esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Evaluation of Path-Based Product Line Integration Testing and Test Coverage Metrics
## 2 Related Work  
In integration testing, it is important to obtain both interaction information between units and the integration order of these units, as not all units interact with each other. [PERSON] [9] and [PERSON] [10] propose incremental integration testing methods, considering the integration orders, while others propose methods only considering interactions among units [11; 12; 13; 14; 15; 16; 17; 18; 19; 20].  
Among studies that consider possible interactions only, one category of studies includes studies that see a component as the unit of integration [11; 12; 13; 14; 15; 16; 17; 20], and the other category of studies regards a feature as the unit of integration [18; 19]. SPL integration testing studies use control flows [11], method calls [12], data dependencies [13], deltas [16; 17; 20], or feature dependencies [18; 19; 21] to obtain interaction information. Because [18; 19] deal with integration at the feature level, it is hard to see that it considers all possible interactions even though integration testing covers all feature interactions. [PERSON] [11], [PERSON] [12], and [PERSON] [13] use an activity diagram as a test model tailored to depict interactions between components. They use interactions among components of the system or the subsystem for integration testing. However, the focus of [11] and [13] is on reducing the complexity of test models due to variabilities and [12] deals only with variability in requirements. The fundamental limitation of this kind of specification-based testing is that it is difficult to know either the extent of redundancy or the possibility of gaps corresponding to the way a set of test cases covers an IUT [3].  
These studies either do not measure test coverage or they primarily rely on feature coverage or feature combination coverage. Feature coverage and feature combination coverage, commonly utilized in existing SPLT studies, are types of coverage related to sample product testing. Most of them are about a selection of products to test [18; 22]. [PERSON] [23] tracks execution traces for the purpose of avoiding redundant testing by checking whether each test case covers the same code units. However, the method of this study is applicable to system testing rather than integration testing. Thus far, test coverage of integration testing has rarely been studied. SPL Integration testing is typically performed in both platform testing (testing of common components shared by products in a product line) and product testing (testing of individual products of a product line). It is essential to define clear test coverage for these two testing processes to avoid redundant testing while still achieving quality goals.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
## 1 Introduction  
Forage hay is a major conserved forage used worldwide for ruminant production. However, the high concentration of cell walls in hay products could limit ruminant intake and access to nutrients by rumen microbes [1; 2; 3]; therefore, it often results in suboptimal ruminant performance ([PERSON] and [PERSON] 1996).  
A range of physical, chemical, and biological treatments have been previously explored to investigate the effect on hay digestibility [4]. Microwave (MW) treatment is a non-ionizing electromagnetic physical heating method which proved to be effective for the improvement of the nutritional value of food [5; 6] due to its safe, quick, and energy-efficient properties [7]. In the human food industry, MW heating is widely used for thawing frozen food, heating up, pre-cooking, blanching, baking, pasteurizing, and sterilizing food [8]. The rapid increase of inner temperature in the MW treated material due to heating, generally known as \"thermal run-way,\" is usually linked to the dielectric properties of the material that may cause plant cell wall destruction [9] and might increase the utilization of previously unavailable cell nutrients [10].  
There are a few studies have explored concentrate feed (e.g., grain) nutritive value change in relation to MW heating [11; 12]. These studies demonstrated that MW treatment reduced rumendry matter (DM) degradability and increased availability of bypass protein in the small intestine, which may potentially lead to improved ruminant performance. [PERSON], et al. [13] found that MW treatment increased in small scale reppsin-cellulase in vitro dry matter digestibility (15%) of lucerne hay compared to control, [PERSON], et al. [14] found similar improvement in MW treated wheat straw under in sacco organic matter degradability (20%) study in Yak compared to a control. Based on these studies, it can be hypothesized that MW treatment can enhance the nutritive value of animal feed (grain/roughage) independent of compositional and structural differences (e.g., high-quality concentrates vs low quality roughage, legume vs non legume, etc.) However, the information related to MW treatment effect on forage hay nutritive value and exploration on the potential mechanisms of the changes is limited. Therefore, this study aimed to explore microwave heat treatment effect on forage hay nutritive values and cell microstructure.
