esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 6 Results
### Dataset Acquisition
#### 6.1.1 The Performance of Various Open-Set-Detection Techniques  
Initially, we assessed the effectiveness of three open-set-detection methods, as detailed in Table 4. The MaxLogit method surpassed both MSP and OpenMax across all metrics, achieving the highest recorded performance with 88.8% in AUROC and 53.1% in AUPRC. An intriguing finding from the evaluation was the notably poor performance of OpenMax in detecting unknown rhythm patterns, where it achieved an AUROC of only 52.6%, thus significantly lagging behind the other methods. We hypothesize that this inefficiency may stem from the method used to calculate rejection probabilities. According to Ref. [13], OpenMax modifies the softmax layer and employs the libMR FitHigh function Ref. [45]to conduct Weibull fitting on the top \(\eta\) largest distances between correctly categorized instances. Subsequently, rejection probabilities are recalculated using the Weibull model, based on values from the penultimate layer. Given that the audio-segmentation task generates millions of activation vectors, the computational complexity involved means that the libMR library can fit only a limited number of samples, potentially introducing substantial bias in the test data.  
Despite these challenges, our proposed methodology demonstrated enhancements in performance across all three open-set-detection techniques, with MaxLogit showing the most remarkable results by accurately recognizing unknown rhythm patterns with an 88.8% success rate. For the sake of simplicity and based on these findings, we chose to proceed with the MaxLogit method in subsequent experiments, unless specified otherwise.
# Design, Implementation, and Field Testing
## 5 Field Study  
For the field study, we installed PACTS in six rooms at a local nursing home with seven participating nursing assistants. All rooms are single-occupancy rooms where there is only bed and one patient per room. The study lasted 80 days. Because one of the nursing assistants was not assigned to the bedside care duty, only the activities of six nursing assistants were monitored. Furthermore, the patient in one of the rooms did not require bedside care, and another room had several deaths, which made the room unavailable for extended period of time during the field study. Therefore, only the data collected from four rooms are worthy of analysis.  
The field study exposed a number of issues with our original design. First, as we mentioned earlier, the manual registration mechanism for privacy protection significantly reduced the usability of the system. Second, the use of a smartphone to relay the communication between the smartwatch and the KinectServer also created usability issues for our system. The smartphone was on the critical path of the registration and alerting process. If the smartphone is in deep sleep and fails to relay the registration request from the smartwatch, the nursing assistant would not be able to register with the KinectServer.
# SwitchFuzz: Switch Short-Term Goals in Directed Grey-Box Fuzzing
## 3 Motivation
### Unreachable Path  
As mentioned in Section 3.2, users of the fuzzer often cannot set the exploration time well. This can result in other problems.  
Considering Figure 5 as an example, assume that the vulnerability triggering requisite is at D on the long path. However, D is not set as the target point. After a long exploration, there are seeds in the queue for paths A-B-C-T and A-B-E-F-G-T to reach the target point. For AFLGo, if the A-D path is not discovered during the exploration phase, it will be difficult to execute the path again at a later stage. The result of mutation of the seeds after this is that path A-B is executed with high probability. Even if the path to A-D-E is occasionally generated by mutation, it will be greater in distance than A-B-E. Over time, it will gradually become submerged in a pile of seeds at a much smaller distance.  
For the fuzzer, the A-D path is a path to the target point, but we cannot obtain the seeds for this path. Unlike the problem mentioned in Section 3.2, test cases for the route A-D path are unlikely to be generated because the time setting of the exploration phase is not reasonable.  
Based on the above analysis, we pose the following questions.  
Q1: For directed grey-box fuzzing, how can the target point be reached more quickly?  
Q2: How can path diversity be better preserved and multi-path coverage be achieved using the directed grey-box fuzzing test?  
Q3: For a seed that may be submerged in the queue with low energy, how can it be given a chance to be scheduled and when should this be considered?  
Figure 5: The case of possible missing vulnerabilities.  
Q4: How can speed and multi-path coverage be balanced? What strategy should be adopted?
