esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 3 Adaptive Buffering Scheme
### Determining the Ratio of Caching and Prefetching Areas
Figure 4 shows the ratios of caching and prefetching areas when employing our proposed adaptive buffering scheme and the conventional fixed buffering scheme. Note that the two same peers in both \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Definition** \\ \hline \(P_{i}\) & peer with index \(i\) \\ \(T_{i}\) & playback time position of \(P_{i}\) \\ \(T_{earliest,i}\) & current playback time position of the earliest peer in group \(i\) \\ \(T_{latest,i}\) & current playback time position of the latest peer in group \(i\) \\ \(\alpha\) & maximum ratio of caching area \\ \(R_{prefetch,i}\) & ratio of prefetching area in \(P_{i}^{\prime}\)’s buffer \\ \(R_{cache,i}\) & ratio of caching area ratio in \(P_{i}^{\prime}\)’s buffer \\ \(F_{i}\) & buffer adjustment cycle of playback period \(i\) \\ \(AF_{i}\) & average cycle between playback period 1 and playback period \(i\) \\ \(UNA_{i}\) & number of peers that have joined and left per time unit between playback period 1 and playback period \(i\) \\ \(UIN_{i}\) & number of peers that have joined and left per time unit during playback period \(i\) \\ \(N_{i}\) & number of peers that have joined and left during playback period \(i\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of symbols. cases have the same playback position while having different ratios of caching and prefetching areas. Since the ratio of caching and prefetching areas in a peer's buffer is almost fixed in the conventional fixed buffering scheme, it does not need to consider the buffering status of other peers in the group. On the contrary, since, in our adaptive buffering scheme, the ratio of the caching and prefetching areas in each peer's buffer can be adjusted adaptively according to its relative playback position within a group, it caches more data to provide to other peers as its playback position moves earlier in time. For example, in Figure 4, the number of chunks that \(P_{i}\) can provide to other peers is two in the fixed buffering scheme: one chunk cached in the caching area of its buffer and the other in the prefetching area received from an earlier peer. However, in the adaptive buffering scheme, since the ratio of the caching area of \(P_{i}\) increases, five chunks in the caching area can be provided to other peers. Therefore, the adaptive buffering scheme can improve the overall performance, because it can increase the degree of data duplication among peers by increasing the overlapping periods among peers' buffers even though the intervals among peers' playback positions increase.
## 10 Social Impact and Clinical Applications of Robotic Prostheses with 3D Printing
### Democratization of Access to Customized Prosthetics  
Historically, traditional prosthetics have been costly and difficult to obtain, especially in resource-limited settings. However, 3D printing allows for the production of low-cost  
Figure 11: Workflow for the production of robotic prostheses using 3D printing.  
robotic prosthetics, expanding access to a wider population. Nonprofit organizations, such as e-NABLE, have adopted 3D printing to create and distribute affordable prosthetic hands and arms, making functional devices accessible to individuals who might otherwise be unable to afford them [16].  
Example and Impact: In e-NABLE projects, volunteers collaborate globally to design custom prosthetics that can be tailored to the needs of each user, including options for color and style. This initiative exemplifies how 3D printing democratizes medical device access, significantly improving the lives of economically disadvantaged patients and demonstrating the impact of open-source prosthetic design.  
3D Printing Advantage: The speed and low cost of 3D printing allow for rapid and affordable customization, providing a viable solution for populations in need of frequently adjusted prostheses, such as children who outgrow devices quickly.
_Applied_  
_sciences_  
_Article_  
Piezoelectric Normally Open Microvalve with Multiple Valve  
Seat Trenches for Medical Applications  
**[PERSON] 1,2,*\(\copyright\), [PERSON] 1\(\copyright\), [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
1 Fraunhofer EMFT Research Institution for Microsystems and Solid State Technologies,  
80866 Munich, Germany; [EMAIL_ADDRESS] (S.T.G.); [EMAIL_ADDRESS] (P.K.M.)  
2 Chair of Physics of Electrotechnology, Technical University of Munich, 80333 Munich, Germany; [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (G.S.)  
