esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Confidentiality  
Confidentiality refers to the \"property that information is not made available or disclosed to unauthorized individuals, entities, or processes\" (ISO/IEC 15408-2008 [29]). Thus, the preservation of Confidentiality requires that the disclosure of information happens only in an authorized manner, i.e., non-authorized access to information should not be possible. This implies leveraging a number of security controls to protect the confidentiality of data, which prominently include encryption mechanisms and, depending on the specific use case, will also typically encompass authentication and authorization provisions.  
Formal definitions of Confidentiality are typically based on the concept of information flow (IF) [30], separating users in classes with different access rights to the system's information and distinguishing the information flows within the system according to the user classes to which they should be accessible. Herein, Confidentiality is considered a property that refers to a high-level problem and, thus, depends on specific patterns to solve lower-level problems, creating the context for them. The relationships of Confidentiality with the specific patterns are depicted in Figure 2. Three core patterns have been identified  
Figure 1: Hierarchical graph of Security and relevant patterns.  
for achieving Confidentiality: Encrypted Channel, Encrypted Storage, and Encrypted Processing, respectively covering all data states, i.e., data in transit, data at rest, and data in processing, respectively.
## 2 Disadvantage of Classical Object Detection Methods Based on Regression
The mapping of the default bounding box coordinates to the original image coordinates on the feature map is as follows:  
\[x_{\text{min}}=\frac{c_{\text{x}}-\frac{w_{\text{b}}}{2}}{w_{\text{feature}}}w _{\text{img}}=(\frac{i+0.5}{|f_{k}|}-\frac{w_{k}}{2})w_{\text{img}} \tag{2}\]  
\[y_{\text{min}}=\frac{c_{\text{y}}-\frac{h_{\text{b}}}{2}}{h_{\text{feature}}}h _{\text{img}}=(\frac{j+0.5}{|f_{k}|}-\frac{h_{k}}{2})h_{\text{img}} \tag{3}\]  
\[x_{\text{max}}=\frac{c_{\text{x}}+\frac{w_{\text{b}}}{2}}{w_{\text{feature}}}w _{\text{img}}=(\frac{i+0.5}{|f_{k}|}+\frac{w_{k}}{2})w_{\text{img}} \tag{4}\]  
\[y_{\text{max}}=\frac{c_{\text{y}}+\frac{h_{\text{b}}}{2}}{h_{\text{feature}}}h _{\text{img}}=(\frac{j+0.5}{|f_{k}|}+\frac{h_{k}}{2})h_{\text{img}} \tag{5}\]  
where \((c_{\text{x}},c_{\text{y}})\) denotes the center coordinates of the default bounding box, \(h_{\text{b}}\) is the height of the default bounding box, \(w_{\text{b}}\) is the width of the default bounding box, \(h_{\text{feature}}\) is the height of the feature map, \(w_{\text{feature}}\) is the width of the feature map, \(|f_{\text{k}}|\) is the size in the \(k\)-th feature map, \(h_{\text{img}}\) is the height of the original image, \(w_{\text{img}}\) is the width of the original image, and \((x_{\text{min}},y_{\text{min}},x_{\text{max}},y_{\text{max}})\) denotes the mapping coordinates of the default bounding box, which is centered at \((\frac{i+0.5}{|f_{k}|}),(\frac{i+0.5}{|f_{k}|})\) and scaled to a height of \(h_{k}\) and a width of \(w_{k}\) in the \(k\)-th feature map. If the SSD_300 \(\times\) 300 model is adopted, such that the size of the input image is 300 \(\times\) 300, the feature maps of the model are mainly produced from the Conv4_3, Conv7, Conv8_2, Conv9_2, Conv10_2, and Conv11_2 layers. The sizes of the convolution receptive field and the mapping region of the default bounding box on each feature map are shown in Table 1.  
Table 1 shows that at the Conv9_2 level, most feature points of the feature layer are produced by utilizing the information of the entire input image, which results in comparatively weak differentiation among different objects. Additionally, the mapping region of the default bounding box is more than half the size of the input image; thus, when several objects are simultaneously located in the same region, they cannot be distinguished. This problem is common in object detection based on regression. In such cases, small objects can be recognized and located only by using the information from the previous feature layers. Therefore, SSD has a weak object detection capability for small object detection.
# Consumer Acceptance of Cultured Meat: An Updated Review (2018-2020)
## 4 Consumer Acceptance
### Personal and Societal Considerations  
One of the major themes identified in the previous literature on cultured meat acceptance is the consideration of costs and benefits to the individual, as well as to society overall. Our previous review found that people typically perceive the benefits of cultured meat as accruing to society, but the risks accruing to themselves [12]. More recent work appears to support this view [34]. [PERSON] and [PERSON] [28] demonstrated that Italian consumers were in agreement with respect to societal benefits, but there was less agreement regarding the personal benefits such as taste, nutrition, and food safety.  
