esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
Other statistical ranking methods are also considered here: Pattern probability assuming either an i.i.d. distribution of symbols based on amino acid frequencies (Probability) or an equal distribution of symbols (Equal Probability); _z_-score, computed using the probability value as in [12]. We can easily see that our pattern priority achieves the best scores among all methods for the detection of representative patterns. In addition, our heuristic ranks on average the reference patterns of \(Ni\) in the top three out of 2239 candidate patterns in the input and those of \(Fa\) in the top five out of 10,842 patterns, while for the other families, the reference patterns range from the top three to the top five selected patterns, on average. The definition of pattern priority was conceived especially for degenerate patterns, like those presented in this section. However, this framework can be used in conjunction with other comparison functions designed specifically for patterns with profiles or variable gaps, e.g., [38]. These preliminary experiments support the validity of the theoretical results presented in the previous sections and also prove their effectiveness for protein analysis. However, a more comprehensive experimental setting is desirable, in order to improve the evidence on the performance and to compare this method with other existing tools, like MEME [42]. \begin{table}
\begin{tabular}{c c c} \hline
**Quorum** & \
\begin{tabular}{c} **Max Similarity \(Fa_{1}\)** \\ **(underlying/original)** \\ \end{tabular} &
\begin{tabular}{c} **Max Similarity \(Fa_{2}\)** \\ **(underlying/original)** \\ \end{tabular} \\ \hline
15 & 11/12 & 11/12 \\
20 & 11/12 & 12/12 \\
25 & 12/12 & 8/10 \\
30 & 10/12 & 8/10 \\
35 & 10/12 & 9/10 \\
40 & 10/12 & 8/8 \\
45 & 12/12 & 8/8 \\
50 & 12/12 & 8/8 \\
60 & 10/10 & 8/8 \\
70 & 10/10 & 8/8 \\
80 & 9/10 & 8/8 \\
90 & 9/10 & 8/8 \\
100 & 9/10 & 8/8 \\ \hline \end{tabular}
\end{table} 
Table 6: Normalized maximum similarity with the reference patterns of the family coagulation factors 5/8 type C domain, for different quorums.
## 3 Differences from Existing Work  
### Differences in Message Sequence  
The OLSRv2 message sequence mandates MANET/D2D nodes to exchange two HELLO messages before a TC broadcast. The two HELLO messages enable devices to identify their symmetric one-hop and two-hop neighbors and the selection of willing symmetric one-hop neighbors for flooding or routing MPRs.  
This paper proposes a different approach to the OLSRv2 message sequence by introducing a HI message as a response to HELLO messages. The proposal equally introduced ample listening periods to enable the controlled completion of database chores. It is imperative to allow nodes to complete their link state commitment to the database if OLSRv2 is to operate as desired. While OLSRv2 mandates one-hop connectivity between nodes, in practice, the one-hop neighbors of related devices that are equipped with powerful transceivers could be in their hundreds or thousands, for example, in a packed stadium, as shown in Figure 2. Saving thousands of reported one-hop neighbors to OLSRv2 information bases using OLSRv2 existing message intervals will be problematic for OLSRv2 because frequent control messages broadcast will disrupt the process.  
Figure 2: Stadium scenario. The number of one-hop neighbors reported by OLSRv2 is determined by the broadcast range of transceivers on the various MANET devices owned by users.
## 2 Experimental Arrangements  
The laser under investigation is two-section InAs/InP QDash SMLLs with active layers consist of 9 InAs QDash monolayers [39]. The laser is similar to that described in [32, 34]. The emission wavelength of laser is \(\sim\)1.55 \(\upmu\)m and operating frequency is around \(\sim\)21 GHz. The total cavity length of the laser was 2030 \(\upmu\)m including 240 \(\upmu\)m length of absorber section. The gain section of the laser was operated at fixed gain current 300 mA with a average free-space output powers of a few mW. The L-I characteristics of SML QDash lasers are shown in Figure 1a from the current ranges from 50 mA to 300 mA with the threshold at 120 mA. Furthermore, the temperature of the laser was fixed at 19 \({}^{\circ}\)C.  
