esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 4 Small Intestine
### Diagnostics  
The slow wave patterns in the small intestine are both temporally and spatially dynamic [102]. This is related to the function of the small intestine to breakdown, mix and absorb the nutrients of the ingested foods. Unique to the small intestine, the slow wave frequency decreases in a stepwise fashion aborally (see Figure 2b) [103]. In humans, the duodenum has a frequency of 12 cpm, and this frequency drops to approximately 8 cpm at the distal ileum [104]. In the small intestine, contractions of the muscle are also associated with spiking activity or action potentials occurring in conjunction with the slow waves [102].  
Although rarely used due to their invasiveness, one of the most reliable methods for the measurement of the small intestinal electrical activity is the use of serosal or intraluminal electrodes. Serosal electrodes can be implanted on the serosal surface of the small intestine [104; 105], whereas intraluminal electrodes can be placed under endoscopy [106; 107]. The reliable assessment of SWsignals in the small intestine is complicated, as the signals are extremely weak compared to background noise and other biological signals (such as the heart or even the stomach).  
Despite the weak nature of these signals, a number of groups have reported success at non-invasively detecting the resultant activity on the skin surface using electroenterography (EENG) [108; 109]. Another study has also reported the ability to non-invasively detect increased spike burst activity in the presence of higher levels of bowel contractions [110]. However, such techniques rely on sophisticated filtering methods and electrode platforms to overcome the low SNR of the EENG signals [111]. Most likely, such techniques work best on subjects with low body mass index due to the insulating effects of the fat layers [112].  
An alternative approach involves non-invasively detecting the resultant magnetic fields using magnetoenterography (MENG). [PERSON] et al. were among the first to report the ability to non-invasively and spatially map the biomagnetic activity from the different regions of the normal human small intestine [79]. Subsequently, MENG signatures from chronic mesenteric ischemia patients have been reported [113]. In that study, MENG recordings were obtained pre- and post-revascularization surgery on chronic mesenteric ischemia patients. Prior to revascularization surgery, MENG recordings reported a significant decrease in slow wave frequency post-prandially (8.9 vs. 7.4 cpm). Post-surgery MENG recordings reported consistent slow wave frequencies before and after the food was ingested (9.3 vs. 9.4 cpm).
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 7 End-User Study
### Inferential Statistics  
The insights presented in Section 7.4 are merely based on descriptive differences. To evaluate whether the differences in the descriptive results reach statistical significance, independent-samples \(t\)-tests [62] were conducted to compare BPMNE4 IoT and BPMN 2.0. All statistical tests performed were two-tailed and the significance value was set to \(p<0.05\). Table 7 summarizes the independent-samples \(t\)-tests.  
The results demonstarte that the participants had a statistically significant lower (t(47.4) = 13.1, \(p<0.001\)) **mental load** when modeling IoT-driven BPs with BPMNE4 IoT (M = 4.2, SD = 1.8) than when modeling them with BPMN 2.0 (M = 12.8, SD = 3.0). **Physical demand** was significantly lower (t(43.7) = 4.4, \(p<0.001\)) when modeling IoT-driven BPs with BPMNE4 IoT (M = 3.1, SD = 2.1) compared to BPMN 2.0 (M = 8.2, 5.5). Regarding **temporal  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Factors** & \multicolumn{2}{c}{**BPMNEAIoT Modeler**} & \multicolumn{2}{c}{**BPMN Modeler**} & & & \\ \cline{2-7}  & **Mean** & **SD** & **Mean** & **SD** & \(t\) & **df** & **Sig(2-Tailed)** \\ \hline Mental load & 4.2 & 1.8 & 12.8 & 3.0 & 13.1 & 47.4 & \textless{}0.001 \\ Physical demand & 3.1 & 2.1 & 8.2 & 5.5 & 4.4 & 43.7 & \textless{}0.001 \\ Temporal demand & 3.1 & 1.9 & 13.2 & 4.2 & 11.9 & 40.4 & \textless{}0.001 \\ Performance & 2.1 & 1.6 & 9.3 & 4.4 & 8.2 & 37.1 & \textless{}0.001 \\ Effort & 3.5 & 2.0 & 12.8 & 3.4 & 12.7 & 47.3 & \textless{}0.001 \\ Frustration & 2.6 & 2.5 & 13.3 & 4.2 & 11.7 & 46.8 & \textless{}0.001 \\ Completion time & 6.0 & 1.3 & 17.6 & 6.1 & 11.0 & 32.0 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Two-Sample T-Test Results for BPMNE4 IoT and BPMN 2.0.  
