esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 3 Measurement System Architecture  
In its most basic form, the measurement system architecture revolves around the following three main roles. A sender \(S\) transmits test packets with programmable length, content, and frequency. The DUT receives, processes, and then possibly forwards them to the receiver \(R\), as it would do if the network were normally operated. Informally speaking, the measurement goal is to evaluate the time needed by DUT to process packets. By varying packet length, content, frequency and possibly other operating conditions, complete and sophisticated performance figures can be obtained, thus enabling users to understand whether or not the considered DUT is well-suited for their purposes.  
In order to consistently measure the time taken by each packet to traverse the DUT, the \(S\) and \(R\) ought to share the same reference clock or be synchronized in some way. Moreover, the time measurement should ideally be unaffected by possible delays in the output and input queues of \(S\)'s and \(R\)'s network interface ports, respectively. A convenient way to meet both requirements is to host both \(S\) and \(R\) on the same node/board, called the test manager \(TM\), and let them use the same (multi-port) network interface card (NIC). The NIC must be able to timestamp outgoing and incoming traffic in hardware according to the same time base or, at least, the time bases having a well-known mutual relationship.  
Flexibility in changing the configuration, in particular in remote cases, can be achieved by introducing a manageable switch (\(SW\)) in the system. This non-functional requirement is a key aspect in practical situations, as it enables users to run tests on multiple devices without recabling the system and operating \(SW\) remotely.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 4 Uplink Transmission
### Uplink Spectral Efficiency  
According to the channel model in [23], the signal components are categorized as the desired signal (DS), beamforming uncertainty gain (BU), multiuser interference (UI), and noise interference (NI). These components are defined as follows:\[DS_{k} =\sqrt{\rho\eta}\mathbb{E}\Bigg{\{}\sum_{m=1}^{M}\delta_{mk}^{H} \delta_{mk}\Bigg{\}}, \tag{72}\] \[BU_{k} =\sqrt{\rho\eta}\Bigg{(}\sum_{m=1}^{M}\delta_{mk}^{H}\delta_{mk}- \mathbb{E}\Bigg{\{}\sum_{m=1}^{M}\delta_{mk}^{H}\delta_{mk}\Bigg{\}}\Bigg{)},\] (73) \[UI_{kk^{\prime}} =\sqrt{\rho}\mathbb{E}\Bigg{\{}\sum_{m=1}^{M}\sqrt{\eta}k\delta_{ mk}^{H}\delta_{mk}\Bigg{\}},\] (74) \[NI_{k} =\sum_{m=1}^{M}\delta_{mk}^{H}w_{u,m}, \tag{75}\]  
where each expression corresponds to the terms in (77).  
Invoking the arguments from [5], the achievable uplink Spectral Efficiency (SE) for \(UE_{k}\) is given by (76), which can be written with the effective SINR, as shown in (78).  
\[SE_{k}=\left(1-\frac{\tau_{p}}{\tau_{c}}\right)\log_{2}(1+\text{ SINR}), \tag{76}\]  
\[\text{SINR}_{k}^{ul}=\frac{|DS_{k}|^{2}}{\mathbb{E}\{|BU_{k}|^{2}\}+\mathbb{E} \{|UI_{kk^{\prime}}|^{2}\}+\mathbb{E}\{|NI_{k}|^{2}\}}, \tag{77}\]  
\[\text{SINR}_{k}^{ul}=\frac{p_{k}|\mathbf{a}_{k}^{H}\mathbb{E}\{\mathbf{g}_{kk }\}|^{2}}{\mathbf{a}_{k}^{H}\Big{(}\sum_{i=1}^{K}p_{i}\mathbb{E}\{\mathbf{g}_{ ki}\mathbf{g}_{ki}^{H}\}-p_{k}\mathbb{E}\{\mathbf{g}_{kk}\}\mathbb{E}\{\mathbf{g}_{ kk}^{H}\}+\mathbf{F}_{k}\Big{)}\mathbf{a}_{k}}. \tag{78}\]  
where \(\mathbf{g}_{ki}=[v_{k}^{H}h_{i1},\ldots,v_{k}^{H}h_{iL}]^{T}\), \(F_{k}=\text{diag}(||v_{kL}||^{2},\ldots,||v_{kL}||^{2})\), and \(\mathbf{a}_{k}=[a_{k1},\ldots,a_{kL}]^{T}\).  
