esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 5 Outlook: An Architecture for Compliant and Accountable Robot Application Development
### Conclusions and Future Work  
In this work, we presented an architecture to protect the integrity of robot workflows and seal them after they passed verification and validation procedures.  
We use a dedicated trusted authority to perform a digital signature of the presented workflows. The robot, before executing a workflow will check the signature and have proof that the transmitted workflow has not been tampered with. It will only execute such workflows.  
Figure 11: The distributed ledger architecture of the compliant development and execution environment.  
Figure 12: A simplified flow of the development within the compIAI environment.  
Our evaluations show that the introduced overhead for integrity preservation is minimal and will not impact a robot in its operation.  
The concept here will be integrated into the larger infrastructure for compliant application development presented above to result in a holistic environment for the trusted development of robot application.  
However, we want to point out that a single security solution within a system is never sufficient. Also, we highlight that security should part of the whole lifecycle of a robotic product [47] where it is constantly re-evaluated according to the most recent threat situation since there can never be a 100% security guarantee.  
As an extension to our current approach, we will also integrate sanitizable signatures [48]. This will allow authorized entities to modify a program after it has been signed by the CSP to a limited extent. This will open up new application use-cases for our approach.  
Conceptualization, B.B. and B.D.; methodology, B.B., B.D.; software, B.B.; validation, B.B.; investigation, B.B., B.D.; data curation, B.B.; writing--original draft preparation, B.B., B.D.; writing--review M.P., S.R.; visualization, B.D., B.B.; project administration, B.D.; funding acquisition, B.D. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Austrian Research Promotion Agency in the project complAI under the program \"Ideen Lab 4.0\" (FFG No. 878804) and by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology within the project \"Credible & Safe Robot Systems\".  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WFM & Workflow Manager \\ CSP & Compliance Service Provider \\ V\&V & Verification and Validation \\ \end{tabular}
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 3 Methodology
### Fingerprint Post-Processing  
The ActiveTLS-generated fingerprints (example shown in Figure 2) underwent initial post-processing, to decompose them into their core elements: TLS version, cipher suites, extensions (including decoded Application-Layer Protocol Negotiation), encrypted extensions and certificate extension parameters. These TLS features were reduplicated across all ten ClientHello scans and categorized by feature type, to create a complete profile of the TLS. While the original fingerprint was preserved, a SHA256-encoded version was also maintained, to enhance readability. The data were enriched by domain source information, classification labels (good, bad, or unknown) and Autonomous System (AS) numbers, with AS resolution performed using the pyasn.dat file from the original ActiveTLS dataset [27].
# Novel Drug Combinations in Lung Cancer: New Potential Synergies Between 5-FU and Repurposed Drugs
## 3 Results
### The Effect of Levosimendan as a Single Agent and in Various Combinations with 5-FU  
For the A549 cell line, Levosimendan did not exhibit significant anticancer activity at any concentration, as indicated by the MTT assay results (Figure 6). When combined with 5-FU, Levosimendan also did not show significant cytotoxic effects compared to its effects alone, except at the highest concentrations (50 and 100 \(\upmu\)M), where there was a notable reduction of up to 40% in cell viability compared to Levosimendan alone. Morphological assessments revealed that these changes were primarily due to the effects of the antineoplastic drug 5-FU, since Levosimendan alone did not impact the cell phenotype (Figure 7).
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 4 Results  
### On-Site Implementation and Effectiveness of the Case Study  
#### 4.1.1 Exhibition Environment Status Detection Device and System  
This study proposes an Exhibition Environment Status Detection Device, which includes the following components: an environmental model composed of environmental block units; an environmental status sensing unit installed in the environmental model to obtain environmental status information of the location; a display unit to show environmental status information and real-time sensing information; an environmental control unit that generates environmental control signals based on the environmental status information to control corresponding environmental equipment; and a real-time sensing module installed in the real environment, which is electrically connected to the environmental control unit. The real-time sensing module includes sensors for detecting information in the real environment and control modules installed on real equipment, electrically connected to the environmental control unit, to control the corresponding real equipment based on the environmental control signals.  
