esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Temperature Measurements Procedure  
Temperature measurements were taken using a K-type thermocouple (SAB-T207, [PERSON], Viersen, Germany), designed for high-temperature industrial environments (up to 800 \({}^{\circ}\)C). Its slim 0.25 mm diameter rod and flexible sheath enable easy installation in the thin-sheeted dough layer. The sensor rod is 100 mm long, with a high-temperature cable and armor to withstand extreme temperatures.  
The bread's lower surface is in contact with the baking conveyor, while the upper surface is exposed to hot air. Due to the dough's large expansion and high baking temperatures, measuring temperature inside and on the dough's surfaces is challenging. The dough thickness is about 1.5 mm, and it expands to approximately fifty times its initial volume during baking.  
One thermocouple was placed beneath the dough at the lower surface, while the other was firmly fixed to the upper surface to ensure stable contact during baking. A third  
Figure 2: Mass loss measurement system: (**a**) Detailed view showing the load cell used for weighing the baked bread. (**b**) Assembled system with the load cell positioned at the oven exit for precise mass measurement.  
thermocuple was used to measure the internal dough temperature. Due to the very thin diameter of the thermocouple wire, direct insertion into the dough was not feasible without deformation. Therefore, a 1 mm-diameter rigid stainless-steel tube was embedded into the dough prior to baking to act as a guide channel, into which the thermocouple was inserted. The sensor was secured by fitting it into precision-drilled holes (0.25 mm) along the length of the tube to ensure stability and accurate internal measurement during baking. At the end of the baking process, the internal sensor remains within the air pocket formed inside the bread.  
Before each experiment, the thermocouples were calibrated using a reference probe (accuracy \(\pm\) 0.5 \({}^{\circ}\)C) in a 25 \({}^{\circ}\)C water bath, ensuring accurate measurements during the baking cycle. The thermocouples were verified to be accurate within \(\pm\)2 \({}^{\circ}\)C under high-temperature conditions. After baking, sensor depth and position were confirmed visually within the bread pocket.  
For each baking trial, measurements were recorded every 0.1 s over a 12-s baking period, resulting in 120 data points per trial. A total of ten independent repetitions were conducted under identical conditions, and the results were averaged to ensure accuracy and repeatability. The average measurement uncertainty was estimated at \(\pm\)4 \({}^{\circ}\)C, which accounts for thermocouple precision, variability in placement on the deforming dough surface, and steep local thermal gradients during baking. Surface temperature monitoring was particularly challenging due to the intense heat (\(\sim\)650 \({}^{\circ}\)C), the thin dough layer (1.5 mm), and the dynamic inflation of the dough during the short baking period. Statistical analysis and curve fitting methods applied to the data are detailed in the Section 3. Figure 3 shows the thermocouple positions inside the dough before and after baking.
# Application of Canola Oil Biodiesel/Diesel Blends in a Common Rail Diesel Engine
###### Abstract  
In this study, the application effects of canola oil biodiesel/diesel blends in a common rail diesel engine was experimentally investigated. The test fuels were denoted as ULSD (ultra low sulfur diesel), BD20 (20% canola oil blended with 80% ULSD by volume), and PCO (pure canola oil), respectively. These three fuels were tested under an engine speed of 1500 rpm with various brake mean effective pressures (BMEPs). The results indicated that PCO can be used well in the diesel engine without engine modification, and that BD20 can be used as a good alternative fuel to reduce the exhaust pollution. In addition, at low engine loads (0.13 MPa and 0.26 MPa), the combustion pressure of PCO is the smallest, compared with BD20 and ULSD, because the lower calorific value of PCO is lower than that of ULSD. However, at high engine loads (0.39 MPa and 0.52 MPa), the rate of heat release (ROHR) of BD20 is the highest because the canola oil biodiesel is an oxygenated fuel that promotes combustion, shortening the ignition delay period. For exhaust emissions, by using canola oil biodiesel, the particulate matter (PM) and carbon monoxide (CO) emissions were considerably reduced with increased BMEP. The nitrogen oxide (NOx) emissions increased only slightly due to the inherent presence of oxygen in biodiesel.  
c 2017 7 34 [Applied Sciences]Applied Sciences
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model
### TSCH Time Slots
#### 3.1.2 Time Slot RxDataTxAck  
This time slot can be considered the opposite of the TxDataRxAck. The states to handle the data in TxDataRxAck are found in handling the ACK in RxDataTxAck and vice versa. All states of the RxDataTxAck time slot can be found in Table 2.  
