esa-sceva/satcom-chunk-collection huggingface dataset:
# Wheat Bread with Dairy Products--Technology, Nutritional, and Sensory Properties
## 2 Materials and Methods
### _Physical Characterisation of the Wheat Dough_
#### 2.3.2 Rheology Characterisation  
Fundamental viscoelastic behaviour of wheat dough (WD) after fermentation was studied by small amplitude oscillatory shear (SAOS) measurements using a controlled stress rheometer (Haake Mars III-Thermo Scientific, Karlsruhe, Germany) with an Universal Temperature Control-Peltier system to control temperature. The procedure was in agreement with the conditions previously optimized [12]. Stress sweeps were performed at 0.1 and 1.0 Hz to determine the linear viscoelastic region and select the stress to use. Frequency sweep tests, performed at 5 \({}^{\circ}\)C to inhibit the yeast fermentative activity, were applied to evaluate the effect of the Yg and Cc additions on dough structure. The evolution of the dough after fermentation time, expressed in terms of storage (G') and loss (G\") moduli, were obtained, ranging the frequency from 0.001 Hz to 100.0 Hz at a constant shear stress within the previously determined linear viscoelastic region of each sample. All determinations were repeated at least three times to ensure reproducibility of the results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ingredients** & **CB** & \(\mathbf{Yg_{10g}}\) & \(\mathbf{Cc_{10g}}\) & \(\mathbf{Yg_{20g}}\) & \(\mathbf{Cc_{20g}}\) & \(\mathbf{Yg_{30g}}\) & \(\mathbf{Cc_{30g}}\) & \(\mathbf{Yg_{50g}}\) & \(\mathbf{Cc_{50g}}\) & \(\mathbf{Yg_{70g}}\) & \(\mathbf{Cc_{53g}}\) \\ \hline Wheat flour & 100.0 & 98.9 & 97.4 & 97.7 & 94.8 & 96.6 & 92.2 & 94.3 & 87.1 & 92.0 & 78.5 \\ Deionized water & 61.8 & 53.0 & 54.4 & 44.1 & 47.0 & 35.3 & 39.6 & 17.6 & 24.7 & 0.0 & 0.0 \\ Yoghurt \({}^{\star}\) & 0.0 & 10.0 & - & 20.0 & - & 30.0 & - & 50.0 & - & 70.0 & - \\ Curd cheese \({}^{\star}\) & 0.0 & - & 10.0 & - & 20.0 & - & 30.0 & - & 50.0 & - & 83.3 \\ \hline \multicolumn{10}{c}{\({}^{\star}\) 100 g of Yg: moisture—88.5 g; dry extract—11.5 g; \({}^{\star}\) 100 g of Cc: moisture: 74.2 g; dry extract: 25.8 g.} \\ \end{tabular}
\end{table}
Table 1: Bead formulations optimized to the control bread (CB) and different levels of yoghurt (Yg) and curd cheese (Cc) addition, considering the moisture content and dry extract of each dairy product (DP) \({}^{\star}\).
# 25 Years of Bluetooth Technology
## 2 Bluetooth Technologies
### Bluetooth BR/EDR
#### 2.1.1 Bluetooth Protocols  
Bluetooth operation utilizes seven different protocols: radio protocol, baseband protocol, Radio Frequency Communication (RFCOMM), the Service Discovery Protocol (SDP), Link Management Protocol (LMP), the Logical Link Control and Adaptation Layer Protocol (L2 CAP), and the Host Controller Interface (HCI) protocol. Figure 5 illustrates the Bluetooth protocol stack.  
The radio protocol runs in the digital radio-processing component of the Bluetooth radio. Bluetooth utilizes the GFSK modulation. The Bluetooth ISM band is divided into 79 channels of 1 MHz each. Each channel is further divided into 625 microsecond timeslots which results in 1600 slots per second. Data is transmitted through these slots and channels. The baseband protocol processes the sent and received signals. The handling of error correction, links, packets, and flow control is implemented by the baseband protocol.  
Bluetooth enabled devices can establish Synchronous Connection-Oriented (SCO) and Asynchronous Connectionless links (ACL). Data packets use ACL primarily while voice packets use SCO. ACL links are primarily defined for data transmission. They support asymmetrical, symmetrical and packet-switched connections. Multi-slot packets use the ACL link type. With ACL,  
Figure 5: Bluetooth protocol stack [1; 7].  
the maximum achievable data rate is 723 kbps in one direction and 57.6 kbps in the other direction. The master device controls ACL link to allocate bandwidth for a piconet slave. The ACL link supports broadcast messaging from the master node to all slaves in the piconet.  