Figure 1 shows the block diagram of the Exhibition Environment Status Detection Device. Figure 2 is a schematic diagram of the System proposed in this study. The environmental block units are at least divided into multiple exhibition space blocks and shared space blocks. The real environment control unit in the exhibition can include various control modules, such as lighting control, exhaust fan control, gas valve control, air conditioning control, and door and window control modules. For example, the air conditioning control module in the real equipment uses the environmental control unit to electrically connect with the real control module. The real-time sensing module obtains temperature data through the temperature sensing module, and based on the environmental control signals, AI processing can intelligently control the automatic air conditioning to adjust the temperature. The real equipment can also include automatic lighting, using the environmental control unit to electrically connect with the real control module. The real-time sensing module detects indoor brightness to generate brightness data, and based on the environmental control signals, AI processing can intelligently control the automatic lighting to adjust the exhibition environment's brightness.  
This technology has currently obtained four domestic and international patents (France, Japan, China, and Taiwan) and has won three international invention exhibition awards (Paris International Invention Exhibition, Seoul International Invention Fair, and Poland International Invention Fair). It has been applied in art collection storage
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
###### Abstract  
Imbalanced data are a major factor for degrading the performance of software defect models. Software defect dataset is imbalanced in nature, i.e., the number of non-defect-prone modules is far more than that of defect-prone ones, which results in the bias of classifiers on the majority class samples. In this paper, we propose a novel credibility-based imbalance boosting (CIB) method in order to address the class-imbalance problem in software defect proneness prediction. The method measures the credibility of synthetic samples based on their distribution by introducing a credit factor to every synthetic sample, and proposes a weight updating scheme to make the base classifiers focus on synthetic samples with high credibility and real samples. Experiments are performed on 11 NASA datasets and nine PROMISE datasets by comparing CIB with MAHAKIL, AdaC2, AdaBoost, SMOTE, RUS, No sampling method in terms of four performance measures, i.e., area under the curve (AUC), \(F_{1}\), AGF, and Matthews correlation coefficient (MCC). [PERSON] sign-ranked test and [PERSON]'s \(\delta\) are separately used to perform statistical test and calculate effect size. The experimental results show that CIB is a more promising alternative for addressing the class-imbalance problem in software defect-prone prediction as compared with previous methods.  
software defect prediction; defect proneness; class-imbalance learning; oversampling; ensemble learning +
Footnote †: journal: Applied Sciences
## 3 Cherry Phenols Bioavailability and Physiological Roles
### Bioactivity of phenolic compounds from sour and sweet cherry
#### 3.1.1 Antioxidant activity  
The antioxidant capacity of extracts from cherries has been widely investigated using different methodological approaches. Using an ORAC (oxygen radical absorbance capacity) assay, it has been reported that the antioxidant capacity ranges from 1,145 to 1,916 \(\upmu\)mol Trolox equivalents (TE)/100 g in sour cherries (Amarena Mattarello, Visciola Ninno and Visciola Sannicandro) [PERSON] _et al._ (2010). It has to be stressed that the values found in sour cherry fruits are comparable to those found in some berry fruits, for example, strawberry, and are higher than in apple and kiwi fruit [PERSON] _et al._ (2010).  
The antioxidant activity of cherry extracts has been studied also in different biological samples and [PERSON] _et al_. [PERSON] _et al._ (2010, 2011) demonstrated, among the anthocyanins tested, cyanidin 3-glucoside, showed stronger antioxidant activity. Using liposomes as model membranes, it has been reported that anthocyanins, cyanidin and hydroxycinnamates isolated from tart or sweet cherries are more active compared to those of other berries (such as blackberries, red raspberries, blueberries or strawberries) [PERSON] _et al._ (2011). Furthermore, extracts of sweet cherries are able to inhibit copper triggered low density lipoproteins (LDL) oxidation, an atherogenic modification of this lipoprotein class [PERSON] _et al._ (2011). The antioxidant activity of cherry phenolics was confirmed using neuronal PC 12 cells exposed to oxidative stress [PERSON] _et al._ (2011).  
