esa-sceva/satcom-chunk-collection huggingface dataset:
# Piezoelectric Transformers: An Historical Review
## 1 Historical Introduction
### Late 1960s-1980s: Period of Proliferation  
Until the mid-1960s, piezoelectric transformers were not practical due to the lack of materials able to operate with low mechanical losses at high amplitudes. This situation improved with the introduction of hard-doped piezoelectric ceramic compositions, such as PZT8, showing high mechanical and dielectric Q factors at high amplitudes.  
Several U.S. and Japanese companies, like Motorola, Inc. (Chicago, IL, USA) [30; 31; 32], RCA Corp. (Wilmington, DE, USA) [33; 34], Denki Onkyo Co. Ltd. (Tokyo, Japan) [35; 36; 37; 38; 39; 40; 41], and Matsushita Electric Industrial Co. Ltd. (Osaka, Japan) [42], focused their attention to PTs for generating the high voltage required for the cathode-ray tubes in black and white television receivers (see Figure 7b for a typical example). Several attempts were also reported for using piezoelectric transformers as igniter in gas-based stoves (Matsushita Electric Industrial Co. Ltd. [43]), in small engine applications (Briggs & Straton (Milwaukee, WI) [44], Figure 7a) and in automobiles (Nippon Soken, Nishio, Japan) [45]). In the 1980s, Siemens (Berlin, Germany) [46], Figure 7c, and General Electric (Schenectady, NY, USA) [47] were among some companies working on the application of PTs for triggering power switch gates such as triacs, thyristors, Mosfets, _etc._, with galvanic decoupling.  
None of these PT applications reached commercial success because trial production revealed the piezoelectric transformer to be fragile and the overall piezoelectric-solution to be more expensive than the electromagnetic coil it was meant to replace. Some of the technical problems were related to: immature materials fabrication technology, lack of mechanical reliability, inadequate mounting, and bulky driving circuits.  
Figure 6: Configurations of piezoelectric transformers suggested by [PERSON] [29].
## 4 Experiments and Results
### Optimization and Evaluation Metrics  
To evaluate and optimize the performance of the model, the root mean-squared error (RMSE) and correlation (CORR) were used. These metrics were used by considering only the frame in which \(F_{0}\) extracted from natural speech and its predicted value were voiced. Hence, they were modified as:  
\[RMSE=\sqrt{\frac{\sum_{t\in V}{(f_{t}-\hat{f}_{t})^{2}}}{|V|}} \tag{9}\]  
\[CORR=\frac{\sum_{t\in V}{(f_{t}-\mu f)(\hat{f}_{t}-\mu\hat{f})}}{\sqrt{\sum_{t \in V}{(f_{t}-\mu f)^{2}}}}\sqrt{\sum_{t\in V}{(f_{t}-\mu\hat{f})^{2}}} \tag{10}\]  
\[V=\{t:f_{t}>0\wedge\hat{f}_{t}>0,0<t<T\}\]  
where \(V\) is the set of time indices where both the extracted \(F_{0}\) for natural speech and its predicted value were voiced. \(f_{t}\) and \(\hat{f}_{t}\) denote the extracted \(F_{0}\) and the predicted \(F_{0}\), respectively. \(\mu f\) and \(\mu\hat{f}\) are mean values of the extracted and predicted \(F_{0}\), respectively. \(T\) is the total number of frames. The average  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Description**} & \multicolumn{2}{c}{**Values**} \\ \hline  & **Training** & **Validation** & **Test** \\ \hline Number of syllables & 62,005 & 4619 & 6755 \\ Number of words & 51,098 & 3815 & 5580 \\ Number of phrases & 7389 & 565 & 826 \\ Number of intermediate phrases & 3784 & 260 & 374 \\ Length of utterance (phrase) & 1.86 \(\pm\) 0.77 & 1.92 \(\pm\) 0.76 & 1.97 \(\pm\) 0.83 \\ Length of phrase (syllable) & 12.26 \(\pm\) 5.59 & 12.41 \(\pm\) 5.29 & 12.61 \(\pm\) 5.84 \\ Length of word (syllable) & 1.21 \(\pm\) 0.48 & 1.21 \(\pm\) 0.49 & 1.21 \(\pm\) 0.48 \\ \(F_{0}\) (Hz) & 111.80 \(\pm\) 19.27 & 111.56 \(\pm\) 19.22 & 111.90 \(\pm\) 19.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical information of training, validation, and test sets.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Tone**} & \multicolumn{2}{c}{**Number of Syllables (\%)**} \\ \hline  & **Training** & **Validation** & **Test** \\ \hline Mid-tone & 14,004 (22.59) & 1055 (22.84) & 1572 (23.27) \\ Low tone & 10,448 (16.85) & 775 (16.78) & 1155 (17.10) \\ Mid-falling tone & 9522 (15.36) & 705 (15.26) & 1056 (15.63) \\ High-falling tone & 11,680 (18.84) & 906 (19.61) & 1209 (17.90) \\ High tone & 7955 (12.83) & 558 (12.08) & 852 (12.61) \\ Rising tone & 8396 (13.54) & 620 (13.42) & 911 (13.49) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total number of syllables for each tone in training, validation, and test sets.  