The disconnect between societal benefits and perceived personal costs results in some consumers being in favor of cultured meat in principle, but preferring not to eat it themselves. [PERSON] and [PERSON] [22] found this to be the case with cultured meat more than with other alternative proteins, though views on these issues were generally congruent. This may be because consumers are more likely to perceive personal risk from cultured meat compared to other alternative proteins [26]. People with this view may be late adopters, waiting to observe whether and how others adopt the technology (see [PERSON] [42]).  
Interestingly, experimental work has found no significant difference in consumer attitudes between frames which emphasize personal benefits and frames which emphasize societal benefits [15; 19]. However, when compared to other frames also used in these studies, we do see some significant differences: in the case of [PERSON] and [PERSON] [15], both a frame highlighting societal benefits and a frame highlighting personal benefits led to significantly more positive perceptions than a frame highlighting the technological/scientific aspect of cultured meat. In the case of [PERSON] et al. [19] the personal benefits frame, but not the societal benefits frame, led to significantly more positive attitudes than a third \"meat quality and taste\" frame did. This suggests that there could be some advantage of emphasizing personal over societal benefits.  
The relative salience of societal vs. personal perspectives may differ for different people. In Dutch focus groups, [PERSON] and [PERSON] [37] report that younger participants tended to prioritize whether and how they would interact with cultured meat personally, whereas older participants tended to be more reflective on what a societal transition towards cultured meat might look like. Likewise, [PERSON] et al. [21] found that different attitudes were associated with cultured meat acceptance in different countries--while disgust predicted rejection in the US, a positive ethical evaluation predicted acceptance in India.
## 3 Results and Discussion
### Simultaneous Viscosity Measurement of suspended Blood and Plasma with Respect to Hematocrit
The plasma was loaded into a disposable syringe. After the syringe was installed in the syringe pump, the flow rate of the plasma was set to 1 mL/h. In contrast with the previous method, the present method did not require the loading of blood into a disposable syringe, and the syringe pump was replaced with a micropump. Figure 4(C-i) shows the steady viscosity values of blood and plasma  
Figure 4: Sequential viscosity measurement of suspended blood and plasma with respect to Hct. (**A**) Temporal variations of \(\alpha_{b}\), \(I_{b}\), and \(U_{b}\) with respect to Hct = 20%, 30%, and 40%. (**B**) Temporal variations of \(\mu_{b}\) with respect to Hct. (**C**) Quantitative comparison between the present method (i.e., blood delivery with micro pump) and previous method (i.e., blood delivery with syringe pump). (i) Steady viscosity value of blood and plasma obtained using the present method with respect to Hct. (ii) Viscosity value of blood and plasma obtained by previous method with respect to Hct. obtained by the present method with respect to Hct. In addition, Figure 4(C-ii) shows the viscosity values of the blood and plasma obtained by the previous method with respect to Hct. As expected, the blood viscosity tended to increase linearly with respect to Hct (R\({}^{2}\) = 0.98); the plasma viscosity remained constant (R\({}^{2}\) = 0.17-0.46). The present method overestimated the viscosity values of both the fluids by approximately 14-25% (for suspended blood) and 7-21% (for plasma) when compared with the previous method. The two methods only had differences in blood delivery (i.e., previous method: syringe pump, and present method: micro pump). According to a previous study [35], blood velocity is substantially influenced by Hct. Therefore, it is inferred that the blood flow rate was underestimated by the micro-PIV technique. Additionally, with respect to the suspended blood, the delivery time must be increased to guarantee a steady range of \(U_{b}\). From the experimental results, it was concluded that the present method has the ability to sequentially measure the viscosities of suspended blood and plasma. This method simplifies blood handling procedures (i.e., blood collection and loading), and the syringe pump is replaced with a micropump.
## 3 Results and Discussion
### The Behavior of VOCs in the Food Samples After Irradiation
#### 3.2.3 Alcohols  
The formation of alcohols, particularly ethanol, in animal products is usually due to enzymatic processes involving glucose and various microorganisms, in particular _Pseudomonas_, which subsequently cause food spoilage [45; 48; 49]. However, there are other pathways for its formation, such as the reduction of ketones and aldehydes formed during lipid oxidation. In the non-irradiated chicken samples and the samples irradiated with 0.25 kGy, ethanol was absent. However, an increase in ethanol content to 40-90% was observed as the irradiation dose increased from 0.5 kGy to 5 kGy. In turkey samples irradiated at a dose of 0.25 kGy, a 35% increase was detected. With further dose increases, ethanol content rose by 50% relative to the values of the non-irradiated turkey samples. In the irradiated salmon samples, a steady increase in ethanol content was observed with irradiation doses ranging from 0.25 kGy to 5 kGy, while beef samples exhibited non-monotonous dose-dependent behavior of ethanol content within the same dose range.