SCO links support symmetrical, point-to-point connections and are mainly used for voice transmissions. For a SCO link, every sixth slot is reserved for a transmitting channel and reservation of a subsequent slot is done for the receiving channel. After the establishment of the connection, both master and slave units send SCO packets when they need to do so. Data and voice transmissions are allowed in one SCO packet type with retransmission of the data portion carried out when the packet gets corrupted.  
The Host Controller Interface (HCI) provides a command interface to the baseband controller and link manager. It also provides access to control registers and hardware status. HCI is accessible at the host, host controller, and the transport layer. L2 CAP is structured over the baseband protocol which is present in the data link layer. L2 CAP segments packets for Bluetooth transmission and when the packet passes through the L2 CAP of another Bluetooth device, its original form is restored through reassembly. L2 CAP supports only ACL and not the SCO links.  
RFCOMM is a transport protocol which allows RS232 serial port emulation over the L2 CAP protocol. The Service Discovery Protocol (SDP) uses a request-response message style such that each transaction has a response Protocol Data Unit (PDU) and one request PDU [1; 7].
## 4 Results
### Robustness Experiments in Scenario 2  
Figure 10 displays the impact on the patient's cells based on the treatment plans created by the three parameters under Scenario 2's limit. The three treatment regimens both used MIER-MO-DQN.  
According to the slope of the curve in Figure 10a, all three treatment regimens had virtually the same rate of tumor cell inhibition. Figure 10b shows the patient's change in effector-immune cell count, and the mean value of effector-immune cells differs by 0.9% and 0.3% between the treatment regimen with the original parameters and the other two regimens with changed parameters. The changes in the patient's circulation lymphocyte counts are shown in Figure 10c, and the difference between the three regimens' average circulating lymphocyte counts does not surpass 1%. Figure 10d,e show the drug concentrations and doses given for each of the three dosing regimens, and it can be seen that the doses and usage times for each regimen almost follow the same pattern. Table 6 displays the various experimental indices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **MIER-MO-DQN** & **W\_DQN** & **DQN** \\ \hline Time to completion of treatment & 481 & 1635 & 22 \\ Value of tumor cells at 50 days & 11.9\% & 55.4\% & 0.8 \\ Value of tumor cells at 100 days & 2.1\% & 33.7\% & 0.8 \\ Average value of effector-immune cells & 72.6\% & 104\% & 18.7\% \\ Minimum value of effector-immune cells & 70.3\% & 93\% & 6\% \\ Average value of circulating lymphocytes & 32.3\% & 68.4\% & 15\% \\ Average value of drug concentration & 0.047 & 0.019 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental indicators in different methods.  
Figure 9: The change of (**a**) tumor cells, (**b**) effector-immune cells, (**c**) circulating lymphocytes, (**d**) drug concentration, according to different methods in Scenario 2.  
Figure 10: The changes of (**a**) tumor cells, (**b**) effector-immune cells, (**c**) circulating lymphocytes, (**d**) drug concentration, (**e**) drug dose according to different parameters on cells in patients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **Original Parameter** & **Parameter with +10\%** & **Parameter with \(\mathbf{-10\%}\)** \\ \hline Time to completion of treatment & 481 & 450 & 508 \\ Value of tumor cells at 50 days & 11.9\% & 11.1\% & 13.5\% \\ Value of tumor cells at 100 days & 2.1\% & 1.7\% & 2.7\% \\ Average value of effector-immune cells & 72.6\% & 71.7\% & 72.9\% \\ Minimum value of effector-immune cells & 70.3\% & 70.2\% & 70.1\% \\ Average value of circulating lymphocytes & 32.3\% & 31.8\% & 31.9\% \\ Average value of drug concentration & 0.047 & 0.048 & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental indicators using different parameters.
# Ultrasound in Telemedicine: A Brief Overview
#### Tele-Ultrasound in a Historical Context  
According to the World Health Organization (WHO), in clinical situations, medical imaging is needed to make a diagnosis in 20-30% of cases, and ultrasound and/or conventional radiography are sufficient for 80-90% of those cases. However, two thirds of the world population does not have any access to medical imaging [5].  