The effective SINR in (78) can be further maximized as shown in [7] by setting  
\[\mathbf{a}_{k}=\Bigg{(}\sum_{t=1}^{K}p_{\text{ul},t}\mathbf{g}_{kt}\mathbf{g} _{kt}^{H}+\sigma^{2}\mathbf{F}_{k}^{-1}\Bigg{)}^{-1}\mathbb{E}\{\mathbf{g}_{ kk}\},\]  
which leads to the maximum value [38]:  
\[\text{SINR}_{\text{max},k}=\frac{p_{\text{ul},k}|\mathbf{g}_{kk}^{H}|^{2}}{ \sum_{t=1}^{K}p_{\text{ul},t}\mathbb{E}\{\mathbf{g}_{kt}\mathbf{g}_{kt}^{H}\} +\sigma^{2}\mathbf{F}_{k}-p_{t}^{\text{ul}}\mathbb{E}\{\mathbf{g}_{kt}\}}. \tag{79}\]
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 4 Experimental Procedure
### Comparison to State-of-the-Art Methods  
To further verify the generality of the algorithm proposed in this paper, 46 sets of data were verified in the dataset. A total of 399 frames of data were taken from any of the 46 sets for effective analysis to facilitate a comparison of the visual effects. A total of 16 frames, 32 frames, 48 frames, and 64 frames of infrared images were taken as the original data comparison images. The multi-target tracking algorithm reported in this paper was experimentally compared with the algorithms proposed in [5,6], and the target tracking comparison results are shown in Figure 11.  
Comparing the number of targets identified and the stability of the target tracking process in Figure 11b-d, the actual number of targets marked in frame 16 is 9, and the target identification rate of the proposed algorithm is optimal. Comparing the 16 th frame with the 32 nd frame, the proposed algorithm has the best target recognition rate and stability of target tracking. Therefore, it is clear that the proposed algorithm has the desired effect in terms of the number of targets identified and the accuracy of target tracking.  
Using a large number of experimental data statistics, data analysis was carried out for two indicators: the number of targets identified and the target accuracy tracking rate, as shown in Figure 12.  
The purple curve in Figure 12a is the curve of the number of real markers in the image, and the red curve is the characteristic curve of the number of recognized targets after the operation of the algorithm proposed in this paper. The red curve in Figure 12b is the curve of the target tracking accuracy of the proposed algorithm for different frame frequencies, and the overall target tracking accuracy is better than that of the other two algorithms. The target tracking accuracy of the algorithm in this paper is lower when the frame rate is less than 15 Hz. The highest target tracking accuracy is achieved at a frame rate of 30 Hz.