The guard time for the data is however larger than the AGT that is used for ACKs. The Packet Guard Time (PGT) determines how long the radio listens for the data before the radio is turned off. When no data are received during the PGT period, we classify the time slot as RxIdle instead of RxDataTxAck. In OpenWSN, the PGT is defined as \(2600\,\mathrm{\SIUnitSymbolMicro s}\).
## 3 Results
### Case Study
This variability is particularly significant in mountainous regions like Cuenca, where weather conditions can change rapidly. Figure 2: Testing platform developed in the Microgrid Laboratory at the University of Cuenca, Ecuador. The blue curve represents an emulated profile generated in the laboratory as part of a modeling process designed to replicate the dynamics of PV systems under controlled conditions. This approach enables the analysis of the impact of climatic variations on generation and validates the methodology used in the microgrid to ensure operational stability and control strategies. Comparing the actual and emulated curves highlights the laboratory's ability to model dynamic systems with high accuracy. At the bottom of the graph, images of the sky taken at different times of the day are included, correlating with specific points on the (\(P_{PV}\)) curve. These photographs demonstrate how cloud cover directly affects PV production. For instance, during hours of clear skies, there is a significant increase in generated power, while during periods of greater cloud cover, generation decreases sharply. This correlation between visual meteorological data and power measurements strengthens the analysis by providing both qualitative and quantitative insights into the impact of weather conditions. From a technical perspective, this graphical representation shows the importance of evaluating and modeling the dynamic behavior of PV systems in microgrids. The measured data, combined with the emulation, support the proposal of advanced control strategies to mitigate energy generation fluctuations and ensure more stable microgrid operation, particularly in scenarios with high renewable energy penetration. This work assesses the effectiveness of three fuzzy logic-enhanced power smoothing methods--low-pass (LP, V2 GGlide), mean average (MA, V2 GSUN), and ramp rate (RR, V2 GSmooth)--under three initial SoC conditions for the LI-ION BESS:  
* **Case 1:** Maximum SoC (Overcharge)  
The LI-ION BESS is charged to over 80% of its capacity before initiating the smoothing process. * **Case 2:** Medium SoC (Range 20-80%)  
The LI-ION BESS operates within a mid-range SoC, ensuring sufficient energy without reaching overcharge or undercharge limits. * **Case 3:** Minimum SoC (Undercharge)  
The LI-ION BESS begins the smoothing process with an SoC below 20% of its maximum capacity. Figure 3: Daily variation of photovoltaic power output with hourly photographic records of the sky.
# Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
## 2 Materials and Methods
### The Intrusion Scenario Discovery and Prediction Framework
#### 2.2.2 Intrusion Session Reconstruction  
An intrusion session is a sequence with all the unique actions extracted from the communication of a pair of devices in it. As described before, the time-ordered action sequence will be split into many subsequences by calculating the variance of the time gaps between actions. Each subsequence is named intrusion session in this paper.  
In practice, the intrusion session always contains long, repeated, and disordered actions, which lower the performance of the sequence learning system. In most situations, the types of actions involved in a particular intrusion are more important than the order of them; in other words, the order of actions can be altered due to variety of situations, such as the network delay, the system error, even the intended disorders made by the attacker. Thus, the session reconstruction is proposed to refine the sessions.  
The extracted session in Figure 4 is the ordinary state after applied the pruning algorithm proposed in [24]. If the session is converted into a more precise and clearer one, the performance of the system can be highly enhanced.  
In order to discover the potential skeleton of the intrusion, a session refining method is proposed in this paper to highlight the real information hiding in the sessions. It removes the repeated actions from intrusion sessions by calculating the similarity of actions. When reconstructing the session, the new arrived action will be compared with the existing actions of the converted session, and it will discard the actions if they have been in the converted session. The reconstruction process keeps all the action information and the basic orders of actions, and it is more effective than our pruning algorithm proposed in our previous work [24]. Figure 5 shows how the method works.  
Figure 4: The extracted session and the converted session. The numbers denote the intrusion actions.  
Figure 5: The proposed session refining method.  