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology
### The Object Detection Methods  
**Pattern Matching** is a classic algorithm in image processing that involves comparing a predefined template against different regions of the target image. This technique is particularly effective when the appearance of the object is well defined and can be represented by a template. However, its performance may be influenced by factors such as variations in scale, rotation, illumination, and background.  
The **scale-invariant feature transform** (SIFT) is a robust algorithm that operates by identifying distinctive local features, or key points, within an image that is invariant to changes in scale, rotation, and illumination [4]. The SIFT generates descriptors that encapsulate the local image information around each key point, enabling efficient matching between key points in different images.  
The **Oriented FAST and Rotated BRIEF** (ORB) algorithm is an efficient method for feature detection in image processing [6]. ORB starts by identifying key points using the FAST algorithm, assigns an orientation to each key point for rotation invariance, and extracts binary descriptors using BRIEF to represent local intensity patterns. This algorithm enables efficient matching between key points in different images.  
Finally, the **Haar cascade classifier** is a machine learning-based object detection method widely used in image processing [35]. This classifier is trained on positive and negative samples of a target object, creating a model that can efficiently identify instances of that object in new images. The Haar cascade model excels in detecting objects with specific structural patterns, making them particularly suitable for tasks like face detection. Each of these computer vision algorithms offers unique strengths and limitations, and their suitability for depalletizing system implementation depends on the already mentioned indices, specifically tuned to take into account the time constant of edge computing tasks.
# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 3 Background
### Rough Set Theory: Foundations and Advancements  
RST-based feature selection has become increasingly relevant in modern machine learning (ML), offering solutions to challenges like high dimensionality, uncertainty, and the need for interpretability. Its ability to derive minimal feature subsets while preserving the data structure makes it a powerful tool across diverse ML applications. In healthcare, RST enhances predictive models by identifying critical features for tasks like disease classification, balancing accuracy with transparency essential for clinical decision making. In natural language processing (NLP), RST aids in processing noisy text data, enabling robust sentiment analysis and topic modeling by focusing on key linguistic attributes. Computer vision benefits from RST through efficient feature selection for image classification and segmentation, where it reduces computational overhead while maintaining performance.  
Beyond traditional domains, RST supports ML in resource-constrained environments, such as IoT systems, by optimizing feature sets for real-time predictive tasks like anomaly detection or equipment monitoring. Its integration with deep learning further exemplifies its versatility, where hybrid approaches combine RST's interpretability with neural networks' predictive power, as seen in domains requiring explainable outcomes, such as autonomous systems. These applications underscore RST's potential to address scalability and interpretability challenges in ML, paving the way for its continued evolution in data-driven innovation. At its core, RST is based on the concept of indiscernibility, which partitions a dataset into equivalence classes based on descriptive attribute values. These partitions form the basis for deriving lower and upper approximations, which capture the certainty and possibility of classifying objects within the dataset [22]. The reduct algorithm, a key component of RST, identifies the minimal set of attributes that maintain the discernibility of objects, thereby enabling efficient feature selection [26].  
Recent advancements in RST have focused on extending its applicability to more complex datasets. For example, Fuzzy Rough Sets (FRSs) combine the principles of RST with fuzzy logic to handle imprecise or overlapping data, making them suitable for real-world applications where uncertainty is inherent [27]. Additionally, specialized algorithms like SpecialReduct have been developed to optimize attribute reduction, achieving higher accuracy and computational efficiency compared to traditional methods [28].