## 4 Algorithm
### Demand Enqueue Rules
The log-normal distribution, through effective analysis of historical data, can accurately simulate the volatility of demand capacity, ensuring that the required capacity better aligns with the asymmetric characteristics in real-world scenarios. Ultimately, the access demand generated by each queue must satisfy the following constraints:  
\[\sum_{1}^{E_{i}(t)}C\leq R_{i}\leq M_{i},\quad i\in\{low,medium,high\} \tag{21}\]  
The remaining demand capacity \(R_{i}\) of the queue must always be less than or equal to the maximum demand capacity \(M_{i}\) of the queue. Only when the actual number of generated scheduling demands satisfies the above constraint can the generated demandsbe loaded into the corresponding demand queue \(Que_{i}\); otherwise, the generated demand is retained and will be loaded into the demand queue once the condition is met. The specific enqueuing process is shown in Algorithm 1. ```
Parameters: Expected access demand \(\theta_{i}(t)\), adjustment factor \(\zeta\), maximum request capacity \(M_{i}\), remaining available capacity \(R_{i}\)
1: Initialize demand queues \(Que_{i}\), \(i\in\{low,medium,high\}\);
2: Initialize \(\theta_{i}(t)\) for each queue \(Que_{i}\);
3: Initialize \(\zeta\) as an adjustment factor to regulate demand generation rate;
4: Compute the mean of the Poisson distribution \(\lambda_{i}(t)=\zeta\cdot\theta_{i}(t)\);
5:for each round do
6:for each queue \(Que_{i}\)do
7: Generate demand quantity \(E_{i}(t)\sim\text{Poisson}(\lambda_{i}(t))\);
8:if\(\sum_{i}^{E_{i}(t)}C\leq R_{i}\leq M_{i}\)then
9:for each generated demand Dem do
10: Set enqueue round: \(T^{\text{in}}=T^{\text{now}}\);
11: Set dequeue round: \(T^{\text{out}}=\emptyset\);
12: Enqueue the demand into \(Que_{i}\);
13:endfor
14: Update remaining available capacity of \(Que_{i}\): \(R_{i}-=\sum_{1}^{E_{i}(t)}C\)
15:else
16: Retain generated demand until condition is met;
17:endif
18:endfor
```  
**Algorithm 1** Demand Enqueue Rules
## 4 Result and Discussion
### Comparative Analysis with Other Methods
Figure 26 shows the training and testing accuracy of the proposed and existing methods for different epoch values. In the training stage, the accuracy of the proposed method increases up to 50 epochs and after the 50 th epoch, there is a sudden rise in accuracy up to the 100 th iteration, and after that, a rise to the 150 th epoch. After an epoch value of 200, and from 250 and 300, the training accuracy remains constant for the proposed methods. Also, during testing accuracy level, after the 50 th epoch there is a gradual rise in accuracy up to the 100 th epoch, and after that, a sudden rise up to 200 epochs. After an epoch value of 200, and from 250 and 300, the testing accuracy re-mains constant for the proposed methods. Therefore, the proposed approach is effe-tive as, with an increase in repetitions, accuracy likewise rises to produce better out-comes. Figure 25: Throughput (Kb/s) of the proposed and existing methods by varying nodes. Figure 26: Training and testing accuracy. Figure 27 shows the training and testing loss of the proposed methods for different epoch values. In the training stage, the loss of the proposed method decreases up to 50 epochs, and after the 50 th epoch, there is a sudden reduction in loss value. After an epoch value of 100, and from 150 to 200, the loss value shows a sudden drop, and after that, at epoch values of 250 to 300, the loss value remains constant. So, the proposed method demonstrates better outcomes than comparing to training and testing loss value. Figure 28 shows the comparison of the proposed and existing methods, with four videos for training and one for testing. For a pause time of 20, the proposed method achieves an average end delay of 0.06 s, compared to the average end delays of the existing SRABC, AODV, DSR, and OLSR methods, which are 0.11, 0.21, 0.199, and 0.09 s, respectively. For a pause time of 60, the proposed method's average end delay is 0.134 s, compared to the existing methods' average end delays, which are 0.16, 0.189, 0.22, and 0.154 s, respectively. For a pause time of 100, the proposed method's average end delay is 0.174 s, compared to the existing methods' average end delays, which are 0.2, 0.23, 0.24, and 0.21 s, respectively. Thus, the existing methods have more processing time than the proposed method. Figure 29 shows the comparison of the proposed and existing methods with five videos for training and two for testing. At a time of 200 seconds, the proposed method achieves a packet-delivery ratio of 94.3 s, compared to the packet-delivery ratios of the existing SRABC, AODV, DSR, and OLSR methods, which are 92.7, 86, 72.5, and 84 s, respectively.