Although the session refining method is simple, it is effective in discovering intrusion patterns. The most important is that it can stabilize the association patterns of actions in the session, and provide a clean sequence for the HTM algorithm to learn.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
###### Abstract  
Macelignan, from _Myristica fragrans_ (nutmeg), is a bioactive compound with various pharmacological properties, including anti-inflammatory and neuroprotective activities. The purpose of this work was to investigate the antioxidant and anti-apoptotic effects of macelignan in glutamate-treated HT22 mouse hippocampal neurons. Macelignan was extracted and identified in a methanol extract of _M. fragrans_ seeds. The DPPH was used to assess the antioxidative activity of macelignan. Glutamate (5 mM) was used to induce neurotoxicity in the HT22 cells. neuroprotective effects were measured using relevant biochemical and imaging assays, including cell viability, ROS production, nuclear staining, apoptotic cell death, and protein expression. Macelignan markedly and concentration-dependently enhanced DPPH radical scavenging activity. In the HT22 cell model, glutamate induced cell damage by decreasing cell viability, promoting ROS generation, and increasing apoptotic cell death according to cell morphological changes. However, macelignan treatment restored cell viability, inhibited ROS generation concentration-dependently, and reduced apoptosis. Moreover, glutamate significantly up-regulated the phosphorylation of MAPK-pathway-related proteins, which was reversed by macelignan treatment. In conclusion, macelignan shows notable neuroprotective effects on oxidative stress and apoptotic cell death in glutamate-induced cells, and this study provides useful information on its potential therapeutic implications in neurological disorders.  
macelignan; _Myristica fragrans_; apoptosis; neuroprotection; oxidative stress; free radical
# Hydrogenation of Adiponitrile to Hexamethylenediamine over Raney Ni and Co Catalysts
###### Abstract  
Hexamethylenediamine (HMDA), a chemical for producing nylon, was produced on Raney Ni and Raney Co catalysts via the hydrogenation of adiponitrile (ADN). HMDA was hydrogenated from ADN via 6-aminohexanentirite (AHN). For the two catalysts, the effects of five different reaction parameters (reaction temperature, H\({}_{2}\) pressure, catalyst loading, and ADN/HMDA ratio in the reactant) on the hydrogenation of ADN were investigated. Similar general trends demonstrating the dependence of ADN hydrogenation on the reaction conditions for both catalysts were observed: higher temperature (60-80 \({}^{\circ}\)C) and H\({}_{2}\) pressure, as well as lower ADN/cataly and ADN/HMDA ratios, led to higher HMDA yields. A further increase in temperature from 80 to 100 \({}^{\circ}\)C increased the HMDA yield from 90.5 to 100% for the Raney Ni catalyst, but did not affect the HMDA yield (85-87%) for the Raney Co catalyst. A 100% HMDA yield (the highest yield reported to date) was also achieved via ADN hydrogenation over the Raney Ni catalyst, with a high HMDA content in the reactant (e.g., ADN/HMDA volumetric ratio of 0.06). No sign of metal leaching into the product solution was found, meaning that the Raney Ni and Raney Co catalysts were stable during ADN hydrogenation.  
Keywords:adiponitrile; 6-aminocapronitrile; nylon; hydrogenation; hexamethylenediamine; polymer industry +
Footnote †: journal: Applied Sciences
# A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
## 4 Borehole Image Error Analysis and Restoration
### Grayscale Error Analysis and Restoration Methods
In quantifying the effectiveness of the grayscale restoration method, the grayscale error matrix, calculated from the original borehole image, had a maximum value of \(+0.1717\) and a minimum value of \(-0.1433\), resulting in a total error range of \(0.315\). After grayscale restoration, these errors are fully corrected, achieving an improvement of \(31.5\%\) relative to the original grayscale range of \([0,1)\). Therefore, the grayscale restoration method designed in this paper can repair the brightness errors caused by probe eccentricity in borehole images.
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **mAP(0.5)** & **mAP(0.5:0.95)** \\ \hline all & 0.972 & 0.977 & 0.988 & 0.888 \\ \hline person & 0.99 & 0.988 & 0.996 & 0.949 \\ \hline truck & 0.978 & 0.977 & 0.987 & 0.921 \\ \hline safety helmet off & 0.998 & 1.000 & 0.998 & 0.908 \\ \hline safety helmet on & 0.992 & 0.980 & 0.954 & 0.885 \\ \hline safety gloves off & 0.983 & 0.963 & 0.988 & 0.784 \\ \hline safety gloves work on & 0.976 & 0.949 & 0.981 & 0.804 \\ \hline safety gloves insulated on & 0.999 & 0.997 & 0.999 & 0.982 \\ \hline safety rubber insulated sleeve off & 0.968 & 0.989 & 0.984 & 0.858 \\ \hline safety rubber insulated sleeve on & 0.992 & 0.963 & 0.991 & 0.874 \\ \hline safety boots off & 0.992 & 0.972 & 0.981 & 0.788 \\ \hline safety boots on & 0.993 & 0.999 & 0.999 & 0.983 \\ \hline safety belt off & 0.952 & 0.993 & 0.978 & 0.836 \\ \hline safety belt basic on & 0.987 & 1.000 & 0.996 & 0.823 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of object detection with the customized YOLOv7 model according to various metrics: precision, recall, mAP(0.5), and mAP(0.5:0.95). Figure 6: Experimental results of object detection with the customized YOLOv7 model applied to ensemble models in an on-site AI engine. Figure 7: Experimental results of data-driven risk management (assessment and alarm notification) between the safety autonomous platform (**left**) and the supervisor with a tablet PC (**right**).