values of RMSE and CORR on the test set were used as objective metrics. A lower RMSE and higher CORR indicated better prediction performance.
# Influence of Pre-Radiation and Photo-Bleaching on the Yb-Doped Fiber Laser Radiated with Gamma-ray
###### Abstract  
To improve the radiation resistance of Yb-doped fiber lasers, we investigate the influence of pre-radiation and photo-bleaching on the gamma-radiated laser's performance. When the gamma radiation dose is within 10 krad(Si) with a radiation dose rate less than 0.4 rad(Si)/s, compared to the output power of a non-pre-radiated Yb-doped fiber laser, the pre-radiation technique could enhance the radiation resistance against gamma-ray. However, the mode instability threshold was decreased, which was caused by the cumulated radiation-induced attenuation of pre-radiation and radiation. Based on an electronic probe micro-analyzer, the Yb-doped active fiber was Yb-doped aluminophosphosilicate ternary fiber; therefore, the radiated defects were mainly hole-related defects. A laser diode centered at 532 nm was chosen as the photo-bleaching laser source, which could recover 45.2% of the radiated-induced attenuation and increase the mode instability threshold. This work demonstrates the influence of pre-radiation and photo-bleaching on the radiation resistance against the gamma-ray of Yb-doped fiber lasers, which are of significance in the design and fabrication of related fiber lasers.  
Yb-doped fiber; fiber laser; gamma radiation; pre-radiation; photo-bleaching +
Footnote †: journal: Applied sciences
## 3 Results and Analysis
### Effects on the Normal Force of Unloading  
Values of instantaneous normal forces corresponding to the slip velocity of 3.7 mm/s, 2.5 mm/s, and 1.5 mm/s during the final fast sliding stage in test Group A and Group B are shown in Figure 6. As can be seen, the corresponding normal force has a strong relationship with both normal unloading rate and shear force with different sliding velocities. With a higher normal unloading rate, the corresponding normal force during fast sliding decreases. The change in corresponding normal force is more drastic when the unloading rate is too low or too high. When the unloading rate is 0.3-0.7 mm/s, the normal force is less sensitive to the change in the unloading rate. For the first several tests of Group A, due to the low normal unloading rates, the decrease in the corresponding normal force is insignificant. So,  
Figure 5: (**a**) Relationship between shear displacement and shear force under \(F_{\mathrm{N}}\) = 30 kN in a conventional direct shear test. Within the displacement range, the shear strength of the granular material keeps increasing with larger shear displacement. (**b**) The peak shear force (shear strength) for different normal force in 0.0833 mm/s shear-displacement-controlled conventional direct shear test. (**c**) Shear displacement (solid lines) and normal displacement (dash lines) versus time for low (0.08 kN/s) and high (0.8 kN/s) unloading rates extracted from Figure 3a and illustration to explain sliding deceleration/intermission.  
the three curves in Figure 6a are very close at the beginning. Thanks to the effects of a high unloading rate, the interval between these three curves enlarges gradually. In Figure 6b, the corresponding normal force during fast sliding increases with larger shear force. To better demonstrate the influence of normal unloading rates and constant shear forces on the corresponding normal force at different velocities, we fit the results above to synthesize 3D surfaces in Figure 7. When both shear force and unloading rate are in their respective middle ranges, the variation in normal force is not very conspicuous.  