demand**, study participants had a significantly higher load ((t(40.4) = 37.1, \(p<0.001\)) when modeling IoT-driven BP with BPMN 2.0 (M = 13.2, SD = 4.2) than with BPMNAE4 IoT (M = 3.1, SD = 1.9). The **performance** of modeling IoT-driven BPs is significantly better (t(37.1) = 8.2, \(p<0.001\)) in BPMNE4 IoT (M = 2.1, SD = 1.6) in comparison to BPMN 2.0 (M = 12.8, SD = 3.4). To model the given IoT-driven BP, study participants had to exert significantly more **efforts** (t(47.3) = 12.7, \(p<0.001\)) when using BPMN 2.0 (M = 12.8, SD = 3.4) than when using BPMNE4 IoT (M = 3.5, SD = 2.0). When modeling IoT-driven BPs with BPMN 2.0 (M = 13.3, SD = 4.2), study participants were more **frustrated** (t(46.8) = 11.7, \(p<0.001\)) compared to the use of BPMNE4 IoT (M = 2.6, SD = 2.5). Finally, participants require significantly less **time** (t(32.0) = 11.0, \(p<0.001\)) to model the IoT-driven BP with BPMNE4 IoT (M = 6.0, SD = 1.3) than when using BPMN 2.0 (M = 17.6, SD = 6.1). Overall, the independent-samples t-tests statistically confirms Hypotheses H1 and H2.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 6 Electricity Load-Forecasting Model  
### Load Influencing Factors  
Electric load is influenced by various factors, including weather, location, time, electricity prices, user consumption habits, and unexpected events. The load curve can be decomposed into regular components that change periodically, uncertain components that change non-periodically, and noise components from unexplainable physical factors [32]. Key weather factors, such as temperature, humidity, and atmospheric pressure, significantly impact short-term load variations.
## 3 Results and Discussion
### Evaluation of Sensory Quality of Bread  
Sensory evaluation (Figure 8) of bread samples was performed 24 h after baking. The bread samples received up to 9 points regarding general acceptability with the following average scores: the bread samples with the addition of 5% hemp oil scored 7.66; samples with the addition of 10% hemp oil scored 8.20; samples with the addition of 15% scored  
Figure 6: Analysis of the main components for the correlations between the bread samples and the rheological and textural characteristics.  
Figure 7: Graph of the distribution of bread samples in relation to the quality characteristics and the proportion of added hemp seed oil.  
7.12. On average, the evaluators said, with regards to the addition of 5-15% hep oil, \"I like it very much\" and \"I like it moderately\". The evaluators appreciated the samples of black bread with the addition of 5% and 10% for its crumb pore and specific aroma. Although using hepp seed oil benefits is beneficial to bread quality, it cannot be used in excess of 10%, as, according to the evaluators' ratings, this causes the taste to worsen. Related to this, is that other several studies have also evaluated consumer satisfaction with oils, such as sesame, chia, pumpkin, and flax, finding that 5-10% additions did not significantly alter sensory properties.
## 3 Results and Discussions
### Surface Quality Measurements
#### 3.1.1 Surface Quality Resulting from CNC Milling of Wood
The waviness for the other angles, such as 15\({}^{\circ}\), 30\({}^{\circ}\), 75\({}^{\circ}\) and 60\({}^{\circ}\) does not show a clear trend, perhaps being biased by either raised fibers or incidental gaps (Figure 10). The cumulating effect of roughness and waviness appear in the primary profile, which is the measured profile after subtraction of form error. Even at this level of magnitude of the surface irregularities, from the smallest (roughness) to the largest (waviness) wavelengths, the processing at 15\({}^{\circ}\) and 60\({}^{\circ}\) appears to be the worst for all angle variants. Somehow similar, Pa, for Douglas Fir processed by milling at 5m/min feed speed, suddenly increased for 30\({}^{\circ}\) and then again for 70\({}^{\circ}\), in down-milling against the grain [28]. The best quality was the one parallel to the grain, followed by 90\({}^{\circ}\) and 75\({}^{\circ}\), which confirms previous findings in the analysis of the surface roughness (Figure 11a). The surface quality, evaluated by the combined effect of roughness and waviness, decreases with an increase in the feed speed,  
Figure 10: The variation of the arithmetic mean deviation of the waviness profile, Wa, for various cutting angles, with the feed speed. Figure 9: The variation of the arithmetic mean deviation of the roughness profile, Ra, for various feed speeds with the cutting angles. with the exception of the cutting angles of \(15^{\circ}\) and \(30^{\circ}\) which seem to be biased by outliers and without a clear trend (Figure 11b).