The beginnings of tele-ultrasound date back to the era of space exploration in the 1960s, when the first remote ultrasound scans were carried out on astronauts with remote guidance from experts in the Mission Control Center [6] (Figure 1). Those ultrasound systems were capable of high-definition sonographic imaging for the cardiac, vascular, general, abdominal, thoracic, and musculoskeletal systems, among others ([PERSON] 2006).  
The technology remains relevant in aeronautics and recently several studies have assessed the use of ultrasound by non-physician crew members at the International Space Station, which entailed prior training in ultrasound [6; 7; 8; 9; 10].  
From space, the focus has shifted to earth. Here, especially in remote areas in New Zealand and Sub-Saharan Africa, tele-ultrasound has proven successful. Focusing mainly on emergency ultrasound [11] and Point-Of-Care Ultrasound (POCUS), it has been shown that remotely-guided Focused Assessment with Sonography for Trauma (FAST) ultrasound examinations with minimally trained health care workers are possible and effective [12; 13]. In obstetrics and gynecology, several successful prenatal tele-ultrasound projects have been described in literature [14; 15]. Many countries harbor only a small number of qualified fetal-medicine specialists who are capable of pre-natal ultrasound imaging, which does not adequately cover demand, but which is highly significant in diagnosing and preventing potential birth defects. Studies, mainly from remote areas such as rural Australia, have tried to identify the need for fetal therapy [16].  
Tele-ultrasound performed in resource-limited settings can produce satisfactory images of diagnostic relevance which have an impact on medical treatment and outcomes [17].
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 4 Simulation Verification Analysis
### Early Failure of Inner Raceway  
Figure 28 shows the sound signal of an early failure of the bearing inner raceway. Since the sound signal is extremely susceptible to interference and the interference of strong noise from various bench operations in the test environment, it is difficult to find periodic pulse characteristics from the picture, and the bearing cannot confirm the health status.  
The method proposed in this article and the adaptive VMD method were respectively used to perform noise reduction processing on the sound signal, as shown in Figure 28. The convergence curves of the SBOA and the CPO algorithm obtained based on the minimum envelope entropy value are shown in Figures 29 and 30. After 30 iterations of the SBOA, FMD reached the optimal combination. The filter length \(L\) is 34, the frequency band cutting number \(K\) is 14, the mode number \(n\) is 5, and the cycle period \(m\) is 34. CPO was iterated 21 times, and VMD reached the optimal combination. The number of decomposition modes \(K\) is 5, and the penalty factor \(\alpha\) is 4985. The comprehensive index ISEI of each mode of the decomposition results is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline F-IMF & IMF1 & IMF2 & IMF3 & IMF4 \\ ISEI & 1.28 & 1.60 & 2.49 & 1.19 \\ V-IMF & IMF1 & IMF2 & IMF3 & IMF4 \\ ISEI & 0.89 & 0.70 & 0.97 & 1.55 \\ \hline \end{tabular}
\end{table}
Table 3: ISEI values of the decomposed modes via parameter-optimized FMD-VMD (inner raceway fault).  
Figure 27: Shell.  
Figure 28: Time domain with incipient inner raceway fault.  
As shown in Table 3, the F-IMF3 component in S-FMD has the largest ISEI value and contains the most fault information. The envelope spectrum analysis of F-IMF3 is shown in Figure 31. Two peaks with prominent amplitude energy are observed, corresponding to the inner raceway fault characteristic frequency \(f_{i}\) and its harmonic components, respectively. Although the signal noise floor is high and there are serious interference components near low frequencies, the proposed method can still effectively extract bearing inner raceway fault information, showing its strong anti-interference ability. The ISEI value of the V-IMF4 component in C-VMD is the largest. The envelope of this component is shown in Figure 32. The fault frequency is not intuitive, and \(f_{i}\) and its harmonic components cannot be observed.  
Figure 31: Envelope spectrum of F-IMF3 decomposed via parameter-optimized FMD (inner raceway fault).  
Figure 30: Convergence curve of the CPO algorithm (inner raceway fault).  
Figure 29: Convergence curve of the SBOA (inner raceway fault).
