtained for closed and intrabony, but not for extrabony defects, and this limits it [10]. The prognosis of GBR using a resorbable membrane is further worsened in an extrabony defect that does not exist within the envelope of adjacent bone [11].  
Various materials have been used to compensate for the limitations of the resorbable membranes. Some methods use titanium-reinforced d-PTFE or titanium (Ti) mesh [12]. These methods can facilitate satisfactory results for GBR for extrabony defects, but they have high technical sensitivity because of the need for additional surgery to remove the membrane and the high membrane exposure rate. As a material for compensating for the disadvantages of the conventional Ti-mesh, a three-dimensional preformed Ti mesh (3-D preformed Ti mesh) was introduced. [PERSON] et al. reported that a 3-D preformed Ti mesh can improve the prognosis of alveolar ridge augmentation, which is also easy to use because it does not need to be cut or bent, and it reduces the surgery duration and the exposure rate of the mesh by avoiding sharp edges. In addition, it has been reported that minimal invasion of the bone graft site during the second surgery is advantageous because GBR application to the bony defect around the implant can be simultaneous with implant placement [13].  
This study aimed to retrospectively evaluate the clinical and radiological findings of GBR in 100 consecutive patients (129 sites) who underwent the simultaneous application of the 3-D preformed Ti mesh to the non-contained horizontal defects around the implant. The study's working hypothesis was that the use of additional two types of resorbable collagen membranes with a 3-D preformed Ti mesh would affect the hard tissue gain rate.
## 4 The Methodology for the Proposition Validation
### Total Logistics Cost of Combined Road-Rail Transportation
* \(TC_{rail}\): To obtain pricing information for the main leg, we consulted the sales team at a railway operator for data related to a sample CRRT service. They provided us with a cost of 800 EUR for a single 60-foot wagon travelling an on-rail distance of 822 km and having a capacity of three 20-foot containers, which are commonly referred to as 20-foot equivalent units (TEU). Based on this information, we estimate the cost of transporting one 20-foot ILU per kilometre as: \[TC_{rail,km}=\frac{800\text{EUR}}{3\text{TEU}^{*}822\text{km}}=0.32\frac{ \text{EUR}}{\text{TEUkm}}\] We are aware that rail transport pricing differs between operators, relations, and time, but regarding the efficiency of the research, we were not able to collect more detailed cost information empirically. Nevertheless, the 0.32 EUR/TEU used in this study is in the range of 0.46-1.35 EUR per forty-foot container provided by [75]. From this, we deduce that the exemplary price resulting from our research is representative of the European market. Figure 4: Cost structure of combined road–rail transportation from a freight owners’ perspective (Icons from Freepik on Flaticon.com, accessed on 11 September 2023). * \(HC\): For the hub cost, we use primary data from the involved terminals' homepages, if available. If not, the value of: \[HC_{hundling}=48\;\text{EUR}\;\text{and}\;HC_{storage}=0\;\text{EUR}\] is used in line with [75]. For gateway movements, we use: \[HC_{hundling}=27\;\text{EUR}\;\text{and}\;HC_{storage}=0\;\text{EUR},\] in line with [75] as no other data is available to us. * \(ILUC\): The ILU rental fee per day was retrieved from an Austrian container rental company, compared with data from [76; 77], and set to \[ILUC=7.5\frac{\text{EUR}}{\text{day}}\]
* \(OC\): Since actual road freight costs are included in the input data, the road \(OC\) is already included in the road freight charges. * \(DC\): As no data was available to us on the production delay, we initialized the simulation with: \[DC=500\frac{\text{EUR}}{\text{hour}}\]
* \(CC\): In the first case, we cannot integrate \(CC\) in our evaluation due to the abundance of the shipment monetary values. For the second case, we compute the costs of capital by utilizing a literature-based interest of 12% from [62].