# Object Detection-Based One-Shot Imitation Learning with an RGB-D Camera
## 4 Experiments and Results  
In order to validate this proposed object detection-based one-shot imitation learning framework, a series of simulated experiments are conducted in an object placing application scenario in the V-REP environment, shown in Figure 5. In the object placing task, the robot is guided by an RGB-D camera to place the object into the target container in the presence of two distractors on the table. The simulated RGB-D camera outputs an RGB image of size \(240\times 320\) and a depth map with the same size. The manipulator is a simulated UR5 robot equipped with a two-finger gripper and two grippers are used in the data collection phase to increase the diversity, which can be found in Figure 5. A workstation with a NVDIA GTX 1080 Ti GPU and the machine learning platform of Tensorflow 1.70 are used for training. Twenty containers and twenty objects are used for collecting the training data and four cameras that are placed vertically above the table in different positions are applied for recording perception information at the same time. Containers and objects are shown in Figure 6. 1200 manipulation trajectories are collected and there are total 4800 demonstrations recorded by four cameras. 4000 samples are used as the training data and 800 trajectories are chosen for validating. A manipulation trajectory consists of a series of perception information \((\mathbf{o},\mathbf{d})\) and motion actions \(\mathbf{a}=(\mathbf{P},\mathbf{G})\). As mentioned before, the position change of the end-effector \(\mathbf{P}\) and the state of the gripper \(\mathbf{G}\) are chosen as the action. With these demonstration trajectories, the visuomotor policy can be trained in a BC manner. Experiments are conducted to address three questions as followed: (1) Can the robot finish the object placing task efficiently for unknown containers in new environment with only one demonstration under this object detection-based framework? (2) Do the integrated kernels in the object detection module improve the performance of this approach and what is the effect of different kernel size? (3) Can this proposed framework trained in simulation be transferred to the real world directly without any fine-tuning?  
Figure 4: The structure of the motion policy network.
## 1 Introduction
The new control system proposed in this paper is a coordinated system between an ASG and battery that is designed to make up for each weak point. Therefore, the proposed system is significantly novel. Simulation analyses by using a PSCAD/EMTDC simulator were performed on a model system consisting of usual synchronous generators, a squirrel cage induction generator (SCIG)-based WF, and a battery. The simulation results are presented in the paper to show the validity of the proposed method. The remainder of the present paper is organized as follows: Section 2 presents the models used in the simulation analyses, that is, the model system, governor models, ASG, and battery; Section 3 explains the proposed coordinated control method between the ASG and the battery; Section 4 presents the conditions and results of the simulation analyses regarding the coordinated stabilizing control and the minimum necessary power rating of the battery; and Section 5 concludes the paper.
## 3 Mechanical Design
### Analysis of the Frequency Drift during Pumping  
As the 1.3 GHz deflecting cavity operates in TM\({}_{110}\) mode, the coefficient of frequency shift with temperature is about \(-20.52\) kHz/\({}^{\circ}\)C through calculation in CST, so the frequency shift of less than 20 kHz can be easily compensated either by adjusting water temperature or during the design. The deflecting cavity works in an ultra-high vacuum state, and the changed pressure would cause the surface deformation during pumping. For a uniform thickness cylindrical cavity, the deformation of the curved surface is smaller than the end walls, so the thickness of the curved surface and end walls are optimized separately for  
Figure 6: Electron beam profile at the screen in the GPT simulation. (**a**) Deflecting cavity is off; (**b**) Deflecting cavity is on.  