* Correspondence: [EMAIL_ADDRESS]; Tel. +49-89-54759-497  
**Citation:** [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Piezoelectric Normally Open  
Microvalve with Multiple Valve Seat  
Trenches for Medical Applications,  
_Appl. Sci._**2021**, _11_, 9252. [[https://doi.org/10.3390/app11199252](https://doi.org/10.3390/app11199252)]([https://doi.org/10.3390/app11199252](https://doi.org/10.3390/app11199252))  
Academic Editor: [PERSON]  
Received: 6 August 2021  
Accepted: 30 September 2021  
Published: 5 October 2021  
**Piezoelectric Normally Open Microvalve with Multiple Valve**  
Seat Trenches for Medical Applications**  
**[PERSON] 1,2,*\(\copyright\), [PERSON] 1\(\copyright\), [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
1 Fraunhofer EMFT Research Institution for Microsystems and Solid State Technologies,  
80866 Munich, Germany; [EMAIL_ADDRESS] (S.T.G.); [EMAIL_ADDRESS] (P.K.M.)  
2 Chair of Physics of Electrotechnology, Technical University of Munich, 80333 Munich, Germany; [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (G.S.)  
* Correspondence: [EMAIL_ADDRESS]; Tel. +49-89-54759-497  
**Abstract: Microfluidic systems for medical applications necessitate reliable, wide flow range, and low leakage microvalves for flow path control. High design complexity of microvalves increases the risk of possible malfunction. We present a normally open microvalve based on energy-efficient piezoelectric actuation for high closing forces and micromachined valve seat trenches for reliable valve operation. A comprehensive investigation of influencing parameters is performed by extensive fluidic 3D finite element simulation, derivation of an analytical closed state leakage rate model, as well as fabrication and test of the microvalve. Additional valve seat coating and a high force actuator are introduced for further leakage reduction. The microvalve has a wide-open flow range as well as good sealing abilities in closed state. Extensive fatigue tests of 1 \(\times\) 10\({}^{6}\) actuation cycles show that additional coating of the valve seat or increased actuator strength promote sealing performance stability. Analytical calculations of leakage are suitable to estimate experimentally obtained leakage rates and, along with computational fluidic dynamic (CFD) simulations, enable future microvalve design optimization. In conclusion, we demonstrate that the presented normally open microvalve is suitable for the design of safe and reliable microfluidic devices for medical applications.**  
**Keywords:** microvalve; piezoelectric; microfluidics; modeling; FEA; CFD; fatigue
## 4 Parsimonious Network Based on Fuzzy Inference System (PANFIS)
### Rule Pruning Scenario of PANFIS  
PANFIS is equipped by a rule base simplification strategy, namely the extended rule significance (ERS) method, which discards inconsequential rules. The inconsequential rules which are less important during their lifespan can be detected and will be pruned in the latter process. In the realm of EIS, the rule pruning strategy is vital to alleviate the risk of overfitting and to improve the interpretability of rule semantics. The rule pruning scenario adopts the same principle of the DS method, which approximates the statistical contribution of fuzzy rules. The ERS method is formalized as follows:  
\[ERS_{i}=|\delta_{i}|\frac{\left(V_{i}\right)^{n}}{\sum\limits_{i=1}^{C}\left( V_{i}\right)^{n}} \tag{10}\]where \(\delta_{i}=\sum\limits_{j=1}^{n+1}W_{i,j}\) stands for the total output weight of the \(i\)-th rule. The salient feature of the ERS method is in the approximation form of statistical contribution during the training process under the assumption of uniform distribution. In addition, (10) also measures the contribution of output weights. The fuzzy rule contribution is deemed poor provided it has a low output weight because it results in a small output which can be negligible to the overall predictive output of PANFIS. A fuzzy rule is pruned once the following condition is fulfilled:  
\[ERS_{i}\leq g_{2} \tag{11}\]  
where \(g_{2}\) is the rule pruning threshold. The higher the value of the rule pruning threshold, the higher the number of fuzzy rules that are pruned during the training process, and vice versa. This is because the ERS method and the DS method share a similar working principle, and \(g_{1},g_{2}\) are often selected close to each other.