# NOMA Clustering for Improved Multicast IoT Schemes
## 3 System Model and Problem Formulation
### Selection of Coalition Heads and NOMA Pairing  
In this first phase, the selection of users who will be leading the coalitions is carried out. These users will be served by the BS in NOMA fashion. With the objective of maximizing the sum rate of NOMA users and hence, the system sum rate, we choose the NOMA pairs optimally as is [21] and we calculate the optimal NOMA power coefficients similarly to [6]. The BS selects \(M/2\) \"best\" users (\(M<N\)), based on their channel gains, to be paired in NOMA fashion with \(M/2\) \"worst\" users. We denote the obtained pairs, respectively, by \([\text{UE}_{1},\text{UE}_{N}]\), \([\text{UE}_{2},\text{UE}_{N-1}]\), \(\ldots\), \([\text{UE}_{M},\text{UE}_{N-M+1}]\).  
The formed NOMA pairs are designated as coalition heads \(\text{CH}_{i}\), \(i=1,\ldots,M\).
## 2 Materials and Test Programs  
### Materials  
The subgrade silty sand was obtained from Songyuan County of Jilin Province. The particle analysis test results and the basic physical properties of cemented silty sand are presented in Figure 1 and Table 1, respectively, based on the Chinese specification of JTG 3430-2020, Test Methods of Soils for Highway Engineering [42]. Based on the previous research of our team, the optimal cement content of stabilized silty sand is a 2% dosage [43]. The cement content is measured by the percentage of cement in the mass of the dry soil, as [PERSON] et al. [44] proposed. From Table 1, the initial water content of plain soil is 5.47%. The specific gravity obtained by the pycnometer test is 2.68. The liquid limit and plastic limit are obtained by the liquid-plastic limit combined test method. The optimum water content of plain soil is 8.53% and that of cemented soil is 9.69%. The maximum dry density of them, which was tested by the heavy compaction test, is 2.030 g/cm\({}^{3}\) and 2.042 g/cm\({}^{3}\), respectively. The coefficient of uniformity, C\({}_{\text{u}}\), and coefficient of curvature, C\({}_{\text{c}}\), of plain soil are 5.8 and 1.33, respectively. In summary, the plain soil is a well-graded low liquid limit silty sand classified by the Chinese specification of JTG 3430-2020.  
The cement used in this study is Portland cement (P.O 42.5) produced by Yatai Cement Co., Ltd., of Jilin Province (Changchun, China). The chopped BF used as Figure 2 shows, has a monofilament length of 12 mm obtained from Anjie Composite Material Co., Ltd., of Zhejiang Province (Haining, China). The physical properties of BF are given in Table 2. The diameter of BF is 7-15 \(\upmu\)m. The density is 2.63 g/cm\({}^{3}\), similar to the maximum dry  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Silty Sand** & **Cement Reinforced Silty Sand** \\ \hline Cement content (\%) & 0 & 2 \\ Initial water content \(w\) (\%) & 5.47 & 4.17 \\ Specific gravity & 2.68 & - \\ Liquid limit \(w_{\text{L}}\) (\%) & 22.4 & 26.6 \\ Plastic limit \(w_{\text{P}}\) (\%) & 14.1 & 17.6 \\ Plasticity index I\({}_{\text{P}}\) (\%) & 8.3 & 8.9 \\ Optimum water content \(w_{\text{opt}}\) (\%) & 8.53 & 9.69 \\ Maximum dry density \(\rho_{\text{d}}\) (g/cm\({}^{3}\)) & 2.030 & 2.042 \\ Coefficient of uniformity C\({}_{\text{u}}\) (\%) & 5.8 & - \\ Coefficient of curvature C\({}_{\text{c}}\) (\%) & 1.33 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic physical properties of silty sand and cemented silty sand.  