Figure 7: Three-dimensional mechanical model of the deflecting cavity.  
the deflecting cavity. Due to the weight and mechanical strength, the outer diameter of the cavity is chosen to be 296 mm, and the wall thickness of the end and middle cell are 11.76 mm and 15.61 mm, respectively. The frequency drift caused by the deformation of the curved surface is about 5 kHz, which can be ignored. The largest deformation and frequency drift with different end wall thickness during pumping are calculated. The results are listed in Table 4. When the end wall thickness is more than 21 mm, the frequency drift is within acceptable range. Simulation shows that maximum deformation is located around the root segment of the beam pipe, so setting stiffening rings around the beam pipe is proposed. Finally, the thickness of the end wall is 21 mm, the thickness and outer diameter of the stiffening ring are 5 mm and 118 mm, respectively. The cavity frequency deviation is \(-17.22\) kHz due to the mechanical deformation from the atmosphere pressure to vacuum under the design.
## 1 Introduction
Existing power simulation platforms and approaches integrating existing simulators like Matpower (V6.0, [PERSON], [PERSON], and [PERSON], Ithaca, NY, USA, 2017), PSCAD (X4, Manitoba-HVDC research centre, Winnipeg, MB, Canada, 2014), and PSASP (V7.0, China electric power research institute, Beijing, China, 2011) can be used for analyzing both steady states and transient states. However, when dealing with a simulation of the propagation of cascading failures, these simulators are not convenient because the topology of the power grid will be changed  
Figure 1: A complete evaluation procedure of vulnerability analysis. after a splitting accident. Detailed comparisons with existing solution approaches of vulnerability analyses with ECPSs are shown in Table 1.  
The second contribution is a comprehensive approach to vulnerability assessments. The five aspects of the vulnerability assessment include robustness, economic costs, the degree of damage, vulnerable equipment, and trip point. Associated with the simulation results obtained by our platform, we can have a thorough comprehension of the vulnerability in ECPSs. Additionally, decision-makers can get specific advice and defence strategies from this system against cyber attacks based on the results. Existing indicators (such as node degree, cluster coefficient, and node betweenness) are mainly proposed in the research area of complex networks. These indicators cannot represent the relationship of power flows and topologies. The rest of this paper is organized as follows. The modeling framework for ECPSs is presented in Section 2. Section 3 introduces the solution approaches for fault detections, protection procedures, and adjustment strategies. The aspects of vulnerability assessment and simulation platform are illustrated in Section 4. Section 5 analyzes the vulnerability with numbers of examples with the IEEE 39-bus system based on our proposed simulation platform. Section 6 concludes the paper. Section 7 discusses future works.
## 1 Introduction  
To meet the rapid increase in power demand, power generation, transmission, and distribution facilities should be expanded. However, power generation facilities cannot be constructed close to the area of demand. To address line stability risks, studies have focused on the application of technologies using power semiconductor devices for power transmission lines. To address the decrease in power quality because of long-distance transmission, power conversion devices for power quality compensation are used in distribution networks. With renewable energy and the increase in demand for power electronics and large-load devices due to industrial development, various power conversion devices have been used for power quality compensation, such as UPS, CVT, STATCOM, and DVR [1, 2, 3].  
As shown in Figure 1, in power transmission systems voltage controllers are generally divided into first, second, and third stages [4, 5]. The first-stage control is performed by an automatic voltage regulator (AVR) mounted on the generator, which operates very quickly in the range of a few seconds. The second-stage control is performed by flexible AC transmission systems (FACTS), static var compensators (SVCs), and a synchronous compensator. The secondary controllers usually have an operating constant of several seconds. The third-stage control, voltage control, is performed through a device such as a DVR, taking into account the stability or economy in the state of the secondary control. Secondary control has been actively researched. The representative technologies are FACTSand SVC [6; 7; 8; 9]. In this paper, the DVR of the tertiary control method suitable for high-quality consumers, such as distribution networks and smart factories, was studied. In the existing DVR method, the complex implementation of high-speed DSP calculation and DFT application, which are applied as a harmonic compensation method for power quality compensation, is intended to present a modified topology and a sampling technique equivalent to time interval.