Figure 6: Variation in normal force at different sliding velocities. (**a**) For different unloading rate (Group A) and (**b**) for different shear force (Group B).  
Figure 7: Normal force versus unloading rate and shear force at sliding velocity of (**a**) 3.7 mm/s, (**b**) 2.5 mm/s, and (**c**) 1.5 mm/s.  
In the process of normal unloading, the sheared sand is in an unstable and fast motion state, where the normal constraint is constantly relaxed. Therefore, its shear strength characteristics cannot be explained by the Coulomb theory obtained from conventional low-rate direct shear tests. In Figure 6a, the normal forces are much lower than the theoretical value calculated by the Coulomb theory (10/0.6897 kN \(\approx\) 14.50 kN). In addition, as shown in Figure 6b, the curves are not straight lines, and the corresponding normal force is always less than the theoretical value given by Equation (2).
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 4 Numerical Results  
We tested our algorithm on synthetic data, obtained with \"true\" dictionaries \(\mathbf{D}_{\text{true}}\) whose unit norm atoms are generated randomly following a Gaussian distribution. Given the sparsity level \(s\), the representations \(\mathbf{X}\) are also generated randomly, with nonzeros on random positions. The data are \(\mathbf{Y}=\mathbf{D}_{\text{true}}\mathbf{X}+\mathbf{U}\), where the entries of \(\mathbf{U}\) are statistically independent, Gaussian distributed, with zero mean. The variance of the additive noise is chosen to have four different values for the signal-to-noise ratio (SNR): 10 dB, 20 dB, 30 dB and 40 dB. The dictionary sizes are \(n\in\{128,192,256\}\); the overcompleteness factor \(n/m\) has moderate values, as in most applications.  
Some of the input data for Algorithm 1 are constant throughout all the experiments. The number of signals is \(N=4000\) and their size is \(m=64\). The number of iterations is \(K\in\{200,300,400\}\), increasing with \(n\). The dictionary size changes are made every \(c=5\) iterations. The size steps are \(\delta_{+}=\delta_{-}=5\). The number of size candidates is \(n_{\text{cand}}=20\). All the results are obtained with 50 runs for the same data, but with different realizations of \(\mathbf{D}_{\text{true}}\) and \(\mathbf{U}\).  
We report in this section only a few representative results. More results can be found in Appendix A. Representative Matlab sources are given at www.schur.pub.ro/download/itc-adl.zip.
# Stabilized Sewage Sludge as Fertilizer: Risks Related to the Presence of Microplastics
## 2 Materials and Methods  
### Materials  
The material for the study consisted of fertilizer samples produced from stabilized sewage sludge (SSS). The stabilized sewage sludge used had previously been analyzed for microplastics [14].  
Stabilized sewage sludge samples were collected in June and July 2021 from a wastewater treatment plant located in southern Poland, serving approximately 62,000 inhabitants. These months represent the summer period characterized by stable operational conditions at the treatment plant and typical influent composition. They were selected to minimize seasonal variability and provide a consistent basis for evaluating microplastic content. The influent is primarily domestic wastewater, with typical variations in organic load and flow rate. Additionally, sewage sludge from these months has been previously analyzed for comparison purposes. This information helps to better understand the representativeness and variability of the sludge samples used in the study. Four subsamples (approx. 1.5 kg each) were taken over 12 h and combined into a single composite sample. The sludge was analyzed by an accredited laboratory (No. AB 213) according to the Polish standard [1]. No viable eggs of intestinal parasites or _Salmonella_ bacteria were detected. The collected sludge, derived from methane fermentation and dewatered using screw presses, was then processed into fertilizer according to Polish patent No. Pat 233754, dated 7 October 2019 [15]. From each batch, 4 kg of material was weighed and transformed into a high-quality fertilizer product.  
To the mixture, dolomitic flour was added to absorb moisture, alkaline the soil, and enrich the fertilizer with calcium and magnesium. Additionally, hydrated lime was included to ensure proper hygiization, and cellulose fiber was added to improve the physical durability of the pellets. The percentage composition of the individual compo nents was as follows: sewage sludge (74%), dolomite flour (20%), hydrated lime (5%), and microcellulose (1%).  