Cherries show antioxidant effects also _in vivo_ (Figure 2). An increased activity of the antioxidant enzymes SOD (liver, blood) and Gpx (liver) and a decrease of lipid peroxidation were observed in animal model fed with sour cherry juices obtained from an autochthonous cultivar (_Prunus cerasus_ cv. Maraska) [42]. Studies in human subjects, reported that the consumption of 280 g of cherries (about 45 sweet Bing cherries) increased plasma lipophilic antioxidant capacity [43, 44]. Moreover, in a double-blind, placebo-controlled crossover design, tart cherry juice intake (240 mL twice daily for 14 d) was associated with a decrease of F(2)-isoprostane levels, a marker of oxidative damage, in response to forearm ischemia-reperfusion. In the same subjects a decrease of urinary excretion of markers of oxidized nucleic acids (8-hydroxy-2'-deoxyguanosine, 8-hydroxyguanosine) was demonstrated [45]. In addition to anthocyanins also phenolic acids could contribute to antioxidant activity of cherries [40].
# FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning
## 6 Discussion and Ablation Study  
Our method simply combines FixMatch [15] and focal loss [17]. The main idea behind focal loss is to address the problem of class imbalance. The class imbalance can make it difficult for the model to learn useful information from the minority class. Focal loss is commonly used in the object detection area since the number of images of target classes is much smaller than that of background classes. We find that focal loss is also useful for image classification when the amount of labels is imbalanced. Our experiments set an equivalent number of labels for each class. Therefore, the class imbalance problem can hardly occur in the supervised learning part. However, in FixMatch, pseudo labels are self-generated from unlabeled data. Therefore, the class imbalance can happen on  
Figure 3: The comparison of accuracy and loss between FixMatch and FocalMatch on CIFAR-10 dataset. The numbers in legends of (**c**,**d**) represent the 10 classes in CIFAR-10 dataset. (**a**) top1 accuracy. (**b**) loss. (**c**) Accuracy for each class of FixMatch. (**d**) Accuracy for each class of FocalMatch.  
the generated pseudo labels, which will affect the unsupervised learning of the model. To investigate the effectiveness of focal loss, we conduct an ablation study using different \(\gamma\) values in the focal loss.  
Figure 4 demonstrates the number of pseudo labels generated (i.e., the predicted confidence of a specific class in the soft label is greater than \(\gamma\)) by the model for each iteration on the CIFAR-10 dataset. Figure 4a shows the result of not using focal loss (i.e., \(\gamma=0\)), whereas Figure 4b shows the result of using focal loss with \(\gamma=1\). It is evident that when not using focal loss, there is a significant quantitative imbalance in the pseudo labels of each class. The class imbalance problem can seriously affect the ability of the model to learn from classes with a small number of pseudo labels. In the early stages, the number of pseudo labels generated from a single class (i.e., class 2) is even higher than the aggregated number of pseudo labels generated from all other classes. This indicates that the unsupervised loss is dominated by a single class instead of all classes.  
In contrast, the difference between the number of pseudo labels generated for each class is notably reduced when using focal loss. Therefore, the model can extract useful information from all classes. Focal loss does not present a stricter condition to reduce the number of pseudo labels. Instead, the total number of pseudo labels generated with focal loss is much higher than without focal loss (7 billion to 10 billion). Focal loss provides a smoother learning curve for the model to learn from all unlabeled data, which also shortens the iterations required to reach a stable phase of generating pseudo labels.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 4 Software Implementation  
The sensor node in the monitoring system can do nothing without software. Software is the soul of the sensor node to execute sensing, computing, and communication. In addition, the operating system of sensor networks is the cornerstone of software. Taking the features of WSNs into account, many embedded operating systems special for sensor networks like TinyOS, SOS, MANTIS OS and Contiki have been developed by researchers. TinyOS [23; 24] is an open source, lightweight operating system developed by UC Berkeley for low-power wireless sensor nodes. It provides a set of important services and abstractions, making it easier to build sensor network applications. Applications and the system itself are written in a C dialect, called nesC language. It has been run on many generic platforms and used in a wide variety of monitoring applications for WSNs [7; 12; 25]. SOS [26] is an operating system for more-class WSNs developed by the NESL at University of California (Los Angeles, CA, USA). It  
\begin{table}
\begin{tabular}{c c c} \hline
**Operation Mode** & **Operation Current** & **Power Consumption** \\ \hline Idle & 2 mA & 2 mAh \\ Data sampling & 80 mA & 0.178 mAh \\ Data transmitting & 60 mA & 0.067 mAh \\ \hline \end{tabular}
\end{table}
Table 2: Operation current and power consumption in different modes.  
