esa-sceva/satcom-chunk-collection huggingface dataset:
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 4 Evaluation
### Perceptual Listening Tests  
To evaluate the perceptual effect of DFE on timbre in binaural Ambisonic rendering, two listening tests were conducted on 20 participants aged between 20 to 38 (17 male, 2 non-binary, 1 female) with normal hearing (ISO Standard 389 [46]) and prior critical listening experience (such as education or employment in audio or music engineering).  
Tests were conducted in a quiet room using a single set of Sennheiser HD 650 circum-aural headphones and an Apple MacBook Pro with a Fireface UCX audio interface, which has software controlled input and output levels. The headphones were equalised using a Neumann KU 100 dummy head from 11 measurements using the swept sine impulse response technique [47] with re-fitting of the headphones between measurements and 1 octave band smoothing in the inverse filter.  
Prior to the tests, participants were given the ANSI S1.1-1994 definition of timbre [48] and taken through a training exercise to familiarise themselves with the graphical user interface and task.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{M}\) & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**5**} \\ \hline
**DFE** & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** \\ \hline QE (\%) & 11.9 & 9.8 & 3.6 & 2.8 & 9.8 & 6.2 \\ PE (\({}^{\circ}\)) & 35.8 & 35.2 & 27.6 & 26.6 & 30.6 & 27.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Predictions of binaural Ambisonic rendering using the Sagittal Plane Localisation Model [44], with and without DFE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{M}\) & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**5**} \\ \hline
**DFE** & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** \\ \hline \hline PSD (sones) & 1.92 & 1.80 & 1.61 & 1.58 & 1.28 & 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Solid angle weighted mean values of PSD between reference HRTFs and Ambisonically rendered HRTFs for left and right ears.  
The base stimulus was one second of monophonic pink noise at a sample rate of 48 kHz, windowed by onset and offset half-Hanning ramps of 5 ms. Each test sound was generated by convolving the pink noise with a HRIR; either Ambisonic or not. The test sound locations (\(\psi\)) corresponded to the central points of the faces of a dodecahedron. To reduce the total number of trials, symmetry was assumed and thus only locations in the left hemisphere were used, amounting to 8 locations (see Table 5). Test sounds were normalised to a corresponding A-weighted level of between 65 and 70 dB SPL which is in line with conversational speech listening levels.
## 5 Performance Evaluation and Analysis
Thus, more information is gathered. In addition, performance tends to be stable as \(\mathbf{M}\) increases. Figure 14 shows the performance of CS-DDSG under different packet loss ratios \(\mathbf{p}\) and probabilities \(\mathbf{p_{1}}\) when \(\mathbf{M=40}\). As \(\mathbf{p}\) increases, the value of NMAE remains stable, i.e., \(\mathbf{NMAE\approx 0.014}\). This result indicates that CS-DDSG effectively resists the packet loss and maintains high reconstruction accuracy even when unreliable links exist. Additionally, its accuracy is not influenced by \(\mathbf{p_{1}}\) due to the very sparse measurement matrix. Figure 12: The number of fusions and receiving nodes during the forwarding process: (**a**) CS-DDSG; (**b**) CNCDS; (**c**) CStorage; and (**d**) ICStorage. Finally, we investigate how the proportion of source nodes \(\mathbf{p_{1}}\) impact the recovery accuracy in Figure 15. The simulation results show the following: (1) When \(\mathbf{p_{1}}=\mathbf{0.4}\), the value of NMAE decreases as \(\mathbf{M}\) increases because more nodes participate in data reconstruction as \(\mathbf{M}\) increases. (2) When \(\mathbf{M}\) is fixed, CS-DDSG performance is improved and the trend of NMAE values is very close to the value of \(\mathbf{p_{1}}\), varying from 0 to 0.6. This effect occurs because, when there are more source nodes, more nodes will receive broadcast packets before the sink obtains data. Hence, the amount of information used for reconstruction increases. However, due to the reception condition, the measurement matrix \(\mathbf{\Phi}\) is  
Figure 14: Performance of CS-DDSG with different \(\mathbf{p_{1}}\) and \(\mathbf{p}\). Figure 13: Performance of the algorithms when \(\mathbf{p_{1}}=\mathbf{0.3}\). sparse. Thus, information is increasingly limited. As a result, the trends of the NMAE values are close to the different value of \(\mathbf{p_{1}}\).