The diagram of the experimental arrangement is shown in Figure 2. The light from the QDash MLL was coupled into lensed fibre through port 2 of an optical circulator and then coupled into a semiconductor amplifier through port 3. After amplification, the light was then equally splited via 3-dB coupler. Half of the light was used for the analysis of optical and RF spectra. RF spectrum was captured using RF spectrum analyzer (Keysight E-series, E4407B) via 21 GHz photodiode. Figure 1b shows RF spectra under full frequency span [0-26.5 GHz]. The minimum RF linewidth was measured to be 100 kHz for the case of free-running laser. Besides the optical spectrum of the QDash MLL was recorded using a diffraction-grating based commercial optical spectrum analysers (Ando AQ6317B) covering the infrared wavelength range up to 1700 nm with a minimum resolution bandwidth of 0.01 nm. Measured optical spectrum of SML QDash laser under free-running condition is shown in Figure 1c. The spectrum was centered at 1570 nm with a 3 dB bandwidth spanning 12 nm and containing 68 longitudinal modes, with 0.18 nm free spectral range (FSR). The other half was fed into a dual-loop feedback configuration. Each feedback loop composed of a polarization controller (PC), variable optical attenuator (Att) and optical delay line (ODL). In each feedback loop the coupling strength was adjusted using a variable optical attenuator. The overall feedback ratio was fed into the gain section of the laser through port 1 of an optical circulator and feedback strength was monitored using a power meter. The microscopic length of the fibre loops were optimized by optical delay lines attached with each feedback loop. Single side-band (SSB) phase noise trace of the free-running laser under integration limits 10 kHz-100 MHz is shown in Figure 1d and minimum timing jitter was measured to be as low as 3.9 ps.  
Figure 1: (**a**) L-I curve of QDash MLLs; (**b**) RF spectra of free-running QDash MLLs across the full frequency span [0–26 GHz]; (**c**) Optical spectra of free-running SML QDash laser at gain current 300 mA; (**d**) SSB phase-noise trace of the free-running laser using integration limits 10 kHz to 100 MHz.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 3 Background  
This section presents a brief introduction to the LTE network architecture and the resource block allocation in the eNB in order to better understand the later sections.
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 1 Introduction  
Computer Vision (CV) technologies have become indispensable in agri-food industry tasks, such as quality control, counting and grading, where camera hardware is paired with dedicated software pipelines [1, 2, 3, 4]. The latest leap in performance has come from deep-learning, particularly Artificial Neural Networks (ANNs) [5, 6, 7, 8, 9, 10]. Although ANNsdeliver high accuracy and real-time throughput, they demand extensive datasets, careful annotation, hyper-parameter tuning and long training cycles [4; 8; 11; 12].  
Manual annotation can inadvertently introduce noisy labels that impair supervised learning. Recent literature describes two complementary mitigation strategies: (i) PSSCL, a progressive-sample-selection framework with contrastive loss that detects and prunes potentially mis-labelled instances [13]; and (ii) BPT-PLR, which partitions the data into balanced mini-batches and employs a pseudo-label-relaxed contrastive loss to dampen label noise during training [14]. Within our table olive workflow, this risk is virtually eliminated because SAM is used in zero-shot mode, obviating the need for a dedicated dataset or additional annotation; nevertheless, the aforementioned strategies would be valuable if domain-specific models are trained in the future.  
A different paradigm emerged with the Segment Anything Model (SAM), released by Meta AI in 2023 [15; 16]. The word Anything underscores SAM's capacity to generalize to previously unseen objects and scenes, enabled by its billion-mask pre-training and prompt-agnostic interface. SAM was trained on >1 billion masks and can segment virtually any object after receiving only a simple prompt (point, box or text), without any additional fine-tuning. Its zero-shot capability offers an attractive shortcut for industrial environments where building task-specific models is costly. Even so, SAM has been explored mainly in remote sensing and precision agriculture [17; 18; 19; 20], while its potential for inline food product inspection remains largely untested.  