## 3 Animal Health Monitoring
### Physical Health  
An infectious disease epidemic in dairy cattle, where thousands of animals are kept together, can result in substantial losses, and in such a circumstance, a contagious disease outbreak will be difficult to prevent unless the farmer gets involved immediately. When symptoms show, it is generally too late to intervene. The advancement of biosensors enables them to spot disease in its early stages and, with the help of a communication protocol, inform dairy cattle farmers. Sensors and wearable technologies are implanted in dairy cattle to measure body temperature [32; 33], sweat constituents [34; 35; 36], detect stress [37], observe behaviour and movement [38; 39], detect pH [40], analyse sound [41], prevent disease [42], detect analytes, and detect the presence of viruses and pathogens [43; 44]. Wearable sensors aid farmers in detecting illness early and preventing animal deaths. Sensors, AI, and ML are used to continuously monitor important animal health aspects e.g., movement, air quality, and food and fluid consumption, rather than reacting to problems as they arise or using the services of doctors. Farmers may now identify, forecast, and prevent disease outbreaks even before they occur on a wide scale by continuously collecting data and applying modern AI and ML algorithms to predict deviations or abnormalities. This type of system has two major advantages. One advantage of such a system is that it allows fewer farmers to care for many more animals, lessening production costs. Two, such a system can alert farmers to the possibility of a disease, even if it is still in the pre-clinical stage. This, in turn, will assist farmers in taking timely action to avoid catastrophic losses.
## 4 Results and Analysis
### Comparative Testing of Transmission Mechanisms and Results Analysis
#### 4.2.3 Comparison of Transmission Delay  
During the program initialization phase, the size of memory blocks used for transmission varies from 64 B to 8 MB. Subsequently, the latency of both the sliding window RDMA transmission mechanism and the status-byte-assisted RDMA transmission mechanism for transmitting a single memory block is measured. The transmission delay of each mechanism is recorded and averaged over 100 independent experiments.  
Figure 12 compares the transmission delay of various RDMA transmission mechanisms under different packet sizes. As illustrated in Figure 12, the transmission latency of the status-byte-assisted RDMA transmission mechanism is reduced by 2 ns to 3.4 us compared to the sliding window RDMA transmission mechanism. The sliding window RDMA transmission mechanism uses RDMA Write With Immediate to transmit data, which requires the receiver to issue a receive request and generate a CQE. This results in significant latency due to interactions with the receiver. In contrast, the status-byte-assisted RDMA transmission mechanism performs two RDMA Write operations in a linked list: the first request does not generate a CQE and the second request uses inline data transmission. This approach reduces both NIC and memory exchanges, eliminates the need for receiver interaction, and thus achieves lower transmission latency due to its true unilateral nature.  
Figure 11: Distribution of synchronization control latency versus the number of transmissions: (**a**) the sliding window RDMA transmission mechanism and (**b**) the status-byte-assisted RDMA transmission mechanism.  
Figure 12: Comparison of the transmission delay for various RDMA transmission mechanisms under different packet sizes: (**a**) small data packet sizes (64 B to 4 KB) and (**b**) large data packet sizes (8 KB to 8 MB).
## 2 System Model and Fading Statistics
Using the generalized power series representation of the modified Bessel function of the second kind [18] (8.445, 8.485), the PDF in (6) can be expressed as  
\[f_{I_{f}}(x)=\sum_{j=0}^{\infty}a_{j}(\alpha,\beta)x^{j+\beta-1}+a_{j}(\beta, \alpha)x^{j+\alpha-1}, \tag{7}\]  
where  
\[a_{j}(\alpha,\beta)=\frac{\pi(\alpha\beta)^{j+\beta}\csc(\pi(\alpha-\beta))} {\Gamma(\alpha)\Gamma(\beta)\Gamma(j-\alpha+\beta+1)\Gamma(j+1)}, \tag{8}\]  
Using (5) and (9), the CDF of \(I=I_{I}I_{p}I_{f}\) can be expressed by  
\[F_{I}(x) = \int_{0}^{\Phi}F_{I_{f}}(x/t)f_{I_{p}}(t)dt \tag{10}\] \[= \sum_{j=0}^{\infty}b_{j}(\alpha,\beta,\delta)x^{j+\beta}+b_{j}( \beta,\alpha,\delta)x^{j+\alpha},\]  
where \(b_{j}(\alpha,\beta,\delta)=a_{j}(\alpha,\beta)\delta^{2}/\{(j+\beta)(\delta^{ 2}-j-\beta)\Phi^{j+\beta}\}\). By using (10), the CDF of \(\gamma_{FSO}\) is finally obtained by  
\[F_{\gamma_{FSO}}(x)=\sum_{j=0}^{\infty}\frac{b_{j}(\alpha,\beta,\delta)}{(\eta ^{w}\mu_{w})^{\frac{j+\beta}{w}}}x^{\frac{j+\beta}{w}}+\frac{b_{j}(\beta,\alpha,\delta)}{(\eta^{w}\mu_{w})^{\frac{j+\alpha}{w}}}x^{\frac{j+\alpha}{w}}, \tag{11}\]  
where \(\mu_{w}\) denotes the average electrical SNR. More specifically for \(\mu_{w}\), when \(w=1\), \(\mu_{1}=\gamma_{FSO}\) and when \(w=2\), \(\mu_{2}=\gamma_{FSO}\alpha\beta\delta^{2}(\delta^{2}+2)/\{(\alpha+1)(\beta+1) (\delta^{2}+1)^{2}\}\)[5].