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 2 Materials and Methods
### Preprocessing Client-Declared Sample Sizes
#### 2.2.4 Convergence Analysis
We restate Theorem 4 (for the trimmed mean aggregation procedure), where \(\Delta\) is an upper bound that we prove on the distance between the trimmed mean aggregation of client-reported stochastic gradients and the true gradient (i.e., \(\|\mathbf{g}(w)-\
abla F(w)\|\)). **Theorem 4** ([12] Theorem 4). : _Consider the trimmed mean aggregation procedure. Suppose that Assumptions 4 and 5 hold, \(F(\cdot)\) is \(\lambda_{F}\)-strongly convex, and \(\alpha\leq\beta\leq\frac{1}{2}-\epsilon\) for some \(\epsilon>0\). We choose a step-size of \(\eta=1/L_{F}\). Then, with a probability of at least \(1-\mathcal{O}\Big{(}\frac{d}{L^{d}D^{d}K^{d-1}m^{d}}\Big{)}\), after \(T\) parallel iterations, we have_  
\[\Big{\|}w^{T}-w^{*}\Big{\|}\leq\bigg{(}1-\frac{\lambda_{F}}{L_{F}+\lambda_{F}} \bigg{)}^{T}\Big{\|}w^{0}-w^{*}\Big{\|}+\frac{2}{\lambda_{F}}\Delta,\]  
_where \(w^{t}\) denotes the model-weights vector at the server at the end of step number \(t\) such that 0 is the first step of the algorithm. For brevity, we do not repeat Theorems 5 and 6 from [12]. Note that they similarly depend on \(\Delta\)._  
In the following lemma, we prove the upper bound \(\Delta\). This lemma is an adaptation of Theorem 11 in [12] to our setting. Given this updated bound, the proofs of Theorems 4-6 follow precisely as in [12]. **Lemma 1**. : _Define_  
\[\mathbf{g}^{i}(w^{t})\leftarrow \begin{cases}*&i\in\mathcal{B}\\ \
abla F_{i}(w^{t})&\text{else}\end{cases} ,\quad\mathbf{g}(w^{t})\leftarrow \mathsf{truean}_{\beta}\Big{\{}\mathbf{g}^{i}(w^{t}):i\in[K]\Big{\}}.\]  
_Suppose that Assumptions 4 and 5 are satisfied, and that \(\alpha\leq\beta\leq\frac{1}{2}-\epsilon\). Then, with probability at least \(1-\mathcal{O}\Big{(}\frac{d}{L^{d}D^{d}K^{d-1}m^{d}}\Big{)}\),_  
\[\|\mathbf{g}(w)-\
abla F(w)\|\leq\mathcal{O}\bigg{(}\frac{vdU}{\bar{m}\sqrt{m }}\bigg{)}\sqrt{\log\bigg{(}1+\widehat{L}D\sum_{i=1}^{K}n_{i}\bigg{)}+\frac{1} {d}\log K}+\widetilde{\mathcal{O}}\bigg{(}\frac{U}{K\bar{m}m}\bigg{)}. \tag{7}\]  
Proof. : See Appendix D.  
It can be seen that \(\Delta\) increases with \(d\) and \(U\) and decreases with \(m\) and \(\bar{m}\), with the latter three showing that the bound becomes less tight when the distributed dataset becomes more imbalanced.
# Key Schedule against Template Attack-Based Simple Power Analysis on a Single Target
## 1 Introduction
#### Challenge & Contribution  
An adversary should acquire the identical device in order to build the templates in the profiling scenario. If an adversary creates the templates without identical devices, the template attacks will be more attractive compared to non-profiling attacks. But, how can we create templates in a non-profiling scenario? The answer is that we can utilize public information such as plaintext and ciphertext. Then, Pols can easily be found without secret information. Afterward, the templates can be built. Despite this fact, in usual scenarios, the templates are only used to recover the public information, not secret, due to the fact that the templates are built from public information. From a different angle of view, in this paper, we utilize the template created from public information to the key schedule operation, which is composed of secret information. More precisely, after searching the loading/saving parts in public and secret information, we can apply the templates to the secret information part. Additionally, although assuming that an adversary cannot recover any secret information in encryption/decryption parts, our suggestion allows us to recover the secret key.  