## 6 Results and Discussion
### Analysis of Residual Energy, RSSI, and PDR  
The RSSI, PDR, and residual energy study using the BEA-SSA method compared with other conservative models, GWO [26], MOFPL [22], PRO, SSA [36], BES [7], ROA, HGS, SSO, RCSO [21], and FCR [20], is portrayed in this section. The more superior the remaining energy, the more superior will be the performance of the system. In Figure 7a, the remaining energy continues to reduce with the rise in rounds. In Figure 7a, the remaining energy for BEA-SSA and conservative models is 0.6 at round 0; however, as the rounds increase, the remaining energy starts deteriorating. Similarly, in Figure 8, the RSSI is high at the 250 th round for 100 nodes, while for 200 nodes the RSSI is high at the 750 th and 1000 th rounds. In Figure 9, the PDR fluctuates for every round. In Figure 9a, the PDR is high at the 500 th round for 100 nodes, while for 200 nodes the PDR is high at the 1000 th round. However, when compared with GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20], the BEA-SSA model poses high residual energy, high RSSI, and high PDR. An improved Deep Convolutional Neural Network (DCNN) is utilized to choose the best CH in WSN by predicting the energy level. The WSN chooses the best CH using the BEA-SSA. The Bald Eagle Assisted SSA (BEA-SSA) model has high RSSI, PDR, and residual energy. These developments are due to the installation of the BEA-SSA-based CHS and the improved energy evaluation and prediction using DCNN. These enhancements are due to the improved energy evaluation and prediction using DCNN and the adopted BEA-SSA-based CHS.  
Figure 6: Alive node analysis using BEA-SSA compared with the other models [20; 21; 22; 26] for node counts of (**a**) 100 and (**b**) 200.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 2 Typhoon Soulik and Field Measurement  
Typhoon Soulik (international number 1307) originated in the northwest Pacific on 8 July 2013. At 2:00 on 10 July, it was upgraded to a super typhoon and was moving at a speed of 20 km/h. At 16:00 on 13 July, it first made landfall along the coast of Ningde City, Fujian Province, China, carrying the highest wind speed of 33 m/s. Simultaneously, at 16:20 on the 13 th, the observation location experienced a minimum distance of merely 87 km, after which it diminished in intensity and transformed into a tropical storm. Figure 1a depicts the center track of super Typhoon Soulik characterized by great intensity, stable route, and extensive impact.  
The observation of this typhoon was conducted in collaboration between the State Grid and the China Electric Power Research Institute. The measurement tower was situated in Ningde City, Fujian Province, at coordinates 26\({}^{\circ}\)25\({}^{\prime}\) N, 119\({}^{\circ}\)46\({}^{\prime}\) E. The measurement site is surrounded by hills in the north and south directions, with open terrain on the east and west sides, characteristic of a typical valley topography. Wind field observations were conducted using a 3D ultrasonic anemometer at heights of 15, 27, 53, 67, and 82 m above ground level on the 550 kV 52# pole transmission tower, as depicted in Figure 1b. The R.M. Young 81000 ultrasonic anemometer was used with a measurement frequency of 10 Hz. The wind velocity can be measured within a range of 0 to 50 m/s, with an observation accuracy of \(\pm\)0.2 m/s, and the wind direction may be measured within a range of 0\({}^{\circ}\) to 359.9\({}^{\circ}\), with an observation accuracy of \(\pm\)0.3\({}^{\circ}\). The analysis period for the measured wind speed sequence was from 0:00 on 13 July 2013 to 21:00 on 13 July 2013, and the sampling data were recorded with 10 min as the average time interval.
# Augmented Reality in Cultural Heritage: An Overview of the Last Decade of Applications
## 3 Top Trending Applications of AR in Cultural Heritage
### Intangible Cultural Heritage  
Intangible cultural heritage (ICH) plays an important role in the life of each community because it is an expression of the history, traditions, and culture of the people who have lived and worked in a certain area. UNESCO includes in ICH the following: \"oral traditions, performing arts, social practices, rituals, festive events, knowledge and practices concerning nature and the universe or the knowledge and skills to produce traditional crafts\" [148].  
Digital solutions could help to preserve and protect ICH. AR technology has great potential to enhance the perception of intangible cultural heritage [149]. In [150], the authors proposed an AR system designed for the digital protection of ICH that uses an intelligent terminal to fuse its content and realist video images in the scene.  
AR technologies can be used to overlay ICH-related virtual decoration in living space, which brings a new enjoyable perspective to the user experience [151]. Additionally, based on AR technologies, the environment can become similar to a stage where traditional dances (for example Australian Al Ardha aboriginal dance [152] or Greek traditional dance \"Sytros in Three\" [153]) can be learned.  
In the field of digital display for ICH-related content, AR equipment can provide virtual try-on experiences of traditional costumes and accessories [154]. By experiencing the AR environment, users could transpose themselves to a traditional atmosphere [155]. Interactive AR walls that combine markers, projection mapping, and animations are also suitable for presenting folklore heritage, national traditions, and legends [156]. Visitors can also experience an immersed impression by using HMD in conjunction with ICH-related materials [157]. Despite all the advantages of using AR technology, one downside is the increased amount of time needed for the production process of AR applications [158].