Figure 1: Particle size distribution curve of silty sand.  
density of cemented silty sand. The tensile strength, elastic modulus, and melting point of monofilament of BF are 3000-4800 Mpa, 91-110 Gpa, and 1050 \({}^{\circ}\)C, respectively. Those mentioned data manifest that fiber has great mechanical properties and great resistance to temperature.
## 3 Results
### Objective Collected Data
#### 3.1.1 Heart Rate and Oxygen Saturation  
Oxygen and heart rate values collected show the effort and stress levels of the participants during the tests. The charts in Figure 8 show the evolution of the heart rate, oxygen saturation, and motion of a patient during the five minutes of the testing procedure, with pulse rate and motion peaks during the first minute (whack-a-mole) and third minute (collecting fruits). The information recorded in the pulse and motion charts shows the clear differences between the physical activity and the relaxation exercises (Figure 8a). The final motion inputs observed on the chart are explained by the PMR gestures of the respective hand or by the movement of the patient's hand when removing the VR headset to end the testing process. The pulse rate range and distribution (74-88 beats/min), as well as its average (80 beats/min), show that the patient was not stressed during the entire procedure (Figure 8b).  
For statistical analysis, three sets of ANOVA tests were conducted in order to find correlations between average heart rate or oxygen saturation and the aforementioned subgroups of patients, divided based on technological experience, VR experience, and preferred level of difficulty for games.  
The results did not prove statistical significance in the correlations related to the technological experience (\(p\) = 0.767 for heart rate, \(p\) = 0.226 for oxygen saturation) or preferred level of difficulty (\(p\) = 0.824, \(p\) = 0.113), which might imply that the levels of stress and effort of patients were not related to their technological savviness or previous gaming experience. Interestingly, results related to the correlations with VR experience were lower (\(p\) = 0.174 for heart rate, \(p\) = 0.140 for oxygen saturation), and further investigations on a higher number of patients are necessary to understand a possible influence of VR on the stress and effort of patients measured through physiological parameters.  
Figure 7: Boxplot of average oxygen saturation level for the 3 subgroups of patients based on level of experience with technology (Little e = T1, Medium e = T2, Very exp = T3).
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### Fiware  
Fiware is an open cloud platform, illustrated in Figure 24, under development and created in a European FP-7 Project to support future Internet. Considered important to several areas, the Fiware has a set of generic enablers (GEs) [94]. According to standard IoT-RA (Figure 1), only the member function is implemented in Fiware Technologies. The platform offers support to various management protocols and standards. It supports OMA NGSI9/10, OMA LWM2M, MQTT, CoAP, and IPv6 [95]. The heterogeneous wireless networks have specific communication protocols to connected devices. Different data encodings make it difficult to find a global deployment.  
The platform, illustrated in Figure 24, has a modular architecture that supports several IoT protocols, in which modules are called IoT Agents. However, the integrators must determine the protocol that will be used to connect and select the IoT Agent correct. The IoT Manager collects or sends data to devices that use heterogeneous protocols and translates them to a standard platform, simplifying the Device Management and integration [96].  
_Pros: Simplicity and interoperability. Cons: Vulnerability, privacy, and security._  
Use Case  
The Fiware project is used for orthopedics, podiatry, physiotherapists, and related health services producing prosthesis. This work is time-consuming, cost-inefficient and causes many inconveniences to patients. The Ortholab aim is to produce advanced scan and manufacturing solutions to the insole sectors. With the Ortholab solution, orthopedists or physiotherapists will be able to take digital information of the patient's body part in an easy way and specify the parameters to 3D printers [97].  