While performing our suggestion, the trace points used in the profiling phase might not be identical to those used in the extraction phase. To resolve this issue, we should estimate the statistical difference between the traces used in the profiling and extraction phases, before executing the extraction phase. Intuitively, the success rate seems to depend on the statistical difference. In this paper, we show the possibility of our suggestion, which means that the template attack from the fact that the profiling is created by only the public information is feasible.  
In generic non-profiling attacks, the key schedule operation of a block cipher cannot be targeted since all intermediate variables in the key schedule operation cannot be changed. However, even though we do not employ the original profiling concept, the Hamming weight (HW) information can be recovered on key schedule operation. When performing a template attack to create the templates, we can utilize public operations like plaintext or ciphertext. In other words, while the plaintext or ciphertext are moved to the local variables, the templates can be constructed. Despite the mitigated assumption, the success rate is quite high when the power consumption model is considered as a HW model. After the HW value of the secret key is recovered using our suggestion, we can employ an attack scheme like an algebraic attack to completely retrieve the master key. Consequently, by our suggestion, the master key is directly retrieved in the key schedule operation without assuming an original profiling attack. In this paper, to show our suggestion, we decide the main target as the key schedule of a block cipher Advanced Encryption Standard (AES) [19].
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 5 Simulation Results  
In this section, we evaluate the performance of the clipped OTFS-BFDM system by simulations. Firstly, the PAPR distribution for clipped OTFS-BFDM signals is given, then the BER performance between the MP-AIC and MP-A methods in the clipped system is compared. In the simulation below, the delay-Doppler profile considered is shown in Table 2, while other relevant simulation parameters are summarized in Table 3, where the channel has five taps with uniform power [30].
# Querying on Federated Sensor Networks
## 1 Introduction
As intra-island querying protocols, we discuss the adoption of querying protocols in the literature which are selected from each of categories of geometrical, hierarchical cluster-based, hash-based, and tree-basedcategories [4]. We found that a layered FSN management by L-FSN protocol provides efficiency in terms of energy-efficiency, query resolving distance, and querying latency compared to the flat management. We found that, as intra-island querying protocols of L-FSN, running infrastructure-free protocols in small islands while running infrastructure-based protocols in large islands provides a benefit on both energy-efficiency and query resolving latency.
## 5 Discussion of the Results and Limitations  
The proposed NMP possesses interesting properties to meet the requirements of matrix profile indexing. It is faster than the STAMP and STOMP algorithms and occupies less space compared to the other algorithms. The proposed NMP has computational complexity O(n), whereas STOMP has complexity O(n2) and STAMP has O (n2 log n). The most important feature of the proposed NMP is that it handles queries of variable lengths. There is a very marginal chance of producing false positives or false negatives. With respect to the segmented representation, our NMP is guaranteed to produce no false negatives. However, we cannot make the same claim with respect to the original results. Because such an occurrence involves pathological conditions and has never been found in any of our studies, we do not feel that this concern is a major limitation. Nevertheless, we will briefly address an extension to our representation that would allow us to change our indexing method to ensure no false negatives with regard to the original data. A different type of samples involves 11 features that take longer to deal with. As a result, this complexity challenges the limitations of STAMP and STOMP algorithms. On the other hand, the proposed NMP leverages the feature of sorting confidence level intervals that offer data about a series in which the exact value involves a confident degree of probability. Furthermore, the data representation section can be extended from a 4-tuple to a 5-tuple, where the additional element is the residual error of approximating the rows. It is possible to adjust the distance measures stored in the index to provide a confidence bound, which is simply the sum of all the residual error terms from all of the segments that are compared. To determine the best segmented match for a query, an initial run of an indexing scheme can be used. The raw data can be indexed using this best fit, and the real Euclidean distance  
Figure 6: (**a**) Detection efficiency of the proposed NMP and contending STAMP and STOMP algorithms with patient’s infected data. (**b**) Detection efficiency of the proposed NMP and contending STAMP and STOMP algorithms with patient’s recovered data. (**c**) Detection efficiency of the proposed NMP and contending STAMP and STOMP algorithms with patient’s death data.  
between the query and the best segmented raw data can be computed. Table 2 shows the performance of the proposed and compared algorithms.