Table olive processing is an ideal test bed. Fitting, slicing and stuffing machines (DRRs) run at 2500-3000 olives \(\min^{-1}\) but still suffer from the so-called boat error: olives that arrive vertically oriented are pierced off-axis, producing pulp damage and pit splinters that jeopardize product safety [8; 9]. CV can either (i) diagnose the root causes of these misorientations or (ii) close the loop with electromechanical actuators. In both cases, fast and reliable segmentation of each olive on the carrier chain is a prerequisite.
# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 4 Discussion
To address concurrent OPAs happening simultaneously, sequential modeling could be used to make multiple predictions. A prior study used sequential artificial neural network models to estimate hand posture before estimating hand exertion force [41]. Perhaps a similar approach that predicts whole body posture before predicting upper extremity movement could be used to improve OPA prediction accuracy. Continual challenges to estimating prediction accuracies remain because of the transitional time between OPAs when performing simulated work. This can be minimized by training models to make accurate predictions from the beginning of the activity to the very end. For example, lifting would need to be predicted from the first moment there is hip and knee flexion to the moment the person returns to standing. This has obvious challenges for a model that makes a prediction every second and will require proceeding information for accurate classification. Exploring additional types of deep learning models may help to address this issue and improve prediction results. In this study, a recurrent neural network (RNN) model was first applied to predict OPAs when performed in isolation. The prediction result of the RNN model did not meet expectations. Upon reflection, RNNs are designed to predict an outcome for each timestamp in a time series that was not the best fit for OPA classification. Further, CNN Resnet models have been shown to outperform other deep learning approaches significantly for time series classification [42]. Thus, the current model chosen for this study was from ImageNet contest winner, Resnet. Its 18-layersimplementation is robust to train the model efficiently and retain high accuracy for single OPAs prediction. The window size of the current CNN model was 60 rows of sensor data, roughly corresponding to 1 s of video data which is enough for a single activity to be repetitive and recognizable. However, in MMH tasks, activities change at a very fast pace. Thus, further investigation on window size selection is needed.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Piezoelectric Sensors
#### 3.4.1 Quartz Crystal Microbalance (QCM) Sensors  
QCM sensors possess quartz crystal resonators functionalized with different appropriate sensing elements (e.g., metalloporphyrins [131; 132], sensitive polymers, MOS, or nanomaterials) [6; 40; 167]. The acoustic wave propagates through the bulk of the crystal perpendicularly or parallel to the surface [51]. When a gas is absorbed on the sensitive surface of a crystal the mass changes thus alternating the resonance frequency [167]. Typically, mass increase results in a decrease in the oscillation frequency of the resonator [143] which comprises the measured physical quantity [167]. The sensing mechanism of QCM sensors is presented in Figure 11a.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 2 Bonding of Embedded CFRP Rod in Concrete G40 and UHPFRC
### Material Properties  
#### 2.2.1 Grade 40 Concrete and UHPFRC  
In the realm of construction materials, the distinction between conventional concrete and advanced composites plays a pivotal role in determining structural performance. Grade 40 concrete serves as a fundamental starting point. Comprising primarily concrete, steel reinforcement, and a Carbon-Fibre-Reinforced Polymer (CFRP) rod, G40 concrete adheres to well-established practices. It is crafted following a precise recipe to serve as a control specimen, featuring a steel rebar cage with 9 mm diameter rebars intended for pull-out test facilitation. This standard mix, designed in accordance with BS 5328-Part 2 [17], undergoes a standard curing process of 28 days to ensure optimal strength and durability.  