## 4 Results and Discussion  
### Probability Distribution Function Parameter Result  
The estimation of Weibull and Rayleigh parameters for year 2010 at the South Plains region of Texas, Southern Offshore region of Texas, Hills region of West Virginia, and Hills region of Arizona are presented in this section. The shape and scale parameters were estimated for the whole year's data for all locations. Details are shown in Table 1.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### 70-30 Split Ratio Models  
These initial models are developed by combining all datasets from various devices of the same brand, model, and gain range. The combined datasets are then split into a training set, used for model construction, and a test set, used to evaluate result accuracy. Six different models are created:  
1. Cisco EDFA-17 [32], low-gain range, 1 dataset;
2. Cisco EDFA-35 [30], high-gain range, 2 datasets;
3. Cisco EDFA-35, low-gain range, 4 datasets;
4. Cisco L-band EDFA [33], low-gain range, 6 datasets;
5. Juniper EDFA [34], high-gain range, 4 datasets;
6. Juniper EDFA, low-gain range, 4 datasets.  
The training and test sets are divided using a 70%/30% ratio, following common practice in machine-learning applications [35]. The results for the cumulative distributions of the absolute and relative errors are shown in Figure 14.  
The EDFA-17 model demonstrates superior performance since it relies on data from a single device, thereby avoiding potential errors introduced by manufacturing variations and resulting in higher overall measurement correlation. If additional datasets were available for this model, its performance might be negatively impacted, but unfortunately, none were available. For absolute errors, the 90%-threshold ranges between 0.05 and 0.28 dB, depending on the model, while the 99%-threshold ranges between 0.10 and 0.46 dB. For relative errors, the 90%-threshold ranges between 1.19 and 4.83%, depending on the model, while the 99%-threshold ranges between 2.08 and 7.19%. The maximum values for absolute and relative error are 0.81 dB and 10.56%, both originating from the Juniper devices in the high-gain range.  
Figure 13: Cumulative distribution of measurement errors for Cisco EDFA-35 devices operating in the low-gain range (blue) and 90%-threshold (red), 99%-threshold (green) error values evaluation.
## 3 Results
### Verification of TFs  
KEGG enrichment of TFs was carried out in DEGs at each stage, and then 14 TFs that had significantly differently expressed in TS and US at each stage were selected and verified by RT-qPCR. The comparisons of the RNA sequencing and RT-qPCR results are reported in Figure 3, and detailed expressional profiles are listed in Table S1. In addition, the Pearson  
Figure 2: Transcriptomic profile of grape berries. (**A**): Comparison of the significantly up- and downregulated DEG numbers between TS and US at each stage (FC > 2, \(p<0.01\)). (**B**): Venn diagrams of DEGs between TS and US at each stage. (**C**): KEGG classification of DEGs between TS and US at each stage. All the pathways were classified into seven categories, including (1): Organismal Systems; (2): Metabolism; (3): Human Diseases; (4): Global and Overview Maps; (5): Genetic Information Processing; (6): Environmental Information Processing; (7): Cellular Processes. TS: Turpan sample; US: Urumqi sample.  
correlation was carried out on TFs and volatiles (Table S7). Results showed that at S2, the expressional value of _LOB21_ in US was higher than that in TS. Among the five ripening stages, _LOB21_ was generally highly expressed in US. The expressional values of both _ERF62_ and _ERF5_, from the ERF family, were higher in US than in TS at S3. Additionally, the results of RNA sequencing and RT-qPCR found upregulations of _VvWRKY22_, _HSF30_ and _HSFA_ in TS at later stages. Furthermore, three TFs from bHLH family showed their high expressional values in TS at S3 or S4.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential
### Monitoring of Ground Deformation (GD)  
Seismic activity, sediment compaction, other geodynamical processes, moisture accumulation, excessive exploitation of groundwater, hydrocarbon extraction, and the presence of quarries and industrial areas usually cause vertical and horizontal ground movements. They affect railway infrastructure components, in particular the ballast substructure, whose condition is extremely important for the durability of the railway tracks. Ground movements cause loss of elevation slope change and affect the smoothness of the railway track. Conventional methods for monitoring, such as on-site geotechnical measurements, leveling, usage of settlement meter and inclinometer, and GPS, are effective and give insight into the composition of layers and condition of the railway substructure [23; 28; 29]. However, they are labor-intensive, imply substantial costs, and are carried out in a targeted manner on sites indicated by preceding survey train measurements. Ground deformation rates sufficiently precise for railway infrastructure monitoring can be obtained using SAR technology, as in the case of rail track deformation measurements, explained just above (Section 2.1). Using SAR technology, the railway ground area of interest can be monitored on a regular basis without the need for any ground instrumentation (e.g., [20; 21]).