# A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts +
## 6 Discussion
### A Robotic Arm-Based Batching System Configuration  
One way to increase the time budget allowed for the scheduling problem solving is to have a long buffer zone or slow down the conveyor belt. Importantly, by this managerial solution, more items can be considered for a single run of a series of sub-problem solving, and thus efficient try utilization can be achieved. On the other hand, having more items to schedule means a larger scale of scheduling problem solving. Note that the number of decision variables and constraints are exponentially increased as the number of items to schedule increases. Additionally, slowing down a conveyor belt obviously reduces the productivity of the system.  
Using the simulation model, we can test the impact of such managerial decisions on the performance level of a robotic arm-based system. As an example, we evaluate the impact of the buffer zone length on the performance of the robotic arm-based batching system and the computational feasibility of the proposed solution approach. With 15 different conveyor belt length settings, keeping the rest of the simulation setting as described in the previous section, we run simulations for this trade-off analysis, and the results are summarized in Figure 9.  
Figure 8: Computation time comparison.  
Figure 7: An example of a profit function.  
From the figure, where the current conveyor belt setting is represented by a dotted line, it can be first observed that give-away, the key performance measurement of the target food batching system, is decreased as the buffer zone becomes longer and more items are considered at the moment of the scheduling. On the other hand, one can observe that the computational time for solving the scheduling problem increases as the buffer zone becomes longer and, importantly, this makes the proposed solution approach with a commercial optimization solver no longer feasible.  
As observed, it is clear that there is a trade-off between the quality and computational burden of the proposed solution approach. Importantly, this observation implies that the configuration of a robotic arm-based batching system (e.g., the number of robotic arms on a conveyor belt and the length of a buffer zone) should be set carefully, considering not only the physical performance of the system but also the performance of a scheduling solution to the system that actually determines the system performance.
## 4 Materials and Experimental Methodology  
### Material and Specim Fabrication  
Both plain concrete and unidirectional reinforced concrete (UDRC) specimens were fabricated for this study, as illustrated schematically in Figure 13. The UDRC specimens featured a centrally positioned 6 mm diameter steel bar, which could be either plain round or deformed with a standard rib pattern. This bar was precisely fixed in the mold prior to concrete casting to ensure its central alignment. All specimens were manufactured with a nominal diameter of 74 mm and a length of 1000 mm. With an aspect ratio exceeding 10, the stress state along their longitudinal axis during dynamic loading can be considered approximately uniaxial [14].  
The concrete used for all specimens was specified as grade C40. The detailed mix design is presented in Table 3. The maximum size of the coarse aggregate (continuously  
Figure 12: Simulated strain field evolution and spall fracture process in a deformed UDRC specimen, showing the influence of reinforcement.  
Figure 13: Schematic diagram of the specimen types: (**Top**) Plain Concrete (PC), (**Middle**) Plain UDRC with a smooth steel bar, and (**Bottom**) Deformed UDRC with a ribbed steel bar.  
graded crushed stone) was 10 mm, selected to ensure material homogeneity relative to the specimen diameter.  
The specimens were cast in custom-designed steel molds. After casting, they were cured in a saturated lime-water environment for 28 days according to standard protocols. Following curing, the specimens were dried in a forced-air convection oven at a controlled temperature of 60 \({}^{\circ}\)C for approximately 48 h until they reached a constant weight, ensuring a consistent moisture state before testing.  