Figure 24: Illustration of the Fiware architecture.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 4 Proposed Broadcast Algorithm  
In this section, we describe the new proposed broadcast algorithm known as the KTFW algorithm. The KTFW algorithm is designed for the category of non-adaptive, full-sensing algorithms [19]. A broadcast algorithm is non-adaptive when no node can attach additional information to packets, and it is full-sensing when a node continues listening to feedback from the channel at all times. The new proposed algorithm is distributed because each node executes the broadcast algorithm deterministically based on the feedback from the channel. The algorithm does not use randomization and does not require a central node; see the pseudocode for the algorithm in Algorithm 1. Although the algorithm's specification is described for synchronous communication, this does not impose any restriction on its design. The algorithm can be implemented in asynchronous communication, even without requiring synchronized clocks. This limitation is overcome through a simple yet effective solution. The solution is a _synchronization_ step that applies only to new nodes joining the network, allowing them to synchronize with existing nodes. This synchronization step avoids the necessity for global synchronization and enables both existing and new nodes to communicate on the same network. Therefore, the KTFW can be implemented and used in practical network scenarios. The synchronization step is described further in this section. The main idea of the KTFW algorithm is to divide the execution into segments consisting of \(k\) rounds, and a segment terminates when all activated nodes of the segment have successfully transmitted all their packets. Although the algorithm looks similar to existing tree-based algorithms, it works differently from them as it uses a novel approach in handling collisions and deciding which nodes participate in the broadcast. For each segment, a binary search is repeated to process the packets until all packets in all nodes have been transmitted successfully. An example of how the binary search works is presented in Figure 1, in which the search starts from top to bottom and from left to right. Although the KTFW algorithm resembles tree-based broadcast algorithms, it uses novel approaches such as _withholding_ the channel and it does not rely on randomization. Next, we describe how the KTFW algorithm works at the beginning of a segment. All nodes that are activated during a segment enter a segment processing phase, in which each node is allowed to transmit its packets on the channel. In a segment, each node is identified by its activation round, such that all activated nodes are labeled with consecutive IDs starting from 1. In this work, the injection model is 1-activating such that a maximum of one node can be activated in a single round. Therefore, in a segment of \(k\) rounds, a maximum of \(k\) nodes can be activated, and no two nodes share the same ID. The segment nodes are assigned IDs according to their activation rounds such that a node that was activated at round \(j\) is assigned the ID \(j\).
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 3 DR-Net and CTAM
### DR-Net
#### 3.1.2 Residual Blocks Design  
We use dense blocks to enhance information transmission and feature reuse, but the increase in the number of layers in the feature extraction network can easily lead to overfitting. DR-Net introduces two residual blocks after two dense blocks to enhance the deep feature extraction capability and reduce the risk of overfitting.  
Figure 3 shows a sketch of the residual block in DR-Net. The downsampling residual module reduces the size of the input feature map to half of the original size and doubles the channel number, while the residual module does not change the size of the input feature map or the channel number. The first residual block consists of 1 downsampling residual module and 10 residual modules. The second residual block consists of one downsampling residual module and two residual modules. The downsampling residual module uses \(1\times 1\times 1\) convolution to fuse the input feature maps, which keeps the number of feature maps unchanged and preserves the input feature information to the maximum extent. The \(3\times 3\times 3\) convolution is replaced by \(1\times 3\times 3\) and \(3\times 1\times 1\) pseudo-convolution with a step size of two, which reduces the size of the feature maps to half of the original size and improves the real-time performance. The \(1\times 1\times 1\) convolutions (n = 640, 1280) separately extract features from the original input feature maps (with stride = 2) and the half-sized feature maps (with stride = 1). The output feature maps of these two operations are added together as the output of the downsampling residual module.  
The residual module is similar in structure to the downsampling residual module, except that it does not have a downsampling operation, because its input feature map and output feature map have the same size.  