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 2 Medical Data Management Platform  
Since [PERSON] published the white paper of Bitcoin [8], the blockchain technology that supports Bitcoin has made great progress in the fields of Internet of Things [9; 10; 11], healthcare [12; 13], finance [14; 15], etc. At present, there are some review papers that have described blockchain technology from the aspects of consensus algorithm [16; 17; 18], application scenarios [19; 20; 21], and transaction data management [22; 23]. As the foundation of smart healthcare, medical data are closely related to personal privacy, medical research, and drug research. In order to explore the value of medical data, many research works have used technologies such as data storage, data fusion, privacy protection, and federated learning to break the data island and realize the management of medical data [24; 25; 26; 27]. However, the traditional methods lack a trust mechanism, and it is difficult to meet the needs of trusted sharing and secure provenance of data in a multi-party untrusted environment. In view of the decentralized, tamper-resistant, traceable, and other features of blockchain, some studies have used blockchain technology to solve problems such as storage and sharing of medical data, privacy protection, and provenance, so as to achieve trusted management and provenance of medical data among multiple organizations.
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
## 5 Impact of the Proposed Antenna on the NoC Infrastructure Performance  
This section evaluates the impact of the proposed antenna on the performance of the whole NoC infrastructure, and hence on the performance of an MPSoC, with respect to the use of a zig-zag antenna, as in [31; 37]. It is worth noting that the higher HPBW of our design vs. zig-zag antenna in [31; 37] leads to a larger number of cores that can be reached by the WiNoC.  
Moreover, the increased antenna gain of about 19 dB (\(-\)7.5 dBi in our case vs. \(-\)26.5 dBi in [31; 37]) can be exploited to reduce the power consumption of the NoC infrastructure, and hence of the MPSoC. Indeed, in the link budget of a wireless connection, the received power in dB depends on the transmitted power plus the gain of the transmitting and receiving antennas (i.e., twice the gain of the antenna in dB if the same antenna is used during transmission and receiving phases), minus the path loss. By increasing the antenna gain by 19 dB, adopting the design proposed in Section 3 instead of a zig-zag antenna topology, then to achieve the same distance with the same path loss of [31; 37], the transmitted power can be decreased by about 38 dB. For example, instead of transmitting 1 mW of power (which is drained from the power supply), the WiNoC transceiver can transmit about 200 nW, with a power saving by a factor 5.  
Finally, the larger antenna bandwidth, measured in GHz (see Table 2), can be exploited to sustain a higher bit-rate vs. [31; 37], measured in Gb/s, when using the same transceiver and the same modulation and coding scheme (i.e., with equal spectrum efficiency, measured as b/s/Hz).
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 4 Methodology of MAPE's Analysis Service
### Analysis Flow and Components
#### 4.3.1 Metric Collector  
The subcomponent responsible for the collection and combination of these raw metrics according to time, origin, NS, and VNF is the metric collector component, written in Scala and deployed as an Apache Spark application. This component subscribes to the central Kafka bus and to the appropriate topics of interest, receives the raw metrics, and forms them into proper feature vectors, grouping them by running the VNF and VDU. Obviously, the topics receive all the enabled metrics supported by a monitoring tool, hence we should note that metrics published to and received from Kafka that are not included in the list of employed metrics are simply omitted.  
Having received the raw metrics, the MC performs the necessary normalization actions and transforms them into feature vectors, before grouping them per running VNF. A timing deviation of one second is considered acceptable for two measurements of the same VNF being packed into the same feature vector. If a round of measurements is not properly synchronized, then the stale measurements will be discarded and will be updated with the most recent values until a complete and synchronized feature vector is formed.  
When a synchronized set of measurements is collected, forming a feature vector, a simple min-max normalization is applied to the features. It is important to note that an extensive profiling of the incoming measurements was conducted a priori, to enable the min and max values and the statistical data of each metric to be known and thoroughly understood. Following the realization of a measurement vector, the MC component publishes the outcome to the central Kafka bus. Thence, it is consumed by the RL agent of the AS module, and in retrospect, after being processed, a possible suggestion intended for further consideration by the planning module is published to the Kafka bus.