Material characterization was performed to determine the quasi-static mechanical properties of the concrete. Compressive strength and Young's modulus were evaluated from tests on 150 mm cubic concrete specimens using an MTS materials testing system (MTS-180-50), MTS Systems Corporation, Eden Prairie, MN, USA adhering to ASTM C39 and ASTM C469 standards, respectively. The static tensile strength of the concrete was determined through quasi-static Brazilian disk tests conducted on 74 mm diameter by 37 mm thick specimens, as per ASTM C496. The key quasi-static mechanical properties obtained for the C40 concrete are summarized in Table 4. The steel reinforcement (both plain and deformed 6 mm bars) had a nominal yield strength of approximately 335 MPa and an ultimate tensile strength around 400 MPa, with a Young's modulus of 210 GPa and density of 7800 kg m\({}^{-3}\).
# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Atlas or Library-Based Methods
The algorithm applies an iterative refinement approach to transfer labels from multiple atlases to a specified target brain image utilizing image registration. A consensus label was generated at each refinement level of the algorithm. The search for brain boundary was confined to the neighborhood of the boundary of this consensus label at the successive level. This algorithm (_Pincram_) is also publicly available at [77] as free software. Recently, a new multi-atlas brain segmentation (MABS) base brain masking technique was implemented by [PERSON] et al. [78]. In contrary to other atlas-based algorithms, MABS allows flexibility by assigning weights to the atlases with respect to their resemblance to the target image and circumvents the issue of atlas selection. The atlases most closely resembling the target image are given more weight in a set of heterogeneous atlases, than the atlases with less resemblance. Moreover, the MABS technique decreases the risk of averaging out individual differences by the inclusion of both pathological and control images in the training set. The authors also compare their results with FreeSurfer (FS; version 5.3) [39], BET [25], and Brainwash [79]. [PERSON] et al. [80] developed a novel method named ALFA (accurate learning with few atlases) for brain extraction of multimodal neonatal brain MRIs. The algorithm is combined with a new sparsity-based atlas selection strategy with a machine learning-based label fusion technique that requires a very limited number of atlases in low dimensional data space. Another recent method, \"sparse patch-based multi-contrast brain stripping method (MONSTR),\" published by [PERSON] et al. [81] is also used non-local patch information from multiple atlases and integrate them to produce a final brain mask. This algorithm is also publicly available and insensitive to pathology. Other recent algorithms include [82]. The accuracy of Atlas or library-based Methods depends on the quality of brain masks, atlas registration, intensity and spatial normalization in each brain MRI volume. Furthermore, these approaches are generally computationally intensive.
## 2 Materials and Methods
### Evaluation of the Antioxidant Properties of GPP and PE  
The antioxidant properties of GPP and PE were evaluated by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, the ferric reducing/antioxidant power (FRAP) assay, the cupric ion reducing antioxidant capacity assay (CUPRAC) and total phenolic content (TPC). For the DPPH assay, PE and GPP were added to a 0.2 mM ethanolic solution of DPPH (final dose 0.0133-0.8 mg/mL) and, after 10 min under stirring at room temperature, the absorbance of the solution at 515 nm was measured. Trolox was used as a reference antioxidant. Experiments were run in triplicate [23; 24]. For the FRAP assay, PE and GPP were added to 0.3 M acetate buffer (pH 3.6) containing 1.7 mM FeCl3 and 0.83 mM TPTZ, (final dose 0.0013-0.2 mg/mL) and, after 10 min under stirring at room temperature, the absorbance of the solution at 593 nm was measured. Results were expressed as Trolox equivalents (eqs). Experiments were run in triplicate [25]. For the CUPRAC assay, 1 mL of a 10 mM CuCl\({}_{2}\) solution in water, 1 mL of a 7.5 mM neocuproine solution in ethanol, 1 mL of 1 M ammonium acetate buffer (pH 7), and 900 \(\mu\)L of distilled were mixed and PE and GPP were subsequently added (final dose 0.0027-0.2). The mixtures were stirred for 30 min at room temperature. After incubation, the absorbance was measured at 450 nm. Results were expressed as Trolox equivalents (eqs). Experiments were run in triplicate [26]. For the TPC assay, GPP and PE were added at a final dose of 0.0133-0.5 mg/mL to a solution consisting of Folin-Ciocalteu reagent, 75 g/L Na\({}_{2}\)CO\({}_{3}\), and water in a 1:3:14 \(v/v/v\) ratio. After 1 min incubation at room temperature, the absorbance at 765 nm was measured. Gallic acid was used as reference compound. Experiments were run in triplicate [27].