## 3 Results
### Reactive Strength Index, Average Power Output, and Vertical Jump Height
#### 3.2.1 Reactive Strength Index  
[PERSON]'s test indicated a violation of the sphericity assumption; thus, Greenhouse-Geisser correction was applied. Significant main effects were observed for time (F(3, 117) = 5.12, \(p\) < 0.01, \(\eta^{2}\)P = 0.12, and 1 \(-\)\(\beta\) = 0.78) and velocity (F(2, 78) = 4.23, \(p\) < 0.05, \(\eta^{2}\)p = 0.10, and 1 \(-\)\(\beta\) = 0.65) on the reactive strength index (RSI). Under the fast-speed condition, the RSI significantly increased at 4 and 8 min compared to baseline (\(p\) < 0.05), with effect sizes of d = 0.53 (95% CI [0.09, 0.97]) and d = 0.49 (95% CI [0.05, 0.93]), respectively. No significant change was observed at 30 s (\(p\) > 0.05). Under the medium-speed condition, a significant increase in the RSI was observed at 4 min (\(p\) < 0.05), with d = 0.25 (95% CI [-0.19, 0.69]), while the change at 30 s was not significant (\(p\) > 0.05). Under the slow-speed condition, the RSI significantly increased at 30 s compared to baseline (\(p\) = 0.01, d = 0.52, 95% CI [0.08, 0.96]), but no significant changes were observed at 4 min (\(p\) > 0.05).  
Figure 2: **(A)** Effects of time and flywheel speed on reactive strength index. **(B)** Reactive strength index across different flywheel speeds. **(C)** Average power output across different time points. **(D)** Average power output across different flywheel speeds. **(E)** Vertical jump height across different time points. **(F)** Vertical jump height across different flywheel speeds. * indicates a significant difference compared to the baseline value; # indicates a significant difference compared to the 30 s mark.  
Across all velocities, the RSI values exhibited a linear trend at 4 and 8 min, with the fast-speed condition producing the most substantial improvements, followed by medium and slow speeds (see Figure 2A,B and Table 4).
## 3 Results
### Simulation Results of Electric Field Distribution Analysis  
The electric field distribution analysis was performed with the FEMM 4.2 software, which adopts the finite element calculation method and is frequently used in high voltage studies. Breakdown voltage was applied to create the electric field. As a result of the simulation study, the image of the electric field exposed to the insulation material in the capacitor is shared in Figure 10. Here, the maximum electric field value (Emax) and the electric field distribution on the insulation materials to which the breakdown voltage is applied are clearly seen.  
Figure 10 shows the electric field distribution for PLA. Due to the similar relative permittivity values, the electric field distributions are nearly identical; therefore, the electric field distribution analysis results for other insulation materials are not included. It has been revealed that the electric field distribution becomes irregular at the bending points of the capacitor insulation material and reaches its maximum value. In this context, a graph of the electric field distribution values on the bending points of the capacitors was created and shared in Figure 11. Here, the length distance on the x-axis defines the insulation material's thickness.  
According to the results of the simulation study conducted to examine the electric field distribution, the maximum electric field value in the ABS capacitor was determined to be 34.419 kV/mm. This value was determined as 30.655 kV/mm in the PETG capacitor, 26.254 kV/mm in the PLA capacitor, and 23.108 kV/mm in the ASA capacitor.  
Figure 10: Results of the simulation for electric field distribution.  
In the simulation study, the energy stored (\(W\)) on the insulator can be obtained as output. In this context, the capacitance value (\(C\)) of the capacitor can be obtained as the result of the simulation study with the help of the expression given in Equation (5).  
\[W=\frac{1}{2}\cdot C\cdot U^{2} \tag{5}\]  
Here, \"\(U\)\" defines the voltage value.  
The capacitance values obtained in the simulation study were determined to be 0.981 nF for the PLA material, 0.9493 nF for the ABS material, 1.044 nF for the ASA material, and 0.9493 nF for the PETG material.
## 3 Hybrid Test
### Test Results and Discussions  
The hybrid simulations were conducted on the steel frame, which has been shown in Figure 3. Prior to hybrid simulation, the stiffness matrix of specimen used for iteration was obtained based on the data of the static loading test. For comparison, both the ECM and the AAM were used in the hybrid simulation. The integration time step was 0.02 s. Figure 7 shows the horizontal displacement responses of the experimental column when the frame was subjected to the El Centro (NS, 1940) earthquake scaled to a PGA of 35 Gal. The results with two integration methods were almost identical. Figures 8 and 9 show the force-displacement responses of the experimental and numerical columns. It is seen that the numerical column was in elastic range, while the experimental one exhibited slight hysteretic behavior.  