# Combined Investigation of Indoor Climate Parameters and Energy Performance of a Winery
## 3 Results
### Thermal Comfort and IAQ Parameters

Table 5: Meteorological and environmental parameters at the installation site and at the storage rooms (1 January to 7 March). and hexane might be due to the movement of the hand pallet truck that took place at the vinicitation place at the non-winemaking period. This is also reflected for the winemaking period. Except those values, the predominant compounds for the storage room are a-pinene and 3-carene, i.e., terpenes, which are naturally emitted compounds and their concentration mainly come from the cork and wood (pine) made barrels of the wine storage tanks. For the office room, the VOC concentrations for both measurement periods present differences with regard to those of the vinicitation area, as office constitutes a separate, \"isolated\" space from this area (Figures 1 and 2); separation is more intense by wintertime as, due to heating purposes, the connecting door remains strictly closed. Especially, by wintertime (non-wine making period), the indicated high values for Benzene and Toluene are related to the occasional use of a woodstove for heating purposes of the office. Moreover, the high concentration of d-limonene in the office by non-winemaking period is probably due to the presence of some person wearing a personal care product which emits such terpen [31]. With regard to the detection of compounds which are directly related to the wine making procedure, the GC-MS analysis of the sampling air of the basement room detected compounds such as iso-amyl alcohol, ethylacetate, iso-amylacetate, hexanoic acid ethyl ester, N-hexyl acetate, octanoic acid ethylester and decanoic acid ethyl ester [32,33]. These compounds are not found at the non-winemaking period for the vinicitation area which is the place of wine making procedure.
## 5 Experimental Setup
### NN Architectures  
We tested HyperGE on two widely used pre-trained networks, viz., VGG-19 [37] and ResNet-50 [6]. VGG-19 has 19 layers, while ResNet-50 is a larger network with 50 layers of convolutions. Both of these models are pre-trained on the ImageNet benchmark dataset for image classification with 14 million instances, making them an ideal choice for transfer learning tasks. The intuition behind choosing these models was twofold: (1) they both are from different families of pre-trained networks; (2) they have a different number of layers, specifically ResNet-50 having almost double the amount of layers as that of VGG-19.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 2 System Model
### Receiver  
At the receiver side, the cyclic prefix is first removed from the received signal \(\mathbf{\tilde{y}}_{gb}(t)\) on each antenna using the cyclic prefix removal module to obtain the vector \(\mathbf{\tilde{y}}_{b}(t)\) of the length \(N_{\text{FFT}}\). The signal is then converted to the parallel form and transformed into the frequency domain by the FFT block, which gives a frequency domain signal \(\mathbf{y}_{b}(t)\) of  
\[\mathbf{y}_{b}(t)=\text{FFT}\{\mathbf{\tilde{y}}_{b}(t)\}. \tag{10}\]  
The pilot signal is excited from the frequency-domain signal for channel estimation purposes. After estimating the channel, the received signal \(\mathbf{y}_{b}(t)\) is equalized and congregated into a serial sequence from all the receiver antennas by the layer demapping module. The signal is then demodulated by the demodulation scheme, which corresponds to the approach used by the transmitter. At this point, the output of the MIMO-OFDM system model is obtained as the final binary data sequence.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 2 System Model  
### Task Model  
When performing tasks, there is generally a considerable spatial distance between the swarm of drones and the ground station, requiring the involvement of relay drones for real-time information transmission. Suppose that relay drones assisting in communications suffer from malicious interference by an enemy, causing communication disruptions. Relay dronesneed to sense enemy interference, intelligently select channels, and optimize their transmission power, trajectory, and modulation methods to maximize communication rates, thereby forming real-time, efficient, and stable relay communications. Figure 1 shows a schematic diagram of relay communication by a swarm of drones under interference conditions.  
Assuming a relay drone assists in communication for a duration of \(T\), it is divided into \(K\) time intervals, \(\delta_{T}\) and \(M\), where \(\Omega=\{1,2,...,M\}\) drones form a cluster, with the coordinates \(F_{i}=(x_{i}(k),y_{i}(k),z_{i}(k))i\in\Omega,k\in K\). The coordinates of the ground station are \((0,\ 0,\ 0)\), the jammer's coordinates are \((x^{I},y^{I},0)\), and the relay drone's coordinates are \(Q=(x_{r}^{uav}(k),y_{r}^{uav}(k),z_{r}^{uav}(k))k\in K\). Changes in positions during each time slot \(k\) are negligible. Additionally, the relay drone updates the position coordinates of the cluster drones every \(\delta\) time interval, during which changes in the cluster drones' coordinates can also be neglected. The relay drone uses NOMA multiplexing to communicate, enhancing the real-time transmission efficiency of the cluster drones, and employs amplify-and-forward in a half-duplex communication mode for data transmission.