## 4 Calculation and Results
### Decision Distance \(S_{1}\)  
It is considered that the vehicle maintains a constant vehicle speed during the driver's response to the sign, as determined in Equation (11):  
\[S_{1}=\frac{V}{3.6}t_{f} \tag{11}\]  
where \(V\) is the vehicle speed (km/h) in the decision process, which can be taken as the design vehicle speed of the mainline. \(t_{f}\) denotes the decision time (s).  
According to the _Intersections Guidelines_, ideal decision times of 2.5 s, 2.5 s, and 2.4 s are specified for design speeds of 120 km/h, 100 km/h, and 80 km/h, respectively. When drivers lack anticipation regarding driving decisions and must abruptly decide whether to exit from the mainline after suddenly discovering the exit, a longer decision time is required compared to situations where anticipation exists. The road environment's complexity faced by drivers in RTRL differs from that of a traditional exit, necessitating a more extensive decision time for drivers. The length of the decision time is influenced by the complexity of the decision, notably the extent of the information involved in the decision-making process [32]. The correlation between the drivers' decision time and information capacity without anticipation is elucidated in Equation (12):  
\[t_{f}=1.878384e^{0.261087x} \tag{12}\]  
where \(x\) signifies the information capacity. In conventional exit sections, drivers can continue straight or change lanes within 1-bit information capacity [32]. To ensure drivers promptly adapt to the RTRL and make a timely and accurate choice of the correct path, considering an information capacity of 2 bits, the calculation of \(t_{f}\) takes 3.166 s, thus adopting a reaction time of 3.2 s.
## 2 Theory
### Fast Fourier Transform (FFT)  
The Fourier transform is a well-known and important tool in signal processing. In lock-in thermography (LIT), thermographic data is transformed from the time domain to the frequency domain using the one-dimensional discrete Fourier transform [37; 38]. Discrete Fourier transform (DFT) can be used in any waveform to extract amplitude and phase angle from the transient signals. The DFT is applied to each pixel of the thermal sequence to compute amplitude and phase angle image sequences. Figure 2 shows the principle of amplitude and phase extraction by FFT.  
The one-dimensional DFT can be expressed by Equation (6) [(39; 40)]:  
\[\mathrm{F_{n}}=\Delta\mathrm{T}\sum_{k=0}^{N-1}\mathrm{T(k\Delta T)}\exp^{\frac{ \mathrm{i2 mk}}{N}}=\mathrm{Re_{n}}+\mathrm{Im_{n}} \tag{6}\]  
where \(\Delta\mathrm{T}\) is the sampling interval; \(\mathrm{i2}\) is the imaginary number; N is the total number of thermography sequences; \(\mathrm{Re_{e}}\) and \(\mathrm{Im_{m}}\) are the real and the imaginary parts of the transform, respectively.  
\(\mathrm{j2}=-1\) is the imaginary number, \(n\) is the frequency increment (\(n=0\), \(1\),..., N), \(\Delta\mathrm{t}\) is the sampling time interval, N is the total number of thermographic images, and \(\mathrm{Im_{n}}\) and \(\mathrm{Re_{n}}\) are the imaginary and real part of the transform, respectively. The lock-in frequency modulated thermographic data are converted into the frequency domain, and amplitude and phase angle images can be extracted. Real and imaginary parts of the complex transform are then applied to evaluate the amplitude (\(\mathrm{A_{n}}\)) and phase angle (\(\varnothing_{n}\)) and can be expressed by Equations (7) and (8), respectively [(41)].  