## 5 RT Real-Time Simulation and Experimental Validation
### Experimental Validation  
Experimental tests have been carried out for a 1 kW five-phase IM using TMS320C32 DSP based control board. Figure 5 shows the experimental results of IRFOC of the five-phase IM using only the fundamental components of the currents. The (\(\alpha\)-\(\beta\)) and (\(x\)-\(y\)) components of stator currents when the motor runs at 250 RPM without load (\(T_{L}=0\)) are shown in Figure 5. The same waveforms are presented in Figure 5 when a 50% of the rated load is applied to the motor at time t = 1.3 s. Figure 5,d illustrates the waveforms of the stator currents in the (\(d\)-\(q\)) and (\(x\)-\(y\)) planes and the motor speed when the motor runs at 250 RPM in both cases (without load and 50% of the rated load). The waveforms are consistent with the theoretical analysis; the stator current is a sinusoidal waveform as desired, sharing the similarity with the air-gap flux profile. From the actual speed profile, it can be realized that the IRFOC system is very stable during the steady state operation. It is observed that the speed regulation and the steady-state operation is realized very fast. However, the speed is attributed with the chattering phenomena, but it is improved by replacing the signum function with a saturation function.  
Figure 5: _Cont_.  
The experimental tests are also carried out when operating the motor at very low speed (20 RPM), this is to investigate the validity of the SMO for a wide range of speed operation while exposing the system to uncertainties (i.e., rotor resistance variation). From Figure 5(a), it can be observed that the estimated speed tracks the actual speed with high precision. Figure 5(b) also confirms this fact, as it shows the speed error profiles which is almost zero. Figure 5(c) shows the torque profile, whereas Figure 5(d) illustrates the estimated rotor flux, and from this figure, it can be realized that the proposed SMO observer has managed in achieving an accurate estimation of the rotor flux.  
Figure 5: Experimental validation at operating speed of 250 RPM.  
Figure 6: Cont.
## 2 Materials and Methods
### Procedures  
#### 2.5.1 Voice Recording  
Participants had their voices recorded in an acoustically treated room at the Speech-Language Pathology and Audiology Clinic. We used an AKG microphone (model C 44 PP; AKG Acoustics GmbH, Viena, Austria) positioned at a 45\({}^{\circ}\) angle from the participants' mouth and 2 cm away from their labial commissure. The microphone was connected to a computer with a Creative Sound Blaster sound card (model Audigy II; Creative Technology Ltd., Singapore). We used the Sound Forge program (version 10; MAGIX, Munich, Germany) with a sample rate of 44,100 Hz. A 16-bit.wav extension was used to record the voices samples. Participants were asked to sustain for approximately 5 s, at a comfortable pitch and loudness, the vowel/a/ and then count from 1 to 10 (i.e., assessing continuous speech).
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
## 2 Methodology
### Theoretical Model  
The SIMECK round function is described by Equations (1) and (2).  
\[\text{SIMECK}(L_{i},R_{i})=(R_{i}\oplus f(L_{i})\oplus K,L_{i}) \tag{1}\]  
where  
\[f(L_{i})=(L_{i}\odot(L_{i}\lll 5))\oplus(L_{i}\lll 1) \tag{2}\]  
The TEA round function is defined in Equation (3):  
\[TEA_{K_{i}}(L_{i},R_{i})=(R_{i}+((L_{i}\ll 4\oplus L_{i}\gg 5)+L_{i})\oplus(K_{i }+\Delta),L_{i}) \tag{3}\]  
where  
* \(L_{i}\) is the left part of the input.
* \(R_{i}\) is the right part of the input.
* \(K_{i}\) is the round key.
* \(\Delta\) is the key schedule constant (\(\Delta=0x9x3779b9\)).  
In order to apply 3 rounds of SIMECK each including 5 cycles of TEA, Equation (4) defines \(SIMECK_{3}\):  
\[\text{SIMECK}_{3}(L_{i},R_{i})=\text{SIMECK}_{2}(\text{SIMECK}_{1}(\text{SIMECK}_ {0}(\text{TEA}_{5}(L_{i},R_{i})))) \tag{4}\]  
where each SIMECK function call includes the TEA function in Equation (6):  
\[\text{SIMECK}_{j}(L_{i},R_{i})=\text{SIMECK}_{\text{TEA}_{5}}(L_{i},R_{i}) \tag{5}\]  
for each round \(j\) from 0 to 2.  