In a marked departure from conventional concrete practices, Ultra-High-Performance Fibre-Reinforced Concrete (UHPFRC) emerges as a sophisticated composite material that surpasses the performance of traditional concrete by incorporating a unique blend of steel fibres, superplasticisers, and Ground Granulated Blast Furnace Slag (GGBS), among other additives. These carefully selected constituents coalesce to form an advanced, homogeneous cementitious composite, meticulously engineered to achieve extraordinary compressive strengths exceeding 150 MPa, as elucidated in Table 2. UHPFRC's remarkable properties make it an invaluable asset for the restoration and reinforcement of structural  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Notation** & **Length** & **Width** & **Height** & **Embedment** & **Concrete** \\  & **(mm)** & **(mm)** & **(mm)** & **Length (mm)** & **Cover (mm)** \\ \hline Top Concrete & 430 & 430 & 400 & 300 [16] & 50 \\ Bottom Concrete & 700 & 700 & 400 & 300 [16] & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimension List of Specimens.  
Figure 1: Dimension of Top and Bottom Concrete with embedded CFRP Rod.  
components that face the rigours of severe environmental and load-related challenges. Its versatility extends to its role as an integral component in the skeletal strength of bridges and skyscrapers. Not only does it fortify structural integrity but also serves as an exceptionally resilient, waterproofing membrane.  
Diving deeper into the reinforcement aspect, the UHPFRC employs copper-coated micro steel fibres (WSF0220), adhering to industry standards such as ASTM A820 [18] and BS EN 14889 [19], with specific fibre details tabulated in Table 3. [PERSON] et al. introduced a fibre factor, a measure that encapsulates both volume fraction and aspect ratio, ranging between 0.8 and 2.0 [20], setting a benchmark for the expected performance of fibre-reinforced composites. Further specifying the operational thresholds, [PERSON] and [PERSON] recommended strain limits for straight steel fibres to be in the range of 2.5% to 7% [21], underlining the formidable tension-bearing capacity of these discreet yet integral components of UHPFRC.
## 2 Related Works
In response to a query, MiLAN creates an execution plan, which specifies the source nodes and the routing tree, such that satisfies the QoS requirement while maximizing energy efficiency. However middle-wares play an essential role in easy development of applications, providing a useful communication protocol may be a challenge in literature. For example PEMuR has been proposed as a dual scheme protocol for efficient video communication in [21]. This protocol aims at both energy saving and high QoS attainment by combining an energy aware hierarchical routing protocol with an intelligent video packet scheduling algorithm. The routing protocol tries to select the most energy efficient paths and manages the network load according to the residual energy of the nodes. Additionally, the packet scheduling algorithm enables the reduction of the video transmission rate with the minimum possible increase of distortion. TinySOA [22, 23] facilitates the use of wireless sensor networks in traditional application development by using a service-oriented model. It provides a set of Web services and tools which make possible to send and receive information from a deployed sensor network. TinySOA architecture consists of four main parts: TinySOA Node, Gateway, Registry, and Server. When one of the sensor network nodes turns on, it detects and identifies what sensor types are available and announces them by sending a registration message to the Gateway. The Gateway uses the gathered sensor types to register this information into the Registry. Once the sensor types are registered, the Gateway subscribes itself to the services provided by the sensor nodes. When sensor nodes receive the subscription message, they start constantly sending sensed data to the Gateway. The Gateway records sensor readings into a subcomponent called Historical Registry. The Server uses the Registry to prepare a Web service. This Web service leads methods to query for sensor readings. When a query for readings is requested from a user, the Server communicates with the Historical Registry to obtain the requested information and returns it to the Web services. However, this approach improves the response time; it imposes energy consumption overhead due to continuously sending unnecessary data to the registry by sensor nodes.
## 5 Results
### Metrics Analysis  
In a deeper analysis of the metrics obtained previously, you can focus on a particular metric to achieve a certain objective. There is a common example where it is explained that, in the medical and health field, a false positive (_precision_), although it would cause an avalanche of emotions, does not have as serious repercussions as a false negative (_recall_). Following this analogy, in this work, the _recall_ metric is of greater importance, that is the measurement of the number of anomalies that have been detected.  
By focusing on a particular metric, this causes a change in development as well. It is possible to determine the boundary that will be used by the technique for anomaly detection employing the isolation forest technique. The number of iterations was increased to 100 to collect all the scores that the technique assigns to the generated anomalies, and the distribution over all the iterations is shown in Figure 9.  