Figure 2: Dense blocks.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 5 Resource Optimization Process
### Server Interference  
The error server execution may interfere with any message, having different interference patterns, depending in error sequence and server configuration. We define Indirect Interference on one message when this message does not suffer errors itself, but is delayed by the server executing on behalf of other messages. The Direct Interference corresponds to scenarios where one error affects the message being analyzed. When calculating the response time with direct interference we must also account, at first, with the recovery of \(n\)-\(1\) errors (of indirect interference), in a scenario with \(n\) errors in total. This aspect is further clarified in Section 5.3.2. The worst-case response time for any message is the maximum of both types of interference. As we will see later on, direct interference is normally more penalizing but it is not always the case, thus the need to compute both scenarios.
# v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers +
## 3 Application-Awareness Concepts in Cloud Datacenters  
Conventional networking-based cloud resource administration techniques fail to address the dynamicity in the networking arena, especially ones that require a lot of human intervention to provide the network resources in an on-demand manner. Software-defined networking is a key factor driving the software-defined cloud (SDC) research community. Figure 2 presents a typical software-defined cloud environment. To effectively manage the middlebox processing capabilities in SDCs, the concept of outsourcing enterprise middlebox processing to the cloud was proposed in [20] with a system the authors called APLOMB (Appliance for Outsourcing Middleboxes). APLOMB delegates middlebox tasks to the cloud to ensure the ease of management and capital expenditure (CAPEX) functions. In multi-tenant datacenters, tenants need to perform migration of unmodified workloads from enterprise networks to the service provider networks. This is a very challenging task and was addressed by Network Virtualization Platform (NVP) technology in [21]. The proposed solution uses SDN to decouple network management from the physical infrastructure in two steps; first, by decoupling the tenant control planes from the physical infrastructure, and then by decoupling the modified workload of NVP from the physical infrastructure.  
Figure 2: A view of the services and functions in a software-defined cloud data center.  
There is a dire need to support the easy development of software-defined cloud environments. In this regard, a recent data centre solution, Topology and Orchestration Specification for Cloud Applications (TOSCA) [22] has been presented. TOSCA also provides a flexible combination of declarative and imperative models of workloads that can be seamlessly integrated with automation frameworks for task-level automation and optimized resource orchestration. TOSCA was exclusively designed to overcome the interoperability challenges across the cloud and virtualization infrastructure and to simplify the management and deployment of applications in cloud datacenters. To effectively monitor service-centric cloud environments, a unified solution was presented in [23], which can rapidly migrate and optimize the use of cloud resources in ways that are consistent with the service profiles and policies. This solution also address interoperability concerns for various cloud service platforms. The reliable implementation of application-aware concepts through SDN implementation is now an essential requirement for leading data center solutions. We believe that a combination of open standards technology (SDN technology) with a best-of-breed hardware platform (SDN-enabled hardware peripherals) can deliver flexible, scalable and high performance networking solutions for the future networking industry.
## 5 Experimental Evaluation
### Results and Discussion  
#### 5.4.1 Performance Evaluation under Cache Size  
The relationship of the cache size variation on cache hit rate, latency, and backhaul traffic is depicted in Figure 3. The cache hit ratio significantly increases with increasingcache size for all schemes. The proposed scheme outperforms the other schemes by small margin gains. This shows that an increase in the cache size of MAN servers improves the cache hit ratio of all caching schemes.  
The second sub-figure in Figure 3 depicts the latency performance relationship with increasing cache size. Similar to the hit rate performance, the proposed is the best scheme in this comparison. However, the performance of the LFRU and Greedy schemes comes close to that of the proposed scheme. The increase of the cache size had a significant impact on the network latency, as larger cache storages allow for the placement of many content files thereby decreasing the file fetching time.  
An increase in cache size also increases the backhaul traffic, as depicted in the third sub-figure of Figure 3. In this performance comparison, DPoM shows an average performance, better than the LFRU, at par with Greedy, and lagging behind TLRU and FIFO. Since DPoM parts the files into several parts, with most files in the small files range, the view duration thresholds of many files are easily reached, and hence there are more file transmissions over the backhaul network.