# 5G NPN Performance Evaluation for I4.0 Environments
## 3 Hybrid 5G and I4.0 Concept Architecture
### Tsn  
5G, the next generation of 3 GPP technologies, offers capabilities specifically designed to meet certain industrial needs. These include URLLC in 5G-NR (New Radio), support for TSNs, and network deployment scenarios for Non-Public Network (NPN) operations, ranging from standalone NPNs to public-network-integrated NPNs. In Release 16, 3 GPP adopted 5G-TSN [33] integration for time-sensitive communication. This framework focuses on 3 GPP standard adoption from Release 17 and onwards, with respect to TSNs, and fosters the integration of relevant industrial automation profiles for TSN activities, as defined in IEC/IEEE 60802. The manufacturing industries involved have requirements associated with guaranteed high availability, high throughput, real-time transmission, low latency, and low jitter, and currently utilize vendor-locked protocols and solutions, such as Profibus, Profinet, and EtherCAT. To overcome this open challenge, we provide a vendor-independent TSN solution over 5G with OPC UA Pub/Sub compatibility, embracing end-to-end interoperability for every industrial automation application, as detailed in Figure 4. More specifically, this solution can address the following industrial automation traffic types through its proposed 5G-enabled TSN: (i) time synchronization IEEE 802.1 AS; (ii) scheduled traffic IEEE 802.1 Qbv; (iii) IEEE 802.1 Qbu recommended frame pre-emption; (iv) IEEE 802.1 Qbu--optional strict priority IEEE 802.1Q; (v) redundancy IEEE 802.1 CB; and (vi) TSN configuration IEEE 802.1 Qcc.
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 3 Proposed Scheme  
The binary search with a conventional Bloom filter reduces the search range by half with each Bloom filter access. In contrast, the proposed method further reduces the search range to the span of descendant prefixes, making it less than half. Additionally, since the proposed Bloom filter identifies a prefix node at the final access, the hash table only needs to store prefix nodes. This reduces the number of table entries, helping to minimize hash collisions.
## 6 Evaluation and Result Analysis
### Lake Experimental Results  
To further verify the proposed algorithms, a real lake experiment was performed at Huating Lake, Anhui Province, China, in May 2018. Huating Lake covers an area of about 70 square kilometers, with an average water depth of about 17 m. We hung the transmitter and receiver on the side of a passenger ship and a speedboat, respectively. The passenger ship has a semi-open roof and is about 10 m long, while the speedboat is about half the size of the ship. During the experiment, the transmitter depth was about 10 m and the receiver depth was about 13 m. Affected by wind and current, their relative speed was about 0.5 m/s, and the distance between them was more than 4.5 km. The parameter settings of the experiment are in Table 2. In addition, the linear frequency modulation (LFM) signal was added at the beginning and end of each frame for time synchronization and Doppler estimation. The transmitted data bits were encoded by a 1/2 rate convolutional code, and the encoded bits were mapped by QPSK constellation. As showed in Figure 1, using the proposed NC-CSCE algorithm, we obtained the estimated CIRs for a 14 frame history with five consecutive OFDM blocks in one frame. The CIRs are estimated after Doppler compensation, and the total delay spread is around 15 ms. From the estimated CIRs, we notice that the paths are distributed in multiple clusters, and the channel shows a clustered-sparse structure.  
Figure 8 shows the BER performance after convolutional decoding. The BERs of the proposed two algorithms are still the lowest for most frames, followed by the TMSBL and the SOMP, while the OMP has the worst BER performance. Obviously, the performance gap  
Figure 7: The performance comparison of different algorithms under colored noise: (**a**) the MSE and BER performance; and (**b**) the estimated SNRs over all subcarriers.  
between proposed algorithms and other methods further verify the superior performance of the proposed algorithms.