All ingredients were thoroughly mixed in a counter-current dynamic mixer, then directed to a disc pelletizer, where the fertilizer particles were formed and solidified. The final pellets were dried at ambient temperature (approximately 25 \({}^{\circ}\)C) for about three weeks before further analysis.  
To ensure consistency and accuracy in the microplastic content analysis, all fertilizer samples were initially subjected to freeze-drying (lyophilization). This process aimed to remove residual moisture and obtain a constant dry mass, which is critical for subsequent mass-based quantification of microplastic content. Samples were frozen and maintained under vacuum at -50 \({}^{\circ}\)C for seven consecutive days until a stable mass was achieved. The lyophilization was conducted using a laboratory-grade freeze-dryer (e.g., Laboconco FreeZone or equivalent), with pre-cleaned, inert sample containers to minimize external contamination.
## 4 Experiment
### Experiments in Outdoor Real-World Scenarios
#### 4.5.2 Preparation Phase
Specifically, the average translation error in Scene 2 is 0.377 m, with Test2 showing the best performance, yielding an error of only 0.205 m. The largest error occurred in Test3, with a value of 0.544 m. In all test cases, the errors were kept within 0.6 m, with minimal fluctuations, indicating that the proposed method can effectively achieve vehicle localization in diverse environments. Figure 11 presents the map displaying the coordinates predicted by the relocalization algorithm across different scenes. The red dots represent the predicted position coordinates, while the green dots indicate the true coordinates obtained through RTK measurements. The experimental results from two real-world scenarios demonstrate that the proposed relocalization method achieves high accuracy in both outdoor environments, effectively determining the autonomous vehicle's position on the map and enabling successful relocalization.
## 5 The ATLAS MAC Protocol
### Medium Access Design
#### 5.2.1 Cluster-Head to Gateway (Ch-to-G) Communication
The \(TS_{c}\) slot is the closest time-slot to the next superframe. When there is any additional request for a slot, the gateway assigns the \(TS_{c-1}\) slot and so on. As a result, the CAP and CFP move toward the middle of the superframe, and the rest of the period is maintained as the IP. Thus, all the periods or components of the superframe are dynamically adjusted in moderate-load status and can maintain a fair contention environment. * _High-load_ (Use of CFP and IP): Upon detecting high-load traffic in the network, the gateway switches to the CFP mode. Hence, in the initial beacon (\(B\)) it fairly assigns a guaranteed time-slot to each of the cluster-heads (Figure 3(c)). Each cluster-head becomes active only in the assigned slot for data transmission and remains inactive otherwise. The transmitted data at each time-slot is acknowledged by the gateway. Thus, if any cluster-head requires multiple time-slots for transmission, this request can be made through the data packet header and an additional time-slot can be allocated for the same cluster-head using the data-Ack beacon of the current slot. Following all slot assignments and data receptions, it is expected that the rest of the superframe would be the IP. Therefore, the CFP and IP are also dynamically adjusted under high-load state. * _Over-load_ (Use of CFP): When the network load reaches the over-load state, it is anticipated that the network capacity reaches its saturation level. Hence, in order to achieve maximum throughput the gateway uses the full CFP mode throughout the superframe (Figure 3(d)). The IP is useless in such a state, since it might cause packet dropping due to buffer overflow at the cluster-heads. The ATLAS protocol stops packet reception from the sensors at the cluster-head, unless the load-status drops to high-load status or below (see S-to-Ch communication in the next section). This concept is similar to the rate-control techniques proposed in [24, 28] where the source nodes throttle down the rate while sending their data to the heavily loaded node. In assigning the time-slots in CFP, the gateway maintains weighted fairness according to the estimated load status of the cluster-heads. More specifically, the cluster-head that have a higher load would be assigned more time-slots than the relatively lower loaded cluster-heads. Figure 3: Cluster-head to gateway (Ch-to-G) communication snapshot: **(a)** Low load; **(b)** Moderate load; **(c)** High load; and **(d)** Overload.
###### Abstract  
To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation capability of the space station's protection radar system, this paper proposes a task scheduling method based on time shifting constraints and pulse interleaving. The time shifting constraint is designed to minimize the deviation between the actual execution and the desired execution time of the task, and it is negatively correlated with the threat degree of the target. Pulse interleaving is intended to utilize the idle time between the transmitted pulse and the received pulse of a task to perform other tasks, thereby improving the utilization of radar resources. Through computer simulation under typical parameters, our proposed method reduces the average time shifting ratio by about 60% compared to traditional task scheduling methods, and the scheduling success ratio is also higher than that of traditional scheduling methods. This demonstrates the effectiveness of the proposed method in enhancing scheduling efficiency and overall system performance.  
space station; phased array radar; scheduling algorithm; time pointer; pulse interleaving 2025  
19 May 2025  
Revised: 27 June 2025  
Accepted: 1 July 2025  
Published: 10 July 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Research on Task Interleaving Scheduling Method for Space Station Protection Radar with Shifting Constraints.  
_Telecom_**2025,** 6, 49  
**Copyright**: 0 2025 by the authors.  
License MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Quantum Computing for Healthcare: A Review
## 6 Security of Quantum Computing for Healthcare
### Defense Against General Security Risks  
A system that relies on quantum computing for healthcare processing and security is vulnerable to a variety of security risks. These include, but are not limited to, authentication, interception, substitution, man-in-the-middle, protocol, and denial of service. In this section, we present existing defense approaches to withstand different general attacks against quantum computing systems. For instance, [PERSON] et al. [140] proposed a defense strategy for BB84 that enforces security with random individual imperfections concurrently in the quantum sources and detectors. Similarly, [PERSON] et al. [141] proposed a semi-device-independent defense scheme against individual attacks that provides security when the devices are assumed to devise quantum systems of a given dimension. In [142], authors presented a defensive scheme for a greater number of quantum protocols, where the key is generated by independent measurements. A comparative analysis of secret keys that violate Bell inequality is presented in [143]. The authors suggested that any available information to the eavesdroppers should be consistent with the nonsignaling principle. [PERSON] et al. [144] evaluated \"the security of Gaussian continuous variable QKD with coherent states against arbitrary attacks in the finite-size scheme\". In a similar study, [PERSON] et al. [145] presented a method to evaluate the security aspects of a practical distributed-phase-reference QKD against general attacks. A framework for the continuous-variable QKD is presented in [146], which is based on an orthogonal frequency division multiplexing scheme. A comprehensive security analysis of continuous-variable MDI QKD in a finite-sized scenario is presented in [147], and defense against generic DI QKD protocols is presented in [148]. In [149], authors presented a method \"to prove the security of two-way QKD protocols against the most general quantum attack on an eavesdropper, which is based on an entropic uncertainty\" relation. In [150], authors particularly defined the perspective of [PERSON]'s original entanglement protocol against a general class of attacks. A taxonomy summarizing different defenses against general security attacks is presented in Table 6.
## 2 Fifth-Generation Network Slicing and Related Work
### Resource Capacity Planning  
Resource capacity planning can be achieved through either static resource partitioning or dynamic resource reallocation. Static resource partitioning involves allocating resources to network slices that do not change over time. On the other hand, dynamic resource reallocation involves changing resource allocation among slices over time, as determined by the controller, which periodically checks the need for change. Various mechanisms and criteria are considered during resource capacity planning, including slice priorities, weights, resource utilization, fairness, availability, and isolation (see Table 1).  
Network slice isolation is a crucial factor in resource capacity planning. Although not yet fully formalized in specifications, it is generally understood as minimizing the impact of one slice on the performance of other slices. Isolation is often provided until the number of users exceeds a given threshold. In cases where there is a shortage of resources, users who violate isolation may be interrupted by users from other slices. Slice degradation, defined as a state where one or more active users are not provided with the necessary resources to fulfill bit-rate guarantees, is closely related to isolation.  
The efficiency of resource capacity planning depends on the resource reallocation policy among network slices. Mathematical methods commonly used to solve corresponding optimization problems include optimization theory for linear and nonlinear problems, game theory, cooperative game theory (Nash equilibrium), machine learning [37], MDP, continuous-time Markov chain (CTMC), and queuing theory. A retrial queuing system with an infinite orbit was proposed for analyzing prioritization schemes and resource reservation [38]. In another study [39], a resource queuing system with signals, where upon arrival, the signal interrupts a user to try again with a new bit-rate requirement, was analyzed.