# Towards Deep Integration of Electronics and Photonics
## 2 Perspective Materials
The realization of this mechanism in electro-optical devices requires the incorporation of ITO/dielectric capacitors, suitable for charge accumulation. Another approach is to mix TCOs with noble metals to obtain hybrid plasmonic structures with desired properties. For instance, Ag-ITO co-sputtered composite films [35] and thin Au spacer layers incorporated inside ITO films [36] allow to control the ENZ-point wavelength and thus provide better tunability of the system. The development and structuring of metallic/dielectric composites leads to the idea of plasmonic metamaterials supporting the ENZ effect with many intriguing properties, like, for example, optical nonlocality [37]. Surface plasmons can be excited in 2D materials as well. One of the most studied examples is graphene. Optical properties of such structures are obviously qualitatively different from 3D bulk materials or films. The dispersion relation of massless Dirac electrons and its influence on the overall material properties are well known. Plasmonic behavior of graphene can be engineered using geometrical variations, doping and stacking [38; 39]. Thus, one of the characteristic features of 2D structures is high tunability. Gating works particularly well there [40]. It is experimentally demonstrated that in graphene/SiO\({}_{2}\)/Si structures it is possible to alter both the amplitude and wavelength of plasmons by applying a gate voltage [41]. Typical propagation length of the order of micrometers (several plasmon wavelengths) is reported in these experiments. Fast and efficient gating leads to various applications including graphene-based optical transistors and modulators [42]. Fundamental limits of graphene plasmonics due to losses were probed recently in cryogenic experiments, revealing the physics of plasmonic dissipation [43]. At the temperature of liquid nitrogen, when certain mechanisms of dissipation can be excluded, the plasmon propagation length grows and can exceed 10 \(\upmu\)m (approximately 50 wavelengths). High quality dielectrics are among auxiliary materials which are important in plasmonic applications [44; 45]. One good example is hafnium dioxide, which was introduced by Intel as an advanced MOSFET gate insulator about a decade ago [46]. Materials like HfO\({}_{2}\) with the static permittivity approximately 6 times larger than silica allow to significantly decrease the operating voltage and energy consumption of the resulting devices.
## 2 Reactive Routing Protocols and Application of Jitter
### Basic Operations of Reactive Routing Protocols
#### 2.1.1 Route Discovery  
During route discovery, route request (RREQ) messages are flooded through the network, with each intermediary router forwarding the RREQ also recording the reverse path, _i.e._, the next hop towards the originator of the RREQ. When the RREQ reaches the sought destination, that destination generates an RREP (route reply), which is unicast along the installed reverse path. RREQ and RREP messages carry a monotonically-increasing sequence number, permitting both duplicate detection and detecting which of the two messages contains the most \"fresh\" information. The RREQ message carries also the metric semantics of a path, _i.e._, the \"cost\" of the path to the originator is recorded and updated by each intermediate router. Two flooding modes are possible: the shortest-delay mode and the shortest-path mode. Depending on the flooding mode, RREQ forwarding and RREP generating rules may be slightly different.  
**Shortest delay mode** Routers in the network only forward the first RREQ message received from a given source to a given destination; later arriving RREQs with the same pair \((\)source, sequence_number\()\) will be dropped, even if they advertise better paths than the first RREQ received and forwarded. The requested destination behaves similarly: it generates an RREP onlyupon the first reception of an RREQ from a given source. Routes discovered in this mode may be suboptimal, but they are acquired with minimal delay.
**Shortest path mode** Routers may forward the first RREQ message received from a given source to a given destination; later arriving RREQs with the same pair \((\mathrm{src},\mathrm{sequence\_number})\) will be forwarded only if they advertise better paths than all previously forwarded RREQs with that same \((\mathrm{src},\mathrm{sequence\_number})\). The destination behaves similarly, generating an RREP for the first RREQ and then for any subsequent RREQ that advertises a better path than all previously received RREQs with that same \((\mathrm{src},\mathrm{sequence\_number})\). Depending on what metric is used for calculating \"better\", this can improve the quality of the acquired routes, at the cost of increasing considerably the overhead associated to route discovery processes. This is the most common mode, and used in protocols, like LOADng [20].