Figure 2: The customized sensor node and its logical structure. (**a**) The sensor node; (**b**) The logical structure of the node.  
uses a common kernel to implement basic services. In addition, the prominent characteristic of SOS is its dynamic reconfigurability, which allows dynamic addition, modification, and removal of network services. However, currently, SOS is no longer under active development. MANFIS OS [27] is an open source, multi-threaded operating system written in C for wireless sensor networking platforms. It supports many platforms and has been used in several applications. Contiki [28] is a tiny, highly portable operating system for small devices developed by a world-wide team of developers. It is a very promising operating system as it is easy to connect to the Internet. We have not currently introduced any operating systems into our monitoring system, and we just utilize C language to implement the software functions. In initialization stage, route discovery and time synchronization of nodes are considered, and we focus on transmitting protocol and duty cycling in monitoring stage. The graphic user interface (GUI) at the sink node side is also mentioned.
## 3 Results and Discussions
### Order Picking Performance
#### 3.2.1 Performance of Routing Policies  
In terms of subset performance, midpoint and traversal heuristics perform equally well in 6 (E, ERb = O, E = 0, \(e_{3-4}\) = 0, R \(e_{3}\) = 0, and R \(e_{4}\) = 0) of the 10 scenarios. In each case, equal number of subsets contain either midpoint or traversal heuristic. In order words, based on the selection method, they are equally likely to be selected by a decision maker based on the 10 best performing subsets. Furthermore, in terms of the efficiency level, 7 of the 10 scenarios yield equivalent results for both midpoint and traversal heuristics. This is contrary to the literature, which show either one of those heuristics outperforms each other in many instances. This result may be due to the fact that, since items are almost evenly distributed across the warehouse, in both cases, both sides of the aisles are equally likely to be traveled by a picker. Return policy remains the least performing heuristic. Over all the scenarios, most of the return policies are present in subsets containing small warehouse layouts. This confirms the results of the literature, which found that return routing to perform the best in cases of small pick density or order length [35]. However, to evaluate the effect of errors on its performance, we assess its efficiency without errors given that errors are observed in both midpoint and traversal routing. According to the results, return routing without propagation errors and midpoint routing with errors equally perform. Likewise, return policy without error outperforms both midpoint and traversal policies with errors. Therefore, picking errors largely contribute to the underperformance of return policy.
## 2 CFD Model
### Data Processing  
By non-dimensionalizing the physical quantities, the variations in the fall trajectories and aerodynamic load can be studied in a more unified and comprehensive manner, facilitating the analysis and interpretation of the results. The physical quantities involved are non-dimensionalized using Equations (5)-(9). The non-dimensionalization is applied to physical time (\(t\)), displacement (\(x\), \(y\) and \(z\)) in three directions, velocity (\(v_{i}\), where  
Figure 4: Computational domain: (**a**) longitudinal section of the train; (**b**) longitudinal section of the falling debris.  
Figure 3: Geometric model and boundary conditions of numerical simulation.  
i = x, y, and z) in three directions, aerodynamic force (\(F_{i}\)), and moment (\(M_{i}\)) around the \(x\), \(y\), and \(z\) axes.  