Figure 5: Displacement on top of column.  
Figure 6: Iterative vertical displacement on the top of column. (**a**) Reduction factor used in the first iteration; (**b**) Reduction factor used in the first two iterations  
In the hybrid simulations, the factor \(\theta\) = 0.75 was adopted for the first iteration in every time step. Part of the responses during iteration for the case of 220 Gal with the ECM is shown in Figure 13, where no overshooting can be observed in all three DOFs.  
To further reveal the difference of the two integration methods, the energy errors are shown in Figure 14. The energy error is defined as the difference of external work and the sum of internal, damping and kinetic energy. It is seen from the energy errors from the hybrid simulation as shown in Figure 14a,b that the ECM was not shown to be advantageous compared to AAM. The reason for this may be attributed to the small response which resulted in insignificant geometric nonlinearity, and the small advantage of the ECM was covered by the experimental errors. Unfortunately, the specimen was used for another test, and hence no further hybrid simulation with higher PGA was carried out to reveal the advantage of the ECM experimentally. Due to this, we have to rely on the results of pure numerical analysis which is shown in in Figure 14c for the case of 400 Gal. It is clearly seen that the energy error of the ECM is much lower than that of the AAM.  
Figure 11: Hysteretic loops of specimen (220 Gal).  
Figure 12: Hysteretic loops of numerical column (220 Gal).  
Figure 13: Displacement response in iteration (220 Gal), (**a**) Horizontal displacement; (**b**) Vertical displacement; (**c**) Rotation.
###### Abstract  
This study aims to compare the effects of different laser wavelengths, exposure times, and low-power-intensity laser irradiation on maize seeds. Seeds were exposed to He-Ne (632.8 nm) red laser, Nd:YAG second-harmonic-generation (532 nm) green laser, and diode (410 nm) blue laser. Four different exposure times (45, 65, 85, and 105 s) with different intensity (2 and 4 mW/cm\({}^{2}\)), for each laser were tested. Phenology and yield components (plant height, leaf area, number of rows per ear, seed yield, harvest index, yield efficiency, and grain weight) were determined. The experiment was conducted in a randomized complete block design with three replications. Plant height was found comparatively high in blue laser light--211 cm at 85 s. Blue and green laser lights showed significant increases in the number of rows per ear to 39.1 at 85 s and 45 at 65 s, respectively, compared to the control of 36 rows/ear. The order of seed yield was blue (7003.4 kg/ha) \(>\) green (6667.8 kg/ha) \(>\) red (6568.01 \(\mathrm{\SIUnitSymbolMicro h}\)) based on different exposure times of 85 s, 85 s, and 105 s, respectively, compared to the control of 6.9 kg/ha. The findings indicate the possibility of using blue laser light to manipulate the growth and yield of maize.  
Zea mays L.; laser stimulation; yield components; wavelengths +
Footnote †: journal: Applied Sciences
## 3 Similarity Analysis Approach
### The Case Study for the SAA Approach  
This section contains a case study for understanding the SAA approach and its work in detail. We conducted a case study where the data transmission distance is a key but changeable factor contributing to overall energy consumption. It is straightforward thatthe shorter the transmission distance is, the more the total energy consumption will be reduced. For example, we have two users, [PERSON] and [PERSON], working at the same university but on different campuses. [PERSON] is a professor at the university, and [PERSON] is studying there; they normally have to meet to discuss the research work. [PERSON] intends to send a file to [PERSON], which will be required by [PERSON] after seven days. At the moment, [PERSON] is working on another task. In this case, how can [PERSON] send the file to [PERSON]? There are several options available Option A: [PERSON] can send the data through the internet in a traditional way. Option B: A direct file-transfer service can be used; for example, [PERSON] and [PERSON] can exchange the data through point-to-point transfer through routers.  
Options A and option B are the most popular ways; overall energy consumption depends upon the distance and the size of the data. If we know [PERSON] and [PERSON] will meet, we can consider option C. We have fewer requirements of time, data are not more urgent, and the delay-tolerant indicator is decided to reach the data between that duration. In option C, human mobility traces are matched according to behavior. [PERSON] and [PERSON] have similar mobility preferences as they are at slightly distant locations (on any floor/room of the building). They are also connected to the same network.