# Discrimination Accuracy of Sequential Versus Simultaneous Vibrotactile Stimulation on the Forearm
## 4 Results
### Simultaneous Versus Sequential Presentation  
We also investigated the effects of two distinct presentation methods: sequential and simultaneous. Our results revealed a large and highly significant difference in accuracy between these two methods. Sequential presentation yielded an impressively high average accuracy rate of 93.24%, indicating that participants were highly adept at recognizing vibrotactile patterns when presented sequentially.  
In contrast, the average accuracy for the simultaneous method was substantially lower (26.15%). This very large difference clearly demonstrates that the presentation order of the vibrotactile patterns greatly affects accuracy (Figure 5).  
Figure 4: Box–whisker plot comparing the accuracy (\(y\)-axis) between the two different pattern lengths (short- and long-length patterns) with different vibrotactile presentation methods (\(x\)-axis): the graph shows the average accuracy in percentages on the upper side of the forearm for sequential (blue) and simultaneous presentations (orange). The box length shows the interquartile range (IQR) and the 25% and the 75% limits, and the horizontal bar inside the box shows the median value. The ends of the box plot represent minimum and maximum values.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 3 Experimental Results
### Experimental Results on the Challenging Dataset  
We experimented with HMG dataset, which has a different viewpoint from Cityscapes. This dataset used a SVM camera road image dataset. It consists of 1079 road images acquired by an SVM camera attached to a vehicle, provided by Hyundai Motor Group. The experimental environment is the same as previously described. However, the last of the FCL is set to 12 because the number of segmentation classes is 12. Training set and validation set comprised 80% (863 images), and the remaining 20% (216) of the images were used as the testing set. Considering that the total number of images is 1079, which is less than that of the Cityscapes dataset, the learning rate was reduced by half every 50 epochs with 150 total epochs of training. The GT mIoU value distribution of HMG dataset was composed as depicted in Table 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function** & **Failure Detection Accuracy (\%)** & **mIoU Prediction Accuracy (\%)** \\ \hline Mean squared error & 90.8 & 78.9 \\ Modified loss function & 91.7 & 80.8 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Failure detection and mIoU prediction accuracy for the different loss function for the Cityscape dataset with DeepLabV3+.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**GT mIoU** & **0–0.1** & **0.1–0.2** & **0.2–0.3** & **0.3–0.4** & **0.4–0.5** & **0.5–0.6** & **0.6–0.7** & **0.7–0.8** & **0.8–1** \\ \hline numbers & 0 & 0 & 4 & 17 & 27 & 43 & 54 & 61 & 10 \\ fraction (\%) & 0 & 0 & 1.8 & 7.8 & 12.5 & 19.9 & 25 & 28.2 & 4
###### Abstract  
Nowadays, underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water-air interface is also highly desirable. Air-to-water wireless transmission is crucial for sending control information or instructions from unmanned aerial vehicles (UAVs) or ground stations above the sea surface to autonomous underwater vehicles (AUVs). On the other hand, water-to-air wireless transmission is also required to transmit real-time information from AUVs or underwater sensor nodes to UAVs above the water surface. Previously, we successfully demonstrated a water-to-air optical camera-based OWC system, which is also known as optical camera communication (OCC). However, the reverse transmission (i.e., air-to-water) using OCC has not been analyzed. It is worth noting that in the water-to-air OCC system, since the camera is located in the air, the image of the light source is magnified due to diffraction. Hence, the pixel-per-symbol (PPS) decoding of the OCC pattern is easier. In the proposed air-to-water OCC system reported here, since the camera is located in the water, the image of the light source in the air will be diminished in size due to diffraction. Hence, the PPS decoding of the OCC pattern becomes more difficult. In this work, we propose and experimentally demonstrate a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM). Due to water turbulence and air turbulence affecting the AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. OCC can provide wide FOV without the need for precise optical alignment. Results revealed that the proposed air-to-water OCC system can support a transmission rate of 7.2 kbit/s through a still water surface, and 6.6 kbit/s through a wavy water surface; this satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER).  
visible light communication (VLC); optical wireless communication (OWC); rolling shutter; optical camera communication (OCC); machine learning; CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM)