esa-sceva/satcom-chunk-collection huggingface dataset:
# Guidelines for Experimental Algorithmics:
## 5 Guidelines for the Experimental Pipeline
### Repeatability, Replicability and Reproducibility  
Terminology differs between venues; the Association of Computing Machinery defines _repeatability_ as obtaining the same results when the same team repeats the experiments, _replicability_ for a different team but the same programs and _reproducibility_ for the case of a reimplementation by a different team. Our recommendations are mostly concerned with replicability.  
In a perfect world scenario, the behavior of experiments is completely determined by their code version, command line arguments and configuration files. From that point of view, the ideal case for replicability, which is increasingly demanded by conferences and journals (for example, see the _Replicated Computational Results Initiative_ of the _Journal on Experimental Algorithms_, [[http://jea.acm.org](http://jea.acm.org)]([http://jea.acm.org](http://jea.acm.org))) in experimental algorithms, looks like this: A single executable program automatically downloadsor generates the input files, compiles the programs, runs the experiments and recreates the plots and figures shown in the paper from the results.  
Unfortunately, in reality some programs are non-deterministic and give different outputs for the same settings. If randomization is used, this problem is usually avoided by fixing an initial seed of a pseudo-random number generator. This seed is just another argument to the program and can be handled like all others. However, parallel programs might still cause genuine non-determinism in the output, e.g., if the computation depends on the order in which a large search space is explored [71; 72] or on the order in which messages from other processors arrive. (As an example, some associative calculations are not associative when implemented with floating point numbers [73]. In such a case, the order of several, say, additions, matters). If these effects are of a magnitude that they affect the final result, these experiments need to be repeated sufficiently often to cover the distribution of outputs. A replication would then aim at showing that its achieved results are, while not identical, equivalent in practice. For a formal way to show such _practical equivalence_, see Section 7.4.1.  
Implementations often depend on libraries or certain compiler versions. This might lead to complications in later replications when dependencies are no longer available or incompatible with modern systems. Providing a virtual machine image or container addresses this problem.
## 4 Control Strategies
### Control of the Microgrid Inverter
In addition to the phase margin (PM) and the gain margin (GM), the sensitivity index \(\eta\), which defines the minimum distance from the Nyquist diagram to the critical point \((-1,\,0)\), is also verified. As a design criterion, we considered GM \(\geq\) 3 dB, \(\eta\)\(\geq\) 0.3, and PM \(\geq\) 30\({}^{\circ}\). The proportional controller gain needed to ensure the desired crossover frequency is \(k_{p}=0.09899\). Then, the SSI-based resonant controllers are added in parallel and tuned with the harmonic components h = 6k \(\pm\) 1 until h = 9. The same resonant gain of \(k_{i}=1\) was chosen for all resonant units, and it was observed that the desired phase margin was maintained. Concerning the inevitable delay in digital implementation, a computational delay of two samples was considered. The Bode and Nyquist diagrams of the resulting current control loop are presented in Figure 11a,b, respectively, demonstrating that the design criteria were met. After defining the internal current control loop gains, the much slower external DC bus voltage loop tuning was considered. The 0 dB crossover frequency of the external loop was chosen so that  
\[f_{cv}<\frac{f_{ci}}{10} \tag{21}\]  
where \(f_{cv}\) and \(f_{ci}\) represent the 0 dB crossover frequencies of the external and internal control loops, respectively. This design criterion is selected so that the DC bus voltage regulation does not compromise the dynamics of the current control. Therefore, for \(f_{ci}=1500\) Hz, \(f_{cv}<150\) Hz must be chosen. For practical reasons, in order to avoid the too frequent saturation of the controller output, \(f_{cv}=6\) Hz was adopted. Furthermore, the indices GM \(\geq\) 3 dB and PM \(\geq\) 30\({}^{\circ}\) were considered as design specifications. The frequency response of the open-loop transfer function for \(k_{i}=1.0562\) and proportional controller gain  
Figure 11: The Bode and Nyquist diagrams of the resulting current control loop. (**a**) Nyquist diagrams of the open-loop system with computational time delay compensation. (**b**) Bode diagrams of the open-loop system with computational time delay compensation. Results were obtained for the proportional controller (blue) and P-SSI in parallel (green). \(k_{p}=0.084\), which is the value that results in the desired crossover frequency \(f_{cv}=6\) Hz, is shown in Figure 12. The compensated system has a gain margin GM \(=65\) dB and phase margin PM \(=71.5^{\circ}\), meeting the design specifications.
# Qualitative Analysis of Sleeper Supporting Condition for Railway Ballasted Tracks Using Modal Test
## 4 Analysis and Discussion
### Analysis of Changes in Natural Frequency and Damping Ratio of Graveled Track Sleepers  
Figure 21 presents the variations in natural frequencies and damping ratios of concrete sleepers before, during, and after the adjacent excavation work, providing a comprehensive overview of the impacts on sleeper integrity.  
The analysis of the natural frequencies before and during the adjacent excavation work on the up track, shown in Figure 21, reveals that most left-side concrete sleepers had lower natural frequencies during construction than before, except for sleeper #10, which showed a significant increase. Similarly, most right-side sleepers exhibited lower natural frequencies during construction than before, with sleeper #9 showing a significant decrease.  
Figure 19: Concrete sleeper under poor support conditions on both sides.  
Figure 20: Measurement results for poor under-sleeper support on both sides.  
The results of the natural frequency analysis before and during construction for the down track are shown in Figure 22.  
Figure 21: Natural frequency analysis before and during construction (up track). (**a**) Left side; (**b**) right side.  
Figure 22: Natural frequency analysis before and during construction (down track). (**a**) Left side; (**b**) Right side.  
The natural frequency analysis before and during the adjacent excavation work on the down track, shown in Figure 22, reveals that, except for steeper #6 on the right side, which showed a significant increase in natural frequency, most other sleepers exhibited similar natural frequencies before and during construction.  
The analysis of damping ratios before and during construction for the up track is shown in Figure 23.  
The damping ratio analysis before and during construction on the up track, shown in Figure 23, reveals that most left-side concrete sleepers had lower damping ratios during construction than before, except for steeper #7, which showed a significant increase. Similarly, most right-side concrete sleepers showed a lower damping ratio during construction than before, with sleeper #5 showing a significant reduction.  
The results of the damping ratio analysis for the down track, before and during construction, are shown in Figure 24.  
The damping ratio analysis before and during construction for the left-side concrete sleepers on the down track, shown in Figure 24, reveals that most exhibited a lower damping ratio during construction than before. Similarly, most right-side sleepers showed a reduced damping ratio during construction than before, but those of sleepers #2 and #8 increased significantly.  
Figure 23: Damping ratio analysis before and during construction (up track). (**a**) Left side; (**b**) Right side.
## 3 Results and Discussion
### Monitoring Copper within Hydrolysates  
The optimized processes were carried out on biomass harvested from a Cu contaminated soil with 1464.1 \(\pm\) 250.18 mg/kg Cu at the beginning of the experiment. For the control, biomass was harvested from a control soil with 15.77 \(\pm\) 2.60 mg/kg Cu at the beginning of the experiment (n = 6). Hence, we analyzed Cu content in the waste hydrolysate. The experimental and control hemicellulose hydrolysates had Cu concentrations of 0.2 mg/kg and below detection limit, respectively. The experimental biomass was found to have 51.6 \(\pm\) 11.9 mg/kg Cu and the experimental cellulosic hydrolysate contained 0.10 \(\pm\) 0.02 mg/L Cu. The Cu content between the control cellulosic hydrolysate and experimental cellulosic hydrolysate was significantly different (_p_ = \(<\) 0.0001).
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## V Advanced Viability Experimental Activity  
### Test-Bed Description  
This section describes the experimental set up and results of a joint 5G-VLC networks for vehicular applications. In particular, road safety as well as emergency alarm services have been tested. The test  
Figure 3: I2V measurements campaign using a VLC-based traffic-light (city of Prato).  
bed was implemented at the Polo Universitario Citta di Prato (PIN). The experimental set-up scheme is reported in Figure 4.  
In particular, the system was composed by three Raspberry Pi 3 Model B+, 3 WeMos D1 mini, an Italian regulation compliant traffic light, a VLC transmitter, a VLC receiver, and several different IoT sensors:  
* Flame sensor: To simulate a fire alarm.
* Gyroscope/accelerometer: To detect an incident between to (scale model) cars.
* Temperature, humidity and pressure sensors: To detect the presence of ice on the road.  
The first two raspberries are used to collect data coming from the sensors and forward, through the 5G network, to the VLC network. The third raspberry was used only to display in the vehicle the sensors values as well as the alarm. It is important to point out that the temperature/humidity/pressure sensors send the data periodically, while the other sensors send an alarm only if an event is triggered. The raspberries are connected to the 5G customer premise equipment (CPE), provided by the 5G network operator, though ethernet gigabit ports. The traffic-light and the car were connected using custom-designed TX and RX stages based on open source Arduino device [22]. The second raspberry and the VLC block are connected by a serial cable (USB). The connection between the sensors and the raspberry is through the air: Raspberry provides a Wi-Fi Access Point and all the WeMos have an IP address. All the code in the raspberries is written in Python 3 and all the code used to drive the sensor--WeMos is written in C/C++. The VLC transmitter and receiver are IEEE802.15.7-compliant and they have been specifically designed/prototyped for I2V communications (see [22] for all hardware implementation details). The implementation design of the transmitter includes on-off keying (OOK) modulation with Manchester encoding, while the receiver collects the light by using an aspherical two uncoated lens and it is designed to reject the sun light. The most expensive part is the photodiode. Although the initial cost of the VLC prototype could be considered high (few hundreds of euros), after industrialization the cost could be reduced to few tens of euros.
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
## 4 Discussion
### Recommendations for Future Studies
#### 4.4.1 Finite Element Analysis/Calculations/Networks/Artificial Intelligence  
Since our aim was finding novel technologies, studies using calculations, networks, data processing, finite element analysis, machine learning, and artificial intelligence are not included in this review. However, these assessment approaches seem to be receiving a lot of attention in the literature and there has been a rise in their number [60, 61, 62]. These methods either enable large amounts of data to be processed efficiently and yield results [63, 64] or provide very detailed results from a very small number of subjects with detailed assessments [65, 66, 67, 11, 68, 69, 70, 71, 72]. They seem to have important potential for future studies, as they are approaches for processing data independent of the technology used. However, the obtained information should be manageable and convertible into useful information at the clinical level. It seems that the development of common approaches to understand the PFP characteristic may be useful for further studies.
## 3 Materials
### IoT Hardware and Devices  
The effectiveness of any IoT deployment does not only depend on the software and communication protocols. In rural environments, hardware selection becomes an important decision. The success of IoT deployments in rural environments relies heavily on robust and scalable hardware solutions.  
For long-range communications, the laboratory uses Dragino devices (Dragino Technology Co., Shenzhen, China) compatible with LoRaWAN such as the Dragino sensors, LHT65 (temperature and humidity), LGT-92 (GPS tracking), and LDS02 (door status monitoring). These devices offer reliable performance in low-power scenarios and are suitable for deployment in open fields, and remote or off-grid infrastructure. In addition, the Mikrotik LR8 Kit (SIA Mikrotikls, Riga, Latvia) and Dragino LPS8 gateways (Dragino Technology Co., Shenzhen, China) are used as bridges between end devices and backend services. The laboratory supports both public infrastructure via The Things Network and private, offline deployments, thus eliminating the dependence on cloud services.  
Complementing the LoRaWAN scenarios, WiFi-based scenarios use ESP32 Arduino microcontrollers (Espressif Systems, Shanghai, China), known for their low cost, energy efficiency, and compatibility with various sensors and communication modules [48; 49]. The low-cost sensors employed in this work were purchased through online retailers, as they correspond to generic modules commonly available from multiple suppliers. In WiFi-based scenarios, the Dragino LPS8 router acts as an access point and a Raspberry Pi 3 (Raspberry Pi Foundation, Cambridge, UK) captures network traffic, enabling dataset generation and protocol analysis.  
The entire backend runs on a Debian Linux virtual machine, where the services are deployed in Docker containers. A Windows 11 workstation is also used, serving as a development platform for programming the ESP32 microcontrollers and managing configurations.  
Tables 3 and 4 list all hardware, operating systems, and software tools used. These components were selected not only for their performance and compatibility, but also for their relevance to real-world deployments, prioritizing low-cost, reliable devices suitable for long-term operation in these types of environments.  
In short, the laboratory demonstrates that it is possible to build and maintain a research platform using affordable hardware. This approach reinforces the idea that rural innovation does not require expensive infrastructure and can be implemented through the use of open technologies.
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 5 Conclusions  
In this paper, the improvement mechanism of DC in layered saturated soil ground was investigated by performing fluid-solid coupling simulations. Major conclusions are drawn as follows:  
1. The Dual-Phase Coupling Method in Abaqus can well reflect the development law of excess pore water pressure and the reinforcement effects of DC in saturated grounds with a weak soil layer.  
2. The embedment depth and thickness of the weak layer are important factors affecting the effective reinforcement depth. For grounds with a weak layer of a thin thickness and at shallow depths, sufficient tamping energy can penetrate through the soft interlayer, thus reinforcing the lower part of the soft interlayer as long as the transverse range of the liquefied zone is not large. While the weak soil layer is thick and buried deep, the effective reinforcement depth of DC is hard to exceed the embedded weak layer.  
3. Improving the tamping energy may enlarge the range of liquefied zone (\(\Delta\)Ru > 1.0) in the soft interlayer. However, excessive tamping energy can lead to great excessive excess  
Figure 21: Distributions of excess pore water pressure ratio in grounds of different levels of groundwater. (**a**) groundwater level: 3 m; (**b**) groundwater level: 4 m; (**c**) groundwater level: 5 m.  
pore water pressure in the weak embedded layer, which may lead to the formation of \"rubber soil\".  
4. To achieve a better compaction effect during DC, the groundwater table should be lowered by dewatering or adding a drainage layer (e.g., a layer of coarse-grained material) to shorten the drainage distance between the ground surface and the groundwater.  
Methodology, writing--original draft, C.Z.; software, validation, C.Z. and J.L.; conceptualization, formal analysis, writing--review and editing, C.Z. and C.L.; supervision, G.X., P.Z. and Z.Y.; project administration, G.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 2 Methodology
### Risk Association Analysis and Apriori Algorithm
#### 2.1.1 Relevant Concepts of Association Rules  
**Definition 1**.: (_Association Rules_)_: Let \(I=\{i_{1},i_{2},\cdots i_{n}\}\) be the set of all items, while \(P\) is a subset of 1, \(\{P=\{i_{j},\cdots i_{k}\}\subseteq I,1\leq j,k\leq n\}\). Then, the following type of association rules can be defined as follows:  
\[IF\ Antecedent\ THEN\ Consequent(A\Rightarrow C) \tag{1}\]  
where both the Antecedent (\(A\)) and Consequent (\(C\)) are subsets of \(P\).  
**Definition 2**.: (_Support_)_: The support of association rule \(A\Rightarrow C\) is the percentage of transactions containing sets \(A\) and \(C\) among all transaction items, as shown in Equation (2), and the support can be understood as the frequency of the association rule._  
\[Support(A\Rightarrow C)=\frac{|\{T:A\cup C\subseteq T,T\subseteq D\}|}{|D|} \tag{2}\]  
**Definition 3**.: (_Confidence_)_: The confidence level of association rule \(A\Rightarrow C\) is the percentage of transactions containing sets \(A\) and \(C\) among all transactions containing item set \(A\), as shown in Equation (3), and the confidence level can be understood as the strength of the association rule._  
\[Confidence(A\Rightarrow C)=\frac{|\{T:A\cup C\subseteq T,T\subseteq D\}|}{| \{T:A\subseteq T,T\subseteq D\}|}=\frac{Support(A\Rightarrow C)}{Support(A)}\]  
The lift refers to the dependency between the antecedent and consequent of an association rule. If the lift exceeds 1, \(C\) is likely to be highly correlated with \(A\), which means that the association rule may be useful for predicting future outcomes in the dataset. Therefore, it can help filter and obtain valid results ([PERSON] et al., 2019).
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 4 Theoretical Results
### Classification Task  
We begin by formalizing our semantic communication framework for the classification task, based on the maximum a posterior probability (MAP) principle [26; 27; 28].  
The classifier outputs the probability of each class label, given by \(q(U)\), where \(q(\cdot)\) is the coding function of the classifier. We can write the standard classifier based on the MAP principle as follows:  
\[\hat{s}=\arg\max_{S\in\mathcal{S}}p_{S|U}(q(U)|U). \tag{9}\]  
We can then apply the following divergence with respect to the output of the classification model to develop the classifier. Formally, the loss function under the MAP approach in (1) of [26] can be reformulated as  
\[d_{KL}(p_{\text{data}},p_{\text{model}})=d_{KL}(p_{S},p_{S}) \tag{10}\]  
where \(p_{\text{data}}\) is the distribution of true class labels, which is actually the distribution of semantic source \(p_{S}\), and \(p_{\text{model}}\) is the distribution induced by the classifier, which is actually the distribution of the output probability of the semantic decoder \(p_{S}=p_{q(U)}\).  
Consider the tradeoff relationship between the transmission rate, bit-wise distortion, and the classification accuracy; the objective of the semantic rate-distortion problem in (7) specified for the classification task is defined by:  
\[R_{\text{cls}}(D,P_{\text{cls}})=\inf_{P_{\delta|U}}I(U,\hat{S} |Y) \tag{11}\] \[\text{s.t. }E[d(S^{n},\hat{S}^{n})]\leq D,\] \[d_{KL}(p_{S^{n}},p_{S^{n}})\leq P_{\text{cls}},\]  
where \(P_{\text{cls}}\) is the parameter that quantifies the maximum tolerable level of misclassification. The distortion term \(E[d(S^{n},\hat{S}^{n})]\) measures the bit-wise fidelity and the semantic distance term \(d_{KL}(p_{S^{n}},p_{S^{n}})\) measures the classification accuracy. This allows us to characterize the tradeoff relationship between the reconstruction quality and the classification accuracy. Note that the mismatch between the random variables in the mutual information term and the constraint terms is a characteristic of indirect source codingproblems [29; 30]. This formulation extends our general framework while maintaining consistency with the rate-distortion tradeoff established in previous sections. The KL divergence constraint directly links communication efficiency with classification performance through rate-distortion theory.