\[t^{\prime}=tV_{t}/L \tag{5}\]  
\[x^{\prime}=x/L,y^{\prime}=y/H,z^{\prime}=z/W \tag{6}\]  
\[{v_{t}}^{\prime}=v_{i}/V_{t} \tag{7}\]  
\[C_{Fi}=\frac{2F_{i}}{\rho V_{t}^{2}S} \tag{8}\]  
\[C_{Mi}=\frac{2M_{i}}{\rho V_{t}^{2}Sh} \tag{9}\]  
The dimensionless quantities used in the analysis include the dimensionless time (\(t^{\prime}\)), the dimensionless displacements (\(x^{\prime}\), \(y^{\prime}\), and \(z^{\prime}\)) of the falling debris in the \(x\), \(y\), and \(z\) directions, the dimensionless velocities (\(v_{i}\)/) in the three directions, the dimensionless aerodynamic forces (\(C_{Fi}\)) acting on the falling debris, and the moment (\(C_{mi}\)) around the three axes. \(V_{t}\) is the train speed; \(L\), \(H\) and \(W\) are the length, height and width of the head train, respectively; \(\rho\) stands for the air density, 1.29 kg/cm\({}^{3}\); \(S\) and \(h\) are the characteristic area and height of the falling debris.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Feature Analysis
#### 2.3.5 Fragmentation  
Fragmentation in IPv6 is managed by a dedicated Fragment Header that efficiently segments and reassembles packets, whereas BPv7 uses a custom fragmentation mechanism designed for the challenges of DTNs. IPv6's approach could potentially be enhanced for DTN use by refining fragmentation policies or extending the Fragment Header to account for longer delays and reordering issues typical in disrupted networks.
# Thermogravimetry Applicability in Compost and Composing Research: A Review
## 3 Results and Discussion
### Compost Maturity Evaluation Research by Using a Variety of Techniques
#### 3.2.1 Composting Maturity Research by Thermogravimetry  
Different authors have proposed thermogravimetric methods (sometimes combined with other methods) due to the great advantages that these methods represent both in terms of speed and the no-need to pre-treat of the sample [9; 11; 33; 36; 42; 58; 59]. Moreover, [PERSON] et al. [34] have been able to differentiate the efficacy of different composting methods through these techniques and several authors have reported that this behavior is consistent with the fertilizer stabilization process [11; 33; 38; 60].
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 5 Optimization Formulations
### Formulation FPRM/1: Fixed Frame Length  
In the case when the frame length \(T\) is a fixed parameter, the formulation to minimize the pilot rate is similar to that in [16] for obtaining a static slice allocation.  
\[\min\ \frac{1}{T}\sum_{k\in\mathcal{K}}\sum_{i\in\mathcal{S}}x_{k}^ {i} \tag{1a}\] \[\sum_{i\in\mathcal{T}}x_{k}^{i}\geq\hat{h}(k)T, k\in\mathcal{K}\] (1b) \[\sum_{i\in\mathcal{T}}x_{k}^{i}\geq\hat{h}(k)T, k\in\mathcal{K}\] (1c) \[\sum_{i\in\mathcal{T}}x_{k}^{i}\geq 1, k\in\mathcal{K}\] (1d) \[\sum_{j=i-d(k)+1}^{i}x_{k}^{j}\geq 1, k\in\mathcal{K}(T),\,d(k)\leq i\leq T\] (1e) \[\sum_{j=1}^{i}x_{k}^{j}+\sum_{j=T-(d(k)-1-i)}^{T}x_{k}^{j}\geq 1,\] \[k\in\mathcal{K}(T),\,1\leq i\leq d(k)-1\] (1f) \[\sum_{k\in\mathcal{K}}x_{k}^{i}\leq P, i\in\mathcal{T}\] (1g) \[x_{k}^{i}\in\mathbb{B}, k\in\mathcal{K},i\in\mathcal{T}. \tag{1h}\]  
The objective function (1a) is however changed to minimize the pilot rate, that is, the number of pilots used per slot in the frame. For the static frame length case, this is equivalent to minimizing the total number of pilots used, but simply normalized to the frame length.  
Constraints (1b) and (1c) ensure, respectively, that the uplink and downlink traffic rates are met for each device \(k\in\mathcal{K}\). Constraint (1d) requires that every device transmit at least once in the frame, which is not otherwise assured in the case where a device only has periodic traffic, with no rate demand traffic. Constraints (1e) and (1f) together ensure that the periodic traffic requirements are met for each device \(k\in\mathcal{K}\), with constraint (1e) covering most of the frame and requiring a transmission at least once every \(d(k)\) slots, while constraint (1f) takes care of the beginning and end of the frame, ensuring that no more than \(d(k)\) slots elapse between the last transmission of one frame and the first transmission of the next. Finally, constraint (1g) makes sure that no more than the maximum allowable number of pilots \(P\) is used in any slot, and constraint (1h) gives the domain for the decision variables \(x_{k}^{i}\).