\[\mathrm{A_{n}}=\sqrt{\mathrm{Re_{n}}^{2}+\mathrm{Im_{n}}^{2}} \tag{7}\]  
\[\varnothing_{n}=\tan^{-1}\biggl{(}\frac{\mathrm{Im_{n}}}{\mathrm{Re_{n}}} \biggr{)} \tag{8}\]
## 5 Proposed SVM for CMV Reduction  
### AZS-Scheme  
In this scheme, two appropriate opposite active vectors in all subspaces are used to obtain the same effect of the actual zero states (0, 511) and reduce the CMV magnitude. A similar proposal is presented in [14; 16; 21] but for three-, five- and seven-phase VSIs, respectively. To get the same output voltage vector magnitude as in the conventional SVM scheme, the duty cycles of these phase-opposed voltage vectors must be the same and equal \(d_{z}\) determined for the true zero vectors. With this concept, any two active vectors in phase opposition will generate a zero vector on average and obtain a similar output voltage as in the conventional scheme.  
However, some of these vectors generate a switching pattern with more than one switching cycle per sampling period (\(T\)s), thus increasing the switching frequency. In this case, the only pair of vectors in phase opposition that preserves a constant switching frequency (\(1/T\)s), hence ensuring one switching cycle per sampling period occurs, is (264, 247) for the first sector. These vectors form group 3 (Table 2) and generate a CMV with peak values of \(\pm 5V_{dc}/18\). After searching for all sectors, Table 4 lists the new zero vectors selected to reduce the CMV and ensure minimum switching commutations. Moreover, the selected vectors and the switching pattern of sector 1 in this scheme indicate the generated CMV, as shown in Figures 3b and 4b, respectively. It is noticeable that the elimination of the true zero vectors reduces the peak CMV by 22.22%, with the peak CMV equaling \(\pm 7V_{dc}/18\).
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 3 Visual SLAM and Visual Odometry Using Deep Learning: Survey
### Deep Learning-Based Module for Visual SLAM and Visual Odometry
#### 3.1.1 Depth Estimation
In other words, it tells you how concentrated the data are around the line of best fit. The \(RMSE\) includes the \(RMSE_{linear}\) expressed in Formula (1) and \(RMSE_{log}\) expressed in Formula (2). \[RMSE_{linear}=\sqrt{\frac{||\sum_{i=1}^{N}y_{i}-y_{i}^{*}||\|^{2}}{N}} \tag{1}\]  
\[RMSE_{log}=\sqrt{\frac{||\sum_{i=1}^{N}logy_{i}\quad-\quad logy_{i}^{*}\;||^{ 2}}{N}} \tag{2}\]  
where \(N\) is the number of data points, \(y_{i}\) is the predicted depth map, and \(y_{i}^{*}\) is the GT depth map. The \(RMSE_{linear}\) and \(RMSE_{log}\) have values as small as possible. d. Results and discussions  
The results of evaluating depth estimation on KITTI, NYUDepth, Make3D [50], Cityscapes [51], TUM RGB-D SLAM, and ICL-NUIM datasets with \(RMSE_{linear}\) and \(RMSE_{log}\) measurements are shown in Table 2. The evaluation results of DRM-SLAM_F [52] on the NYUDepth dataset [49] are the best. The evaluation results of Cowan-GGR [53] on the KITTI dataset are the best. The evaluation results of DVO_CNN [43] on the Make3D dataset [50] are the best. The evaluation results of DRM-SLAM_F [52] on the TUM RGB-D SLAM dataset are the best. The evaluation results of PE_N [54] on the ICL-NUIM dataset are the best. In Table 2, the KITTI dataset was evaluated in most studies, and the Make3D and Cityscapes datasets were evaluated in only a few studies. Table 2 also shows studies on the depth estimation evaluated across multiple datasets, so equal comparisons across studies are difficult to make. Therefore, Table 2 has many empty cells.
## 1 Introduction
Given that the randomness in the transmission power can potentially affect the network topology and in some cases balance the power consumption among nodes with different harvesting ratios, the goal of this paper is to see how clustering can affect network performance in the existence of transmission power adjustment. For clustering, we used a simple energy-based clustering where each node connects to the neighbor with higher available energy. Thus, node A connects to node B as cluster head if node B has higher available power than node A. As cluster heads usually handle more traffic, by using this method, we can assure nodes with higher power become cluster head. However, any other clustering algorithm can be adjusted with the proposed transmission power control method. We show that, even by using a simple clustering concept, the network shows a fairer distribution of power consumption, which results in achieving better network end-to-end performance. The rest of this paper is organized as follows: Section 2 presents the related work in clustering and power control for energy harvesting sensor networks. The system model is introduced in Section 3. The numerical results are presented and discussed in detail in Section 4. Section 5 presents the conclusion and future work.