Figure 9: Score distribution assigned by isolation forest to the generated anomalies. Source: own elaboration.  
With this information, an adequate limit that captures the majority of the anomalies generated can be determined. Using a cutoff of 0.015, the results for all 30 iterations can be analyzed in Figure 10. It can be seen how the value of the _recall_ can reach a value of up to almost 80%, while the _accuracy_ and _precision_ remain in a range of 40% and 20%, respectively. In this way, focus can be given and action can be taken in development to achieve certain results depending on the objective or need of the task. Within the context of this work, the _recall_ metric is prioritized to detect as many anomalies as possible.
###### Abstract  
In the multi-satellite and multi-ground station downlink task scheduling problem, the waiting time from the proposal of the task to the execution will affect its validity. If the satellite has multiple communicable ground stations when the downlink task is proposed, the selection problem needs to be solved first. After the selection, since the available time conflict between tasks of different satellites for the same ground station, the specific start time should be determined. To reduce the waiting time, a simulated annealing algorithm with a tabu list and start time decision (SATLD) is proposed. This method uses a two-stage scheduling strategy. In the first stage, the improved simulated annealing algorithm based on a tabu list is used to select the downlink ground station. The second stage combines downlink scheduling algorithm based on task arrival time (DSA-AT) method and downlink scheduling algorithm based on task requirement time (DSA-RT) method to determine the specific start time of each task of a single ground station. Simulation analysis prove the method has better selection efficiency of downlink task and shorter total task waiting time, and has practical value.  
simulated annealing; scheduling; multi-satellite downlink Article  
A Simulated Annealing Algorithm with Multi-Satellite Downlink Schedule Problem  
202292320103390/aerospace0950235
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## 3 Results and Discussion
### Effects of Hollow Electrodes on Machining Speed with Varied Wall Thickness
#### 3.2.4 Analysis of Tool Wear Ratio  
Figure 23 depicts carbon deposition on the block and hollow electrodes of various thicknesses and it can be observed that there is a very small amount of carbon deposition on those electrodes.  
From Figure 24, it was observed that, with the decreased thickness of the electrode, carbon deposition on the electrode also gradually decreased. The average carbon deposition per electrode was 1.22, 0.89, and 0.845 gm for thicknesses of 0.12\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.04\({}^{\prime\prime}\), respectively. It can be said that due to the reduction in the contact area for lower-thickness electrodes, the amount of carbon deposition is also less. The carbon deposition for the block electrode is 0.973 gm, which is slightly less than for the 0.12\({}^{\prime\prime}\)-thick electrode, but greater than 0.08\({}^{\prime\prime}\)-  
Figure 23: Carbon deposition on the block and hollow electrodes for various wall thicknesses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of Electrode**} & \multicolumn{2}{c}{**Percentage of Deviation (\%) at Entry**} & \multicolumn{2}{c}{**Percentage of Deviation (\%) at the**} & \multirow{2}{*}{**Overall Percentage of Deviation (\%)**} \\  & & **Side** & & **Exit Side** & \\ \cline{2-6}  & **Length** & **Width** & **Length** & **Width** & **of Deviation (\%)** \\ \hline Block & 3.244\% & 3.371\% & 4.544\% & 4.669\% & 3.957\% \\
0.12\({}^{\prime\prime}\) & 3.085\% & 3.355\% & 3.514\% & 3.641\% & 3.399\% \\
0.08\({}^{\prime\prime}\) & 3.355\% & 3.083\% & 3.498\% & 3.084\% & 3.255\% \\
0.04\({}^{\prime\prime}\) & 3.095\% & 1.985\% & 2.795\% & 1.701\% & 2.394\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Quality of the machined parts of the workpiece with different thicknesses of hollow electrodes.  
Figure 22: Overall percentage of deviation for block and hollow electrodes of 0.04\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.12\({}^{\prime\prime}\) thicknesses.