## 2 Materials and Methods
### Calculation of MC Exposure, Percentage of Accumulation and Condition Index  
The ingestion of cyanobacteria and of MC were quantified in order to evaluate percentages of accumulation. As free MC only represent a fraction of the total amount of MC measured in tissues, we used total MC values to calculate the two accumulation percentages.  
The cumulated percentage of accumulation (cum % acc) was calculated for each sampling time, using the total MC content in tissues (\(\upmu\)g/g DW) converted in \(\upmu\)g/g FW using pre-established FW/DW ratios (_A. anatina_ digestive gland: 6.4; whole _A. anatina_: 11.5; _D. polymorpha_ digestive gland: 9.3; whole _D. polymorpha_: 14.1), the tissues' weight (g) and the cumulative quantity of ingested MC (\(\upmu\)g):  
\[cum\ \% acc\ at\ day\ n=\frac{Total\ MC\ in\ tissues\ (ug\ FW/g)\ at\ day\ n\times fresh\ tissue\ weight\ (g)}{Cumulative\ quantity\ of\ ingested\ MC\ between\ day\ 1\ and\ n\ (ug)}\]  
However, this ratio comparing the cumulated accumulation to the cumulated ingestion at each date does not reflect the potential dynamics of MC accumulation and detoxification within tissues during the exposure. To better represent those variations, we calculated the temporary percentage of accumulation (temp % acc), using means of accumulated total MC and of ingested MC for several considered periods during the intoxication (between day 0-1, 1-3, 3-7, 7-14, 14-21):  
\[temp\ \% acc\ at\ day\ n=\frac{(Mean\ MC\ in\ tissue\ at\ day\ n+1)-(Mean\ MC\ in\ tissue\ at\ day\ n)}{Mean\ ingested\ MC\ between\ n\ and\ n+1}\times 100\]  
To assess a potential impact of the exposure to cyanobacteria on the overall mussel health condition, the condition index (CI) was measured using the total weight (non-dissected mussel with dried shell) and soft tissues weight as follows:  
\[CI=\frac{Whole\ soft\ tissues\ wet\ weight\ (mg)}{Whole\ body\ weight\ with\ shell\ (mg)}\]
_Algorithms_**2015**, \(8\), 743-753; doi:10.3390/a8030743  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
A CS Recovery Algorithm for Model and Time Delay  
Identification of MISO-FIR Systems  
[PERSON] \({}^{1,2,*}\) and [PERSON] \({}^{2}\)  
\({}^{1}\) Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education),  
Jiangnan University, Wuxi 214122, China  
\({}^{2}\) School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-135-8415-9252; Fax: +86-510-8591-0633.  
Academic Editor: [PERSON]  
_Received: 29 June 2015 / Accepted: 2 September 2015 / Published: 10 September 2015_  
This paper considers identifying the multiple input single output finite impulse response (MISO-FIR) systems with unknown time delays and orders. Generally, parameters, orders and time delays of an MISO system are separately identified from different algorithms. In this paper, we aim to perform the model identification and time delay estimation simultaneously from a limited number of observations. For an MISO-FIR system with many inputs and unknown input time delays, the corresponding identification model contains a large number of parameters, requiring a great number of observations for identification and leading to a heavy computational burden. Inspired by the compressed sensing (CS) recovery theory, a threshold orthogonal matching pursuit algorithm (TH-OMP) is presented to simultaneously identify the parameters, the orders and the time delays of the MISO-FIR systems. The proposed algorithm requires only a small number of sampled data compared to the conventional identification methods, such as the least squares method. The effectiveness of the proposed algorithm is verified by simulation results.  
compressed sensing; sparse; parameter identification; gradient projection pursuit algorithm; time delay estimation
# Designing Social Robots with LLMs for Engaging Human Interaction
## 3 System Design
### System Architecture and Speech Pipeline  
The system is structured around a modular architecture, combining Pepper's native capabilities [40] with external components for speech processing, dialogue generation, and memory management (Figure 1). Communication between modules is handled via socket-based protocols that bridge Python 2.7 (used by [PERSON]'s NAOqi SDK) and Python 3, ensuring compatibility and reliable data exchange.  