\[\text{TEA}_{5}(L_{i},R_{i})=\text{TEA}_{4}(\text{TEA}_{3}(\text{TEA}_{2}(\text {TEA}_{1}(\text{TEA}_{0}(L_{i},R_{i}))))) \tag{6}\]
# Sleep Apnea Classification Using the Mean Euler-Poincare Characteristic and AI Techniques
## 4 Methodology
### Neural Network Classifier  
This work uses a feedforward multi-layer neural network to classify apnea or non-apnea patients. In this architecture, data move in one direction from the input layer through one or more hidden layers to the output layer. FNNs are commonly used for classification tasks, such as determining whether a patient has sleep apnea. The network is fully connected, meaning each neuron in one layer connects to every neuron in the next layer. The model is trained by adjusting the weights between these connections to minimize the difference between the predicted and actual labels using a process called backpropagation. In binary classification, the output typically comes from a single neuron using a sigmoid activation function to produce a probability.  
The main equation governing the computation in a neural network is the weighted sum of the inputs at each neuron, followed by an activation function. For neuron \(j\), the pre-activation output \(z_{j}\) is given by the following:  
\[z_{j}=\sum_{i=0}^{n}\omega_{ji}x_{i}+b_{j} \tag{11}\]  
where \(\omega_{ji}\) denotes the weights, \(x_{i}\) denotes the input values, and \(b_{j}\) denotes the bias. The weighted sum \(z_{j}\) is then passed through an activation function \(f(z_{j})\) to introduce non-linearity. This nonlinearity is essential for the network to model complex patterns in the data. The final output from the network depends on both the network architecture and the activation function choice.  
Figure 9 shows the neural network used in this work to classify apnea, where an input layer, two hidden layers, and an output layer were implemented. A Swish activation function was implemented in the input layer, a ReLU activation function was implemented in the first hidden layer, a tanh activation function was used in the second hidden layer, and a softmax was used in the output layer.  
Table 3 summarizes the values of the parameters of the FNN builder in Python using the TensorFlow library.  
Figure 9: Feedforward multi-layer neural network architecture proposed for classifying sleep apnea; an input layer using a Swish activation function, two hidden layers—one with a ReLU function and the other with a tanh function—and an output layer equipped with a softmax function.  
Below are the results obtained using two types of polynomial orders to smooth the signal. They are also compared with other models.
## 3 Kinematic Analysis of CDFM
### Mapping between Motor and Cable  
The motion of the driving cable was realized by the rotation of the motor. The mapping relationship between the cable length variable and the motor rotation angle was as follows:  
\[\Delta L_{k,i}=\Delta\varphi R \tag{1}\]  
where \(\Delta\varphi\) and \(\Delta L_{k,i}\) respectively denote the variations in the motor angle and cable length, and \(R\) is the winding radius of the cable (here, \(R=32\) mm).
## 3 Results
### Performance of Delirium Prediction Models Based on XGBoost  
The prediction performance of the model based on XGBoost after cross-validation is presented in Table 2. For the discrimination performance of the prediction model, the AUROC was 0.852 for population A, 0.806 for population B, and 0.794 for population C. Similarly, the AUPRC calculated to assess the ability to adequately identify imbalanced events was 0.329 in population A, 0.365 in population B, and 0.177 in population C. Furthermore, the calibration slope was 1.013 for population A, 1.102 for population B, and 1.032 for population C. Discrimination and calibration performances were generally good.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Discrimination**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Overall**} \\ \cline{3-8}  & & **Sensitivity** & **Specificity** & **AUROC** & **AUPRC** & **Slope** & **Intercept** & **Brier Score** \\ \hline A, n = 55,389 & & 0.838 & 0.721 & 0.852 & 0.329 & 1.013 & 0.001 & 0.062 \\  & & (0.015) & (0.022) & (0.005) & (0.015) & (0.038) & (0.049) & (0.002) \\ B, n = 30,043 & XGBoost & 0.783 & 0.690 & 0.806 & 0.365 & 1.102 & 0.002 & 0.098 \\  & & (0.027) & (0.015) & (0.006) & (0.014) & (0.017) & (0.017) & (0.001) \\ C, n = 4293 & & 0.767 & 0.709 & 0.794 & 0.177 & 1.032 & \(-0.047\) & 0.044 \\  & & (0.084) & (0.040) & (0.033) & (0.052) & (0.347) & (0.306) & (0.004) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predictive performance of prediction models.