3.1.1. Speech-to-Text (STT)  
User speech is transcribed in real time using Microsoft Azure's streaming STT API, combined with a voice activity detection (VAD) mechanism to handle end-of-turn detection. This configuration supports open-ended input without hard timeouts, allowing users--especially older adults with slower or more reflective speech--to complete their turns naturally. Silence thresholds are calibrated prior to each session to minimise unintended interruptions.
# Multi-Band Array Antenna Sharing a Common Aperture with Heterogeneous Array Elements
## 3 Verification of the Proposed Multi-Band Array Antenna  
Figure 6 depicts the active reflection co-efficient (ARC) of the central X-band element among the implemented multi-band array antenna. The dashed, solid, and dotted lines present the simulated reflection co-efficient, the measured reflection co-efficient, and the measured ARC (a steering angle of 0\({}^{\circ}\)), respectively. For a fully excited array with a steering angle of \(\theta_{0}\), the ARC of the \(m\)-th element can be calculated as follows [18,19]:  
\[\Gamma_{m.active}(\theta_{0})=\frac{\sum\limits_{n=1}^{N}s_{mn}e^{-ju_{0}(n-1)}} {e^{-ju_{0}(m-1)}}=\sum\limits_{n=1}^{N}s_{mn}e^{-ju_{0}(n-m)}, \tag{1}\]  
where \(d\) is the array distance, and \(u_{0}\) is \(kdsin\theta_{0}\). \(S_{mn}\) represents the 5-parameter with \(m\)-th output and \(n\)-th input in the array. \(e^{-ju_{0}(n-m)}\) is the phase difference between \(m\)-th port and \(n\)-th port. The ARC for 0\({}^{\circ}\) angle steering is under \(-10\) dB from 8.5 GHz to 10.2 GHz and is similar to the bandwidth of the reflection co-efficient, as shown in Figure 6a. Figure 6b shows the projected ARCs onto the UV-plane, when the beam steering vectors are weighted to each X-band element [20,21]. The black dashed line indicates the \(\pm 45^{\circ}\) steering angle region in the Azimuth-over-Elevation (Az/El) coordinate system [22]. The ARC with the dB scale is expressed as a colored level, where the deeper blue color represents better matching characteristics. The maximum ARC value in the \(\pm 45^{\circ}\) steering angle region of the Az/El coordinate system is \(-11.7\) dB at 9.5 GHz. Figure 7 presents the steering beam patterns of the 3 \(\times\) 3 X-band array in the unit-cell at 9.5 GHz. The measured gains with steering angles of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) are 15.8 dBi, 15.6 dBi, 14.4 dBi, and 12.2 dBi, respectively, whereas the simulated bore-sight gain for a 0\({}^{\circ}\) steering angle is 14.8 dBi, where the simulated and the measured steering beam patterns are in good accordance, as presented in Figure 7. The beam steering performance is obtained by super positioning the measured active element patterns of all antenna elements [23]. Phase control for each element is conducted by applying the theoretical phase weighting to the measured active element patterns. This assumes that TRMs connected each antenna are capable of ideal phase control. For better beam steering performance, the polarization direction and currents between S-band and X-band antennas are set to be orthogonal to each other. Thus, we did not observe any significant degradation in beam steering performance even with the common aperture configuration. The overall size, application, gain, and SLL of the proposed antenna system are compared with those of reference antennas in Table 2.